Best Practices Summary:

• Align to overall company strategy, identify business challenges in delivering against that strategy, ensure that the data teams work is tied to specific business outcomes and measure the impact. 

• Gain executive leader commitment to drive usage of data and insights by their teams, including decision making requiring data to support asks

• Embed insights and recommendations in users’ existing tools for ease of access and use

• Institute an Analytics Council with executive leaders to gain and direct investments in the data strategy including, data science, analytics, data management and data engineering 

• Showcase early improvements with business results to garner more support and investment

Case Study:

A large high-tech company on a digital transformation journey with a goal of transitioning from 98% perpetual to 80% subscription for software required new ways of thinking about and using data.  To reach the company’s strategic objective they needed to increase cross-sell revenue and reduce customer churn by driving adoption of data insights and data science to inform decision making in sales, marketing, customer care, and customer success. However, the company faced both cultural and data management challenges. The company had a culture of habitually making decisions without data. To compound this issue the company’s lack of data infrastructure resulted in long wait times to get insights. The analytics team serving sales, marketing, customer care, and customer success functions approached the challenge with data infrastructure, analytics, data science and data management-based strategies.

Data use cases were selected where the work could be tied to tangible outcomes and strategic promises. For example, the sales, marketing, and customer success functions each wanted to increase growth in product cross-selling. A propensity model was developed with personalized cross-sell recommendations by account. At first it was lightly adopted and used. The team focused on relationship building with the heads of the functions to garner alignment on both the targeted business outcomes and leadership commitment to their teams’ use of analytic model outputs. This was coupled with ensuring that recommendations were easily accessible. Propensity scores were made available in both a visual dashboard format and their marketing automation tool, allowing employees to see recommendations in the tools they already used daily. Where leads received higher propensity to buy scores, they received priority treatment. These actions together led to adoption of analytic scores/insights and resulted in sales growth. 

Trust and availability in the data was addressed through data management strategies including executive alignment around investments in infrastructure and data quality improvements. Previously, manual efforts to find, cleanse, and prepare customer data led to long lead times to complete analyses. It could take six to nine months to answer business questions due to the preparatory work. An Analytics Council was created to govern data management and analytics activities and investments. The Council kept leaders informed and gained their support for investments in areas such as master data, metadata management, and data quality. An example of a data management investment that led to improvement dealt with customer hierarchy data management. The sales operations team had a challenge of suboptimal account coverage from its sales team leading to missed sales opportunities. The poor coverage was partly due to the low data quality of commercial customer accounts. The team improved customer parent/child hierarchy matching to over 95% accuracy through master data and data quality efforts. With this higher quality data, it was possible to predict the propensity of customers to buy. This allowed more accurate customer segmentation for an improved sales coverage model with more focused attention on "whales" or customers with a particularly high likelihood of purchase. Field sales increased with the better account coverage model. Wins, such as the positive impact of the new sales coverage model on sales, helped showcase actual improvements that result from data quality initiatives. This helped unlock more investment from the Analytic Council for further data management improvements. 

The analytic team’s experience driving both new analytics and data management activities showed the importance of balancing front-end and back-end investments. With these complementary approaches the analytics team was able to increase adoption of analytics among sometimes skeptical stakeholders and thereby create tangible improvements such as increased cross-sell and revenue growth.