KI am Point of Sale, Pricing, Personalization

Retailers deal with large amounts of data every day – be it in purchasing, inventory management or price management – throughout all processes along the value chain. Artificial intelligence (AI) supports online and stationary retailers alike in the smart processing of data and in optimizing existing processes. And when it comes to customer service, AI also brings great benefits in the form of personalization, because customers receive product offers that are right for them. Let’s take a look at ten exciting application scenarios of AI for the point of sale (PoS).

1. Dynamic Pricing: Initial Pricing, Regular Pricing and Markdown Pricing

Price management is an important sales lever for retailers. Making the best price decisions – for any product, at any time and through any channel – is one of the most difficult tasks.
Artificial intelligence makes it possible to automatically adjust prices to all relevant context conditions such as demand, inventory or competitor prices. It also helps reduce write-offs, curb food waste in retail stores and prevent out-of-stock situations. Depending on the retailer’s goals, different pricing methods can be used.

Learn more in the video, “What does dynamic pricing do?”

Initial Pricing, Regular Pricing, Markdown Pricing, GK Software

Depending on the current phase in the product life cycle of an item, AI can apply the appropriate pricing strategy: Initial Pricing, Regular Pricing or Markdown Pricing.
Initial Pricing calculates the optimal pricing of products that have been newly added to the range or are at the beginning of the product life cycle. The focus is on siphoning off enough income and sales in a demand-oriented manner in order to achieve the specified average target margin in various product ranges.

The aim of Regular Pricing is to design prices in such a way that retailers achieve their specific goals. This can mean optimizing sales, revenue or earnings, building a positive price image, or a combination of these goals. In addition, the AI can recognize the dependencies of products (keyword cross-price elasticity) and raise hidden sales potential through corresponding price adjustments. Consider this example: the price of strawberries is falling and the demand for cake mixes and whipped cream increases. The price can possibly be adjusted upwards to compensate for markdowns on strawberries and to prevent out-of-stock situations if the sales volume forecast by the AI is above the stock. The AI can also suggest optimal price adjustments for these products.

Learn more about this in the video, “Intelligent pricing by observing cross-price elasticities using the example of strawberry cake.”

If retailers want to updates prices to gain market share, AI can support them through the intelligent pricing of focus items.
Markdown Pricing, also known as sales optimization, aims to sell items with an advanced product lifecycle by a specified point in time in order to achieve a stock level of zero. The pricing algorithms recognize how high the demand is for each product. With this knowledge, AI impacts sales via the price, and at the same time exploits the greatest possible gross profit per product. In the food retail sector in particular, this pricing strategy is ideal for curbing food waste (especially with fresh products).
The food retail trade has special features that should definitely be taken into account for successful price management. These include, for example, the often complex organizational structure of many retailers in food retailing, the regional freedom of decision of the individual branches, the availability (or non-availability) of electronic price tags, the enormous amounts of data due to the range of products, short product lifecycles, or special product relationships in the range. You can read how these challenges flow into the dynamic optimization of prices, especially in the food retail sector, in the blog post, “Dynamic Pricing in Food Retail – 5 Solution Approaches.”

2. Intelligent product placement and preventive product placement through AI

Intelligente Produktplatzierung. GK Software

The entrance area at brick and mortar retail locations is a magnet for spontaneous walk-in customers. If there are interesting products in the field of vision of potential customers, the probability is high that they will spontaneously enter the store.

AI can give recommendations regarding which items are currently in high demand and are therefore well-suited for placement in the entrance area. AI not only takes into account the demand in the store, but also the demand in other sales channels such as an app or online shop. Influencing factors such as weather, season and time are included in the calculation of the recommendations.

In preventive product placement, artificial intelligence analyzes the master data patterns of products with inventory differences and calculates the loss probability of all articles. This gives retailers suggestions for products that should get better placement in the store. In this way, retailers can take preventive action against possible cases of fraud and, as a result, reduce the number of fraudulent activities.

3. Effective workforce planning and forecasting of customer traffic through AI

Demand determines supply. But how high is the demand for product X and product Y at certain times of the day, on the different days of the week and months, before and after public holidays or during school holidays? Retailers can plan better and thus save costs if they know roughly how high the customer volume and thus the purchasing power will be. Past data is helpful for this. But how does the number of visitors change due to current influencing factors?

Real-time prediction not only includes historical data in the calculation of demand forecasts, but also takes current events into account, e.g. time of day, weather or special offers. Dealers can determine for themselves which factors are included in the calculation. The self-learning AI learns continuously in real time and can thus continuously optimize its forecasts. On the basis of the forecast demand, the number of employees needed at the point of sale to support the number of customers can be derived. In this way, needs-based planning that incorporates real cost savings is possible.

 

Realtime Prediction, GK Software

 

Would you like to know more about these topics and how you can actually implement these application scenarios in your business? Make an appointment with us.

In my next blog post you will learn, among other things, how you can convince your customers with demand-oriented product recommendations on digital advertising boards, consultant tablets with personalized recommendations and personalized recommendations on receipts.

Mr Personalization