In the world of sports, it is now possible to create accurate odds, simulate games, and understand as much as possible before placing a bet on a team. This is enabled by analyses of past performances. For example, you can look at Harry Kane’s statistics and find out the expected performance for each of his upcoming games, writes Matthew Pavich, Managing Director, Global Strategic Consulting, Revionics, an Aptos Company.
However, with a rookie player, there’s no historical data to help you understand the player’s performance potential. It is anyone’s guess, and you can really only make assumptions about the number of goals or assists they’ll make.
Similarly, when it comes to pricing new products, grocery retail pricing teams must use their best estimates – paired with science – to set the initial price. It’s not an easy task, which is why grocers must understand the top techniques and best practices for introductory product pricing, and how they can closely monitor performance to ensure that they drive the most profitable results.
Identify Similar Products
When introducing a product, retailers haven’t yet been able to experiment with its price to see what happens in demand curve and elasticity as prices fluctuate. Retailers can instead begin by looking at the new product’s attributes and subcategory in order to identify similar products. Understanding how those more-established items performed as a result of price changes over time can serve as a guide for setting an initial price.
For example, when pricing a new type of plant-based meat, grocers can look at existing data from similar categories, such as other brands of meat alternatives, to make pricing decisions based on hierarchy and zone structure. By matching the product with an existing scientific model, retailers can set an initial price. Then, once new data informs the model for that product’s performance, the science prompts smarter, better recommendations that pricing teams can rely on.
Enable a Rules-Based or Cost-Plus Approach
Because a like-item approach can be a logistical challenge, pricing teams can also lean on a rules-based or cost-plus approach for introductory product pricing.
At the most basic level, a cost-plus approach means looking at margins and deciding what markup percentage will yield a profit. If a product has a unit cost of £5, a retailer may add a 40% markup to the cost to drive revenue, making the price £7. However, this simple approach doesn’t consider many constantly changing factors, such as shifts in consumer demand and impacts on affinity and cannibalisation.
That’s why pricing teams can also turn to rules-based pricing, setting up parameters based on margins, competition, brand pricing gaps, volume discounts and more. This is especially important for price perception, allowing grocers to look at prices based on the category and competition and match those prices accordingly. Grocers can then strategically re-evaluate prices based on financial targets and changes in consumer demand.
However, with this rules-based approach, pricing teams must ensure that they’re not ignoring the science behind it. As consumer demand evolves, grocers will need to start with rules and merchant instincts until there’s enough data to create a science-based approach – one that combines rules and science to create a more tailored and profitable response.
Leverage Science-Based Advanced AI
It’s important to note that even after just one week of collecting data on a new product, retailers will have a better forecast than when they started. And the more diverse the data is, the better. Of course, grocers shouldn’t do anything too drastic in the first few weeks – but grocers can still fine-tune their pricing strategies frequently to understand the changes in elasticity as it relates to price adjustments. As time goes on, pricing teams can adopt optimised pricing as the science evolves and ‘learns’ more about the item.
Grocery retailers should closely monitor performance and leverage advanced AI to adjust pricing on a continuous basis. By leveraging big data to fill in the unknowns, pricing teams can transfer the knowledge from the big data to the small data. They can then use these AI-driven recommendations to drive powerful pricing strategies that deliver the maximum impact for each product, especially as it becomes more and more of a crowd favourite.
Make Your ‘Rookie’ Product the MVP
Grocers must remember that every day and every pound matter. They can’t rely on guesses to be successful. Instead, grocers must collect data and make the pricing model smarter from day one. That way, pricing teams have an opportunity to adjust quickly and ensure that they’re not leaving money on the shelf.
Throwing a new player into the pricing game can be a challenge, but with the right approach, grocers can make the ‘rookie’ product the best player in the league.