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How personalization can power customer relationships

Data, coupled with AI and machine learning, can build stronger connections—provided it’s used right

How personalization can power customer relationships

As individuals interact with their world—at work and at home—they produce vast amounts of data on the things they buy and the services they use. Modern, tech-forward companies can gather and analyze this data to better understand their customers’ needs and provide products and services that align more closely to their needs. When done right, this proactive understanding helps companies to present tools and options before customers may even realize they need them.

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At the recent Fast Company Agenda 2022 event, Capital One hosted a panel of innovation and technology leaders to discuss how brands can use customer data to build powerful connections that can result in better outcomes and increased loyalty. Here are four takeaways from that conversation.

1. Connect on a deeper level.

It’s a profound human need to want to be known and understood, says Don Busick, executive vice president and head of enterprise products and platforms at Capital One. “At its core, personalization is about knowing your customers and their preferences so you can surface exactly what your customers need when it matters,” he says. “When done right, personalization can create deep and lasting connections with your customers.”

At Capital One, this means creating personalized digital experiences, like Capital One’s intelligent assistant, Eno. Eno provides proactive alerts and insights that alleviate work for the customer, such as reminders that alert customers before their free trial ends—and the paid subscription begins—so they can avoid any unwanted charges. This helps to instill confidence in customers while giving them control over their financial lives.

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2. Gather the right data.

Personalization starts with the data companies gather about their customers. Those bits of information form a crucial foundation, which is why it’s critical that companies gather the right data from the outset. “In many cases, the most valuable data is the feedback customers provide about what they like and don’t like,” Busick says. This type of data, he adds, may come from asking customers directly about their preferences or learning from how customers interact with a service or product.

Tyler Whitworth, senior vice president of global product and design at Discovery, says this type of behavioral data—what customers do with products—can be much more useful than demographic data, such as gender and age. “It’s more predictive of what customers actually enjoy,” he says.

With data in hand, companies must be thoughtful about how they use it. “There is a science and art to getting personalization right,” Busick says. “The science relates to how well you know your customer and how you use data in a way that aligns with the permissions the customer has given you, their expectations of how you will use data, and that respects their privacy. Too many businesses get this wrong. They collect too much data, or they’re not properly using the data they did collect. All of this can turn off customers at the precise moment they need you the most. It’s important that customers are fully aware of the value trade-off they’re getting between access to information and what the company can provide in exchange.”

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3. Leverage the latest tech.

To make the most of the data they gather, companies rely on artificial intelligence (AI) and machine learning (ML) to organize that information and produce actionable insights. “AI and ML models have a huge part to play in healthcare in general,” says Neetu Rajpal, chief technology officer at Oscar Health. “You need the ability to be able to have lots of anonymized information about the health of a population, and you need to be able to run models around it so that you can nudge members into the right health plan at the right time.”

At Capital One, Busick says big data and machine learning are front and center. For example, Capital One has introduced new personalized insights capability to its award-winning mobile app, resulting in a machine-learning-driven, scrollable series of tiles containing urgent messages, insights, and offers most relevant to each customer and their accounts.

4. Avoid common pitfalls.

Whitworth warns that relying too much on personalization can be problematic. Discovery, for example, hosts a library containing approximately 300,000 hours of programming. He says companies must strike a balance between offering content that will appeal to customers based on their viewing history along with a variety of new shows that may stoke their interest. “Sometimes you can focus so much on the type of content somebody likes that it really can, over time, hinder people’s access to new things, or to novelty,” he says.

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Course correcting and adapting to customer needs is also key. After all, it’s important to understand that a company’s personalization efforts may land wrong or will fail to reflect a customer’s changing preferences. That’s why personalization models should include a built-in feedback loop to help adapt offerings and continue providing value to customers in the future. “The models that you build are based on the assumptions that you have,” Rajpal says. “And there’s no guarantee that your assumptions are correct.”

“Personalization is not a one-and-done proposition,” Busick concludes. “It’s a forever journey of listening and continuously adjusting personalized digital experiences. You aren’t building a one-off transaction. Personalization is about building a customer relationship over time and learning with your customers as their needs change, based on where they are in their life journey.”

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