How One Google Team Built Storytelling Into Analytics

• Data layer: Builds business-aligned data foundations.
• Analytics layer: Designs flexible, interpretable models that anticipate and address executive needs.
• Decision layer: Structures outputs around actionable levers, trade-offs, and constraints.
• Narrative layer: Delivers coherent, contextualized insights that lead to action.

Each layer builds on the one below it to form a structured approach that helps executives make better, faster decisions.

Data Layer: Start With Shared Definitions

One project involved analyzing sales engagement to determine the ideal number of customer meetings that a sales rep could sustainably manage. This insight played a key role in informing sales coverage models and capacity planning. We discovered that teams had varying definitions of what did and didn’t qualify as a customer meeting. Sales teams defined it as everything from a brief email exchange to an all-day conference, while the business strategy team counted only scheduled phone calls or in-person conversations. We found instances where teams reported having had more than twice the number of meetings as other teams within the same region because they used different definitions. This discrepancy resulted in wildly different customer-to-representative ratio recommendations.

We quickly realized that without a common, business-relevant definition for “customer meeting,” our insights were not credible enough to support actual decision-making.

To address this, we aligned our data governance logic with business context. Partnering with cross-functional teams, we defined “customer meeting” based on the specific decision at hand. We agreed to prioritize building substantial, enduring customer partnerships. Only scheduled phone or in-person conversations qualified; emails and similar interactions were recategorized as light-touch engagement. We also built a unified data source that categorized meetings by channel and tagged each one by engagement depth.

These efforts gave business leaders confidence in the data and clarity on what it represented. Reviews required fewer clarifying questions, which allowed teams to move faster during go-to-market planning. As this example demonstrates, consistent data is a critical storytelling enabler and the first step toward action.

Analytics Layer: Build Models That Executives Understand

Analytical sophistication and accuracy are valuable, but business executives prioritize model explainability and practical relevance. Black-box models rarely earn colleagues’ trust. To secure leadership buy-in, teams should focus on transparent, flexible analytics techniques that align with how business decisions are actually made.

With a consistent understanding of customer meetings in place, the next step was to determine the optimal customer-to-representative ratio to inform our global sales capacity planning. We knew from previous experience that a static model would not be enough. Executives wanted not only the numbers but also the ability to understand how assumptions were influencing the results and how they shaped different business scenarios.

We began by identifying the scenarios that were most relevant to executives. In a cost efficiency scenario, we focused on maximizing the ROI per representative. In a growth-first scenario, the goal was to maximize overall revenue coverage. These scenarios defined the model’s structure and boundaries.

We made key drivers — like meeting frequency by customer segment, meeting length, and available rep time, after accounting for internal responsibilities like training — transparent from the start. Instead of keeping these assumptions in the background, we developed an interactive model that allowed executives to adjust inputs in real time.

For instance, executives could evaluate the impact of asking reps to increase customer-facing time by 5% or allocate more time to high-value customers. The model would recalculate the impact in real time, showcasing the downstream effects on staffing, coverage ratios, and projected revenue.

We also prioritized explainability. When results shifted, the model highlighted the most significant drivers, such as changes in customer engagement time or regional meeting norms. This gave executives a clear view into the logic and allowed them to evaluate decisions with confidence and context.

Applying this dynamic model changed the nature of our planning conversations. Debates over model assumptions gave way to discussions about trade-offs, resourcing priorities, and go-to-market execution. It reinforced a key principle: Business analytics must be decision-driven, interpretable, and context-specific to be truly effective.

Rather than treating storytelling as a post-analysis presentation or dashboard polish, we embedded it into the structure and delivery of the analysis itself. In the sales capacity planning project, we built the first three analytics layers by starting with a clear problem statement, highlighting decision principles, identifying constraints, and introducing realistic, data-supported scenarios.

Then, instead of walking through technical logic from the bottom up, we led with the business context and the decisions at stake, such as how a 10% increase in sales team efficiency could affect coverage and staffing. We also used visualizations to surface trade-offs and structured our content to build executive confidence, from high-level implications down to supporting inputs.

Storytelling was not an afterthought; it was a decision enabler. By framing the analysis through a clear, compelling narrative, we helped executives not just understand the outputs but also act on them with clarity and confidence.

At Google SMB, this framework has been applied extensively, particularly in projects involving global sales coverage, resource planning, and go-to-market strategy. The framework’s principles have also been adopted by multiple cross-functional teams, including parts of Google’s finance and marketing organizations.

Our analytics framework achieves that through:

• Clean, consistent data that creates a shared understanding.
• Flexible, transparent models that facilitate strategic exploration.
• Decision framing that clarifies what is and isn’t actionable.
• Narrative delivery that mobilizes leaders from insight to action.

By treating storytelling as a core design feature, we shifted analytics from output generation to strategic enablement. The result was not only better models but also faster alignment, clearer decisions, and more confident action.

The takeaway: When an organization’s analytics are built with decision-making in mind, analytics earns a seat at the table where it truly matters.