Unlock data science value with cross-functional collaboration

Strategic decision-making, agility and performance optimization are essential in the retail grocery industry. Data science enables the deeper insights that companies crave to make smarter, informed decisions.    However, two challenges often arise:   

• How can business and technical teams collaborate effectively 

• How can lean data science teams drive maximum impact?  

 Leading pasta brand Barilla solved both challenges—exemplifying effective collaboration and lean team optimization. Here’s how they did it.   

The insights opportunity

Barilla's shopper marketing team had noticed rising sales of their Protein+ pasta line, recounted Barilla Shopper & Digital Commerce Manager Marianna Sugarman at a joint presentation that we gave at  the Path to Purchase Institute (P2PI) Live conference.   The shopper marketing team wanted to double down on this momentum but needed deeper insights into the driving factors. Key questions included: 

• What are the characteristics of Protein+ pasta purchasers?

• How can we identify Protein+ prospects buying similar products?  -How could these insights inform an engagement plan with current and prospective customers?

Collaborating with a technical team to help uncover those data insights was “critical,” said Sugarman. Together with the Barilla acceleration team—a group that supports business teams with advanced analytics and insights—and 84.51°, they formed a cross-functional team.   

Describe, predict, influence

In collaboration with 84.51°, Barilla utilized an “insights-to-activation” playbook driven by data science. The playbook consisted of three parts:    

• Uncovering descriptive insights about current and priority Protein+ customers 

• Predicting likely and potential new customers 

• Then influencing shoppers along the path to purchase with a layered, cross-channel offers approach 

 Barilla used 84.51° Collaborative Cloud to deploy its data science resources against 84.51's industry-leading shopper behavior data, containing transaction data from over 62 million households that shop at Kroger stores every year. The Barilla data science team leveraged a suite of pre-built data science packages to uncover actionable insights quickly and build science-based audiences to use in their Kroger Precision Marketing Protein+ campaign. After building a propensity model to identify households with a high likelihood of buying Protein+ pasta among other insights, the final step was to construct and deploy personalized offers at scale to current and prospective customers using Kroger Precision Marketing.    

The results

Barilla’s collaborative strategy was successful. Deploying the insights-to-activation playbook produced a 1.8X increase in household penetration uplift and a 1.7X lift in incremental return on ad spend. The collaboration between Barilla’s shopper marketing and acceleration teams also set an example of a cross-functional way for data science and business teams to work together.  

Key takeaways

Fostering connectivity between different teams is essential to unlocking opportunities that drive outcomes—especially among lean teams. With shared goals, contextualized data and collaborative workflows, companies can bridge divisions to generate value. Here are essential tips to bridging the gap  between business and technical teams to drive maximum impact:  

• Start with shared objectives – This may seem simple, but it is foundational and often overlooked.  This step upfront ensures that both teams are charging toward the same goal and outcome.    

• Build teams around initiatives – This is a two-part recommendation. One, prioritize initiatives that are worthy of focused time from cross-functional teams. Two, create a formalized group that brings all necessary parties together.    

• Once together – It is critical for the business teams to take the time to give the technical teams context. When beginning work, design your workflow to drive connectivity and touchpoints throughout the analytic process.  This enables input/iteration where and when needed, avoiding misalignment or the need for rework at the end of the process.

  By enabling cross-functional alignment, utilizing the right data science tools and spotlighting quick wins, companies can maximize value from data science. With the right structure, teams can leverage shared insights to optimize decisions. Connectivity across teams unlocks data science impact, fueling smarter executions at every level.     Unlock the power of data science with 84.51° Collaborative Cloud. Request a demo today to learn more.