Designing a customer-centric data strategy

A recipe for a successful data-driven value chain

From nursing homes to neurotechnology to private equity, companies in all sectors face a similar challenge: they know they need to evolve their offerings and operations to become more customer-centric, and they know they need data to get there. Yet the prospect of gathering, processing and interpreting that data into something actionable is often overwhelming.

If your business offers digital products or services, you are probably flooded with a considerable amount of structured and unstructured data of varying units, scales and time periods. It is likely your organization either collects more data than it can consume, or it struggles to interpret the data in a way that drives value across your business (through new products, features, or business opportunities).

These challenges are so common because traditional data strategies tend to emphasize a streamlined transaction funnel rather than a nurtured relationship with customers. To implement a truly customer-centric data strategy, we have to reconceptualize data as more than an opaque matrix of transactional benchmarks. Instead, we need to see data as a series of small stories. These stories, when mapped strategically, can reveal a bigger picture: a fundamental understanding of current customer behaviors and a series of insights that drive new opportunities.

The recipe for data success relies on viewing data on two levels: first, as an indicator, data can define the current state relationship between a customer and a product. Secondly, as a material, data can drive the design that helps you create and shape new features that meet real customer needs.

Data as an indicator

How do you measure a relationship? With a recent news media client, we measured the user-product relationship by tracking two foundational behaviors: how users found news and how they consumed news. Did users browse extensively, and then deep dive on a specific topic? Or were they dedicated readers of a particular news brand? Using data analytics and other surveying methods, we tracked these two behaviors and were able to define a number of specific archetypes whose relationship to the brand was crystal clear.

Every business is different, but defining specific metrics that make sense for your sector can help you understand how your product is adopted, used or loved. If you are in the airline industry, for example, you might consider measuring the factors that have the highest impact on overall satisfaction with the journey, such as the emotional state of the passenger, the amount of disruption the trip causes for the businessperson, the loneliness experienced when separated from family, or the level of anxiety when waiting for luggage. After all, people tend to choose airlines based on much more than just a flight schedule or price.

Once the relationships of the customers to the product or service are defined, the data gathering exercise becomes less of a complex task. Your business will only need to concentrate efforts on the data that allows you to diagnose and strengthen the relationship between your product and a user segment. You will then have the foundation necessary to begin to use data as a material.

Data as a material

Using data as a material means using data to tailor your product or service to meet user needs. Design and data teams can work together to shape new features, or design whole new offerings that data revealed an opportunity for. If, for example, the digital news publisher noticed that a certain segment of their readers clicked on the stocks before reading anything else, they might have then decided to create a “stocks briefing” newsletter, aimed at that user segment and released every morning.

Spotify “discover” playlists are another great example of this. By going beyond transactional data to relationship data, Spotify can understand and therefore predict their customers’ relationship to various music genres. They then use data as a material to orchestrate the experience of serendipity, nurturing their customer relationships through a combination of data, engineering and design.

Conclusion

The questions that companies ask data to answer — “What do audiences want from our new technology?” “How can we reduce churn?” or “How do we make calculated bets in product feature development to fuel growth?” are complex, and they cannot be solved in a silo. Product portfolios should not mimic the siloed nature of organizations’ internal structures, but instead map to true customer needs. By reconceptualizing data as both an indicator of relationships and a material to drive design, you can begin to define a truly customer-centric data strategy.

This article was written by Dr. Lusine Tarkhanyan with contributions by Erin Peace. Edited by Erin Peace. Illustration by Patrick Egglinger .