Building a Pricing and Promotion Analytics Toolkit for Consumer Goods Clients

Rajveer was given a brief to build an internal capability: a pricing and promotion analytics toolkit that Alvarez and Marsal could sell to consumer goods and retail clients globally. He owned the project independently, reporting directly to the team director.

He started by designing the overall architecture of the dashboard, mapping out what views the end user would need: pricing structure across SKUs, the impact of price changes on sales volume, and a full view of promotional effectiveness across the year. The toolkit was built in Power BI, with a regression model running in the background to simulate how different pricing variables would affect volume outcomes.

During the build, he hit a problem with the backend dataset. The numbers surfacing on the dashboard were off. Rather than assuming the dashboard logic was broken, he worked through the problem methodically: checked the calculations manually, confirmed the formulas were correct, then traced the issue to the backend data layer. He rebuilt just that component, leaving the broader architecture intact.

Once the toolkit was complete, Rajveer presented it to global managing directors and team directors. The session covered what the tool could do, what decisions it enabled, and how clients could operate it. When the MDs pushed on the regression model, asking which variables drove the output and how the implications could be made more transparent, he walked them through the dropdown and filter controls built into the interface and explained how the variable set could be tailored to client context.

The toolkit was positioned as a sellable capability for consumer goods and retail clients, enabling them to run pricing and promotion analysis on their own data.