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?
- Prices are set based on competition, margin, or supplier’s price recommendation. But where is the shopper at the center of interest?
- Both sides agree that Optimal Price is the price the shopper understands and accepts. They believe that in the long term they will have the best economic results at this price.
- Both sides talk about shopper’s Price Perception. At the same time, they can’t agree internally on what it actually is and how to measure it. They often mix Price Perception with Competition Price Index (which in fact measures difference in prices compared to selected competitors).
- Both sides agree that setting the right price is extremely difficult, since you must take into consideration many different factors; from article quality to weather forecast. They both admit that it’s not possible for humans to consider all the necessary factors. BI reports aren’t sufficient as they are time consuming and difficult to implement while not guaranteeing guarantee any results.
- Likewise both sides aren’t sure of which prices are comprehensible for consumers and which are not.
- Both sides confuse the terms Price Optimization and Dynamic Pricing.*
- Both sides often spend money on expensive consultations and reports despite the fact that they provide only limited insight on items & categories to reprice. Additionally, this provided guideline is usually based on fixed-rules and parameters which possess limited applicability as nowadays the business conditions influencing pricing change every day.
What else did we noticed?
- Small retailers look for simple but efficient applications based on different aspects that recalculate prices automatically and whose price settings can be adjusted with just a few clicks. Standard CPQ systems are not suitable as they are complex and counterintuitive solutions which do not guarantee increase in profitability through optimal pricing.
- Small retailers who often use the price recommendation of their suppliers see the potential in improving pricing. At the same time they are quite flexible about making price decisions.
- Large retailers are aware of the fact that difficulty of pricing is related to the assortment scale. Therefore they are not looking for consultants who advise them but for a software that does the job for them.
- Large retailers know proper pricing is a process which needs to be done repeatedly every week, day, or even hour. This applies to small growing retailers, and especially to those in the field of e-commerce.
- Both large and small growing retailers are therefore actively looking for a price optimization tool. Small traditional retailers often do not know that there might be a tool or service which can help them with pricing. This tool/service isn’t necessarily a BI tool or an expensive consultation.
- Managers of large retailers looking to innovate pricing, regularly face the pressure from middle management who refer to their extensive experience. We figured out that if these obstinate employees are in key assortment departments, they can cause millions of dollars of loss in net profit per year.
- Small retailers have to deal with a whole range of other problems than just pricing. If they are growing fast, they are occupied with scalability (marketing, logistics, etc.). If they are traditional retailers (and not growing as fast) they face the problem of a shortage of qualified staff.
- The increase in profitability resulting from the optimization of one article is, naturally, higher for large retailers, where the optimization of one article has impact on large number of purchases.
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.