Editor’s note: This article was published in April 2025. The tariff conditions and company reactions described below reflect that period.
A recent Wall Street Journal article noted that “Major U.S. CEOs warn that constantly changing tariffs is spooking consumers and making business planning virtually impossible” and “several companies considered raising prices” in light of the tariffs.
How do you establish a pricing strategy in this environment?
Brands may absorb added costs, raise prices, change pack sizes, or alter materials, but the commercial effect is difficult to predict. When the offer may change across several of those attributes, discrete choice modeling can isolate how each variable affects preference within the tested scenarios.
A discrete choice study can evaluate how several tested factors affect purchase consideration and/or market share:
- Price Increases or Decreases: The impact of changing the price of your current product offering(s) in its current state
- Competitive Action: Measuring the impact of a competitor’s price increase or reduction, product changes, or new product introduction
- Discounts or Bulk/Multi-Pack Pricing: Determining if demand and the bottom line is positively or negatively impacted by multi-pack offerings, discounts, coupons, or loyalty programs
- Product Size Change: Whether purchase behavior differs if the current product size is reduced or increased
- Product Modifications/Line Extension: Determining whether the addition of new products that may be of lesser quality (e.g., “good/better/best” offerings) is a viable strategy
- Material Change: Evaluating the impact of modifying the materials currently used in products or packaging (e.g., changing an aluminum can to a glass bottle)
A market simulator is a useful deliverable from this type of study. Insights and product teams can run “what if” scenarios in which one or more prices, pack sizes, materials, or competitive offers change. If a competitor changes its price or product later, the team can return to the simulator to estimate the likely effect within the conditions tested.