Predictive optimal pricing with AI
Pricing is a complex and risky task for every retailer.
"How to calculate the price correctly: not to underestimate and not to overestimate?" is a question that accompanies the chain owner every day.
If the price is high, then customers will go to buy products from competitors, and if it is too low, the chain will lose its profit. The optimal price is always about balancing the interests of two parties: the buyer and the seller.
Why is the optimal price so important?
The optimal price is the selling price of the goods, which satisfies two parties: the seller and the buyer. The seller will receive the desired profit, and the buyer has the ability and desire to buy goods at that price.
Keep in mind that the optimal price is not always the highest. Its main tasks are:
- to increase chain profit;
- to retain existing customers;
- to interest potential buyers to make purchases in the chain stores;
- to increase the sales quantity.
In such a situation, it is most difficult to forecast the buying behavior of existing and potential customers. But today there are effective tools that can easily cope with this task.
How to calculate the optimal price?
To calculate the optimal price, a large amount of data is required, such as:
- seasonality of goods;
- competitors' prices and their changes;
- inflation rate;
- actual and predicted marginality of goods;
- cross-effects: halo and cannibalization;
- demographic characteristics of the target groups of the store;
- buying behavior of customers.
Collecting and processing such information is quite difficult.
It is better to ask for artificial intelligence (AI) help. He can easily predict the customers' behavior with all possible options for the sale price and find the best option, which will ensure high profits for the chain.
AI systems for pricing are actively used by the biggest retail chains: Walmart, eBay, and Amazon.
Price optimization with AI: how does it work?
The whole process of determining the optimal price can be represented by the following steps:
✅ Step 1. Data collection. The system collects not only historical data on prices and sales in the chain over a long period, but also examines all available data from external sources (competitors' and suppliers' prices, purchasing ability of buyers, inflation rate, etc.).
✅ Step 2. Modeling the purchasing behavior of customers. The system calculates how many customers will be interested in purchasing this product at a particular price. To do this, AI studies the behavior of customers in the past, evaluates information about the competitors' prices according to Internet sites, the existing goods demand, information about upcoming holidays, the location of the store, etc.
✅ Step 3. Profit forecasting. AI algorithms calculate the value of the predicted profit in each specific situation.
✅ Step 4. Determination of the optimal price. Among all possible options for the AI price, the system selects and offers the price that will satisfy the largest number of customers and bring the greatest profit to the chain.
Such competent and professional advice will allow the chain to avoid excess inventory, minimize storage costs and increase customer loyalty.
Pricing optimization with Datawiz products
The Datawiz BI solution has implemented an AI-based report - Price Recommendations.
It helps to form an effective pricing policy for your chain, namely:
- determine the goods for which prices should be adjusted in chain stores;
- track the correlation between price changes and profits for selected goods;
- predict sales performance based on the recommended price.
Such a report is your professional consultant, who, based on the processed data array, will give immediate recommendations and allow you to manage your business effectively.