One of the reasons why retailers increasingly rely on AI-based Dynamic Pricing is the huge amount of data and influencing factors that need to be considered for price determination: Article master data, purchase prices, sales quotas, inventory levels, competitor data, promotions, real-time transactions, historical data, seasonal trends, regional factors, weather influences – to name just a few. The crucial point is: the amount of data available is huge. Huge, but full of potential – especially in the age of AI! Because an artificial system is easily able to process large amounts of data in seconds and derive meaningful analyses. Every day I see in projects how great success can be achieved with comparatively little effort when work processes are digitally supported and thus optimized. In the area of dynamic price optimization, these successes are particularly visible because they can be measured numerically and the ROI can thus be clearly determined.
What do you need now to use an AI for your price optimization? The list is quite manageable, as you will see:
- Sales data & transaction data
- Product master data
- 4 to 6 weeks time for AI prices in the online shop (for stores about 4 weeks more)
The AI needs your sales and transaction data to calculate the demand for each product in your range. Keyword: price elasticity. (If you need a short recap, you can find it here). This forms the basis for every price decision of the AI. At least all sales information is needed: Which items were sold at what price. All transaction information, which can be used additionally, improves the AI’s forecasting quality and enhances its results again: viewed products, created shopping baskets, cancelled shopping baskets, saved watch lists or entered search terms, to name just a few examples. It makes sense to make this data continuously available – either via real-time tracking (a powerful Dynamic Pricing software provides this feature) or e.g. via data feed from your SAP CAR system.
Product master data is the digital representation of your assortment and thus an important tool for dynamic price optimization. They usually provide a lot of different article information: product ID, master-variant assignment, current price, RRP, lower and upper price limit, seasonal identification, brand, color, size, stock level, expiration date or target sales date and much more. An AI can use these attributes for various tasks:
- The existing supply is combined (via the inventory) with the existing demand – this is how the AI calculates optimal prices in line with the market.
- Using the best-before or target sales date, the AI recognizes when items are going out of stock and prices them down slowly according to their demand, in order to reach zero stock on the given target date and at the same time to work in a margin-saving manner.
- Using attributes such as colour, brand, size and alike, an AI is able to identify similarities between products and can thereby also calculate optimal prices for products with low data availability, longtailers or even new products.
- The price limits determine the AI’s scope for price optimization – i.e. which price it may not exceed or fall below.
- By means of the master-variant assignment, the AI implements family pricing and/or maps product relations for you (e.g. appropriate price range between low-budget and branded products).
Product master data is usually transferred automatically to the AI software once or twice a day with a CSV import or can also be transferred automatically via SAP S4/HANA.
Finally, I would like to share with you some experience about the time you should plan to integrate an AI for Dynamic Pricing into your processes. The implementation of a price optimization software is carried out in three steps:
- The software is delivered as a cloud service.
- The automated delivery of input data is set up.
- The AI is configured and set to your objectives via a Web GUI.
Afterwards the system goes live and optimizes your prices continuously. For projects in e-commerce, this process often takes no longer than 4 to 6 weeks, since many processes – by their very nature – already run digitally. The shortest implementation phase that I have ever seen so far even lasted only 3 weeks (until today one of the most remarkable projects ever)! In stationary retail it usually takes a little more time, but here too, 9 to 12 weeks is very good for planning.
Now only one question remains: When will you go live?