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The ultimate guide to personalized marketing

83% of marketers who exceeded their revenue goals last year said they used personalization techniques. Here’s why and how you should join them.

Navigate to: The marketer's challenge with personalization / The role of data and the Customer Data Platform / The role of your teams in personalization / Evaluating CDPs for personalized marketing capabilities / Implementing personalized marketing with your CDP / Personalized marketing examples

In 2018, customer demand for personalized reached an all-time high. Companies using personalization techniques saw big benefits in both revenue and customer loyalty. And with technology like Customer Data Platforms with machine learning, true personalization has become more possible than ever.

So, what do we mean when we say personalized marketing? We think the best way to understand is to look at companies doing it successfully.

True personalization is when Amazon knows what you like and serves up recommendations that meet your needs before you even know what they are.

It’s when Spotify understands your musical tastes and caters to them. It’s when they offer you discovery playlists that surprise and delight.

It’s when Netflix changes the cover art on shows and movies to showcase the things you are most drawn to. It’s when you and your spouse or kids are served completely different experiences when you log in and every one of those experiences meets that individual’s needs.

Personalization, in other words, is about knowing your customers and using that knowledge to solve their problems, make their experiences smoother and better, and serve them the things they actually want and need.

When we talk about true 1:1 personalization like that at Amazon or Netflix, what we’re really talking about is personalization at scale. Personalization that lets you reach not just hundreds or thousands of customers, but millions. Personalization that grows with your business and treats every customer as an individual even as your customer base grows far beyond what your team could manually manage.

This makes for a better customer experience with your brand and it’s proven to drive real business results. Just ask Forbes, which says 83% of marketers who exceeded their revenue goals in 2017 were using personalization techniques.

(Prefer to read this guide as a PDF? Download a copy of the full guide here.)

The marketer’s challenge with personalization

So, if personalization is both possible and in demand, why are so many companies still struggling to get it right? Why—according to one recent survey—is data-driven personalized marketing still considered the most difficult marketing tactic out there?

In our experience, the answers boil down to both mindset and technology.

The personalized marketing mindset

When we talk about personalization at scale—especially for companies with hundreds of thousands or millions of customers—it can be hard to wrap your head around. How can you treat people as individuals in your marketing process with a million-person customer base and a marketing team of 20?

The answer is in harnessing the latest technologies, but before you can commit to a technology, you need to be committed to true personalization. Instead of seeing the roadblocks—worrying about things like technical implementation and team restructuring—it’s time to look at the possibilities. Instead of thinking about personalizing based on high-level demographics, it’s time to skip over all that and go straight into personalizing for individuals and their specific goals.

This is the new industry paradigm. Instead of centering marketing efforts around content, future-proof companies are centering their efforts around individual users and their unique needs, then developing content and experiences that are tailored to those unique needs. These companies are beginning with the end goal in mind, harnessing the right data within their organizations to understand their users, and implementing the right technology to make 1:1 personalization possible for millions.

The time for mapping out endless customer journeys and shoe-horning your users into them is over. If you want to reap the benefits of true personalized marketing, jump past demographic data and guesswork and straight into the arms of artificial intelligence and machine learning, the behind-the-scenes tools that let you treat customers like true individuals.

Which is where a Customer Data Platform comes in.

Personalization technology

With millions of users, tech giants like Amazon, Spotify, and Netflix aren’t relying on their marketing and tech teams to do the heavy lifting of personalized marketing. They’re not creating hundreds of linear customer journeys and shoehorning everyone into them.

They’re using technology—artificial intelligence, machine learning, and data science—to get to know individual user preferences and automatically serve up song recommendations, purchase recommendations, and graphics most likely to appeal to those individuals.

Now, these companies may have their own proprietary technology platforms doing the heavy lifting, but the good news it that you don’t have to build your own CDP in order to succeed at personalization. In fact, for most companies, building a CDP in-house is a bad idea. It’ll take more time and more money to support than choosing a best-in-breed option that you can start using right away.

The trick is making sure you choose a CDP that’s fully equipped to not only collect customer data but also to orchestrate personalized experiences at scale using smart segmentation, behavioral scores, natural language processing, and machine-learning technology. This means choosing a CDP with real predictive insights: a CDP that knows who is likely to buy and what marketing or sales action is most likely to convince them to make that purchase.

The role of data and the Customer Data Platform

So, to harness the power of true personalization, you need a CDP. But what does that mean, really? How do CDPs support personalized marketing? And are they all created equal—or is personalization a feature of some, not all?

Here are the basics:

All CDPs are about knowing your customers

According to the CDP Institute, “a Customer Data Platform is packaged software that creates a persistent, unified customer database that is accessible to other systems.”

The core function of any CDP is to collect and unify your customer data. This means taking data from different sources—your CRM, your website, your email marketing platform—and across different devices—tablets, phones, computers—and stitching it together into robust user profiles. It also means tracking user behavior across your marketing channels and using that data to understand each customer’s engagement with your brand, ultimately elevating the entire customer experience.

For personalization, you need a CDP that takes data a step further

Knowledge is power—but only if you can use that knowledge to take real action.

For true personalization, it’s not enough just to know who your customers are. You need that information to be actionable. You need it to be accessible across all customer touchpoints in your business. You need to be able to identify customers based on their engagement with your brand and target those customers across your marketing channels.

Sometimes this will mean identifying customers who’ve abandoned shopping carts and targeting them with ads that encourage them to come back and complete their order. Sometimes it means finding your best customers and sending them special offers automatically via email. Sometimes it means identifying the most engaged customers and using them to create a lookalike audience in a tool like Facebook—increasing your effective reach and helping you get to customers who are more likely to love your products or services.

The better your CDP is at behavioral scoring (which allows you to personalize based on actual engagement with your brand), content affinity (which allows you to personalize based on what content people care about), and machine learning (which allows you to make predictions about future behavior), the better you’ll be at predicting and personalizing your marketing.

Choose a CDP with personalization in mind

Not all CDPs are created equal. Some unify your data and stop there. Others let you segment the data to use in your marketing channels. And still others—the best-in-breed CDPs—offer built-in data science, machine learning, and AI to not only segment customers but to actually predict their behavior and provide you with fresh insights about those customers.

And CDPs with built-in data science? They perform better. In fact, users targeted with behavioral scores are 20 times more likely to interact with your campaign than users targeted with demographics alone.

Look again to industry leaders like Netflix and Spotify. They didn’t stop at unifying customer data. They didn’t stop at segmenting customers into a dozen static categories. Their systems use machine learning and AI to understand customers on a 1:1 level and predict the best content and interaction for each customer.

Real people don’t follow linear customer journeys. We’re all different and our journeys are unique, messy. Which is why companies that value personalized marketing don’t stop with those linear models. They invest in machine learning technology like that found in Lytics, which is constantly optimizing experiences for each unique customer based on common goals like a purchase or a sign-up subscription.

If personalization at scale is a priority (and it should be), machine learning and AI tools like natural language processing should be at the top of your “must have” list for a CDP.

The role of your teams in personalized marketing

The technology may be what makes mass personalization possible, but that doesn’t mean your team is off the hook. In fact, according to industry leaders, you should be prioritizing people, process and strategy before choosing the tech that will power your personalized marketing.

To truly harness the power of a CDP, your people and processes must be aligned. Data and technology are an essential foundation, but they are only as powerful as the strategy behind them.

When you commit to personalization, you also commit to:

1. Beginning with the end in mind

It's important to think about customer end-goals well ahead of time. Forget about just improving segmentation and, instead, spend the time thinking about what you ultimately want the customer to do at every stage of the life cycle.

What are your customer journey end goals? What do you want the customer to do at each stage of the process? What data do you need in order to understand where a customer is in the journey, how they feel, what they need, and what would help them move along the funnel?

Its end goals - and the goals of each touchpoint in between - that ultimately inform what data you’ll need to build an effective personalization strategy and ensure your predictive models are looking at the right outcomes for your business.

2. Getting everyone on the same page about personalized marketing

A personalized experience is a seamless experience. It means the customer feels the same level of care from you on your website, your social media channels, your customer service lines, your tech help chat, etc. Because those channels are usually owned by different teams, this means everybody has to be on board with the strategy and making use of your data.

If personalization is the goal, every customer touch-point must align with the company’s core understanding of the customer. If teams aren’t aligned yet, make a plan to get them there and start implementing that plan right away.

Because if your marketing is great but your customer service has to re-ask the same questions five times, that’s not personalization. If customer service is handling a technical issue and your marketing team is still advertising ad-ons to a customer who is already irritated with the company, that’s not personalization. True personalization requires that teams work together and all have access to relevant customer data.

3. Prioritizing the data you'll use and making sure it's accessible

Not all data is created equal, so before you start shopping for CDPs or planning for personalization, it’s important to understand what data you already have, what data you need to serve customers in the best possible way, how you can access it, and what you’ll need to do to get that data into your CDP.

This often means having transparent conversations with leaders across all customer touch-points in your organization well in advance of getting your new technology up and running. You’ll need their inputs and buy-in to understand each end-goal for your customers so you can gather and leverage the right data across the business.

4. Opening lines of communication across teams

Once everyone’s on the same page, it’s important to keep them there. That means open communication. It means making sure as the strategy shifts over time, everyone is informed. It means making sure data is accessible to the right people in a quick and easy way.

Many successful companies are using a Center of Excellence approach where a centralized team owns data strategy and the data itself, and they’re responsible for building and maintaining lines of communication across the organization. This approach brings in the right expertise from key business groups and ensures everyone stays on the same page about data and personalization.

When it comes to communication, there has to be clear responsibility. Who’s responsible for keeping everyone on the same page? Who’s responsible for asking each team how their personalization efforts are going and how the business can better support them? If communication is everyone’s responsibility, it’s too easy for it to become no one’s. And without communication, your personalization efforts may never get off the ground.

5. Learning to use the tool you’ve chosen

Even the simplest tools have a learning curve. Don’t be afraid to ask questions and dig deeper into everything your CDP can do. Check in with your teams to make sure they are fully trained and not intimidated by the new technology. Have a contingency plan for when team members ultimately change positions and you need to train new people to use your tools. You don’t want to implement a solution that doesn’t offer an easy approach to change management.

6. Getting help from the experts

Speaking of learning curves, don’t be shy about asking your vendor to help you get the most out of your CDP.

And when you’re evaluating vendors, look for one that offers educational resources to get your team off the ground, strategic services to help you structure your strategy and teams, and customer service that gets high marks from current clients. Adopting new technologies will and should always be part of a personalization strategy, but it’s the people and processes that will ultimately lead to realized growth.

Evaluating CDPs for personalized marketing capabilities

To find a CDP with true 1:1 personalization capabilities, make sure to ask the following questions when you’re evaluating options:

Does your CDP offer behavioral scoring and, if so, how does it work?

For true personalization, the answer you want is yes. Behavioral scoring should allow you to identify which customers are engaging more with your brand over time and which ones are falling off. It should let you see the intensity of a user’s connection with your brand, how frequently they interact, how recently they’ve interacted and how likely, based on past behavior, they are to keep interacting with your brand.

Does your CDP offer content affinity features and, if so, how do they work?

Content affinity means tracking which content on your site a customer interacts with and which content they’re likely to interact with based on past behavior. Just like Netflix knows which movies you’ll probably like based on your watching history, your brand can suggest articles, products, videos, etc. based on what your customers have interacted with in the past. Knowing what affinities your customers have is the lynchpin for revenue growth and building an abundant pool of loyal customers who invest more with your brand.

To personalize in this way, make sure your vendor has a solid set of content affinity features.

Does your CDP use AI and machine learning and, if so, how does it work?

Machine learning is all about finding relevant patterns in your data that drive next actions. This means a CDP with good machine-learning capabilities can use patterns with your data—most often patterns that wouldn’t be noticed by a person—to predict future behavior and provide new customer insights.

This is where the power of personalization goes beyond just finding your most engaged customers today—to tell you who your most engaged customers will be tomorrow. It’s where the predictive insights of a best-in-breed CDP like Lytics can tell you who’s most likely to buy, when they’re most likely to buy and what’s most likely to convince them to buy. And it’s where the system can make these decisions in real time, serving up true 1:1 personalization.

Implementing personalized marketing with your CDP

So you’ve aligned your team, identified your end goals for each stage in the customer lifecycle, chosen a CDP, and you’re ready to go…now what? Once you have the technology, how do you use it to drive the results?

Start by unifying your data

Personalized marketing starts with data. So before you can do anything else, you’ll need to identify the data you have that will help you personalize the customer experience across every touch-point and centralize that data in the CDP.

This isn’t just about having data. It’s about identifying the right data.

This means digging that right data out of whatever silos it currently lives in and getting it into the CDP. It means cleaning the data up so that different formats can feed into a unified profile. It means making decisions about whether state names come in as AK and HI or Alaska and Hawaii. And so on and so forth.

The power of your CDP starts with the right data. So that’s where the team should start, too.

Power campaigns with behavioral scores and predictive models

Use your CDP’s behavioral scoring to identify customers based on their behavior and target them for appropriate campaigns.

Is one of your goals to keep existing customers engaged? Use the scores to identify people whose engagement is dropping off and target them with special offers or content that will interest them.

Is finding new customers who look like your best customers a top priority? Use behavioral scores and a data model to identify your best customers and then use that data to create lookalike audiences in Facebook and advertise to people who are similar to your best buyers.

Are you focused on making sure customers don’t lose interest before making a purchase? Predictive insights can identify the tactics, ads, offers, and strategies most likely to result in a purchase.

There are so many ways you can use behavioral data and predictive models to drive real business goals. So start strategizing. Start with the end goals and work backward from there. Then use your CDP to power the campaigns that help you reach those goals.

Segment customers to drive results

Use your CDP’s segmentation tools to target campaigns at the customers most likely to engage with them. If you choose the right CDP, you should be able to segment based on customer behavior, interests, demographic data, location, activity, etc.

Want to target users who’ve purchased thriller novels in the past year? You should be able to. Want to send ads to anonymous visitors who look like your best customers and who’ve recently read your blog post on 10 must-read novels for fans of Gone Girl? You should be able to.

And with a tool like Lytics, you can both add segments and subtract them. This means you can add your best customers—the people who have the highest engagement scores with your brand—to a list you’ll use to for targeted advertising, and you can subtract customers who have already purchased the product you’re showcasing. Smart suppression like this allows you to avoid wasting budget on customers who have taken action on the goal you defined, and it keeps you from annoying them with ads that feel irrelevant.

Leverage machine learning and AI for 1:1 personalization at scale

Marketers can only build so many segments before the number of segments becomes far too complex to manage—especially when you have millions of customers. Th

is is where machine learning and AI come in. Choose a CDP with built-in machine learning and AI designed to automatically serve personalized experiences 1:1 at scale.

AI is capable of seeing patterns that humans simply don’t see and can’t define even with segmentation. It knows which customers are likely to buy, what experiences will get them to buy, and which products they’re probably interested in. It knows which customers are likely to disengage—and the best message and channel to try and re-engage them.

Using a tool like Lytics Orchestrate, you can identify more complex customer journeys and use machine learning to predict and power personalized experiences that move a customer toward your business goals.

Grow your results long-term

The more data your CDP collects, the better its insights and predictions will become. The more your team uses it, the more you’ll see what works best and be able to implement better and better orchestrated campaigns and tweak your strategy to reflect your real-world results.

And so over time your personalization efforts become better and better.

Personalized marketing examples

Personalized marketing triples conversions for Nestle Purina

There’s a reason personalization is marketing’s holy grail. When you reach the right customers with the right offers at the right time, the results exceed expectations.

When Nestle Purina implemented Lytics as their CDP, one of their first steps was to use their unified first-party data to identify people who had recently done searches online for puppy adoption and who were, according to Lytics’ behavioral scores, highly engaged with the brand.

They used this information to target those users with Facebook ads personalized to their needs.

And the results? The Facebook ads served to this audience were three times as likely to convert as the same ads pre-Lytics.

Even better, the cost per conversion dropped by 90%. All because their new CDP allowed them to target people not just based on demographics and interests but on behavior.

Read the full case study.

How The Economist grew digital subscriptions by 300%

The Economist offers us another good example. After centralizing and unifying their data within Lytics, the brand took advantage of our predictive scores to personalize their marketing and drive subscriptions.

They identified web visitors who were not already subscribers and who were likely to subscribe based on those predictive scores and targeted those specific users with subscription ads.

The results? The brand decreased acquisition costs by 80%, grew digital subscriptions by 300%, and increased overall time on their site.

Read the full case study.

How one Lytics client re-engaged their shopping cart abandoners

One Lytics client—a large transportation company that offers rental car services—used their data to identify customers who’d almost made a purchase. They had their shopping cart all set to go…and they abandoned their rental reservations before committing.

The brand also knew, through Lytics content affinity tool, what car customers were interested in, how long they wanted to rent for, etc.—data that allowed the brand to personalize ad copy to recapture these lost sales.

The brand used Lytics data segments to export a list of these customers into their Facebook marketing dashboard, targeting ads that encouraged them to come back and complete their reservation.

The campaign resulted in a 10% lift in reservations and revenue. Just from using data they already had on their customers to make their Facebook targeting smarter.

Ready to personalize? Let’s talk.

When we talk about behavioral scoring, content affinity, and machine learning, we’re speaking from personal experience. Lytics is built to not only centralize your data and unify it across channels and devices, but also to make that data truly useful and actionable to power your business goals.

After all, what good is data if you can’t use it to drive real-world results?

It’s our own real-world results (and customer satisfaction) that earned us the title of best overall CDP on the market according to the Relevancy Ring Buyer’s Guide.

If you’re shopping for a CDP, we’d love to show you our personalization capabilities, behavioral scores, content affinity tool, and some powerful case studies that prove just how much a best-in-breed CDP with machine learning can really do for your business. Ready for a demo? Call us at 503.479.5880 or book a meeting with us today.