According to the IBM * report, retail executives expect AI to be actively used to automate many processes in organizations.

Most managers assume the optimization of processes in interrelated areas - supply chain planning and demand forecasting.

4 out of 5 managers plan to use AI to better understand their customers, and 3 out of 4 will apply machine learning methods in planning marketing campaigns. Slightly fewer - in the pricing process and promotions.

Let's take a look at each direction separately.

AI for demand forecasting

If the product runs out before demand is satisfied, the company loses profit. Customers do not receive the product they want and are forced to buy elsewhere. Thus, customer loyalty is lost, and possible additional sales will also be missed. Too frequent orders in small quantities can raise the cost of logistics.

In the opposite case, when the warehouse is always full, some of the goods lose their relevance and expiration date. In addition, it requires a large warehouse and large financial resources, which reduces the profitability of the enterprise.

The response to changes in demand must be flexible and fast enough. Better - in advance!

Predicting data series depending on many factors is a classic task for machine learning and has been effectively used for many years, for example, in industry to predict equipment breakdowns.

Data scientists use historical data on product demand based on seasons, holidays, days of the week, and external factors such as weather. In addition, annual trends in the demand for goods are taken into account.

Thus, thanks to demand planning with the help of machine learning, several tasks are solved at once: The

• procurement plan is improved; 
• arehouse stocks are optimized; 
• revenue is predicted more accurately

According to Ernst & Young, after applying data-driven demand forecasting methods, companies on average reduce stocks by 20-25%, and their profits grow by 3-5%.

In addition, companies using AI models for demand prediction notice market changes 5 times faster and react 3 times faster than companies that have not yet started using these methods.

61% of companies surveyed believe that demand forecast is a gamechanging capability, and 85% said that this capability will grow in importance.

But in addition to forecasting sales, it is also necessary to offer new products for which there is no history, which means that there is no way to make a forecast for them. What then is to be done?

For promoting new products, a personalized offer is better suited to customers who are more likely to like it. How do you recognize them? Machine learning methods are also applied here.

AI personalization of customer offers

Most of the customers have a positive attitude towards personalized offers. At the same time, less than a third of customers receive truly personalized offers.

Online services such as Netflix, Spotify and Youtube have a pretty good understanding of what content to offer us. Complex AI algorithms are involved in the daily calculation of which videos to show to each of tens of millions of users. And judging by the annual double-digit growth in revenue of these services, it is quite effective.

We have written in detail about recommender systems earlier, so for now let's just say that they work very effectively in retail.

Knowing the preferences of each client, it’s possible to offer a new product to those who will definitely like it and they will be very grateful. After a few successful recommendations, the trust and loyalty of customers will increase. Also, average check and product line will be increased. It’s even possible to recommend to customers a more balanced diet or healthier food that they like (in the case of a grocery store).

According to mckinsey, a well-built system of personal offers can increase revenue by 10%, and increase the effectiveness of marketing costs significantly.

Managing Churn

In the same way, you can identify users who stop using your service. Most often it works in services that are sold by subscription, but our experience has shown that in classic sales, the use of machine learning methods for managing churn is highly effective too.

Knowing which customers are loyal and which ones can go to a competitor, you can, firstly, deeply analyze the possible reasons for the churn, and, secondly, prevent it by offering individual discounts and promotions. Our experience has shown that such offers have a long-term effect and many buyers become loyal for months.

Using ML in churn management will also significantly increase the effectiveness of the promo. We described one of the cases for a fast food restaurant chain, when the churn management system reduced churn by 9%.