The Recipe for Optimizing Prices in Retail
Price optimization platforms have been available for retailers since the early 2000s. Initially, they were simple, but with time they became capable of not only analyzing historical data, but also of predicting the demand of items, and most importantly, optimizing prices to achieve the set KPI.
This evolution is based on the scientific approach (repricing algorithms, sales forecasts, econometrics, and analysis), focusing on the advanced data analytics and decision making.
The majority of retailers face pricing difficulties that arise during both data collection and processing, at the decision making stage of repricing, and even during the price replacement stage on the online storefront.
Modern solutions for price optimization allow retailers to take into account local demand for items, the competition in the same category or another, the business goals, the operational restrictions, etc. Price optimization helps establish the right prices, the initial price, the promotional price, the discount price, and the personalized price of the items.
A correctly configured repricing process takes into account the cost of the item itself, the prices and the remains of the competitor's items, and the customer demand.
Regardless of which classification and item position being used by the retailer — KVI- or ABC- analysis, work with a “long tail”, or separated into “cows and dogs” - this process can also be optimized.
In addition to optimizing the base price, it takes into account the promotional offers of competitors and helps the marketing department launch attractive promotions for the buyers.
Effectively helps get rid of the item’s leftovers in the warehouse and of seasonal or unpopular positions.
It takes into account the purchase history of an individual buyer, their preferences, and their reactions to past offers.
The Recipe to Correct Repricing
If you were to simplify the description of effective pricing in retail, it would be similar to a cooking recipe:
- Take the retailer's internal data about sales, the assortment, the behavior of buyers on the site, the abandoned baskets and the successful transactions, and both streamline and structure them.
- Divide the resulting array into categories. You can use any of the popular approaches such as ABC- or KVI-analysis.
- Add market data. Gather all of the necessary information about competitors such as their prices, promotions, discounts and items in stock.
- Mix the ingredients you received: start with the data necessary to optimize the base price, gradually add optimization to key items, then optimize promotions and discounts, and, finally, optimize personalized pricing.
- At each stage (see step 4), re-evaluate the items and be sure to analyze the results obtained in order to get the required business result.
Just like when you cook food, you can grow all of the necessary ingredients on your own (collecting and processing data) by spending a certain amount of resources on it or by buying all of the necessary components in order to enjoy both the cooking process (repricing an item) and the acquired result (price optimization analysis).
For price optimization, it’s important to use clean and up-to-date market data in order to gather the necessary information about competitors and constantly check its quality. For information on how to check market information, see the study on the impact of competitive data on key business indicators of a retailer.
The next stage of optimization is price automation. Using special tools that automatically collect data, proven repricing algorithms to increase profit or turnover releases the necessary resources to work with price strategies.
The described work system on price optimization allows the retailer to effectively achieve their set business goals: increase turnover and profit, get rid of the leftovers in stock, avoid price wars with competitors, and establish work with suppliers. Competera’s Price Optimization software makes this process fast, convenient and comfortable for every department of the online store.