Every day and every dollar matter in retail pricing. Every item sitting on your shelf with less-than-optimized pricing is a missed margin or price perception opportunity. Even newly released items. Setting an initial pricing strategy for a new product is an admittedly difficult, but also important, pricing decision in which many retailers leave money on the table.
If you were to start a new small business – whether selling a product or offering a service – one of the earliest decisions you’d make would be how much to charge. Before launching, you’d analyze the competitive landscape to see how similar companies structure their pricing, and what price customers are willing to pay. You would evaluate how your target demographic might perceive a price as either too high or too low, and what that pricing communicates about the quality of your brand. And certainly, you’d take into consideration your costs or other resources that factor into protecting some amount of margin.
Setting a price for a new retail product in a grocery, drug or convenience store is no different than those early business beginnings. While introducing a new SKU may seem like a less consequential decision than building a business from the ground up, it’s actually just as important. This is because your success as a retailer is the sum of every new product introduction – and therefore every opportunity to set an initial price.
Sales data is the key to setting non-initial prices. Using sales data, you can understand causal effects of various factors on sales for every product in every store. The tangible data demonstrates precisely how sales are impacted by different factors and conditions. For example, when a price goes up, when an item goes on promotion, when it’s a holiday week, or during the summer months.
Price elasticity is an important dynamic to understand as well, further measuring the relationship between price and demand. If you want additional reading on this topic, my colleague Matt wrote a blog about price elasticity, explaining how a reduction in price for an elastic product drives more sales, while inelastic items allow for higher prices without a huge impact on units sold.
The challenge for an initial pricing strategy lies in the fact that you simply don’t have sales data for a soon-to-be launched product. You haven’t had the opportunity to play around with pricing changes to observe what happens to that demand curve.
So, what can you do? Use a combination rules and science to make initial pricing strategy decisions. Then, monitor, experiment and adjust accordingly to make a series of optimizations on that initial price.
Initial pricing sets you up as a retailer on an evolutionary pricing journey. You start with a decision made with a nominal amount of data and a little bit of science. Over time, as more and more data becomes available for analysis, you can let the science take over, leveraging AI and machine learning to automate pricing over the life of that product.
In the absence of data specific to that introductory product, consider these pricing best practices as your starting points:
Any of these exercises will probably be an overestimate or underestimate of what the ideal price ought to be for a new item, but it will at least give you a starting point from which to adjust. You’ll never know the real effect until it hits your store shelf for consumers to purchase.
Inferencing generally works, in pricing as well as in life. But science gets you closer, and gets you optimized. Once you have a few weeks or months of actual performance data, your pricing teams will be more equipped to make educated decisions on subsequent pricing changes.
If you embrace a culture of making more pricing changes (and fair ones, at that), you will simultaneously be strengthening your pricing confidence. This means the next time you introduce a new chocolate bar, for example, you’ll have an even smarter model to inform those initial pricing strategy decisions.
It’s a fortuitous cycle, really. Intelligence grows and everything gets smarter with each discrete piece of data. The quality of the AI model quickly accelerates. In just a couple weeks, the model can already start adjusting, making price recommendations, and helping you identify more product relationships. From there, pricing teams can follow trends closely, monitor and seek explanation for why things happen, and continually tweak pricing and promotions to be optimized for profitable growth.
Think about it: Even cents per unit add up over days and stores. This is why following pricing dynamics in the weeks after product rollout is critical to a retailer’s bottom line. Setting initial price can be a huge challenge, but with the right data landscape and advanced technology to aid in the decision making, you can be successful in setting initial price.
Interested in chatting more about the challenges you face in setting an initial price, and how AI can assist? Let’s talk.