Best Practices Summary:

• Storytelling is imperative for analytic adoption as an analysis should take the audience through a narrative that highlights the most important takeaways and proposed recommendations to drive the business. This means understanding the goals of the audience, adjusting communication to the right level of detail, and anticipating points of view and questions to inform and drive action.   

• Invest in understanding the data source, ensure its quality by leaning into strengths and away from weak areas. Avoid sharing numbers that you can’t stand behind to become the credible and consistent partners your stakeholders need.

• Hire analysts with a natural curiosity, good communications skills, and a willingness to grow technically. Supplement these traits with internal training on storytelling.  

Case Study:

A large media and entertainment company provided advertising-supported video streaming services to customers to generate revenue and subscriber growth. The streaming services’ product and sales leaders had opposing opinions about the volume of ads to be served to customers during breaks in content. The sales team’s approach was to increase ads to drive revenue while the product team sought to reduce ads to deliver a positive user experience and engagement. Neither group backed their approach with data. Both groups had a culture of using opinions based on experience rather than supporting decisions with data and analysis. The impact of these opposing approaches was significant not only financially but also in terms of reduced morale on the sales and product teams due to conflicts on the optimal ad volume.

The Digital Media VP, Forecasting and Analytics led a research team that sought to take a data-driven approach to solving the problem of the right number of ads. Through A/B testing, data analysis, and communication they were able to shift the narrative from personal disagreements based on anecdotes and opinions to a clear, data-driven story of the optimum approach. The teams’ analysts started by understanding their stakeholders better including what they thought was most important to meet their own business goals. They took a curious and listening-based stance when asking questions. Then they walked these stakeholders through the thought process behind a more hypothesis/data-driven approach that could support their goals. They set expectations for the type of insights and timing of those insights. This increased stakeholder buy-in to the upcoming analyses. The curious approach extended further as the analysts dug into the data to identify challenges with the data processing logic that differed from existing documentation. Once the data quality and processing logic issues were addressed, the team applied A/B testing to the streaming advertising experience. The A/B testing showed that both teams were correct to an extent, however there was a sweet spot when the customer was invested in watching the content where four to six ads would lead to only a 2 to 3% drop in viewership while maintaining the original revenue goals. The insights were turned into a story that incorporated an understanding of their audiences’ personas in the sales and product teams, uncovered gaps, identified what was most important, shared insights in a visual and digestible way, and provided clear recommendations for next steps. With the storytelling-based approach, the sales and product teams were more open to the insights than they were previously and readily adopted the recommendation for optimal number of ads. As an additional bonus, the team identified that the order and variability of ads, not just the number of ads in a break presented improved satisfaction significantly. If an ad was shown more than once in a certain period of time or if it was shown in the same ordinal position in the ad lineup, there was a negative impact on viewership. This insight led to the development of a new ad shuffling logic that minimized repeat ads.  

The ad testing analytics and insights were highly valuable. The analytics team was able to build credibility by providing high quality data, great analysis, and compelling storytelling – increasing their stakeholders’ trust. The implementation of the insights on optimal ad volume and variation led to marked increases in both advertising revenue and customer engagement.