Five facts: How customer analytics boosts corporate performance

By Alec Bokman, Lars Fiedler, Jesko Perrey and Andrew Pickersgill

A McKinsey survey reveals how—and how much—customer analytics can improve profits and growth.

Corporations across the world and industry sectors are increasingly approaching their businesses from a customer-centric perspective, amassing vast quantities of customer intelligence in the process. But harnessing this data deluge is a huge challenge.

Even after drawing on sophisticated software systems to portion Big Data into viable segments, countless companies are finding their expectations dashed. Big Data often fails to deliver the big insights hoped for because companies don’t tackle the topic optimally. To do this, it would be of huge benefit to identify and prove a correlation between the use of customer analytics and corporate performance—and to know what the best companies are doing to turn their analytics into growth.

Our 2013 “DataMatics” survey, based on interviews with 400 top managers of large international companies from a wide variety of industries, provides just these insights.

Extensive and best-practice users of customer analytics outperform their competitors

Use of customer analytics appears to have an immense impact on corporate performance (Exhibit 1). The likelihood of generating above-average profits and marketing earnings is around twice as high for those that apply customer analytics broadly and intensively, i.e., the champions, as for those who aren’t strong in customer analytics, the laggards.  The effect on sales is even greater: 50 percent of the customer-analytics champions are likely to have sales well above their competitors’, versus only 22 percent of the laggards.


The champions are also almost three times as likely to generate above-average turnover growth as competitors who evaluate their data only sporadically (i.e., 43 percent of champions manage to do so, compared to only 15 percent of laggards). Return on investment shows roughly the same picture: companies making intensive use of customer analytics are 2.6 times more likely to have a significantly higher ROI than competitors: 45 percent versus 18 percent.

It is not just along such vital key performance indicators (KPIs) that these companies are very likely to outperform their competitors: they reveal a markedly higher likelihood of above-average performance across the entire customer lifecycle. In terms of strategic KPIs, some of the findings are quite extraordinary. Intensive users of customer analytics are 23 times more likely to clearly outperform their competitors in terms of new- customer acquisition than non-intensive users, and nine times more likely to surpass them in customer loyalty. Our survey results also show that the likelihood of achieving above-average profitability is almost 19 times higher for customer-analytics champions as for laggards. Even more impressive is their likelihood of migrating an above-average share of customers to profitable segments, at 21 times that of non-intensive users of customer analytics (Exhibit 2).


A third of all survey participants rated customer analytics as extremely important for business success, positioning it among the top five drivers of their marketing. They consider it as important as price and product management, only a few percentage points below service and actions to enhance customer experience, and far ahead of the management of advertising campaigns (which only 20 percent view as a key driver of success).

These results also differ considerably by industry sector. Banks have the greatest analytical skills, media companies are particularly strong on implementation, while the retail trade—surprisingly—lags farthest behind. Although they have an unprecedented wealth of transaction data available, most retailers began deploying customer analytics comparatively late; industries such as financial services and telecommunications are far ahead. Extreme cost consciousness has held the majority of retailers back from making major investments in the field. Many also appear to lack awareness of how great the impact of customer analytics can be.

While the best performers across all industries rate the use of customer analytics and other customer-oriented initiatives as the no. 1 contributor to their success, retailers whose performance is average view general marketing, pricing, and campaign management as the key factors for success—incorrectly.

McKinsey’s benchmarking has shown that intensive data evaluation has the greatest impact on performance in the retail industry. In the European retail trade, benchmarking shows that the economic impact of customer analytics is in fact more than twice that in the banking sector, and around three times higher than in telecommunications and insurance.

So how do the top performers do it?

The findings showed that winners take a truly integrative approach, seeing analytics as a strategic rather than purely IT issue. Hiring C-level executives who take a hands-on approach to customer analytics is also vital; they need to have the skills themselves and appreciate their importance. These three factors play a large role in the astounding spread in results between high performers and laggards evidenced in the previous section.

Companies need to understand that it is not the IT that counts so much as what you do with it. Many managers associate customer analytics with complex IT systems and expensive analysis tools. It’s true that a company can’t leverage customer data successfully without IT investment, but relying on technology alone isn’t the answer. How companies actually make use of customer information—and the organizational changes they implement to realize these changes—are what make the difference.

A narrow focus on technology and tools rather than staff and processes is another common failing. The ability to swiftly translate data into concrete action is what counts (Exhibit 3). That is where the retail industry, for example, falls short. It doesn’t invest sufficiently in in-house expertise, staff skills, and the development of proprietary analysis models.


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