How small brands can test new prices

Price optimization can be the most effective way to increase your bottom line, but is often neglected, especially by smaller companies lacking expertise and budget. Here’s a case study illustrating one cost-effective testing methodology for a consumer goods company.

Case Study: Archie’s Organic Dog Treats

Last year, Archie’s pitched his line of Organic Dog Treats to Pet Wonderland, who agreed to put them on shelf at his suggested retail price (SRP) of $3.99. The treats were a hit! They sold 500,000 bags in the first year.

Now, because ingredient costs have gone up, Archie must raise the price, which he knows Pet Wonderland won’t like because of the success at $3.99. After some soul searching, Archie decides that the most viable options for a new SRP are $4.49, $4.99, or $5.49

The challenge

What is the best price to maximize sales and profit?

What would convince Pet Wonderland to accept the higher price?

A few different testing options

1 Poll your team: If you have no time and no budget, you could simply ask your sales and marketing teams for their estimate of how much will sell at the different price points. If you have access to retail data on competitors, look what happened to their volume when their prices moved to different levels.

· Pro: Quick and inexpensive

· Con: Lower accuracy

2 A conjoint study shows consumers 15–20 digital shelf pictures, varying the prices on each screen while continuously asking what they would purchase. Responses let you compute how much volume you would gain or lose at each price point (a.k.a. price elasticity). A critical factor running a conjoint is to make sure it represents a real world environment as closely as possible — Archie could accomplish this by limiting survey respondents to people who buy dog treats, or even have bought dog treats at Pet Wonderland in the last month.

· Pro: Highly accurate and effectively mimics store environment. Can test multiple conditions like labels, promoted price points, etc.

· Con: Expensive, lengthy set up, not an actual in-store test

3 A digital survey asks internet browsers what they would purchase, viewing your product against a limited set of competitors (with a few price options.)

· Pro: A data-driven, cost-effective option that lets you communicate data to retailers

· Con: Does not replicate in-store environment, limited to fewer questions than conjoint

4 In-store test: If you have a great relationship with a retailer and don’t mind tipping your hand, you can run a physical experiment where the retailer changes the actual price of the product in one store or region.

· Pro: Real store environment is the best predictor

· Con: Requires a strong relationship and effort from a retailer, and risk if test data doesn’t support your proposal

The experiment — digital survey

Archie decides on a digital survey because he needs to present his proposal to Pet Wonderland next week. Using a research partner, they survey 500 dog-owning Pet Wonderland shoppers, showing them images of Archie’s treats, as well as the top 3 competitor products, and ask which they would purchase. By seeing what percentage of people chose Archie’s Treats at each of the different price points, Archie realized that $4.99 was the price point that maximized sales and profit for both him as well as Pet Wonderland. He planned to use the information to show them how they would both benefit.

Financial Model

Archie currently sells for $2.79 to Pet Wonderland, who makes $1.20 off each sale, or a 30% margin. His cost of goods to make, package, and deliver the product is going up to $2.20.

Key observations from the model

· There’s a big volume drop off at $5.49. While the elasticity was pretty small up to $4.49 and even smaller at $4.99, there was a major drop off at $5.49, indicating $5 is probably a magic price point

· $4.99 is the option that maximizes sales and profit for both Archie and Pet Wonderland. Even though his % margin would be higher at $5.49, his profit dollars would be lower because of the volume drop-off

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Contact the author: Daniel@consumerledpricing.com