Unlocking the Potential of Data-Driven Personalization

Marketers know that data-driven personalization presents a huge opportunity and a meaningful way to deliver on customer expectations.

Research backs up that common sense. In one recent study, 89% of decision-makers say they believe personalization “is invaluable to their business’s success in the next three years.”

Yet most organizations struggle to personalize content. I explained some reasons in a recent episode of Live With CMI. Read on for the highlights, or watch the video interview:

You lack the right data (or don’t have access to it)

You know you need to offer personalized recommendations on content, products, use cases, etc. Yet, you may only be able to do it at a high level of segmentation (if at all) because you lack the data that empowers personalization that matters to the audience.

That obstacle arises when you don’t have an integrated data system or enough data. Your organization likely looks at data in an ad hoc way:

• Customer relationship data sits with the sales team.
• Website data sits with the technical team.
• Customer service data sits with the customer support team.

All that information lives in silos. Without bringing, it together, you can’t create a holistic customer view to which the marketing team aligns its goals and needs.

You don’t have a say in data infrastructure or collection

As marketers, we present the case of why our brand is relevant and the right fit for the customer. That’s why in both B2B and B2C organizations, marketers are the ones who need to know their customers and prospects best.

That means you (or someone on the marketing team) must have a seat at the data table. Marketing should drive the strategy behind the organization’s data collection, infrastructure, and use.

Customers expect brands to give them highly relevant information. According to a 2023 survey I conducted with Researchscape for my book, most people (88%) expect brands to interact with them based on their history with that brand. A similar number (85%) of folks expect brands to share personalized recommendations with them.

That’s not a surprise. Anyone who has shopped on Amazon or browsed shows on Netflix knows well a brand can recommend things based on past consumption. Imagine if every time you signed in to Netflix, you had to retrain it to fit your preferences. That would be a horrible customer experience.

Years ago, in an interview in TechCrunch, Todd Yellin (then-vice president of innovation at Netflix) said their goal was to know a viewer so well that they could give the viewer one button that would play the exact show you want to watch in that second.

Customers want companies to make everything that easy. But to deliver on that vision, marketers must drive data strategy. That’s because you can ask questions that help determine how data should be collected.

If you don’t have a strong voice in how the organization invests in data infrastructure, how the data will be used, and how to tell the data story to your customers to build trust, then you miss the ability to control your destiny.

You overlook this important data

When you think about the information to collect about your prospects, consider some commonly overlooked data points. Behavioral, technographic, and psychographic data is dramatically underused.

With behavioral data, I’m not talking about how often somebody buys with you or how much they spend. I mean:

• Where are they consuming content?
• Which content drives them through to other content?
• How often are they interacting with you for support?
• How often do they interact with each other to learn more about the industry?

You want a holistic view of customer behavior, not just while they’re using your product.

Many B2B companies should consider the technographic data — the tech stack related to your brand’s space.

For example, if you work for Zapier, the automation tool that connects web apps and services, you’d want to know what customers connect to your tool. The data analytics team should track that. Understanding buyers’ tech stack would let you know, for example, which additional connections they want you to enable.

Technographic data is valuable on the B2C side, too. If you work for a video game company, you’d want to understand which headphones or accessories your customers use. A company like Apple would want to understand the other kinds of tools customers would expect to use together. That kind of data lets you anticipate your customers’ needs.

Psychographic data involves your customers’ attitudes and motivations that determine what they value. Think about how people shop online. Somebody who cares about a good deal might look for websites that offer free shipping or a discount for an annual (vs. monthly) subscription.

Other customers aren’t necessarily looking for value. They prioritize ease of use. They would want to hear a story about the ease of onboarding, available support, or how you’ll simplify their lives.

Each person has a different set of motivations. By working with your data analytics team to collect information about those motivations, you can tailor the messages on your landing pages, in your emails, and in all other communication vehicles. Somebody focused on value gets one set of messages, and somebody focused on ease of use gets a different set. That’s a more effective marketing approach — you’re not wasting time delivering an irrelevant message.

Ultimately, you need to identify the right mix of data your organization should collect. You shouldn’t collect every piece of data because that can overwhelm your team. And asking for too much data makes customers uncomfortable.

You should show prospects how giving data benefits them

In personalization, privacy is a big part of this story. It’s possible to collect data and use it in a transparent way rather than creeping people out.

Several brands ask people about their preferences in a way that builds trust. For example, some brands offer the option to opt out of Mother’s Day messaging because it can be a sensitive time of year for some customers.

Questions like that build a customer preference profile for the marketer, but they also build respect and trust with the customer.

Some companies do a great job of explaining what data they collect and how they use it. Lemonade, an insurance company, is a great example. It clearly discloses what it will and will not do with the customers’ data. Its data privacy policy is written to be human and easy to understand. It wants customers to trust that Lemonade won’t do something untoward with their data.

If you ask people their preferred balance of privacy and personalization, about 50% of consumers say they’re happy for their data to be used for more personalized services and experiences, according to a recent PwC study. 

People recognize the tradeoff. Since they receive more messages than ever, many appreciate companies that give them recommendations that save them time.

For example, as a parent of a young child, I appreciate it when Amazon recommends the clothing size to purchase based on the last time you bought the item. If I bought a 12-month size six months ago, Amazon recommends an 18-month size the next time I shop.

That saves me time because I’m less likely to order the wrong size. It saves Amazon time on returns. Both the customer and the organization benefit when you get personalization right.