Case Study

Microsoft Azure Growth & Adoption

The Challenge

Microsoft needed a solution to streamline and enhance its Sales Plays

Microsoft faced challenges in updating its Sales Plays around key Azure technology each year. The existing process, relying on subjective assessments from SMEs, took months and lacked data-driven insights. Definitions for Sales Plays changed throughout the year, making tracking effectiveness challenging. Microsoft needed a solution to streamline and enhance the Sales Plays process while providing consistent year-over-year tracking.

The Action

Calligo built models for various Sales Plays

In collaboration with Calligo, Microsoft implemented a dynamic solution using R Shiny. The tool empowered the Data Science team to build models for various Sales Plays, optimizing the process and enabling more informed decision-making. The solution included:

  • Elastic net regression to identify predictive features for each target.
  • Controls for reducing collinearity via lasso and ridge regressions.
  • Boosted random forest model predicting service adoption or usage increase.
  • Post-processing with cosine similarity to target accounts for growth or adoption.
  • Visualization of model outputs in Power BI reports for easy seller access.

The Impact

A transformative impact on Microsoft’s Sales Plays

The implementation of the solution had a transformative impact on Microsoft’s Sales Plays and data-driven decision-making:

  • Efficiency Gains: Empowered the data science team to build dozens of models efficiently, a process that would have been manual and time-consuming.
  • Enhanced Accuracy: Account targeting for new services saw a significant increase in accuracy, from 10% to 40%, outperforming the prior method. The average expected adoption of a new service rose to 3%.
  • Revenue Growth: Accounts targeted for growth achieved an average YoY growth of 17%, surpassing the previous model’s 11% and the overall average of 7%.
  • Reduced Cycle Time: Targeting logic time was reduced from a 4-month cycle to just 2 weeks, enabling quicker and more agile decision-making.

In summary, the collaborative effort with Calligo empowered Microsoft to revolutionize its Sales Plays, leveraging data science for efficient model building, accurate targeting, and accelerated growth, ultimately optimizing the entire process and driving substantial business impact.

The difference working with Calligo was the team’s mindset. They looked at the problem entirely as a commercial question, not as a data science challenge applied in a business context. And it worked.”

Bo Oslen

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