A CPG client in the snack category was planning to reduce the size of their core product to help with profitability and needed to optimize their lineup at the shelf to increase revenue. During this process, they identified the following facts if they moved forward in this direction:
- Raising the price on the current core product in absence of other changes would decrease total revenue.
- The ability to drive more dollars per purchase had to be done by introducing a new, larger size for their core product.
- Retailers would not accept a premium price for a larger size, the pricing for the larger size had to be a “better buy”.
- Finding optimal price gaps was critical to maximizing total revenue.
The client had already executed an online conjoint study to model price gaps, but they were not confident in the results because they didn’t think the respondents’ answers reflected how they would actually behave if in the aisle buying the products. They client wanted to test the new smaller and larger products using a real-world shelf set context. The trick was to somehow create a statistically sound volumetric conjoint design that would work with a real, life-sized shopping task.
IDG leveraged our Virtual Aisle to create high resolution virtual shelves that participants could shop just as they would in store. Using this portable, life-sized virtual environment allowed us to test in multiple markets as gave participants the realistic stimuli that was missing from the client’s previous online conjoint model.
IDG created a custom volumetric-based conjoint using the client’s old and new product sizes in conjunction with the price points in question as features/levels. We also incorporated the competitor products on the shelf using a current plan-o-gram provided by the client. A solid conjoint design was derived that consisted of 20 different shelf orchestrations. IDG created 20 virtual shelves and each participant shopped 7 of them. As participants shopped the shelves they indicated what they would by and how many of each product they would buy.
The results were used to create a simulator tool that was calibrated to the client’s sales data enabling realistic simulations to forecast how changing the product sizes and prices would impact total revenue. The clients used these simulations to optimize their shelf lineup and they were able to increase total revenue by moving forward with the ideal product sizes and price gaps.