Category: Uncategorized

As anyone who has worked within a marketing team will tell you, analysis of data has become a staple in maximizing important factors of customer experience. When it comes to big data, the impact is even more crucial.

What is big data marketing?

When it comes to marketing, big data helps organizations by offering valuable insight into their customers and future customers. It also helps businesses understand how to streamline their important workflow processes. Big data helps marketing teams evolve their use of analytics.

A group of data on a laptop.

Big data impact on marketing

The ability to thoroughly analyze big data is the main difference between companies who are efficient and companies who fail. Big data is in fact recognized as a staple for organizations, especially when it comes to marketing.

When big data is effectively utilized, marketing teams are able to optimize their campaigns, accelerate workflows and boost customer loyalty. Here are some of the biggest ways big data impacts marketing:

  • Improved performance through superior understanding. Understanding can apply to many things, but in this instance it is referencing the understanding a marketing team has on its tools, budget and content. With big data analysis, marketers can better assess how successful certain campaigns are and evaluate how to better approach these projects in the future.
People discussing a plan using data.
  • Steadier flow of new customers and clients. Big data offers insights into what types of things are effective in bringing in more customers. This often involves data brought in from technology such as email and website interactions.
  • Superior understanding of target market. Lastly, big data provides some of the most innovative, dynamic understandings of their target market. The overall analysis of specific data directly translates to things like enhanced customer experiences.
Animated people in front of a blank background.

Different big data types

All data is different, and this holds true for big data as well. Understanding which type is what helps users understand how to extract, analyze and apply different pieces of information. Here are the three different big data types.


A person planning data on a whiteboard.

Structured, as the name implies, is data that is grouped based on information that is most likely numerical in form. This generally includes addresses, age and payment information. Structured data fits nicely into data maps and outlines that show interconnected information.


Unstructured data is all stored information which is without any organizational form. It’s a large part of the makeup of modern data. The natural state and result of a user’s actions on a computer end up in unstructured form.


Spreadsheets illustrated.

Semi-structured data is a hybrid between structured and unstructured data. It’s often data that is mostly unorganized but that has key identifiers and labels alongside it that help keep it recognizable and understandable.

Now that you’re aware of the different types of big data, let’s dive into some real world examples to show big data’s impact on marketing.

5 big data marketing examples

One of the most helpful aspects of big data is it is already being embraced by marketers and successful companies. Because of this, it’s easy to find real life examples of companies using big data.

These types of real world applications give us insight into how successful companies use big data in their marketing processes. Here are five to consider.

1. Netflix

The Netflix logo on a TV screen.

We’ll start with Netflix, who is prolific in their attempts to use big data in order to improve crucial factors of their services. Where this is most prevalent, at least to the public eye, is in their data-driven recommendation platform.

This has not only increased the company’s connection with customers, but also saved money and influenced what types of content hits the servers. Look to Netflix when searching for examples of marketing efforts driven by big data that worked.

2. Amazon

An Amazon logo on a smartphone.

Similar to Netflix, Amazon uses big data to drive personalization and customer satisfaction. However, Amazon takes a much more comprehensive approach. They have a much wider customer base and different services which require different processes.

As it turns out, quite predictably, Amazon is benefiting greatly from its big data usage, as it is driving a large portion of its sales. Their machine learning also synchronizes with data to maximize the efficacy of things like ratings and reviews for customers.

3. Kroger

The Kroger grocery store.

Kroger uses big data to personalize direct mail coupons to customers. In order to do this correctly, they need data to determine which customers should get which coupons and on which days/times.

One of the most telling results that big data is effective comes from Kruger’s coupon return rate. It’s consistently been outpacing the industry average by over 60%.

4. The Economist

The Economist's main logo.

One of the most important things to The Economist is making the best connections possible with their customers. This means that big data is at the top of their priorities, since they needed a deeper understanding into what their customers wanted.

The Economist supplemented their big data management with a customer data platform, finding the most precise marketing offers to serve to customers at the critical moments. This boosted subscription rates drastically.

5. Airbnb

A person using the Airbnb service.

Airbnb is one of the best success stories when it comes to big data, as they’re a company that structured so much of their processes around gathering key insights from data. The data science they then used increased their marketing efficiency immensely.

Airbnb used big data to understand where the best and worst performances were geographically, and made insightful conclusions based on this data to make helpful adjustments.

Final thoughts

Marketers who commit to utilizing big data are bound to see more success in all their different projects and campaigns. The possibilities are truly endless, and the analysis that comes with big data can change the entire outlook of a marketing team.

Category: Uncategorized

If you’ve ever invested untold amounts of time, energy, and effort in a marketing campaign that ultimately fell flat, you’ll no doubt appreciate just how invaluable predictive marketing can be to your business.

An approach known as predictive analytics marketing can help you to make informed decisions about the most effective ways to achieve your marketing goals.

But why is it so important? How does it work? And what steps should you take to create a successful predictive marketing strategy of your own?

Below, we’ll answer all of these questions so you can start reaping the rewards for yourself.

What is predictive marketing?

Predictive marketing is a process by which you can use data you’ve already gathered to determine which marketing strategies are going to be most successful for you in the future.

For example, you might look at behavioral data such as website views, page views, and click-throughs to help you create new landing pages or to modify your homepage.

You might use purchasing histories to determine which products you advertise to which customers via online advertising, or analyze historical data to determine the kinds of images you use in your social media posts.

Why is predictive marketing so important?

Predictive advertising and marketing can be important for any number of reasons, all depending on the goals you’re trying to achieve.

Improve conversion rates and brand loyalty via your website

Leveraging data on how customers use your website lets you create much more personalized experiences, such as making real-time recommendations. This can not only help to create a short-term increase in your conversion rates but also help to improve long-term customer retention and brand loyalty.

Increase your ROI on email, social, and other marketing approaches

Predictive analytics can help you identify trends in the results of your previous email marketing campaigns, social media content, and approaches via other marketing channels. Those trends can give you a clear idea about what’s likely to work and what isn’t. As such, you’ll be able to avoid wasting time, energy, and resources on things that are less likely to produce the results you need.

Qualify and prioritize leads

Predictive analytics can help you to make the most of audience segmentation and determine which leads are most likely to convert. That way, you can focus more on those potential customers and avoid wasting advertising dollars on customers who won’t be as responsive.

Ultimately, what all of this comes down to is helping your business to do more of what works so you can minimize waste, maximize revenues, or simply take a more efficient approach to getting your business where it needs to be.

What makes a strong predictive marketing strategy?

So far, so good—but what do you actually need to do to make predictive marketing work for you?

Here a few key suggestions:

Focus on quality data

The more data you have, the more opportunities you have to make the most of predictive analytics. But keep in mind that quality matters just as much as—if not more than—quantity.

If your data sets are littered with inconsistencies, duplicates, or inaccuracies, they’re far less likely to be of use to you.

Define your goals

While it may be true that carefully collected quality data can throw up some surprising and useful insights that you may not have considered, you’re going to get the best return on your investment in predictive advertising if you know what you want to achieve in the first place.

Defining your goals will make it much easier to identify what trends and patterns you’re looking for and how to use them most effectively. For example, if you’re a publisher who wants to increase paid subscriptions, you may want to build lookalike models of users who look like your best customers and target campaigns to this audience for anyone who is not yet a subscriber.

Use the right tools

If you have enough data to draw meaningful predictions from, then you probably have too much data to manually sift through, at least not without wasting a huge amount of time that could be better utilized elsewhere.

This is where tools like Lytics customer data platform come into play, helping you harness the power of your data to create effective, cost-efficient solutions for achieving your business goals.

Predictive marketing: The takeaways

To sum up, there are three key points that you should take away from this:

  • Predictive marketing can help you make better use of your current and historical data by applying it to make calculated predictions about which aspects of your marketing are most likely to produce the results you want.
  • This approach can be invaluable for minimizing wasted time, resources, and ad spend.
  • Quality data combined with an efficient customer data tool like Lytics can help you to make the most of predictive marketing in a way that’s effective, efficient, and affordable.

Watch our five-minute demo to see how Lytics’ customer data platform could work for you, or connect with our team today.

Category: Uncategorized

Why You Can’t Ignore Behavioral Data in Marketing

You can focus your marketing efforts on one of two things to grow your business: acquiring new customers or retaining existing ones. Bringing in new customers is significantly more expensive than working to keep your current ones, and according to research conducted by Frederick Reichheld of Bain & Company, a 5% increase in customer retention rates can increase your profits anywhere from 25 to 95%.

The longer you keep a customer, the more value they provide you over time—that’s where customer lifetime value calculations come in.

What is customer lifetime value?

Customer lifetime value (CLV) shows you how much profit your business can bring in from one customer over the time they spend with your brand. This means that your customers have future value to your company; they’re worth more than the amount they spend on any single occasion.

Looking at customer lifetime value also gives you insights into which customers you can expect to stick around. Customers with high CLVs are more likely to make repeat purchases, while ones with lower CLVs are more passive customers who will require additional effort on your part to re-engage them.

Why customer lifetime value is important for your brand

Here’s why customer lifetime value and customer retention should be key metrics that any marketing team looks at, according to a study by Adobe:

  • 40% of a company’s revenue comes from repeat and returning customers even though they only represent 8% of all shoppers.
  • You would have to bring in five new customers to equal the value that one repeat purchaser brings to your brand.
  • Repeat customers bring in even more revenue during slow economic times and holiday seasons.
  • Returning customers generate three times the amount of revenue per visit than other shoppers.
  • Conversion rates for repeat and returning customers are five times higher than first-time shoppers.

How to calculate customer lifetime value

It’s not difficult to calculate customer lifetime value; all you need is the right data. Here’s how to calculate it to get a clear view of where a customer’s LV currently stands and see how you can improve it in the future:

Multiply the average order value and purchase frequency of a customer and divide that answer by the average lifespan of your customers.

There are many variations of this formula, but this calculation is a good starting point to get an idea of customer lifetime value and start taking steps toward increasing it.

There’s no magic number; no one ideal customer lifetime value: It varies drastically by industry and company size. Whatever CLV you get from the calculation, you should aim to keep it higher than your business’s customer acquisition cost.

How to increase your CLV through personalized marketing

Now you know how to calculate customer lifetime value and why it’s such an important marketing metric, but how do you improve it? Here are a few ways you can increase CLV with Lytics:

Build customer segments with real-time personalization

Segmenting customers based on their behavior and customer profile is a common marketing strategy, but segmenting them into categories like age and location isn’t enough. People want recommendations for products and services that actually match their interests. Think about streaming services like Netflix: They don’t recommend content to you by segmenting their audience—they look at your behavioral data to find out what, when, and why you watch the shows that you do.

Using a customer data platform (CDP) like Lytics allows you to collect and analyze that behavioral data to gain important insights into your customers in real-time, ultimately increasing your customer lifetime value.

Talk to your customers—not at them

Personalized marketing means that your efforts are targeting a specific person or group of people, but do you really understand what they want? Building customer profiles can help, but collecting and analyzing their behavioral data lets you gain deeper insights so you can create a digital experience personalized to your customers.

If the product or service you’re marketing toward a customer isn’t relevant to them, you’re wasting both their time and yours. Lytics helps you analyze behavioral data in a way that zones in on what matters to your customers at that specific time, making them feel understood and valued while increasing their lifetime value to your brand.

Find and use the right data, the right way

The quality of the data you collect about your customers is way more important than the quantity. You could have mountains of data and a huge team to analyze it, but without the right data, you won’t get an accurate view of your customers. Lytics focuses on collecting the relevant, usable behavioral data from your customers’ daily interactions.

Now that you have the data you need, you also have to use it in the right way; this means transforming your behavioral data into actionable knowledge. Lytics uses advanced machine learning, AI, and data science to give you the insights necessary to increase customer lifetime value.

Enhance your personalized marketing efforts with Lytics

The bottom line of customer lifetime value is simple: Bring continuous value to your customers, and they’ll return the favor by generating more revenue for your brand. Lytics can help you take a more personalized marketing approach and increase your customer lifetime value by collecting and analyzing the right behavioral data to make data-driven, science-backed decisions. Get started with Lytics, and watch your customer lifetime value grow.

Category: Uncategorized

As marketers scramble to maximize their use of data, software systems have emerged to lighten the load and boost success. However, not all tools are created equally, so it’s important that you know which system to implement.

There are numerous tools, but I’ll use this article to break down two systems in head-to-head fashion: the data management platform (DMP) versus the customer data platform (CDP).

What is a CDP?

A CDP, or customer data platform, is a software system designed to streamline marketing processes through the use of superior data analysis and personalized data. The most common thing marketers refer to with CDP is its helpfulness in analyzing data and building better customer profiles.

A screenshot of the Lytics CDP.

In terms of giving businesses a boost, CDPs help numerous departments by removing data silos and sending the right information to the right places. It also removes the guesswork that marketers previously had to do before in-depth customer data analysis.

What is a DMP?

A DMP, or data management platform, is a software system that specializes in collecting data from all types of sources, then managing the information within. DMPs give organizations the chance to narrow their audience focuses, boosting ad campaign success overall.

A computer and phone on a desk.

When it comes to helping organizations, DMPs sort data efficiently and deliver it to the correct places, all while creating better understandings on how to market to specific audiences. This in turn saves a lot of time and money.

With the definitions of both DMP and CDP in mind, let’s further explore how they differ and what this means for you.

What are the key differences?

Because the two are easy to confuse, let me begin by clarifying this and making it simple to understand right away: DMPs and CDPs both collect and manage data, but a CDP uses this data in a more personalized way. As long as you can remember that, the difference is quite understandable.

A chair in a different color than its surroundings.

The first difference to point out is the way the programs interpret data. A DMP feeds large amounts of data to the correct departments and divides it based on helping marketers connect with the right audience. A CDP does this as well, but in a more personalized manner. Instead of creating large groups of potential and current customers to market to, it narrows it further and more accurately. This gives marketers a much more concerted system.

The next difference comes in the storing of the data. Because a DMP acts as a sweeping net, scooping up as much data as possible for large scale marketing projects, it disregards specific personal information. A CDP, on the other hand, focuses on letting marketers create single-person customer profiles, so it stores informative data like names and emails.

A computer displaying data.

In terms of marketing campaigns, the DMP is optimized for advertisements across different markets. The CDP is more for content that will reach individuals based on their interest. In this regard, think of DMP like a ‘guest’ Netflix account, and CDP like your own account. One might offer suggestions, but they won’t fit your needs the way a CDP does.

Using these differences, it becomes a lot clearer which system will benefit you, based on your current needs.

Which one best suits you?

You understand now how both a customer data platform and a dmp function, as well as the main differences between them. Next it’s time to figure out which one is best for your organization?

A knob that says 'sync' on it.

The good thing is, the differences are pretty cut and dry. As a result, your decision is easy. Once you identify your needs, the rest falls into place.


Let’s consider what would drive you to a DMP. First, you would be most interested in managing data in a way that gave you a chance to release content en masse to a wide but specific audience.

You would also be content with a system which locates new customers based on the traits and characteristics of previous customers. The key thing to consider then is accuracy and quality over quantity. If you want to reach more people with acceptable advertisements, a DMP will do the trick.


A lot of times, people assume a customer data platform is for smaller businesses, while DMP is for large enterprises. On the surface, the logic behind this assumption is reasonable, because a CDP has a much more personalized process. This makes it better for serving niche groups and individuals the content they want, but it doesn’t exclude a larger organization from needing a CDP.

An individual profile of a person.

Conversely to the DMP principles, think of CDP like the system of quality over quantity. The data is used so specifically in a CDP to create customer profiles of individual people and truly understanding their needs. This pushes marketing teams to create better content, as well as boost relationships with customers.

Final thoughts

It’s all too easy to rely on a software system to handle data, especially when we have so much data to manage. Remember though that organizations design popular tools with your needs in mind.

Make sure that whichever system you choose, it fits your particular needs. Finally, check out our similar breakdown between the CDP and CRM.

Category: Uncategorized

Why You Can’t Ignore Behavioral Data in Marketing

The main goal of marketing is to get your products and services in front of customers who will benefit from them . . . but what’s the best way to do that? The last thing that your customers want is to be targeted with emails and ads that don’t relate to them—that’s where behavioral data and analytics shines.

Behavioral marketing is a method of gathering data and implementing solutions based on the past behavior of your customers to discover what type of content engages them the most. This lets you match the interests and meet the needs of your ideal target audience. Here, we’ll discuss how behavioral data and analytics work and the benefits they can bring to your business.

What is behavioral data in marketing?

Behavioral data collection comes from a customer’s interaction with a business. This includes website views, individual page views, newsletter sign-ups, and basically any other important actions. Besides websites, behavioral data collection comes from places like mobile apps, customer support centers, and billing systems.

In behavioral data, a “customer” can be a general consumer, a business, or someone making a purchase on behalf of a business. Here’s the important part: Behavioral data is always connected to one end-user, whether they’re a known or unknown entity (e.g., a signed-in user vs. an anonymous one).

When it comes to behavioral data, using a intelligent automation platform like Lytics helps you aggregate all of your data and integrate it with your marketing campaigns, customer profiles, analytics, and other user activity that leads to impactful results.

What is behavioral analytics?

Behavioral analytics looks at the collected behavioral data to identify what influences your customer’s behavior. This gets determined by tracking the data mentioned above, like page views and site registration, and allows you to optimize your customer experience for conversions and retention.

For example, if you want to find out why users are leaving before they sign up, you can conduct a behavioral analysis aimed to isolate where you’re losing them in the sales funnel. Behavioral data lets you see the journey of a customer, from when they open an email to account creation and everything in between.

Behavioral data collection and analytics are essential in optimizing your conversion rates. Using the right behavioral data platform lets you gain deeper insights into your customers and the motivation behind their actions to keep them engaged and get them to convert.

Why behavioral data is important in marketing

Behavioral data is a crucial tool in marketing; by leveraging behavioral data and analytics, marketers can get the most out of their campaigns and offer their customers a better experience. Behavioral data also lets marketers track how different campaigns are performing. For example, if you run an email campaign with the goal of getting more visitors to your blog, you can use behavioral data to see how many visitors came from the email and identify which pages got the most views.

Marketing teams can benefit from behavioral data and analytics with Lytics by:

  • Comparing and identifying the most lucrative campaigns to bring in new customers
  • Increasing customer lifetime value (CLTV) by gaining a deeper understanding of the shared behaviors of your frequent customers
  • Maximizing customer retention by seeing where your customers are most engaged during their life cycle
  • Leveraging an affinity engine to understand what content or products your customers are most interested in.

The value of behavioral data

While behavioral data is typically seen as a tool for marketers, it also provides limitless value to product managers and data analysis teams. Product managers can use behavioral data to monitor the performance of new products, and analytics teams don’t have to rely on building SQL queries to gain and analyze behavioral data.

Lytics can help product managers to:

  • Understand customer behavior over the entire life cycle
  • Make changes on the fly after product implementation based on real-time user engagement
  • Identify customer behaviors that increase retention and reduce churn

For behavioral data and analytics teams, Lytics helps to:

  • Manage real-time behavioral data that’s not possible using SQL
  • See a customer’s entire journey with full context
  • Make data-driven, science-backed decisions easily without disrupting your daily routine

How to analyze behavioral data to boost conversion rates

Acquiring behavioral data is great, and we’ve discussed how it can benefit your business, but how do you use that data to boost your conversion rates and generate more revenue? With Lytics, you can make meaningful science- and data-backed decisions powered by behavioral data and machine learning. Analyze your behavioral data in real time with Lytics to:

  • Create meaningful customer connections
  • Drive engagement and conversion rates
  • Increase your customer lifetime value

And, you can expect:

  • 4x revenue growth
  • 10x higher click-through rates
  • 20% lower cost per acquisition

Category: Uncategorized

A person using a smartphone with illustrations.

Real-time personalization is the ability to customize a message to an individual user that is relevant and reaches them at the optimal time on the optimal channel. You can think of it as an evolution of the “right customer/right time” goal of targeted marketing or as one-to-one marketing at the tactical level. Even though you may be doing real-time personalization right now, you’re probably not doing it as well as you think.

If you want to know what effective real-time personalization looks like, look to Google and Netflix. They know which users are engaged, what they’re engaged with and serve up content and product recommendations based on that data to keep their customers engaged and move each of them along a personal customer journey. What does bad real-time personalization look like? It looks like a personalized pop-up ad that feels janky or an email that starts off with “Hello Ash” and loses me in the next sentence with an irrelevant offer.

You say hello and I say goodbye

Real-time personalization is only as good as the data you have to support it. When you have the right data used in the right way, your marketing feels highly relevant. When you don’t, it can feel spammy. The right data—and, by extension, the right targeting—will tell you not only what messaging channel is best for the user but also what time for that message is best. If I’ve just visited a brand’s website, do I want to see an email from them six hours later? How soon is too soon to show me retargeting ads on my social media channels? If I get both emails and retargeting ads, will that boost my engagement or drive me away? Answering questions like these are key to getting real-time personalization right.

Marketers worry about over-messaging their customers and with good reason. If I get a pop-up ad that feels intrusive, I might leave the site. If I get too many messages from a brand, I may be prompted to unsubscribe from their marketing entirely. Real-time marketing efforts that fail have real consequences.

CDPs provide real personalization

Using a data management platform (DMP) to build audiences, compared to using a customer data platform (CDP), is like the difference between guessing and scoring. With a DMP, a marketer might use rule-based heuristics to create an audience of users who haven’t been on their website in the last seven days. They would then label this audience “ripe for re-engagement.” 

In a CDP like Lytics, however, marketers have access to data science driven scores such as customer’s engagement level based on cross-channel behavioral data and predictive analytics. For example, they might find that a customer with a momentum score between 70 and 90 (out of 100)  is the best audience for a given offer. They’re showing frequent and high interest in a given product or service, and therefore are likely to act in response to the offer. In our experience, audiences based on data science perform a lot better than audiences based on some arbitrary rules that may or may not be relevant.

Building better content recommendations

Another area where CDPs can have a big impact is with real-time content recommendations. This can be scary for marketers in the beginning, because it requires some faith. Marketers can’t see each recommendation being made to their customers, but they can preview those recommendations with a robust CDP like Lytics and get a good idea of whether their recommendation engine is really working. The difference between a relevant and irrelevant recommendation could be the difference between a site visitor staying engaged and bouncing.

Earning ROI with your CDP

Content recommendation tends to be a more mature use case for CDPs. Generally, we recommend starting off with something that can deliver ROI quickly, like thinning out your email lists to remove users who are either already highly engaged already or unlikely to engage by email. Given the fact that most email providers charge per email or per user, creating a leaner list based on behavioral and engagement scoring can net marketers the same results and save them a lot of money.

Once marketers see the value of using behavioral data plus data science to drive fundamental marketing decisions like email lists, they’re more confident to try things like content recommendations and creating customer journeys. And that’s where real-time personalization takes on real importance, as it becomes more  than a tool to improve channel-based marketing campaigns, but the foundation for great customer experiences across all channels.

Want to see how you can earn ROI with a CDP like Lytics? Try Lytics for 30 days!

Category: Uncategorized

I keep a birdfeeder in my backyard in hopes to see my favorite bird – the cardinal. I love attracting all types of birds, but get disappointed because I rarely see cardinals.

Hoping to change my luck, I researched which type of seed cardinals prefer. Turns out I was using a generic seed that would attract a wide variety of birds. Instead, I needed a specific seed that cardinals enjoyed.

Sure enough, I began to see beautiful red cardinals throughout the entire winter.

Marketers have run into a similar problem when interpreting data to identify and convert specific customers. They’re able to reach a wide range of people, but need a more personalized approach in order to reach the right ones.

A man surrounded by specific customer data.

Fortunately, customer data platforms have solved this issue by giving marketers the information they need to communicate correctly with the right potential customers.

So what is a customer data platform? This article will lay it all out for you as simply as possible. Let’s make sure that you’re attracting cardinals from now on.

What is customer data?

To understand customer data, it helps to look at the different subsections of this important information.

Demographic data

Demographic data includes indicators such as age, ethnicity, education levels and employment status. This type of data is a general yet important part of creating effective customer profiles within a customer data platform system.

The number fifty-two etched into a wall.

Behavioral data

Behavioral data is one of the most prevalent and available sources of data collected about customers. Furthermore, it gives in-depth insight that other forms of information does not. The complex, revealing data comes from the ways a user interacts when navigating the web on different mobile devices. Behavioral data details which sites a user visits and what types of applications they prefer.

Identity data

Identity data is in the same general information category as demographic data, but there are a few minor differences. It handles elements such as personal identifiers (name, titles), social media presence and geographic information.

A woman stands in front of a data screen.

It’s important to note that data, including customer data, available to marketers is growing dramatically. Without the right software to handle it, the data gets stored in silos, which don’t offer harmonious experiences to customers throughout different business touchpoints. Fortunately, a customer data platform solves this problem.

What is a customer data platform?

A customer data platform (CDP) is a software system that simplifies the marketing process using in-depth analysis and personalized data. The key feature of a CDP lies in its ability to collect and interpret data in order to create an accurate customer profile.

Who needs a customer data platform system?

A customer data platform has the potential to help a wide range of company teams, but some will benefit from its impact more directly. This tends to be marketers, since their aim is to boost customer relationships and provide superior user experience throughout different steps of a customer journey.

Marketers working with large amounts of customer data will greatly benefit from the superior data collection processes of a CDP, and also the personalized customer profiles the technology creates. Essentially, anyone who has experienced a lacking performance in marketing using customer data in the past needs to take the next step by using a CDP.

A spiral of data.

Lastly, companies subject to different rights privacy laws need a CDP if they’d like to automate their protections against potential errors that result in lawsuits. A CDP unifies data and simplifies the entire process, ensuring that administrators have full control.

Now you may be thinking that a customer data platform sounds similar to a CRM or DMP. Let’s make sure you understand the key differences.

Difference between CDP and CRM

A customer relationship management system (CRM) handles customer data in order to help businesses improve their relationship with customers. This technology is effective in handling a large amount of information and customers, but it does so in a manner much differently than a customer data platform.

Two people reviewing a laptop.

The customer data platform acts in the same vein, but with a much more specific and complex focus. A CDP uses similar data, but with much different purpose and in a unique way. The customer profile built within a CDP provides valuable insight into each customer, and informs marketers of important factors pulled from the data.

Before we breakdown some of the unique CDP benefits, let’s take a look at the key differences between CDP and DMP.

Difference between CDP and DMP

A data management platform helps businesses sort, access and gather valuable information. It gathers data from a wide variety of sources, including from different connections and mobile devices.

Even though they both are marketing-based data systems, there are some big differences between a CDP and DMP, mainly in the way the information is utilized by each system. A DMP, in fact, only can handle a small portion of the entire CDP process. Whereas a DMP collects data and moves it to the desired departments and tech platforms, CDP interprets this data and gives a unified solution to marketers.

Two laptops next to each other.

Now that you understand why a customer data platform stands out among its peers, let’s dive into the different benefits that come with using this modern technology.

Major customer data platform benefits

There are too many customer data platform benefits to list, but here are some crucial ones you need to know about.

Supercharged data processes

A CDP overhauls data processes from so many different angles that it’s a wonder how anyone goes without. The first thing it offers is data collection that shines above other data systems. This is because it centralizes data, making it easier to access and utilize for future projects. It’s clear that both the collection and organization of valuable information is superior when using a CDP.

Next, it makes this collected data more accessible, available and useful to a wider variety of company departments, ensuring everyone is pushing forward effectively and without error.

A fast-moving blur of cars.

Finally, what really stands out is how customer data platforms remove the dreaded data silos many have unfortunately grown so accustomed to. This has many benefits, but the main one is for customers, who receive much stronger experiences as a result.

Hyper-focused customer experiences

The most oft-referenced benefit of better customer experiences resulting from using a CDP are the personalized customer profiles. What pushes them beyond basic profiles of other systems are the in-depth, single customer view profiles that detail someone based on data pulled from all different areas of a database.

A cup of coffee on a paper that reads 'customer experience'.

These profiles not only serve as a way for marketers to provide that customer with a better experience, but also gives them insight into potential future behavior of the customer. The more focused the profiles built, the better the data is utilized. Because a CDP unifies collected data from a variety of sources, users are able to construct a detailed view of specific customers across more channels. This ties into an improved omnichannel marketing experience as well.

A CDP delivers better marketing and customer experiences across the right devices, in the right places and in the right manner. It offers marketers a chance to take a comprehensive approach to the process without making costly errors that turn the customer off.

Improved customer lifetime value

One of the best ways to improve customer lifetime value is to boost and maintain customer loyalty. Fortunately, a customer data platform goes a long way when it comes to keeping customers around.

A person holding a star in front of a window.

This starts with removing the guesswork that often comes from older tech sorting and utilizing customer data. The CDP delivers the most on-point, personalized experience to your customers, giving them plenty of reasons to stay loyal.

Lastly, it’s important to note that many elements of a CDP improve customer lifetime value, though often indirectly.

Seamless operations

The final benefit we’ll discuss is the level of effective company-wide operations resulting from the use of a CDP. This is one of the most overlooked benefits. By having software capable of connecting numerous tools, organizations are able to accomplish important tasks and projects much faster.

An illustration of how CDP works.

A CDP also minimizes potential time-consuming integration implementation and maintenance. The system then manages all information from a single location and applies it to different company technology and tools.

Choosing a customer data platform

Even though everyone’s needs are different, that doesn’t necessarily mean that one CDP system will work for one user and not the next. In fact, there are some clear standout technology features to consider when selecting your CDP.

In order to stay ahead of the curve, you’ll need a customer data platform that uses the latest tech and has innovative features.

Plenty CDP systems are able to do the basics, but that just won’t cut it any longer. Make sure that when you select your customer data platform system, it focuses on data science, seamless integrations and other customer-driven factors.

Lytics data interface.

This is why marketers overwhelmingly prefer the intangibles Lytics offers over the competition – their features are designed to push the envelope of what modern tech can offer. With things like artificial intelligence, the Lytics CDP system has given marketers the most precise versions of customer profiles.

Closing thoughts

Just as I couldn’t please the cardinals without the right birdseed, marketers won’t please customers without the right customer data platform system. Make sure that whichever CDP you choose breaks free from convention and moves you forward.

For more information about this powerful marketing tool, check out our customer data platforms marketer’s guide.

Category: Uncategorized

Somewhere along the way, the idea of having a 360-degree view of your customers ended up going in circles. Maybe data warehouses were to blame, but companies started to believe that, once they consolidated all of their customer data in one place, a complete, prescriptive, view of their customers would magically appear. 

It didn’t, mainly because data itself does not equal insight. So, marketers went back to the tried-and-true formula of demographic data plus their marketing instinct, and the dream of one-to-one marketing withered in the latest iteration of demographic segmentation

But the dream is still alive, though in a different form: the customer data platform (CDP). Modern, smart hub CDPs are very different from data warehouses. They aren’t interested in all your data. In fact, they’re not interested in 90 percent of your data. Their focus is on the 10 percent of data that captures customer behavior and intent. We call this behavioral data, and it’s some of the easiest data to track in your business, but also some of the most overlooked.

Data freshness matters

Behavioral data tracks a user’s actual behaviors across channels: the products they click on, the pages they visit, the articles they read, how often they read them, and so on. CDPs can collect this data through a tag management system that then exposes the affinities and insights related to this behavior. Netflix is a great example of how companies can use this data. They track behavioral data to know what shows I like to watch, how I like to watch those shows (e.g., am I a binge watcher?), and what the connections are between the shows I watch (e.g., do I like a particular genre, actor, director?).

One of the key distinguishing points between behavioral data and demographic data is freshness. Not every marketing use case requires real-time data (In fact, most don’t.), but they do need recent data. A CDP doesn’t care what you did 10 years ago because that has little to no bearing on your behavior today. And a CDP doesn’t care what your age or income group is interested in because that doesn’t reflect your interests.

The right data + the right data science

Data science is a trigger word for some marketers, synonymous with black-box solutions that replace “trust your experience” with “trust our machines.” For that reason, it’s crucial that CDPs start with tangible, understandable, and measurable use cases that demonstrate their value from the beginning. For some examples of great use cases, check out our recent blog on CDPs in the media industry.

It’s hard enough to get channels to align on a unified marketing strategy; getting them to align on a new technology that directly affects their numbers is an even harder hill to climb. Recognizing this, we designed our CDP solution to support real use cases within days of setting it up, which is really impressive in an industry where months (or years!) to ROI is the norm.

Lytics’ data science technology is complex stuff, but it’s designed for marketers and not data scientists. And we’ve even got a Data Science for Marketing guide that makes it a lot simpler to understand. The content affinity scores, customer momentum scores, and lookalike models that Lytics generates are easy to use and actionable. 

The customer 360 reimagined

Perhaps most importantly, Lytics builds customer behavioral profiles with built-in identity resolution across channels, which gives marketers a 360-degree view of customer intent and interests. Ultimately, that fresh 360-degree behavioral view is far more valuable than a 360-degree view of the 100 percent of customer information collected from the beginning of time and which probably includes third-party demographic data that may have no value at all in discerning customer intent.

As I said at the beginning, there is no magical solution to getting a complete view of your customer; it’s a combination of data selection and science. When you view your customers as changing individuals, rather than a collection of static interactions and statistics, you’ll gain a much better perspective of what your customers really want.

If you want to find out more about the data science underpinning Lytics AI decision engine, check out our Data Science for Marketing Guide.

Category: Uncategorized

We understand you might not be familiar with the brand Comparis, unless of course you happen to be in the market for a car, home, insurance, or investments in Switzerland. Then, of course, you’d probably be a customer, as they are the online comparison tool–think Consumer Reports or Priceline–that Swiss shoppers use over 80 million times per year (more than nine times per person!) to buy or sell cars, real estate, insurance, and more. 

Consumers trust Comparis for neutral product recommendations and transparency that surfaces competing deals from multiple vendors. Companies appreciate Comparis’ reach and ability to serve up their offerings to potential buyers. Comparis earns its revenue through referral and brokerage fees. So clearly, connecting the right customer with the right offer is critical to their success. 

Gaining customer insight

Like many organizations, Comparis had customer data spread all over their organization, in different departmental and technical silos. On top of that, marketers needed support from business analysts in the IT department. Consequently their segmentation suffered and they had to use a generic landing page experience, rather than personalizing it according to a customer’s needs and preferences.

Unifying customer data in actionable profiles

With Lytics, Comparis was able to break down these technical silos and create unified customer profiles based on behavioral data and affinities. Lytics collects this data, analyzes it, and scores each customer based on their interests, producing hypertargeted customer segments. Some segments are as small as a few dozen users, but automation and orchestration make it possible to serve them unique experiences that are relevant–and convert with results.

“Crazy good” personalization success

These profiles allowed Comparis to deliver personalized experiences across multiple channels. Their first was an email campaign that targeted visitors with personalized offers based on the services they had investigated on the website–an email that arrived the day after the customer’s visit to the site!The initial use case worked, delivering over 49% open rate, doubling their standard. As Larissa Ameti, data product owner, observed, “The results were crazy good!”

Next up were website personalization efforts, both of which built on the user profiles Lytics built. A modal display got a 6% clickthrough rate, more than tripling visitors to a subscription page. And best of all, personalized product offerings on the once generic homepage boosted clickthroughs by 80% and revenue per visitor by 10%. 

Download our complete Comparis case study for more details.

Category: Uncategorized

Last year, Lytics partnered with technology market research firm Vanson Bourne to conduct a study of marketing and IT professionals in the US to better understand their attitudes toward a variety of issues around accessing and using customer data today. (You can download the study here.)

It revealed that one of the biggest concerns marketers have, shared by over 2/3rds of them (69%) is the imminent disappearance of third-party data. Traditionally, marketing departments have used third-party data, specifically demographic data, to build personas and segments and then target them. 

But big tech companies like Mozilla, Apple, and Google and regulations like CCPA and GDPR are restricting third-party cookies, reducing their value. Nonetheless, many if not most marketers still use them as an integral part of their customer data strategy, but the value of third-party cookies is diminishing. 

Not all is lost, as organizations are building up vast troves of first-party data they can turn to for insight in scalable cloud warehouses, but this wealth of data poses its own problem. While marketers want to use the data to deliver better experiences to customers, it can be difficult (if not impossible) for them to determine which data offer the most insight. 

You’re not alone if you’re wondering things like: 

  • What types of data are poised to provide value to marketers today? 
  • How can marketers access those data? 
  • What technologies can help marketers sift through vast volumes of data to find the valuable, actionable insights it contains?

If you want answers, we have some for you. Lytics and Vanson Bourne partnered once again to explore these questions in a webinar held on Wednesday, March 3rd, titled How to uncover the right customer data for your business. Featuring Jessica Gillingham, Research Manager at Vanson Bourne, and Jascha Kaykas-Wolff, President of Lytics, the webinar examined these questions, data points from the study, and Google’s recent announcement that it would stop accepting third-party data in its advertising ecosystem.

Watch it today to find out: 

  • Why more data isn’t necessarily the answer to marketers’ dilemmas
  • Why third-party data is less valuable now than before
  • How first-party data may hold the key to unlocking personalized marketing

Watch the replay!

Category: Uncategorized

Customer segmentation is the cornerstone of an effective marketing campaign. By grouping audiences according to behaviors, concrete attributes, purchase habits, and affinities, marketers can create campaigns that resonate with audiences and provide a strong return on ad spend. While the goal of customer segmentation is to create better customer experiences, boost ROI and reduce customer acquisition costs, many segmentation efforts fall short of this. The reality is that even small to medium-sized businesses can spend upwards of $100,000 per month on Facebook or Google ads, yet get underwhelming returns on that investment because of poor or no segmentation. Other channels also benefit greatly from improved customer segmentation, including display ads, email, and mobile, as well.

What’s the difference between good segmentation and bad segmentation? It often comes down to the data. Many brands rely on demographic data to drive their customer segments and campaign decisioning. This includes both first-party demographic data—the data that brands collect through their own interactions with customers—and third-party data purchased from a data reseller. Of these, first-party data is preferable, as it’s more reliable, fresher and less expensive.

Why behavioral data builds better segments

But the best data for building segments is behavioral data. This is the data that marketers can trust the most because it’s based on users’ actions and behaviors, enabling marketers to draw reliable conclusions from that data. For example, if you’re an online media company and you know that a specific user keeps visiting your renewal page but has not renewed, you can reasonably conclude that they’re thinking about renewing their subscription. That, or they really enjoy clicking the “renew” button.

Best practices for customer segmentation

Beyond data, there are best practices for customer segmentation that marketers should follow. Here are a few that stand out from my experience in helping customers achieve segmentation success:

Build a customer segmentation team.

Some of our customers have seen great results by creating a center of excellence (COE) for customer segmentation. This usually starts with a subset of the marketing team tasked with building and maintaining customer audiences. The team should include a technical stakeholder as well as marketers who will build segments for activations. Generally, the time commitment of team members is about five hours per week during CDP implementation, decreasing once the data streams and mappings have been set up for most use cases.  A COE not only leads to a better segmentation strategy, but also has benefits down the road, as the new audiences being built can be synced with downstream marketing tools and used by other marketers for campaigns without requiring them to even touch the CDP.

Work back from your marketing goals.

Let’s say your marketing goal is to acquire new customers or reduce customer churn. Your audiences should align with those goals. For example, you might create a segment of customers who are likely to churn and set up rules to define that segment, such as “anyone who hasn’t clicked on an email in six weeks” or “a subscriber who hasn’t visited our site in two months.” Lytics CDP has easy-to-use rule builders to help with this, including access to explicit behaviorally-based rulesets, as well as implicit data-science-driven likely-to-churn (or likely-to-purchase) scores.

Don’t worry about perfection.

Getting your data into perfect shape for customer segmentation can actually be counterproductive. It’s more important that your data be prioritized and clearly labeled rather than perfectly clean, especially when you’re using a CDP. Otherwise, companies run the risk of going down the data warehouse rabbit hole again.

Mix it up by trying new segmentation tactics.

Experimentation is the key to getting the most value from your customer segments. If you have a hypothesis about a segment, test it. Measure the results along the way, and don’t be afraid to fail. If a segment performs poorly, then it’s an opportunity to apply your learnings to testing new segments. Also note that buying habits can change over time and segments should too.

One final thought to leave you with: using behavioral data to build segments doesn’t mean tossing out your old audiences. The objective should be to both create new audiences that you didn’t have before and improve the audiences you already have. Better customer segmentation is the result of better insights, and the best way to get those insights is with behavioral data and good data science.

If you’d like to learn more about how marketers can apply data science to their efforts, download our guide, Data Science for Marketing today.

Data science guide blurb

Category: Uncategorized

At its core, customer segmentation is grouping your customers according to specific attributes in order to personalize their experience with your brand. This should result in more timely and relevant communication with customers at any point in their lifecycle and is the first step toward reaching every marketer’s goal: one-to-one personalization at scale. But as companies take their first steps towards this ultimate goal, often the segmentation tactics they deploy focus more on the individual activation channels, rather than customers. 

Focus on customers, not channels

Customer segmentation has traditionally been a channel-specific initiative. The email marketing team may have one set of segments and audiences, the website marketing team another, etc. These segments may even have the same intent—e.g., identifying highly engaged customers—but use different, channel-specific data to define them. The email marketing team may define “highly engaged” as someone who has opened four emails in the last six weeks, while the website marketing team defines it as someone who has browsed more than five pages in the last month. This creates a fractured and inconsistent view of the customer across the business and prevents organizations from launching relevant omni-channel campaigns. 

In fact, marketing technology thought leaders are recognizing that organizations must move from this channel-centric perspective to one that is focused on the customer experience.

Small gains from segmentation aren’t good enough

Marketing teams may recognize that their current customer segmentation models aren’t perfect. But they’re good enough to give them measurable lift, whether it’s improving open rates or driving more users to their online properties. While following these models might be familiar and comfortable, the end result can come with an unfortunate cost.

Most importantly, it can cause marketers to make false assumptions about  customers. Someone whose engagement is shifting from email to online, for example, might look like a churning customer to the email team and an engaged customer to the web team.  This can lead to mixed messages and higher marketing spend as channels “overmarket” to engaged customers.

A customer data platform can help segmentation

A customer data platform (CDP) can help begin to address these pitfalls. By bringing siloed data from different channels into a single view for customer-, rather than channel-based segmentation,  marketing teams can gain several cross-channel efficiencies. 

Let’s return to the first example of “highly engaged users.” With a CDP, both the web and email teams can now build out a shared definition of what a “highly engaged” user is, taking into account data points from both channels (and potentially even more!). They only have to build this audience once, centrally located inside the CDP. Then this one agreed-upon audience can be shared to web, email, social, and ad activation tools. 

This results in a consistent user experience wherever a person interacts with your brand. Organizationally, it allows the marketing organization to start to plan and communicate, focusing on shared company goals. They can target their channel campaigns appropriately, making the most of their budgets.

Data science-based behavioral data yields more accurate segments

A CDP can also bring data science and machine learning to support customer segmentation, providing marketers with insights that may not be so obvious at first glance. Having a shared definition of what it means to be highly engaged is great, but it’s even better to have algorithms tracking your entire database of customers to identify and inform marketing teams which customers are engaged, which are likely to churn, which are disengaged, and which have a high affinity for low-heeled women’s boots–all without you having to lift a finger.

When using data science-powered behavioral data, you don’t have to define what it means to be engaged or not, or try to predict behavior by making educated guesses. The AI algorithms do it for you. For example, this month you may say five pageviews and three email opens define an engaged user. Yet with a holiday coming up, or some other new trend taking hold, suddenly a majority of customers may start visiting 15 or more pages. Using a CDP like Lytics, with its AI decision powered by behavioral scores, you won’t have to go back and adjust that definition for engaged users manually. It will be aware of the changing trends and update the definition of “engaged” automatically–and it will look beyond the obvious data points to create a richer, more robust definition.

These types of data science-based behavioral audiences open up new use cases for marketing teams. They can suppress engaged users from a new customer acquisition campaign in Facebook in real time with evergreen data. They could reduce the number of emails that an engaged online user receives to avoid over-marketing them. Marketing teams can pay special attention to users likely to churn with a special renewal offer four months away before their subscription expires.

Behavioral data helps remove bias

Returning to a previous point about making assumptions: As marketers, we often have to rely on third-party demographic data. Doing so exclusively can make us miss key opportunities and allow our own biases to get in the way of what each customer wants. 

Let’s say that a fashion retailer is launching a new line of fashionable women’s shoes. Through demographic data or just plain event-based behavioral data, you may choose to target  “women aged 25-54 with an income in excess of $75,000,” or “people who have viewed or purchased  women’s shoes this year.” Aside from the fact that third-party data may not be totally reliable, this type of segmentation misses out on men who may wish to purchase women’s shoes for themselves or loved ones because of the assumption that only women of a certain age will want to make this purchase.

If we turn to data science-powered behavioral segmentation as a complement or alternative to the above, we can instead target anyone with an affinity for women’s shoes based on their behavior, or run predictive models using behavioral scores and more reliable first-party data to determine who out of your customer base is most likely to purchase a women’s shoe.

Tuning into the customer rather than the channel

To be truly effective, marketers need to escape the channel mindset. That doesn’t mean ignoring channel outcomes, since marketers will ultimately be measured on their own successes. Instead, it means recognizing that a cross-channel view of customers benefits every channel, whether the benefit is improving engagement or increasing return on advertising spend.

Marketers also need to become better at harvesting and leveraging first-party behavioral data in their customer segmentation. Behavioral scoring, predictive scoring, and finely tuned lookalike models all have a place in the modern marketer’s toolkit, particularly if they plan to move beyond the small, incremental improvements of the past. We are, all of us, ultimately segments of one, with behaviors that change even when our age group or income group doesn’t. Adapting to those changes and adjusting our customer interactions to match them consistently across channels is the only segment that really matters.

To learn more about why companies need to focus on customers above all else, check out this webinar featuring Lytics President Jascha Kaykas-Wolff and Wharton Professor Peter Fader.

Category: Uncategorized

A person pressing a red button.

Nobody implements a customer data platform (CDP) on a whim. It’s a calculated, strategic decision to replace marketing-as-usual with marketing-by-individual. So, you would think that companies put a lot of time and thought into how they can make their CDP implementation a success from the get-go. And you would be right… about twenty percent of the time. The other eighty percent, well, not so much.

It’s not that companies don’t recognize the value of preparation. They’re just not sure what they should be preparing for, in large part because CDPs are still a relatively new technology. Even the term “CDP” has become a kind of catch-all description for anything that doesn’t fit into the traditional data management platform category. In fact, some “CDPs”  are nothing more than glorified ETL (extraction, transformation, loading) products. 

But if you’re talking about the actual customer-behavioral-insights-with-the-whole-data-science-thing CDP, keep reading. You’ll thank me for it later.

“I want this CDP implementation to fail so I can be fired!”

Admit it: You probably read that line even before you finished reading the first two paragraphs. Because no one ever says that. And yet, not preparing for the success of your CDP in the early stages of implementation is a recipe for failure. What are the ingredients that make up a successful CDP implementation project? The right people, the right data and the right proof points that show your CDP is working (and by “working,” I mean boosting your bottom line).

Make sure you have the right people in the room, right from the beginning

This piece of common advice applies to any big project, but it’s especially important with a CDP implementation. Teamwork is critical to its success and there is no such thing as a CDP team until you build one. 

A CDP team requires representation from each marketing channel, IT, privacy, and any group that is responsible for collecting and using customer data to drive decision making. The reality is that marketing teams have become fractured over time, splitting into their own channels with their own sets of goals and metrics for success. CDPs are not only about bringing the right data together (which I’ll talk about in a moment) but also bringing the right people together to share that data, ensuring that the customer interactions flow smoothly from channel to channel and from experience to experience.

Take small bites of your data

Your Mom probably wasn’t a data scientist (and if she was, you were sooo lucky), but the motherly advice of taking small bites totally applies to CDPs. We often see customers who want to take too big a bite from their data and bring in everything while they’re still learning how to use a CDP. 

Connected dots.

That’s a bad idea, because most of the data you have doesn’t even need to be in a CDP. In our experience, less than ten percent of all data provides valuable insights that help predict customer behavior. Instead, we recommend starting with a list of 25-30 data fields and then adding fields very selectively to the CDP. If you’re thinking about bringing in 100 different data fields right from the start, that’s a big red flag. CDPs are all about having the right data, not all the data.

Start simple before you go big

The most successful CDP implementations start with a few simple use cases. This allows marketing to demonstrate the value of a CDP early on and build up the executive support needed for bigger projects. Converting unknown users into known users is a good early use case. Suppressing engaged users from your social media ads to reduce ad spend is another way that marketers can quickly attach a monetary value to their CDP. 

Once you’ve got a few early wins under your belt, you can start to expand the reach of your behavioral-based decisioning. Over time, you’ll find that CDP use cases follow a cycle: develop a use case, gather behavioral data to support the use case, enrich that data with insights and analysis, build another use case based on those behavioral insights, etc. It begins to feel very intuitive with time, but at first it can feel a little uncomfortable, kind of like driving a car.

Go forth and prospect

Change is scary. Different is scary. And CDPs represent both a change and a different way of doing things for marketers. The fear of the unknown, however, doesn’t have to come into play with a CDP implementation. Technical consultants (like me) have been through it all. We know exactly what works and what doesn’t work as companies roll out CDP technology in their business. Follow the advice above, and you’ll end up exactly where you hoped to be: closer to your customers on a meaningful journey that feels authentic, because it is authentically their own journey.

Are you ready to implement a CDP? Find out with our CDP Readiness Guide.

Category: Uncategorized

According to Hubspot, almost 7 in 10 marketers believe that paid online advertising is either very or extremely important to their marketing strategy. Digital advertising enables marketers to deliver key brand messages and appropriate offers to customers across the digital landscape. But like any paid channel, it’s critical to evaluate what’s working and optimize spend to deliver maximum impact efficiently.

Creating audiences in Lytics allows marketers to precisely target the right customers for any given message. As such, it’s critical that marketers can deploy ads effectively using their execution channels–in this case, ad networks.

As Product Manager of Integrations at Lytics, I heard from our customers that increased ad network support was critical to their marketing success.

Increasing Lytics’ ad network connections

Over the last half of 2020, we committed to building out our connections to the ad networks marketers are using. We nearly tripled the number of ad network tools available as execution channels for our clients and also increased the functionality of Lytics on ad networks that we already connected to. As a result, marketers can now use Lytics audiences across a wide variety of tools, including:

And we’re not done there–we’re currently also working on our connections to Google Display & Video 360, and we’re committed to connecting Lytics to all the tools marketers need to reach customers with ads.

Leveraging Lytics’ audiences within ad networks

With Lytics audiences, marketing teams already have the ability to select and target highly specific groups of customers for personalized experiences–and these connections expand personalization opportunities across the leading ad networks. Lytics’ AI-powered data science lets you create lookalike models based on ideal audiences, like high-value customers, and the predictive power of behavioral scoring.

Marketers may optimize ad content for these precisely defined customer segments, saving resources, for example, by not hitting satisfied customers with winback promotions.

With a customer data platform that connects to your ad network, like Lytics, you can surface insights from disparate platforms to make data-driven decisions on which ad campaigns to present to users. Additionally, aggregated ad impressions and clicks metrics can further influence automated decision-making along your customer journeys.

Orchestrating omni-channel marketing campaigns

Lytics decision engine can help marketers optimize channel decisions. For example, if ads on a given network, like Google Ad Network, are performing better than an email campaign with the same goal, Lytics can dynamically route users into the appropriate audience and serve them the ads that are helping achieve your goals — whether it be to place an order, join a loyalty program, or any other business objective.

Commitment to innovation

We’re committed to continuously improving Lytics to meet the needs of our customers and users, marketing professionals like you. Our investment in improving connections to ad networks continues, and we look forward to sharing our latest innovations in leveraging data science and AI to achieve greater control over your ad spend.

Visit our use case navigator and select ‘Ads’ to learn how Lytics can help you acquire new customers, optimize spend and reach your advertising goals.

Stay informed of our latest product updates by visiting our Product Updates page.

To explore all of our integrations further, visit

Category: Uncategorized

In marketing, we like to talk about customer journeys, but what we’re really talking about is customer lifecycle management (CLM)—the idea that a customer relationship can be broken into stages, from acquisition to engagement to purchase to loyal customer and, hopefully one day, brand advocate. Implicit in CLM is the idea that marketing will be in the driver’s seat for much of that journey.

In theory, customer lifecycle management is a great idea. In practice, it’s a bit trickier. Why? Let’s just say there are too many drivers.

The problem starts with the fact that many marketing organizations are siloed by channels. There is content marketing, paid marketing, email marketing, and so on. Each has their own unique set of data, objectives and metrics for success, and each is focused on their own performance and goals. The paid marketing team may be interested in return on advertising spend and improving conversion rates. The email marketing team typically obsesses about open rates and click-throughs. The content team focuses on audience reach and engagement measures like pageviews, conversions, social media shares, and time on page.

While each of these channel performance measurements is important, what gets lost is the importance of delivering timely, relevant, personalized experiences. Siloed teams with their channel-specific data struggle to target campaigns effectively because their decisions are based on the siloed data. As a result, customers end up living double (or multiple) lives, with each channel or team managing the customer differently.

Unifying customer data for better lifecycle management

To really get a holistic picture of a customer, you need to centralize your prospect and customer data from your website, emails, ads, and mobile apps. This is a critical step for effective CLM. Only when you stitch data fragments across sources into a single customer profile through identity resolution can you begin looking at the right things: behavioral data, first-party data, multichannel data. 

Stitching identity graph

Bringing this high-value data together from different silos into a single, smart hub is a big part of what a customer data platform (CDP) does, in effect allowing marketers to build customer segments in minutes instead of weeks. When high-value customer data is in one place, a CDP with built-in artificial intelligence and machine learning can add even more value by identifying which customers are engaged (and how much), separating loyal customers from those who are likely to churn, and building lookalike models based on your best existing customers.

Even more importantly, CDPs collect and analyze first-party behavioral data, which is pure gold for marketers. First-party behavioral data drives personalized experiences like those delivered by Netflix and Amazon and helps brands make meaningful content recommendations that move customers along the journey.

The secret life of Customer X

Journeys rarely follow a straight line and customer journeys are no exception. They can fork off unexpectedly, like when you get married or relocate for a new job. They can stop abruptly. (Covid-19, I’m looking at you). And those changes in direction manifest as changes in behavior. Certainly, the pandemic has radically changed consumer behavior, from how we buy our groceries (and what kinds of groceries we buy) to the kinds of books and articles we read.

Customer journeys

Now, take those changes and view them from a strictly channel-specific perspective. Customer X (which I totally admit is me), might not be opening emails from Best Buy anymore. To the email marketing team, I look like a customer ready to churn. In reality, however, I’ve simply developed the habit of reading the subject line of the email and going directly to the website to research whatever product/promotion is mentioned in that subject line. In other words, I’m still very engaged with the brand, but I’ve changed my behavior.

To the online and email marketing teams, I look like two different people, in effect living out two very different lives. To a CDP, however, I’m a loyal customer who has simply shifted my behavior. So, instead of receiving email offers targeted to a customer winback segment or, worse, being sent back to the email prospecting bucket again, a CDP recognizes me across channels and allows all marketing teams to meet me where I am in my journey.

Start customer lifecycle management with small wins

Companies that have success in customer lifecycle management don’t arrive there by accident. They’re guided by a desire to improve and change. Change can be scary for companies that have a set idea about how marketing should be done. I think of it as the inertia of experience. Email marketing teams learn to equate an incremental improvement in open rates with success. Paid marketing teams have historically leaned on data management platforms to optimize ad buys and improve targeting–a practice that is disappearing as browsers tighten their rules around privacy and data use. The changes that a CDP brings to CLM, whether it’s creating customer segments around behaviors rather than demographics or serving up customized slices of your website, can feel risky.

To remove that sense of risk, two things are needed: executive support and small, early victories. The first is self-explanatory. The second is more problematic, since marketing teams don’t know what they don’t know. In our experience, small but measurable wins can build trust and confidence in a CDP. For example, optimizing ad spend by suppressing known customers from an acquisition campaign that you’re running on Facebook or Google. Or setting up personalized modal experiences on your website to collect an email address and create that critical first-party relationship. Targeted projects like these show the value of a CDP in helping to support a more intentional and effective CLM strategy.

There is no golden rule for successful customer lifecycle management. Each industry is different, and each brand is different. The gold lies in collecting the right data, making it accessible to everyone and drawing meaningful insights from that data to create unique customer journeys for each of your customers.

To see Lytics in action, check out our new five-minute demo video.

Category: Uncategorized

This post is a guest post from Lucas Sommer, director of marketing at LeadsRX. Over the last decade, Lucas has worked with hundreds of organizations and marketers to set up their attribution and help them get useful insights from their data. As director of marketing for LeadsRx, he currently focuses his energy on understanding and optimizing their cross-channel stack as well.

Having choices is a great thing. Sometimes, when you have options, choosing one over another is a great luxury. When choosing the right attribution model for your ad campaign, making the right selection is more than a matter of luxury. It’s a matter of assessing whether your marketing dollars are being spent wisely or thoughtlessly. Marketers that use attribution models and are familiar with how they work may still experience some consternation when it comes to leveraging the one that provides the greatest insight.

When it comes to selecting an attribution model, making the right choice really boils down to the specific ad campaign you’re running and what your objectives are. Before we begin identifying campaign types and objectives, let’s first discuss what an attribution model is.

What is an attribution model?

Determining what prospect interactions lead to a final sale can be compared to searching for the holy grail. You know it exists. People have talked about it. Finding it is another story. Marketers know that a sale has occurred, but what led to that final conversion? This is where attribution comes in.

An attribution model is a means of identifying those specific interactions or touchpoints prospects experience with your company on their way toward making a purchase. As we all know, engagement may be a positive or negative experience. In an attribution model, the hope is that all interactions are positive and ultimately propel the prospect forward to making a buying decision.

There are generally two types of attribution models: a single-touch model and a multi-touch model. Both offer benefits, depending on the type of campaign being run, the length of the sales cycle, and the type of product or service being offered. With that said, at least one model is typically suitable for an ad campaign being run.

Choosing the right model

There are several things to consider when deciding which attribution model to use for your campaign. Those things include determining your marketing objectives, your budget, your website content, and other channels. At the end of the day, marketers must understand which attribution model fits the desired insight.

First-click model

First click attribution is a single-touch model that emphasizes the first touchpoint a prospect has with your brand. That touchpoint could be a Facebook ad that is eventually followed by several other interactions. While there could be one additional interaction or several additional interactions, the first one receives all the credit for the sale.

When first-click works best

While possibly missing many other interactions contributing to a sale, the first click model can be a powerful approach to finding new customers. For some campaigns, understanding which action leads to a new prospect entering the top of the funnel is most important. Refocusing more resources to campaigns that deliver on this notion, such as social media ads, eBooks, etc., can truly provide the needed insight.

Last-click and last non-direct click model

Last-click attribution works the opposite way a first-click approach does. Rather than assigning all credit to the first touchpoint, the last-click attribution model allocates all credit to the last interaction before the sale.

A last-click model can be direct or non-direct. When engagements are direct, customers will type in your company’s URL and make a purchase. There may have been other touchpoints that lead to awareness, but when the customer was ready to buy, they went directly to your site. For non-direct engagements, a customer may be referred to your site via search results or a partner site. They arrived at your site, but they did not type in the URL.

When they work best

The benefit of using either of the last-click models is understanding which interaction leads to the actual conversion. The direct model works well with campaigns that use discounts, trials, or specific calls to action that lead directly to a purchase. The non-direct model works well with campaigns that are designed primarily for awareness and brand building.

Linear model

There are campaigns where marketers may want to give every touchpoint an equal share of sales credit. In those instances, a linear attribution model works well. When searching on a related topic, your prospects may find your company, subscribe to your email newsletter, or come across a LinkedIn ad. After each of those interactions, a prospect may find a reference to your company on a partner site, click on your website, and make a purchase.

When linear works best

The linear model works best when you want to measure the effectiveness of your entire marketing strategy. This can be done through brand awareness and other campaigns where a single interaction is not the driving factor for the sale.

U-shaped model

The U-shaped or position-based model attributes 40% of the sales credit to both the first and last touches of a campaign. The remaining 20% is divided evenly among the other touchpoints in the journey, whether it be one more of five more.

When it works best

The U-shaped model is ideal for campaigns where you want to know what works well for the top and the bottom of the funnel. In other words, the engagement that garnered the initial attraction and the one that occurred last to close the sale is best for this model. When campaigns rely heavily on keywords, marketers can determine what engagements opened the door and which ones sealed the deal.

Time Decay model

The model that gives the most credit to those touchpoints that occur closer to the sale is the time decay attribution model. Initial touchpoints are less important, while those that lead up to and occur closer to the conversion are deemed more important.

When time decay works best

Campaigns that assume a long sales cycle, and require educating the prospect, work well with the time decay model. Often, awareness campaigns, partner campaigns, and activities that build your brand over time are ideal candidates for the time decay model. The time decay approach can also help marketers better understand the true length of their sales cycle.

Data-driven model

Data-driven attribution is perhaps the most sophisticated model of the ones we’ve reviewed. This approach relies on using data (whether derived from A.I. or other means) to determine each touchpoint’s importance. The data-driven model makes no general assumptions but relies on each engagement’s performance to determine attribution weight.

When data-driven works best

The data-driven attribution model is best suited for more comprehensive campaigns where a relatively large budget can accommodate enough data to analyze each touchpoint’s effectiveness. Whether the campaigns are focused on clicks, awareness, or non-direct attribution, having enough data to drive detailed analysis works well with data-driven attribution models.

As we stated initially, choice is a great thing to have, especially when you know why you’re making a choice. Using the right attribution model for the right campaign will go a long way in giving you the information you need to implement the most effective campaigns for the best ROI.

Category: Uncategorized

Three different people connecting from different servers.

There’s a temptation for business to business marketers, especially ones operating within an account based marketing approach, to view their prospects as monolithic entities–and to market to them as such. Prospect company X operates within this industry and geography. They have  defined challenges based on their technology stack or organizational structure. They need to accomplish a set of defined goals.

But that’s a big mistake. As much as organizations share goals, challenges, and other characteristics, they are not homogenous. They’re made up of distinct individuals, each of whom have their own specific challenges, skills, goals, and perspectives. These people aren’t solely representatives of their companies; they’re also consumers. Consequently they have become accustomed to and prefer the sorts of personalized marketing experiences that leading consumer brands like Amazon, Netflix, and Spotify deliver–and this has big implications for how B2B marketers should approach developing and deploying content.

Key considerations for B2B content marketers

B2B marketing, especially for software-as-a-service and technology companies, has become increasingly synonymous with content marketing. Provide valuable content to businesses aware of a challenge or problem, develop their understanding, present a solution, and trust that qualified buyers will grow to recognize the value of your offering.

It seems straightforward enough, until you acknowledge the fact that most companies of sufficient scale to invest in software solutions tend to be large organizations with over 1,000 employees and multiple stakeholders involved in the buying process. Complicating that, most likely those stakeholders will come from different parts of the organization like IT, marketing, sales, operations, and legal–all with their own unique sets of challenges and requirements.

For example, content that speaks to the needs of a marketer might be indifferent to an IT professional charged with implementing a solution, and potentially frightening to a legal professional concerned with privacy regulations. To support sales efforts, marketing has to ensure that the right content gets to the right person at the right point in the buying process–but how?

Challenges to personalizing content marketing

Organizations using ABM tools may know when a person representing a target account is on their website, but these tools don’t identify who the person is, what their job function is, their role in the buying process, or their level of awareness of the company’s offerings. Consequently, they can’t be sure they’re delivering relevant content that could lead to a sale.

A traditional customer data platform helps companies resolve site visitors’ identities, which is the first step in the process of serving them up the most relevant content. But B2B companies face an additional challenge in that they have to resolve their visitors’ identity in two ways.
• First, they need to identify the user as a member of a specific account–what prospective customer do they work for?
• Then, they need to identify who the user is, their job function, and where they fit into the buying process.

How Lytics helps B2B marketers

Lytics offers two key features that empower B2B content marketers beyond the capabilities of traditional customer data platforms: graph database identity resolution and content affinity.

Lytics’ identity graph stitches together customer data from different data sources into a single user profile. Rather than solely classify a user as a representative of a targeted account, it gathers behavioral data based on their interactions with the site, and predicts their interests. By identifying users’ preferences and organizations, it can serve up appropriate marketing experiences across B2B organizations’ marketing execution channels, such as personalized experiences on a website, appropriate ad retargeting, and in email marketing.

Lytics user profile with content affinity.

That’s where content affinity comes into the equation. Since target markets are so specific in an account-based marketing approach, organizations can’t afford to deliver inappropriate or irrelevant content. With most B2B companies having a wealth of content across their website, on their blog, and in resource libraries, Lytics reduces the workload by having its content affinity engine crawl all the content and use natural language processing to identify topics. When a site visitor engages with a specific piece of content, Lytics identifies their interest in specific topics, then recommends similar, relevant, content to that user in the future.

Key lessons for B2B marketers

Whether or not you choose to use technology to address your B2B marketing needs, there are some key lessons that you’d be well-advised to incorporate into your content marketing efforts.

  • You may be targeting specific accounts with your marketing efforts, but ultimately you are marketing and selling to people.
  • These people are also consumers, and they expect a certain amount of personalization. Impersonal or irrelevant offers and content risk alienating the people who will make the decision whether or not to buy.
  • Customer data platforms aren’t just for direct-to-consumer packaged goods and media companies. B2B companies that use content marketing strategies can profit by using behavioral data and content affinity to deliver relevant marketing experiences at the right time.

To find out more about how Lytics works with B2B organizations, check out our B2B Technology education page.

Category: Uncategorized

2020 has certainly been an interesting year. (How’s that for understatement?) But it’s safe to say that it’s taught everyone a few things as people and professionals.  We’ve had to restructure our lives around the pandemic. Many have had to change professions, jobs, or at least how we work and interact with our colleagues. Many of these changes have forced us to re-evaluate what is truly essential, what is nice to have, and what wasn’t really so necessary after all. 

And that’s also true of us as marketers and technologists. Looking back over the year, Martechcube [linkto:], the marketing technology-focused media publication house, compiled their list of the top 10 thoughtful martech quotes from martech leaders in their article Top 10 Martech Thought Leaders’ Insights – 2020 Roundup. We’re proud that Lytics’ Co-founder and CEO James McDermott was included in the list for his observation that:

The legacy approach for marketing technology is to solve a specific problem in a specific channel with a specific tool.

Challenges arising from complex martech tasks

The quote, which was taken from a January interview, calls attention to the unintended consequences of that original marketing technology approach. As marketing teams find themselves faced with more channels for digital experiences and more technologies, they may individually solve some of their problems with new tools, but at the same time they are creating other challenges: 

  • Increasing complex technology stacks: He continues, “Today there is a proliferation of over 7,000 marketing tools to execute one-off campaigns in a single channel.” Every marketing team operates in multiple channels, from website to email to ads, SMS, apps, and more. Each channel creates its own data stream that exists in a silo.
  • Difficulty creating accurate consumer profiles: With data in multiple marketing technology sources–not to mention other data sources like purchase transaction data, customer service records, and third-party data sources–this has created challenges in aggregating customer data into a unified profile. There are technologies like Lytics customer data platform that can help marketers stitch together unified customer profiles, but many of them (fortunately not Lytics!) stop at aggregating data, rather than translating it into insight and action.

The rise of the decision gap

Data on its own does not help marketers reach consumers more effectively. To do that, they need insights that drive results, and after that, they need to implement these insights about the data through the execution tools in their martech suite, in the form of personalized website experiences, content recommendations, and next best experiences that advance the customer through. Marketers need answers to questions like:

  • Where should I place ads to reach the right customers?
  • Which customers are satisfied and which are at risk to churn?
  • What content should I recommend to a customer who has displayed an interest in a given topic?

This challenge of translating data into action is a decision gap–which Lytics CDP fills.

The Decision Gap

Delivering optimal experiences to customers with Lytics

McDermott continues, explaining that “the next generation of marketing automation enables companies to automate the delivery of personalized experiences based on changes in people’s behaviors, interests, likelihood to purchase and more, so ultimately [it] incorporates automated triggers and personalization creating the optimal experience for each individual consumer.”

As pioneers in the CDP category, Lytics offers marketers more than a traditional customer 360. It unifies disparate data sources, but additionally its machine learning decision engine offers insights that marketers need and the ability to automate and execute personalized marketing programs across channels, at scale, based on those insights. Rather than aggregating data, it focuses on extracting valuable insights from the data in a marketer-friendly way.

To learn more about Lytics’ place in a best-of-breed marketing technology stack, check out James McDermott’s recent blog post on the New Martech stack for 2021.

Category: Uncategorized

Over 300 million people listen to Spotify to get their music fix. Why? Because Spotify listens to over 300 million people.

It’s amazing, right? In a world where most marketers have to literally bribe customers to fill out a survey, Spotify customers are freely sharing with Spotify what they like and don’t like about the service. And they’re doing the same with your brand too, only you may not be listening.

The purpose of this blog isn’t to make you feel bad, or to present Spotify as some kind of marketing wunderkind. Rather, it’s to make a point that most marketers are listening to the wrong signals. Consider, for example, how you target offers to your customers. Do you target customers based on demographic data (e.g., gender, income, age), or do you target them based on the behavioral signals that are hiding in plain sight–in your customer data?

The difference between segmenting customers by demographics versus behavior is striking. To illustrate that point, look at the two playlists below. The playlist on the top is a selection of songs I haven’t heard based on the songs that I actually listen to. The playlist on the bottom is based on basic demographic data: popular music in the country where I live and what kind of music people in my age group listen to, etc.

Behavioral segmentation playlist Demographic playlist

Which playlist do you think is most likely to keep me engaged as a listener? Exactly, the one on the top.

Demographic segmentation vs. behavioral segmentation

Demographic segments and behavioral segments are after the same thing: personalized marketing. But where demographic segmentation is based on what I think I know about a certain type of customer, behavioral segmentation is based on what customers are actually interested in as demonstrated by their actions. The trouble with assumptions, other than being flat-out wrong an alarming percentage of the time, is that they tend to stay the same from year to year–but consumer behavior can change dramatically from year to year. Just look at the recent pandemic. How many people thought they would be shopping online for groceries twelve months ago or paying top dollar for toilet paper?

As much as marketers would like to achieve segments of one, it’s just not practical when you have millions of customers. What marketers really need are better segments based on real behavior rather than static stereotypes. “Married men with incomes above $100,000” isn’t a meaningful segment because it’s not telling you anything about a customer’s intent and interests. “People who like electronics and social causes” does tell me something that I can use to start a meaningful conversation with that customer and guide them along an intelligent journey with my brand.

Journeys are increasingly where marketing is headed. For example, I’ve recently begun buying pants from Bonobos. Now, there’s nothing in my DNA or demographic data that screams out “Bonobos pants.” By understanding my interests, however, Bonobos was able to take me on a journey, from introducing me to the brand, to telling me a story about the company’s ethical practices and high-quality production techniques, to eventually selling me a pair of pants, and another pair, etc. But, much like my pants, that was a journey that was tailored to me as an individual.

The future of segmentation

So, if building a segment of one is impractical, how do marketers guide customers along their own individual journey? Through personalized offers and content recommendations. With behavioral data and the right tools, marketers can automate content recommendations and product offers to keep individuals engaged and interested. At Lytics, we call this a recommendation engine. Recommendation engines can support and even replace segmentation by presenting real-time, personalized offers and experiences to your customers based on what you know about those customers’ interests and preferences. And, as Spotify has shown, if you’re tuned into what your customers really want, they’ll stay tuned into your brand.

Find out how you can personalize marketing experiences for your customers in Lytics’ guide to personalization at scale.

Category: Uncategorized

There’s no shortage of facts making the case that data–and lots of it–is central to a successful business strategy these days. Media began using the term Big Data in 2005, and escalating gains in processing power, data storage capacity, and the ability to accumulate data on consumers with have only accelerated the volume and availability of data to marketers.

But why hasn’t the promise of big data delivered?

Would anyone argue that marketing in 2020 has kept pace with our ability to accumulate and process data? If more data were the answer, wouldn’t the everyday experience of consumer marketing be… better? Online ads alternate between irrelevant, offering up goods and services you don’t want or need, and creepy, stalking you long after you’ve made a decision. How many emails do you delete… each and every time you check your email? While you might like receiving an offer via text, I might not. On and on it goes, even though marketing is ostensibly smarter.

There are positive outliers, of course, but for most companies’ marketing departments, access to more data about their customers has not catapulted their marketing into the stratosphere occupied by digitally native companies (usually built on an AI infrastructure) like Netflix, Amazon, and Spotify.

Why not? Because information is not insight.

Understanding the customer 360

The impulse to collect more data in order to make better decisions is long-standing, if not intrinsic, to humans. From ancient Greece, to the development of the scientific method, to (yes) modern marketing, generally the first step we take to solve a problem is gathering data in a systematic way. For many marketers, that “gathering of data” means building a 360° view of the customer, that is, collecting all of an organization’s customer data in a single repository, accessible to all pertinent stakeholders, including marketing.

The data stored here might include, but not necessarily be limited to:

  • Demographic data like age, gender, address, ethnic background, etc.
  • Past and current purchasing data
  • Customer service records
  • Social media interactions
  • First-party behavioral data from digital properties

Many vendors in the CDP space suggest that having all the data (assuming you follow data hygiene best practices) gives marketers what they need to make better decisions. But results often don’t follow, because marketers can’t understand, much less process, the massive trove of data that even a moderate size business possesses.

Why a customer 360 isn’t the answer

Accumulating petabytes of data doesn’t produce insight on its own. Huge amounts of information are far beyond human ability to process. For an example of how humans can process information, think about the scientific method. Scientists painstakingly create experiments that isolate one or two variables to gather evidence that validates (or invalidates) a single hypothesis. They don’t randomly observe a wide array of correlated but uncontrolled events and then draw a conclusion.

What this means is that marketers, working with the wealth of data available today, often end up either facing analysis paralysis or going with their instinct: in essence, guessing. Neither of these situations result in the truly personal, relevant, timely marketing that consumers want.

A second flaw with the customer 360 without another technology for activation is the complexity of modern marketing technology stacks. While most platforms billed as customer 360s integrate well with data sources, they are not able to orchestrate and execute marketing activities. Customer data on its own (and even basic analytics of customer data) also doesn’t translate into marketing execution: deploying ads, personalizing website experiences, recommending content, or delivering social media and email messages.

The result is a decision gap

The Decision Gap

On one hand, marketers have a wealth of data about their customers. On the other hand, they have a variety of execution tools that they can use to deliver highly personalized marketing experiences, content recommendations, and next best experiences. The customer 360 fills a role by aggregating data from disparate sources, but it doesn’t answer the questions marketers are asking:

  • What ads do I need to place–and in what channels?
  • Whom should I be targeting with each offer?
  • What content and products should I be recommending to individual customers?

Lytics refers to this gap between information and action as the decision gap.

What is a decision engine?

To translate data into action, marketers need a tool that fulfills two key functions. First, it must provide technical integrations between data sources, data pipelines, and marketing execution channels. Secondly, it must possess a means to interpret data and make concrete recommendations. Given the vast quantities of data involved, the only viable means is through artificial intelligence–what we term a decision engine.

In a best-of-breed CDP stack, Lytics bridges the gap between your data and your activation channels–your website, your mobile application, your ads, social media, and emails. It applies its machine learning capabilities to your data, focusing specifically on the first-party behavioral data that underlies most customer actions, and derives insights from them.

Modern Best-of-Breed CDP

Then, powered by data science but built for marketers, Lytics decision engine:

  • Activates those insights, giving marketers the ability to deploy them across ad platforms and marketing tools
  • Orchestrates them in personalized campaigns delivered through multiple channels
  • Recommends products and content based on customer affinity, increasing engagement and maximizing return on spend
  • Delivers truly personalized, 1:1 marketing experiences to customers

For many organizations, a decision engine like Lytics can function effectively on its own as a full CDP, as it can both ingest data and build holistic customer profiles. On the other hand, data pipelines, no matter how efficient, cannot provide the insights and activation that a decision engine delivers.

If you’re curious to find out if you’re ready for a customer data platform, download our CDP Readiness Guide.

Category: Uncategorized

How used Lytics to boost conversions

For its 30 year history, has helped enrich its customers’ lives and connect their present and future with their past through journeys of personal discovery, whether in the form of building family trees or investigating their genetic past. With over 27 billion online records, 100 million family trees, 330 million user-generated photos and documents, and 3 million subscribers, it’s no surprise that they’re attuned to the power of data.

And through their use of Lytics, Ancestry’s marketing department has provided another proof point. Ancestry’s Hope Twiss, Sr. Marketing Manager, CRM, and Chloe Petersen, Personalization Manager, CRM recently participated in a customer webinar in which they shared how they’ve used Lytics CDP to increase revenue and customer engagement through two key use cases: reducing cart abandonment and optimizing their onboarding experience.

Watch the video below or read on for more highlights.

Aggregating data from multiple sources

They use Lytics to collect data from a variety of internal partners, including Martech, Member Services/Operations, Marketing, UX/Product Management, Legal, Business Development, and more, and they use it to power engagements across multiple channels including their website, email, and social media advertising in real time.

Reducing cart abandonment

Before Lytics, had problems with delayed cart abandonment emails, with notifications to customers taking 48 hours to four days under certain circumstances. They implemented Lytics to address the problem, and reduced their response time to just 30 minutes and aligned email, Facebook retargeting, and onsite experiences to remind customers about their abandoned product selection at 30 minutes, 24 hours, and 48 hours.

With the Lytics-powered experiences, they achieved amazing results:
• 23% increase in email open rate
• 29% increase in click rate
• 220% increase in conversion rate

Increasing customer engagement with contextualized onboarding

Ancestry recognized that users who completed certain basic onboarding tasks, like building a family tree or purchasing a DNA kit, tended to convert at a better rate than users who did not. They worked with their product team to identify four key onboarding experiences or milestones, and then used Lytics to create contextualized customer journeys to provide users with prompts to complete those four conversions.

With these customized Lytics experiences, they were able to increase conversion rates for the first step from 54% to 137%, and for subsequent steps at rates from 2% to over 70%. More importantly, users who completed the onboarding converted at a 29% increase in recurring revenue over non-onboarded customers, forming a core of reliable, engaged customers for their services. By adding on paid social media advertisements, they were able to add an additional 6% to their onboarding completion rate and 3% to their site visit rate.

The next generation of improvements

Ancestry is far from finished accomplishing marketing goals with Lytics. They plan to apply their abandoned cart strategy for more product offerings than their DNA tests and for multiple-purchase cart abandonments as well. They plan to implement additional coaching steps to encourage users to complete the onboarding process and to apply it in international markets as well.

And with Lytics’ deep integrations with Google services like BigQuery, Looker, Display and Video 360, and Ad Manager, they’re just scratching the surface of the ways they can apply their first-party data to optimize customer experiences and lifetime value.

Learn more about how Google + Lytics work together.

Category: Uncategorized

Few people would argue the statement that giving a customer more of what he or she wants is a good idea. If they, for example, spend a lot of time engaging with particular type of content-say, learning about cat behavior, or watching cat videos–then it’s only logical that a good way to engage them further is to suggest another article or video about cats.

But of course consumer behavior is much more complicated than that. People have many interests that they engage with on a regular basis. Our hypothetical cat lover may regularly research recipes for different kinds of ethnic cuisine, read reviews about new British mystery shows, and enjoy virtual window shopping for (if not purchasing) expensive brand name shoes. How can marketers create a meaningful profile of this person–and how can they then take steps to deepen their relationship with them?

Content affinity can help.

What is content affinity?

Since an affinity is a natural liking, preference, or taste for something, in marketing, content affinity is a consumer’s tendency or preference for a specific type of content. In our example, the customer mentioned has displayed affinities for cats, ethnic food, mystery shows, Britain, and certain brands of shoes.

Lytics takes content affinity a step further, creating a quantitative measurement of a user’s content affinity, which can be loosely interpreted as the chance that user will interact with similar pieces or types of content.

Why is content affinity important?

Your message is competing for your customers’ attention, but not just with your competitors’ messages. It’s competing with every other industry’s messages, with YouTube videos of cats, the news, social media, Netflix, and everything else out there. If you’re not giving your customers what they want, they’re not going to pay attention, plain and simple.

But that doesn’t have to be cause to despair. If you’re paying attention to your customers’ behaviors, you already know what they want. If you measure what your customers are interacting with, you can determine which content affinities they have. You can build those affinities into multiple channels to deliver the right message:

  • Personalized content recommendations on the web
  • Customized “You might like…” product recommendations
  • Newsletters with curated content

How does content affinity work?

There are a couple of key components to ensuring that you’re accurately capturing your customers’ affinity for content. One is making sure that you have good content hygiene; that is to say that you accurately capture, classify, and track your content. The second is ensuring that you’re capturing the right data about your customers to capitalize on their affinities.

On the content side, there are a few key strategies to keep your library healthy.

  • Curate your library effectively. Don’t include elements of your digital properties that aren’t customer-facing–for example, user profiles, retired products, or duplicate pieces of content.
  • Classify your library accurately. Make sure content is tagged with the topics it belongs to. These could be product types or uses, topic buckets (like “cats” or “Korean recipes”), or any other classification scheme. The key is that you accurately understand what topics (or products) a user is expressing affinity for when they interact with a given page.
  • Maintain your content library. Your content isn’t static. New pieces are added and old ones are removed regularly. If you remove a page from your website, it won’t do to recommend it to a user only for them to encounter a 404 error.

Fortunately, technology like Lytics’ customer data platform can make content hygiene easier (or even automated for certain tasks).

On the customer data side, the key is capturing their affinity–and that requires first-party behavioral data, not demographic data. Thinking about our hypothetical cat-lover above, whether or not they’re male or female, their age, or where they live doesn’t contribute to our understanding of their affinities. The data points that directly correlate with their interests are behavioral, tracking things like:

  • What types of content does the user visit most often?
  • How many articles, videos, or images display a given content affinity by the user?
  • How long does the user engage with content type 1 vs. content type 2?

Behavioral data shows what individuals actually do–not what a marketer might think they’ll do based on demographic data.

How can Lytics help?

Lytics customer data platform houses our Content Affinity Engine, which takes in information from your library of content and users’ interactions with it to power its content (and product recommendations). At the start of the process, it crawls the pages you designate as having content, extracts metadata to better understand them, and uses Natural Language Processing to identify the topics that are associated with it.

It helps ensure that your content library is healthy, sanitizing and deduplicating URLs so your content library isn’t cluttered with identical pieces of content. It verifies the health of content URLs, so you can take steps to remedy dead or non-functioning URLs. And the good news is we’re continuing to develop and automate these capabilities, making it easier than ever to maintain healthy content libraries.

On the user side, Lytics’ reliance on first-party behavioral data powers an understanding of your customers’ individual content and product preferences. By understanding these preferences, it can determine relevant recommendations of both content and products, and then help you deliver those recommendations through web experiences, ads, and other digital channels.

Learn how Lytics helped Atlassian deliver personalized content recommendations based on its customers’ behavior.

Category: Uncategorized

Many large international businesses, particularly in the consumer packaged goods (CPG) industry, have decentralized marketing operations for common sense reasons. Campaigns that succeed in the US may (or even probably) won’t work in Europe. Products that are popular in Asia may not appeal to Latin American consumers. These sorts of differences in regional tastes, marketing, and product preferences also exist inside domestic markets (like the US), with sub-brands taking advantage of the benefits of scale for production and distribution.

But while decentralization gives the company the flexibility to market what works in each region, it also has negative consequences like siloed data, disparate marketing technologies, and, as a consequence, difficulty in acting quickly or in unison. This was the case for Electrolux, particularly in their APAC and MEA regions.

When COVID-19 hit, they had to suspend their usual model of selling appliances like vacuum cleaners, air conditioners, and washing machines through retail channels–and issues with their marketing technology meant they couldn’t easily pivot to direct-to-consumer marketing and sales.

Andy Chang, Marketing Technology Director for APAC & MEA, recalled that his colleagues “wanted to go very quick to test [direct-to-consumer marketing] immediately, but our data infrastructure just didn’t allow us to do that.”

Lesson #1: Choose a customer data platform (CDP) that plays well with your current technology.

To get value out of a CDP, it needs to connect with your existing data sources and your digital marketing execution tools, from your website CMS to your email marketing solution. Chances are you already have customer records, purchase histories, demographic data, and other valuable data on your customers; you don’t want to ignore or lose it. More importantly, you need to be able to apply the insights you learn from your CDP to create personalized digital experiences, as that personal touch is what will deliver results.

Personalized popup screens on Electrolux’s website tripled conversions for new users and doubled conversions for existing users, while personalized email campaigns produced increases to 26% in open rate and 22% in click through rate.

Lesson #2: Choose a CDP that measures customer affinity.

Understanding your customers’ affinity–both toward content and toward products–lets you deliver relevant offers in a timely manner. To get the most value, you first need to assign content tags to images and text, so you understand what topics are of primary (and secondary) interest to your customers. Once you know what they’re interested in, you can offer them more related content or product offers. Building off a content tagging exercise, Electrolux introduced a “You May Also Like…” section of personalized product recommendations on their website, increasing web conversions by 37.5%.

Lesson #3: Choose a CDP that delivers actionable insights quickly.

Long implementation projects or difficult data integrations delay return on investments and place additional burdens on stakeholders in CDP projects. Finding a CDP that delivers quick wins–insights that you can use to increase conversions–helps ensure organizational support for the CDP initiative. With Lytics, Andy Chang explains that Electrolux was able to use Facebook “ to start retargeting right out of the box”–something that they had never done before.

Lytics CDP gives marketers insight into their customers’ preferences and intentions by uniting existing data sources with powerful behavioral and affinity indicators, then provides strategic direction by enabling marketers to deliver true 1:1 personalization at scale through integrations with their marketing technology stack. A true “smart hub” CDP, Lytics delivers rapid time-to-value across all digital marketing channels.

Learn more about Electrolux’s story by downloading the case study.

Category: Uncategorized

As much as we marketers want to use better, smarter ways to reach out to and engage our customers, marketing from defined lists is still part of the equation. When you need to target a precise audience in a cost-effective way, working from a list of specific customers or prospects is the way to go. You can show them the ad or deliver the message you need that will get them to convert:

  • Make a first purchase
  • Subscribe rather than make a single purchase
  • Renew rather than churning

The list of uses goes on. But the question of how you first assemble and then maintain or update the list starts to bring up additional costs for the process. What if someone takes the desired action, then gets an irrelevant (or worse, annoying) message or experience? What if they move on to a new job and your service is no longer relevant?

Those costs come out of your brand equity. If you’re perceived as demanding attention for irrelevant, poorly timed, or inappropriate marketing messages, your brand suffers. And if you’re using static lists, you’re inevitably going to be delivering irrelevant, poorly timed, and inappropriate messages to your customers and prospects.

But at Lytics, we’re doing our part to make static lists a thing of the past. With Predictive Audiences, you can make dynamic lists that update based on the lookalike audiences you create and then use them to deliver truly personalized experiences across your digital marketing channels: ads, web experiences, and email. The benefits are clear-cut.

Benefit #1: Predictive Audiences’ lists are always up-to-date

The most obvious benefit of dynamic lists is right there in the name–they’re dynamic. They’re always evolving. We can all understand why static lists get stale. People leave jobs or roles. They purchase one-time items (like cars, B2B software, a mattress) that they shopped for at one point in time. They might move, get married (or divorced), or decide they’re not interested in a topic anymore. But if you’re using a static list, they’ll still receive experiences that are no longer relevant to them.

By building a list based on a lookalike audience, you’re taking advantage of Lytics’ ability to collect and interpret behavior data in real-time. As prospects or customers display interest in a topic (or otherwise start to closely resemble the target audience), they will automatically become part of the lookalike audience and start receiving appropriate experiences. Conversely, if they stop sharing characteristics or otherwise display that they’re no longer interested in a given topic or product, they’ll automatically be removed from the list by virtue of no longer belonging to the lookalike audience.

Benefit #2: Predictive Audiences’ lists are more inclusive

Many if not most lists are segmented based on historical or demographic data that holds little predictive value. Whether a customer once bought a product may indicate a likelihood to purchase it again… or it may not. They might not have liked it, or have just been curious. Being male or female may indicate a likeliness to purchase a product, but basing a list on gender would necessarily exclude people who are planning to buy it as a gift.

By basing a dynamic list on a lookalike model in Lytics Predictive Audiences, you’re relying on behavioral and affinity data rather than historical or demographic data. These data capture indicators of intent, which has more predictive value in terms of what a person is likely to do. Your marketing messages or content are far more valuable and relevant to these audiences (increasing your brand equity) even if they choose not to act on them. And by being targeted toward an audience displaying intent, they’re much more efficient in terms of influencing the outcomes you want to see.

Benefit #3: Predictive Audiences’ lists can be used more widely

A final benefit of the lists you create in Lytics Predictive Audiences is their portability, meaning that you can use them across your marketing technology stack in real time automatically. Since Lytics integrates with execution tools for website, email, advertising, and mobile (among others), customers and prospecs on your dynamic list will receive all the appropriate experiences wherever they are. There’s no requirement to update lists across platforms every time there’s a change or to import and export lists in multiple formats.

While static lists have served marketers well for a decade or two, they’re no longer aligned with the experiences customers today expect. Find out how you can use Predictive Audiences to build lookalike models and dynamic lists by requesting a demo of Lytics smart CDP today.