Client Success Stories –
Pricing & Promotion

Optimizing Pricing and Packaging Strategies for Global Beer Manufacturer

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Client Issue/Needs

  • Quantity discounts are prevalent in the beer industry and our client wanted to understand how to optimize revenue by identifying the right pricing and package strategies.
  • Are there certain consumers who will pay more for the right features? How much more? How do we entice the value purchasers to buy more?
  • How can we better understand consumer volume decisions to maximize revenue?

Our Solution: Trade-Up & Quantity Discount Model

  • Identified the price points at which revenue is maximized through understanding of portfolio and competitive dynamics.
  • Determined how strategy must differ in different regions and within different channels.

Activation

  • Based on results of our study, the client put larger pack size into mainstream distribution for certain brands.
  • Optimized pricing across their line-up.

Pricing and Package Configuration Choice Study for Snack Company

Our pricing recommendation, if executed, would have saved the company $30MM.

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Client Issue / Needs

  • Client was planning a repositioning of Brand A that involved changes to product ingredients and nutrition profile.
  • Some in management wanted to take advantage of the improved benefit proposition by reducing the count per package at the same price point.
  • Goal of this study was to determine the optimal price point and package configuration as well as the new optimal promotion deal.

Our Solution: Discrete Quantities Model

  • We presented consumers with a choice task that enabled us to model the price dynamics of several brand-pack combinations, including Brand A and its competitors, accounting for potential switching across sizes and brands. We used our discrete quantities model to do this.
  • Modeling revealed that the weight-out would result in a significant loss in share as well as a loss of $50 MM in retail revenue. In addition, we identified the optimal price point and promotion deal for the current pack size.

Activation

  • Despite the negative prediction, the client decided to implement the price/pack change anyway. In-market results matched our predictions, much to the dismay of many.

Price and Promotion Analysis for a Global Confectionary Company

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Client Issue / Needs

  • Client was interested in understanding the effects of various pricing and promotion structures on both company and category volume and revenue.
  • Due to the complexity of the category (brands, package sizes, etc.) as well as additional factors (price and promotion tactics), the issue required an incredibly innovative and robust study design.

Our Solution: Discrete Quantity Model

  • We conducted a discrete quantity model in two buying situations: checkout (bars) and aisle (bags) purchases. Respondents completed an online-survey based on their previous purchasing behavior which contained three sets of choice tasks. In each of the choice-sets, price variations and promotional discounts appeared at 10%, 30%, and 60%. Various styles of promotion were used (e.g. – "Buy 1 get 1 free," "2 for $3," etc.)
  • We delivered a simulation tool to use in future pricing/promotion analyses.

Activation

  • The client used the data obtained from the choice tasks to identify the pricing and promotion levels that are most beneficial internally as well as to retailers.
  • In addition, the study produced valuable price elasticity information which the client used to identify vulnerabilities as well as opportunities.

Pricing Study for Global Alcohol Beverage Company

In-Market Test: Reduction of Price by 3 Pesos Resulted in a Sustainable $5MM Annual Increase in Contribution Margin.

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Corrected ROI Research

Client Issue / Needs

  • Client wanted to develop a price elasticity curve for a brand that would be used to set a market-wide pricing strategy.

Our Solution: In-Market Test & Discrete Quantities Model

  • We conducted a 10 week in-market price test consisting of price changes and promotions and modeled the sales results using a discrete quantities model. The test took place in 45 stores, with another 20 stores serving as the control.
  • We accurately predicted market changes when price was changed. Moreover, using distributor retail sales information, we were able to forecast fiscal year volume at different price points.
  • With the cooperation of client's finance department, we were able to examine and analyze profit effects in all areas of the supply chain.

Activation

  • We ultimately delivered a forecasting tool that allowed the client to make changes in price points yielding a $5MM annual increase in contribution margin.

Solutions

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