Leading With Decision-Driven Data Analytics
Data analysts often fail to produce insights for making effective business decisions, but that’s not their fault. Leaders need to make sure that data analytics is decision-driven.
The New Leadership Mindset for Data & Analytics
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If you were to ask any major CEO about good management practices today, data-driven decision-making would invariably come up. Companies have more data than ever, but many executives say their data analytics initiatives do not provide actionable insights and produce disappointing results overall.1
In practice, making decisions with data often comes down to finding a purpose for the data at hand. Companies look for ways to extract value from available data, but that doesn’t necessarily mean data analysts are answering the right questions. It’s also not a safeguard against the influence of preexisting beliefs and incentives.
The solution is simple: Instead of finding a purpose for data, find data for a purpose. We call this approach decision-driven data analytics.
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‘Data-Driven’ Often Means Answering the Wrong Question
We’ll use some example companies to make our point. Let’s start with customer relationship management at RollingBoulder, a media company with a subscription-based business model. RollingBoulder’s customers can renew their annual memberships by responding to a renewal letter that they receive when their membership is about to expire. To reduce customer attrition, the organization sometimes adds a thank-you gift to these mailings.2
Over the years, RollingBoulder has developed a rich data set that describes past and current customers along various dimensions, such as location, membership duration, and website usage and behavior. The company has developed a sophisticated predictive algorithm that uses this information to quantify the likelihood that an active member will churn, and it then sends gifts to at-risk customers.
This data-driven approach to churn management is considered a best practice in the industry, but it is flawed.3 To understand why, let’s look at the central question the company is addressing with this analysis: “How likely is a customer to churn?” This is valuable information. For example, it allows the company to make projections about the value of its customer base. However, it does not address the question that is relevant here: “What is the effect of including a gift on a customer’s likelihood to churn?” This question cannot be answered based on data the company has already gathered and requires further data collection and analysis.
Data-driven decision-making anchors on available data. This often leads decision makers to focus on the wrong question.
1. “Closing the Data Value Gap,” white paper, Accenture, Dublin, 2019.
2. RollingBoulder is modeled after a company described in E. Ascarza, “Retention Futility: Targeting High-Risk Customers Might Be Ineffective,” Journal of Marketing Research 55, no. 1 (February 2018): 80-98.
3. “Customer Attrition,” accessed Nov. 3, 2020, https://en.wikipedia.org.
4. “About Measuring Sales Impact,” Twitter, accessed Nov. 3, 2020, https://help.twitter.com.
5. “Best Global Brands,” Interbrand, accessed Nov. 3, 2020, https://interbrand.com.
6. “BrandZ Top 100 Most Valuable Global Brands 2020,” BrandZ (June 30, 2020); and “Global 500 2020,” Brand Finance (January 2020).