The algorithm predicts the best prices for the retailer, taking into account the demand of buyers, the prices of competitors, the remainder of the goods in the warehouse, the terms of its storage, the delivery dates of the next batch, the speed of sale and other factors.
Determining price elasticity
Machine learning is also used to determine price elasticity – the spread of prices for goods, taking into account a niche, audience characteristics, sales season and product position in the general price range. It is important to adjust prices based on market conditions. Self-learning algorithms can provide fast response to market changes and dynamic pricing for thousands of items. As a result, the retailer maintains the required turnover without losing profits.
Sales forecast, demand, assortment management
The algorithm finds and measures all the relationships between products, analyzes past data on sales, competitors and market conditions, and then simulates the influence of various factors on sales. Machine learning algorithms predict customer demand, that is, the needs of customers.This helps to draw up a procurement plan so that there are always items that customers need, that are relevant this season and that make a profit for the store.
Segmentation of buyers
In retail, there is a diverse circle of buyers: they can be of different ages, income levels, social status, interests. The machine learning algorithm works deeper than conventional marketing analysis, the retailer receives more complete information about customers, can take into account not only the volume and amount of sales, but also gender, age, behavior. Thus, machine learning allows you to combine customers into groups using implicit connections. This clustering can be applied not only to the customers themselves, but also to groups of products, for example, to find products that are often bought at the same time. Analyzing exactly how customers buy helps to create a cross-selling strategy and automate this process by increasing the average check. In addition, the store receives information about which customers are promising and can buy more, and which groups are unprofitable and not profitable.
Optimization of marketing and advertising
Machine learning algorithms help increase the profit from marketing campaigns – remove unnecessary promotions and strengthen the work on those that bring results. Also, neural networks find the relationship between sales and advertising channels, helping to keep the most effective and not waste money on ineffective advertising.Marketers can more accurately target advertising campaigns on the Internet – to show the right groups of users those ads that are most likely to interest them. This increases the number of clicks from advertising to the website of the online store and the number of orders. You can personalize emails, SMS and other messages sent to the client. In contextual advertising, machine learning helps you identify the ad groups that are generating the most revenue and increase bids for them.
Analysis of information from video surveillance systems helps to understand how people move around the store, how the location affects the purchase of goods, which counters and showcases are of the greatest interest. It is possible to draw up “customer journey maps” around the sales area. Comparing the obtained data with the filling of shelves and showcases, ongoing promotions and other factors, the system can determine where it is better to place different groups of goods and how to arrange promotional stands in the store. This helps to increase profits and make the buying process more convenient for the buyer.
Increased forecast accuracy
The forecast accuracy in the product category reaches 95%, and the average improvement in the forecast quality in comparison with traditional algorithms is 15-20 points.
Automatic forecast of the volume of goods per share
Machine learning algorithms allow you to refuse manual adjustment of product volumes when planning promotions.
Adaptability of algorithms
In a situation of unstable demand due to the coronavirus epidemic, machine learning algorithms need a week to adapt to changes in consumption.
The main difference between machine learning and traditional analysis is not programming the algorithm, but training the model to solve the indicated problem on the provided data. These algorithms are called machine learning algorithms, and they are beginning to supplant the previous approaches to analytics. The requirement for the implementation of tasks using machine learning methods is the presence of a certain set of historical data for training a model with a storage depth, depending on the solution being implemented.
Self-learning algorithms process large amounts of data, remember successful and unsuccessful decisions, and use this information in further predictions. Algorithms are trained on historical data: it can be transactions, customer interaction history, Internet sources, revenue information, etc. The set of data, the quality and duration of the period for which they are collected determine how accurate the model will be in the end.
In the data array, the algorithm finds relationships, tracks how and why the influence of various factors on the process of interest changes. The machine sees even non-obvious patterns and does it faster than a team of analysts.
Machine learning technologies are now automating many business processes and helping retailers make money. For example, a store has been collecting information about purchases for several years. The system analyzes data and finds patterns: how customer demand depends on the season, the appearance of new products, promotions and other factors. Based on this, she makes a forecast: which goods should be purchased more next month, and which no one will buy.
Machines cannot learn on their own, they need high-quality data for this. If the information on the basis of which the algorithm is trained is incorrect, the machine will not be able to make an accurate prediction . At the same time, only 3% of the total volume of information collected by companies can be called high-quality. In order for neural networks to build models correctly, you need to collect reliable data, carefully clean it from extraneous noise and prepare it for machine learning. The preparation stage is called preprocessing – the information is translated into a format suitable for training the algorithm.
Machine learning should be implemented by high-turnover retailers who operate in daily changing markets, track information about thousands of customers, track prices for thousands of items. The higher the store's revenue and turnover, the more profitable it is to use algorithms that optimize prices and predict sales.
It is easier to implement machine learning into business processes if the IT infrastructure is based in the cloud. This makes it easier to scale solutions to regional branches of the network, and simplifies strategic planning.