Why Smart Companies Are Giving Customers More Data

In 2016, Spanish banking group BBVA offered to its Spain-based customers a personal finance management app. One of the app’s tools used machine learning algorithms to sort customer transactions into common budgeting categories such as rent, food, and entertainment, and then it displayed a customer’s expenditures broken down as a simple chart. BBVA promoted the categorizer on its digital banking website as a way for customers to better manage their personal budgets. In just a year and a half, the tool became the most utilized feature on the BBVA website, second only to funds transfer.

And in 2019, the global consumer packaged-goods company PepsiCo formally launched a suite of data analytics capabilities called Pep Worx that supported a variety of use cases — such as how to successfully launch and manage innovative marketing programs and how to optimize total store space — that helped retail customers increase product turns, profit realization, and net price realization (the latter because there was less of a need to discount products that weren’t selling). PepsiCo developed the capabilities over a four-year period as the company solved problems for select retail customers using data analytics-based shopper insights. PepsiCo has used Pep Worx to help transform the nature of its retail customer relationships from transactional to collaborative by creating a “three-audience win” whereby sales or marketing activities simultaneously deliver value for the shopper, the retailer, and PepsiCo.

These examples demonstrate how companies benefit from an emergent approach to data monetization we refer to as data wrapping. With this approach, a company’s products are “wrapped” in data analytics features and experiences that help and delight customers, with profitable results. The tendency for most companies is to draw upon preexisting business intelligence groups, data platforms, and analytics talent for data wrapping. However, the capabilities, processes, and skills that historically helped the company use data analytics to do things better, cheaper, and faster are insufficient for producing data analytics that delight customers.

What Makes Data Wrapping a Distinct Data Monetization Approach?

Companies are data wrapping when they give data and analytics to customers as product features and customer experiences — such as spend categorizers, automatic sound optimizers, and shopper insights — with the goal of increasing a product’s value proposition. There are four key characteristics that make data wrapping distinctive:

• The data analytics “users” are a company’s customers, not employees.
• Product owners, not IT, lead the product road map because analytics must be developed as a part of the product’s overall feature and experience portfolio.
• Economic returns result from a lift in sales, not from an internal business process improvement.
• It’s risky; unless companies deliver accurate, valued data wrapping, they could confuse, irritate, offend, or drive away the customers they serve.

In a 2018 survey of 511 product managers by the MIT Center for Information Systems Research (CISR), 85% reported they were developing data analytics-based features or had deployed features to the marketplace. The research indicates that companies that get data wrapping right follow three steps that keep their efforts on track.

Step 1: Assemble a multidisciplinary team, led by product. Product owners and managers deeply understand a product’s cost structure and the customers being served — and key risks to mitigate. They also have access to customer-facing processes and channels that help the company sense and respond to customer needs.

To illustrate the advantage of cross-collaboration in product development, take the example of Cochlear. In 2017, the Australian hearing solutions manufacturer released its Nucleus 7 Sound Processor, a device used for patients with hearing implants. The company paired the device with a mobile app offering scene classifier technology, which allowed users to automatically adjust their sound settings based on their surrounding environment (for example, a crowded street corner or quiet waiting room). These types of changes to processer settings had previously been made manually by the patient, but product use data suggested people may not always choose the optimal settings.

Cochlear product managers evaluated data-wrapping opportunities using established market research techniques, patient focus groups, and clinical trials. They explored data-wrapping ideas while they visited clinics and talked to customers. The conversations helped product managers move from dozens of possible data analytics use cases to a handful that customers actually wanted, such as a “find my sound processor” capability for times when the physical unit (a costly device to replace) falls off a user’s ear.

Although product teams need to lead the charge, data analytics and IT colleagues are critical for shaping, developing, and deploying the data wrapping efforts. At Cochlear, product managers meet regularly with members of data analytics to review and interpret processor usage data, which informs possible changes to existing features — and helps prioritize needs that may need to be served in the future. IT colleagues help product managers assess features for technical feasibility, and they make sure the company’s technology can support data analytics features after they “go live.”

Step 2: Design features and experiences that inspire customer action. The value of data wrapping hinges on customer action and experience. Thus, a key design element for data wrapping involves prompting and guiding customers to use the product features and ensuring their experience meets a compelling need, such as saving time, money, or gaining information. MIT CISR research identified four key design characteristics (See “Four Design Characteristics for Data Wrapping”) that help make this happen:

These four design characteristics — anticipate, advise, adapt, act — are reflected in the data wrapping in Cochlear’s sound processor. The device’s scene classifier technology anticipates that an end user’s hearing needs will require adjustments as the person changes environments throughout the day and adapts to contexts, such as to a crowded street corner or quiet room. The feature advises the user of optimal settings through an app and acts by automatically adjusting the sound processor’s settings to deliver the best hearing for the conditions.

According to our research, product features and experiences that anticipate, advise, adapt, and act are more useful and engaging to customers and more likely to motivate customers to participate with or respond to data wrapping. The research also found that companies with the most useful and engaging data-wrapping features achieve top performance in product sales lift — whether through selling more of the product, raising product price to reflect customer value add, or raising customer retainment rates.

In 2015, PepsiCo established a cross-functional shopper insights unit drawing on nearly 200 people from across the company, including those focused on category management, shopper insights, space optimization, and shopper marketing. The new unit created and delivered standardized, data-driven marketing services to PepsiCo sales and marketing teams — and to PepsiCo’s retail customers. One customer solution helped retailers optimize store-level product assortment. Typically, retailers assorted their stores one way across the country, or possibly across a region. A store in urban Denver, however, could have a shopper profile more in common with a store in urban Phoenix than with a suburban Denver store just 15 minutes away. PepsiCo created a data analytics approach to tag individual stores with an identifier that reflected their local shopper base. Then, the team created distinct plans for where to place products on shelves based on a retailer and its store profiles.

Step 3: Measure impact to both the customer and the organization. Top-performing product managers measure — and report — how much value data wrapping generates for both the customers and the company. But, measuring these returns can be tricky. A company may not have visibility into exactly how and when a customer benefits — and the company’s own returns may happen indirectly or over time. Companies tend to draw upon a portfolio of techniques such as usage tracking, A/B testing, controlled experiments, customer surveys, and pilot studies to get a good sense of their data-wrapping outcomes.

At BBVA, the data analytics unit that supported data wrapping developed an economic impact framework to classify data analytics projects according to their intended goal, such as increased revenues. Product owners were accountable for measuring and achieving the appropriate kind of value for the data-wrapping projects, and a director of finance and operations helped the product owners create measurement metrics and methodologies and validate results. Other companies across different sectors can similarly adapt existing testing methods and measurements to monitor and evaluate data-wrapping success.

The BBVA categorizer, for example, encouraged customers to adjust their spending habits based on a better understanding of where their money was going. BBVA conducted A/B testing on new features, which involved providing a subset of customers a new feature and comparing their response with that of a subset of customers who received the preexisting offering. Specific outcomes, such as customer satisfaction, were measured and compared across the two groups over time.

Reaping Rewards From Data Wrapping

On average, data wrapping represents 26% of the value a company creates from data monetization.1 Data wrapping is not only becoming an essential component of the data monetization portfolio but also for the product value proposition. In a fast-moving market, the approach is particularly appealing and useful for companies that need to distinguish products under attack from commoditization.

Companies that report data wrapping more effectively than peers achieve an average return on investment of 61% from data-wrapping projects, versus 5% for those reporting that they wrap less effectively than peers. It’s time for companies to pay attention to data wrapping or else be left behind. In today’s digital world, customers increasingly expect value from data analytics. If we don’t deliver it, our competitors will.