CHALLENGE
Misalignment in sales bonus scheme
A Nordic confectionery company identified a clear misalignment in its field sales bonus scheme. Setting sales targets for field sales had proven difficult. With stores varying in size, concept, and location, estimating sales potential was nearly impossible. And in the world of FMCG brands like candy, one major variable is campaign activity—making accurate forecasting a moving target.
Lacking better tools, area key account managers resorted to setting targets based on gut feeling—essentially a guesstimate. These targets were set as lump sums for each trading area.
Field sales teams embraced the system—but focused their efforts on low-hanging fruit stores with the most obvious sales potential. While they easily hit their quotas and maxed out their bonuses, they neglected the rest of the stores in their areas. Simply because they could.
From the company’s perspective, this meant leaving money on the table. They needed a better understanding of true sales potential. To improve performance and profitability, they had to restructure the bonus scheme — and, most importantly, embed analytics into their business management processes.
SOLUTION
Store-Level Forecasting Reshapes Sales Targets
The key question from the start was this: What are the forecasted sales by product, by store, on a monthly basis?
Answering it required a bottom-up approach — analyzing each store’s demographics and local demand by product. It meant understanding how media campaigns influence demand, and how promotions, discounts, in-store displays, and even available floor space affect sales.
Product-by-product and store-by-store forecasts were then combined into area-level forecasts — a completely new concept for key account managers.
For the field sales bonus scheme, the change was even more significant. Instead of a lump-sum sales target per area, quotas were now set at the store level, broken down by priority products to sell and priority floor space to fill.
RESULTS
How Analytics Boosted Sales by 58.4%
A newly developed scenario tool empowered sales management to test various product combinations and assess their impact on key sales KPIs. With insights into each product’s price elasticity, they also gained a stronger ability to plan promotions more effectively. Sales target setting was automated using 12-month forecasts for each product at the store level.
All analytics were fully automated and made accessible to relevant stakeholders — business and sales management, as well as area key account managers — via Power BI. Field sales teams access their insights directly through the CRM system. Before visiting a store, they can review priorities and goals, gaining a clear understanding of what to focus on and what to sell.
The company has entered on a true analytics journey. With a robust analytics process now supporting sales management, the results speak for themselves: within the first rolling 12 months, revenues increased by 58.4%.
