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.

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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

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.

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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.