Automatic price optimisation as a tool for small and large retailers

Author: David Klečka, CEO Yieldigo

Who deals with prices more: small or large retailers? What should the retailer who decides for price optimization expect? Does price optimization matter because of team size? What are optimal prices and how does one set them? These are the questions that we hear the most often during discussions with our retail customers.

What is the problem?

About a year and half ago, we launched a software for retailers called Yieldigo. We decided to re-open the dialogue between retailers and shoppers on prices of goods. In the past, this dialogue was the initial step of any purchase or sale of goods which enabled shoppers and sellers to find the right price favorable for both sides. Today, when the shopper stands in front of the shelf there is no place for discussion about price with the retailer. Shoppers have simply become used to accepting the retailer’s prices. But honestly, what’s the percentage of shoppers asking for a store manager to discuss the price of milk?

Over the last year and half we have met with both small and large B2C retailers.

What do the small and large retailers have in common?

What else did we noticed?

Who is Yieldigo?

Yieldigo creates an intelligent software plugin for retailers based on modern mathematical knowledge and can simulate shopper’s decision making process while purchasing goods and using this ability for setting optimal prices. The basic principles, on which Yieldigo was built, are its own scientific research in the field of mathematical stochastic methods and technological scalability enabling Yieldigo to use efficiently great computing power. Based on retailer’s data, the machine learning engine learns automatically new patterns of shoppers purchasing behavior every day which guarantees its high flexibility and ability to respond to new situations on the market.

Yieldigo serves as a plugin needed to connect to the existing pricing system of the retailer; over a month the retailer will see a significant increase in profitability of more than 5%. It’s like employing a pricing manager who has the best mathematical education, unlimited working hours, understands perfectly shoppers purchase behavior, and has many years of experience with the assortment. If a modern, easy to use interface for pricing is added, we get a package that is used by Yieldigo clients from retail industries like supermarkets, drugstores and pharmacies. Brick & mortar retailers and e-commerce chains as well. Yieldigo’s strength is a quick integration of the plugin which takes just a few weeks.

If you are a small retailer, and you are considering price optimization we have good news for you! — It’s not just the size of the team; it doesn’t even matter if you have one manager or fifty of them in your pricing team. A machine learning tool managed by one pricing manager is the right way to go.

* We meet with dynamic pricing when purchasing a flight ticket or traveling with Uber. Prices change over the day, according to the current short-term situations. In retail, especially in brick & mortar, similar mindsets do not exist currently and prices are changed based on long-term trends happening over the last few days or weeks, and price changes are less frequent. The example of dynamic pricing can be following competition prices in e-commerce which can take place even several times a day. But then we should ask — is that the optimal strategy? Isn’t there goods that can be cheaper? Or more expensive? If the answer is yes — how much can the price be changed? At this point, it is the right time to start tackling price optimization. In other words, not every dynamic pricing method is at the same time the optimal method. The transition in this direction is non-trivial! On the other hand, the transition from optimal pricing to dynamic pricing is only a question of tool parameterization.

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