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Don't Let Good Data Go to Waste

Don't Let Good Data Go to Waste

by Chris Musser and Scott DeKoster

Story Highlights

  • Organizational structure should reflect the importance of data insights
  • Incorporate customer-insight analyses into decision-making processes
  • Don't ignore qualitative data

During a recent executive team workshop Gallup held with a multibillion-dollar retailer, one of the executive vice presidents (EVPs) noted that the company had an abundance of customer data but lacked insights from them. Heads around the room nodded, including the CEO's. One strategy leader admitted the company has lots of reports, but they weren't very analytical. Another EVP mentioned "analysis paralysis" and more heads nodded in sympathy.

Many leaders know the feeling. Lack of data insight is a ubiquitous problem -- McKinsey research has found that fewer than 20% of organizations have maximized the potential of their advanced analytics.

This challenge is significant -- but the rewards from good data analysis are commensurately high. One client Gallup advised saw a 150% increase in the accuracy of its demand prediction model, which helped optimize the costs along its value chain. Another saw a 50% increase in sales of a new product thanks to Gallup's powerful predictive analysis, which effectively used the data.

Those results are, obviously, not automatic. They depend in large part on leaders' dedication to action. If you're trying to break free from analysis paralysis, incorporate the following into your organization's operating model:

1. Embed the significance of customer insights into the organizational structure.

Most leaders say data insights should be central to their decision-making -- but for many, their organization's insight center is little more than a traditional market research operation that reports to a mid-level VP. If executives want to anchor their strategies in customer insights, they need to invite division leaders who are closer to the rooms where strategic decisions are made.

Elevating the head of an insight center to an authoritative position will send a powerful signal to the company that data-driven decisions are central to how the organization operates.

2. Take customer insights seriously.

According to a Boston Consulting Group study of 90 companies, less than half of business decisions are informed by customer insights. In most organizations, customer insights are not embedded in the decision-making process whether formal or informal.

Each organization has a unique decision-making style, so prescribing a one-size-fits-all approach is futile. However, leaders should ask themselves if customer insights have the same impact on decisions as, say, financial data. For example, many CEOs would never consider making a significant business decision without first having their CFO review it. A similar review process involving the head of the customer insights center should be considered.

3. Translate between leaders and analysts.

In his recent HBR article, Joel Shapiro notes the frequent misalignment of expectations between data-focused teams and leaders is often caused by leaders' unrealistic assessments of their in-house teams' capabilities. One of his recommendations is to "give a dose of reality -- to both sides." Leaders must know what to expect of their analysts, and analysts must understand what their leaders need.

Train your leaders to interconnect their decisions with dynamic customer data.

One reason for this misalignment is that leaders are insufficiently trained in using customer data to make decisions. They may not know where data should fit in the decision value chain, or they may question how heavily they should weight the data versus their own (or their teams') intuition. That's when data is underutilized. Conversely, a starry-eyed belief that data can instantly transform a business leads to overuse. Either way, the data is not properly used.

One client Gallup advised saw a 150% increase in the accuracy of its demand prediction model, which helped optimize the costs along its value chain. Another saw a 50% increase in sales of a new product thanks to Gallup's powerful predictive analysis, which effectively used the data.

For data to be maximally useful, leaders must better understand what data can -- and cannot -- do. Leaders need not be experts, but they should have sufficient familiarity to make optimal use of their expert teams. For example, leaders and leadership teams should generate testable hypotheses that require data experts to validate or invalidate them.

Train your analysts to think strategically -- don't conflate reports and insights.

Many leaders assume that their insight centers should be full of "quants" who needn't concern themselves with firmwide strategic matters. This is a mistake. If insight centers are to be considered an essential partner of the C-suite, they must be staffed with "T-shaped" analysts: analysts with a depth of quantitative expertise and the ability to, in the words of IDEO CEO Tim Brown, "imagine the problem from another perspective -- to stand in somebody else's shoes." Analysts in the insight centers must step into the shoes of the organization's strategists to ensure their analyses are strategically relevant rather than merely backward-looking reports.

4. Add qualitative data to the mix.

Most companies collect massive amounts of quantitative data. While valuable, these data often lack the texture and actionable insights that supporting qualitative data could provide.

Those data are readily available -- inside the heads of salespersons. But those insights go to waste unless they make it to the right internal partners at the right times, and leaders should not assume they will. Probably more often than not, useful qualitative data stays locked in the salespersons' heads.

But leaders may not have the key to that lock -- a neutral third party may be the best means of objectively collecting these data.

As an example, Gallup conducted key account reviews for one of our largest clients. During an interview with the CEO of one of the client's key accounts, Gallup learned that the customer was planning to take their business elsewhere. These insights would not have come from quantitative data, and the customer may not have voiced this to their vendor directly. Consequently, Gallup's client would not have known that one of its biggest customers was at great risk.

That's not unusual: Gallup's key account reviews for B2B organizations find that an alarming 71% of B2B customers are ready to take their business elsewhere. Leaders are often surprised by the results of external qualitative analysis of their customer relationships.

These are solvable problems.

While the suggestions above are not a panacea, they are tangible steps leaders can take to get the most out of their data. Most companies don't extract top value from their data, rendering their data only partially useful at best.

It's a common problem -- bring it up and you'll see a lot of heads nodding in agreement -- but it's a serious one. Fortunately, it's also solvable.

Keep your customers by knowing what they need:


Scott DeKoster is a Senior Managing Consultant at Gallup.

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