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The value at stake isn’t trivial: our analysis suggests that a company’s aptitude at scaling up digital and analytics programs is correlated with its financial performance. In this article, we describe the most common pitfalls that companies encounter in their journey toward digital and analytics scale-up. We also explore an emerging recipe for sustained success.

Measuring digital and analytics maturity—and its value

The consumer-goods industry has some catching up to do when it comes to digital maturity. Among 11 industries analyzed in the latest McKinsey Digital Quotient ¹ survey, consumer goods ranks third lowest (Exhibit 1). The industry does much better in a comparison of analytics maturity, coming in at fifth place. This isn’t surprising: most consumer-goods companies have focused on established analytical areas (such as pricing) that require relatively little direct consumer data. Sectors with more direct consumer connections, such as retail, have focused more on digital capabilities to enable an omnichannel consumer experience.

Within the consumer-goods industry, the companies with the highest levels of digital and analytics maturity are creating significant value. Between 2010 and 2018, the compound annual growth rate (CAGR) for the total shareholder returns (TSR) of the most mature digital and analytics performers—those in the top quintile—was 19.2 percent, approximately 60 percent higher than the 12.3 percent CAGR for bottom-quintile companies.

While that analysis doesn’t prove causality, the correlation is compelling. And in light of the growth challenge that the industry is up against, the call to action is loud and clear: either fully tap into the power of digital and analytics, or get left behind.

The most common failure modes

Drawing on our experience working with consumer-goods players around the world, we have identified the four most common failure modes—the mistakes that hinder organizations from capturing value at scale from digital and analytics:

1. 1. • Neglecting to connect digital and analytics programs to the enterprise strategy. Laggards tend to treat digital and analytics efforts as side projects rather than important enablers of enterprise-wide priorities. Not surprisingly, these efforts struggle to get the attention and resources they require to succeed.
      • Making big investments prematurely. Some companies, enamored of having the latest technology, invest in digital and analytics before they thoroughly understand what the business truly needs and what will deliver significant impact. This failure mode tends to come in two flavors: a company either pursues a costly, all-encompassing “data lake,” without carefully thinking through exactly what that data lake will enable, or invests in a new technology stack in efforts to simplify or harmonize core platforms (such as enterprise-resource-planning systems), only to find that today’s best-in-class tech stack becomes outdated just two years later.
      • Holding out for “perfect” hires. Laggards spend as much as six months searching for two or three data scientists or wait until they feel they’ve found the “perfect” hire to lead the team. While it’s not wrong to look for the best data scientists, data engineers, designers, and other skilled people to fill critical roles, there are several ways to accelerate progress while building your technical bench—such as training internal talent, disaggregating roles, or partnering for new capabilities.
      • Underinvesting in change management. Executives often tell us that they wish they’d spent as much or more on change management as they did on technology. Without senior business leaders committed to role modeling the changes and a comprehensive plan for encouraging adoption by frontline employees, new techniques won’t stick. As a rule of thumb, digital and analytics leaders should allocate their energy and investment as follows: 25 percent on data, 25 percent on technology, and 50 percent on change management.

An emerging recipe for success

While only a few consumer-goods players have delivered impact at scale from digital and analytics efforts, the recipe for success is becoming clear.   The following are four core elements of digital and analytics success. Combined, they help companies avoid or overcome the aforementioned failure modes.

[Here’s the first two of four.]

Set a bold long-term aspiration

Companies should avoid articulating only a vague, generic aspiration (“we will build excellent analytics capabilities”), which will inevitably fail to take hold. Instead, they must begin with a concrete, long-term digital and analytics vision clearly linked to the corporate strategy. One consumer-goods company, for instance, had the following vision for its transformation: to “create a best-in-class sales force using digital and analytics to enable the right actions, in the right outlets, at the right time, executed flawlessly every day.”

This vision then determines priority areas and investments. Importantly, it must be informed by a candid, detailed assessment of the starting point, using a shared vocabulary and well-understood criteria and standards to ensure that people at all levels recognize the magnitude of the change required. One business unit’s definition of “digital and analytics” might be vastly different from another’s, so it’s critical to establish a thorough understanding of the current state of affairs and a common definition of success.

Pursue ‘domain transformations,’ not unrelated use cases

At the heart of any digital and analytics program are “use cases,” which define specific business problems to be solved through new ways of working. Use cases can be found across the front, middle, and back of an enterprise. They can be grouped together in “domains”—subsets of use cases that share a common element, such as a deployment mechanism, data sources, or business users (Exhibit 2). We’ve found that to bring about transformational change, it’s best to pursue use cases within the same domain.

In the early days of digital and analytics transformations, companies prioritized individual use cases, largely in the commercial functions, based on feasibility and impact. To support the highest-priority use cases, companies then established a set of broad-based enablers—for instance, a data lake, a technology stack, and a technical organization that housed all newer talent profiles, such as data scientists. In theory, these enablers would meet the needs of the entire enterprise.

In practice, however, generic enablers rarely meet specific business requirements. Successfully scaling up digital and analytics efforts thus requires a different approach: one that prioritizes fully enabled domain transformations rather than unrelated use cases. Instead of pursuing the three highest-impact use cases in different domains, a company might pursue, say, the first, fourth, and sixth highest-impact use cases, if these reside within the same domain. The company can then develop domain-specific enablers, such as data the domain needs, surgical changes to the tech stack, or capability building for business users. In this way, the company reaps higher returns on its investment because these enablers support all the use cases within that domain.

This approach also allows companies to tackle each domain’s unique challenges. The sales analytics and merchandising domain, for instance—particularly for large, dispersed sales organizations—typically requires an intense focus on handheld tools linked to the core tech stack and deployed through broad-based capability building. On the other hand, the revenue-management and omnicategory-management domain is much more about sophisticated, granular analytics conducted by a relatively contained team, with limited implications for the tech stack. By transforming domains, companies can home in on these domain-specific challenges and more rapidly achieve impact at scale.