How Machine Learning Boosts Sales in E-commerce and Retail
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Large retailers are already deploying artificial intelligence and machine learning technologies to boost sales. For example, the online store can use Machine Learning to recommend products in mailing lists and analyzes customer data: the frequency and amount of purchases, lifestyle, acceptable price levels, and favorite product categories.
According to a survey of 1000 organizations around the world, 78% of companies implement machine learning in order to increase operational efficiency, 75% to increase customer loyalty, 79% to analyze data and get new ideas.
In the article, we will tell how machine learning technologies help offline and online stores increase profits, who are suitable for such techniques and how to implement them in retail processes.
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What is machine learning
Machine learning (ML) is a class of artificial intelligence methods that can improve the results of computers using training on known data. In fact, this is a way of quickly marking and analyzing large amounts of information that a person is not able to process.
Self-learning algorithms process large amounts of data, remember successful and unsuccessful decisions, use this information in future forecasts.
Algorithms are trained on historical data: it can be transactions, the history of interactions with customers, Internet sources, revenue information, etc. The data set, 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 the relationship, 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.
For example, a store collected information about purchases for several years. The system analyzes the data and finds patterns: how consumer demand depends on the season, the appearance of new goods, stocks and other factors. Based on this, it makes a prediction: which goods need to be purchased more next month, and which no one will buy.
Machine learning technologies are now automating many business processes and helping retail make money.
You need to understand that machines cannot learn on their own, they need 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 give an accurate forecast. However, only 3% of the total amount of information collected by companies can be called quality. In order for neural networks to correctly build models, it is necessary to collect reliable data, carefully clear them of extraneous noise and prepare them for machine learning. The preparation stage is called pre-processing — information is transferred to a format suitable for training the algorithm.
How to use machine learning to increase sales
Price optimization
The algorithm predicts the best prices for the retailer taking into account the demand of customers, competitors’ prices, the balance of the goods in the warehouse, the periods of its storage, the dates of delivery of the next batch, the speed of sale and other factors.
Machine learning is also used to determine price elasticity — the spread of prices for goods, taking into account the niche, audience characteristics, sales season and product position in the general price line.
That is, the algorithm is able to calculate that a closet can cost from $300 to $1000. Knowing these boundaries, the retailer determines the optimal cost of the goods, taking into account the goal. For example, if you want to get the maximum profit from sales, you need to bet $1000, but fewer people will buy for big money. If you have to sell it faster — you can sell at stock for $300, this will allow you to get rid of the goods without losing.
It is also important to change prices based on market conditions. If the cost of similar items from competitors has fallen, as a rule, you need to reduce prices so that the goods are not in stock, and you can’t sell at a loss to the store. Self-learning algorithms can provide a quick response to market changes and dynamic pricing for thousands of products. As a result, the retailer maintains the desired turnover without loss of profit.
Forecast of sales, demand, assortment management
The algorithm finds and measures all the relationships between products, analyzes past data on sales, competitors and market conditions, and then models the influence of various factors on sales.
For example, it predicts how a discount on model X will change sales of similar models, or how the July heat will affect sales. This helps optimize logistics and procurement, reduce associated costs, and not lose customers.
So, in the Magnit retail network, neural networks were used to analyze customer demand and adjust proposals. As a result, forecasting accuracy increased by 5%, which, according to experts, will allow the store to increase revenue by 4 billion rubles a year by reducing the deficit of goods by 2%. The retailer can save another 1 billion rubles due to a reduction in write-offs of goods by 5%.
Machine learning algorithms predict consumer demand, that is, customer needs. This helps to make a procurement plan so that there are always the positions needed by customers, relevant this season and profitable for the store.
For example, in the OTTO network, which uses machine learning to purchase relevant goods, 90% of the imported assortment is bought up within 30 days, nothing is stored in the warehouse.
In addition, the demand forecast allows us to predict when most customers will come to the offline store. So, the Econika, RALF RINGER, and ZENDEN shoe chains use forecasting algorithms to build staff schedules for the flow of customers. The withdrawal of the optimal number of employees for traffic increases sales by 6–19%.
Machine learning algorithms help organize the supply of the right products and control the assortment.
Customer segmentation
Retail has a diverse range of customers: they can be of different ages, income levels, social status, interests. The machine learning algorithm works deeper than ordinary 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, and behavior.
So, ML allows you to combine customers into groups using implicit relationships. For example, we can distinguish the groups “young mothers who always buy educational toys with children’s shoes”, or “people who are prone to impulsive purchases of the proposed complementary goods”. Such clustering can be applied not only to the customers themselves but also to groups of products, for example, to find products that often buy at the same time.
In M.Video, buyers are segmented by machine learning into values: practical, ambitious (they want the best), family people, profit hunters. Each category needs its own approach.
So you can make portraits of customers, find out what products they prefer, set up personal offers, develop loyalty programs, improve the user experience.
An analysis of 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 do not bring profit.
The Rive Gauche cosmetic network is testing Machine learning to predict customer behavior. The system identifies loyalty program participants who can purchase store products in the next two weeks and predicts what they will buy. Then the retailer offers these customers individual discounts on the necessary goods. The first results showed that the accuracy of personal recommendations is more than 30%.
Optimization of marketing and advertising
Machine learning algorithms help increase profits from marketing campaigns — remove unnecessary promotions and strengthen work on those that bring results. For example, they can analyze previous promotions and choose such combinations “store/product/discount value” in order to fulfill the desired task: expand the market, increase profits or attract new customers.
Neural networks also find the relationship between sales and advertising channels, helping to leave the most effective and not lose 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 more likely to interest them. This increases the number of clicks from advertising to the online store website and the number of orders. You can personalize email newsletters, SMS and other messages sent to the client.
In contextual advertising, ML helps determine the ad groups whose customers generate the most revenue and raise bids for them.
Merchandising
The analysis of information from video surveillance systems helps to understand how people move around the store, how location affects the purchase of goods, which counters and display cases are of the greatest interest. You can draw up “customer journey maps” for the sales area.
By comparing the data obtained with the filling of shelves and display cases, promotions and other factors, the system can determine where it is better to place different groups of goods and how to place promo stands around the store. This helps to increase profits and make the purchase process more convenient for the buyer.
X5 Retail Group has tested video analytics and computer vision technologies based on artificial intelligence and neural networks. So they controlled the display of goods, tracked the queues, determined the most visited departments in stores. As a result, the number of people leaving without shopping was reduced by 10%, and stores lost by 20%.
In general, demand forecasting, assortment selection, and pricing using ML can increase retail store profit before interest and taxes (EBIT) by 2%, reduce inventory by 20%, and reduce product returns by 2 million per year.
Who is interested in machine learning technologies and how to implement them
Machine learning should be introduced to high-volume retailers who work in daily changing markets, track information about thousands of customers, and monitor prices for thousands of product items. The more the store’s revenue and turnover, the more profitable it is to use algorithms that optimize prices and predict sales.
Embedding Machine learning in business processes is easier if the IT infrastructure is based in the cloud. This makes it easier to scale solutions to regional network branches, simplifies strategic planning.
How Machine Learning Helps Retail Increase Sales
- Self-learning algorithms can analyze data sets that a person is not able to process, find the relationship between various factors and the amount of profit.
- Machine learning in retail allows you to more accurately predict demand and sales, segment customers, optimize marketing and advertising and manage the warehouse.
- Implementing machine learning is beneficial for retailers with high annual turnover or high business margins.
Machine learning is very promised technology, it opens great opportunities for future technologies! I’m always ready to discuss your ideas and answer your questions about that technology, just join me on linkedin:
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