Machine learning in e-commerce – predicting customer behaviour and personalising offers

The internet and computers have revolutionised the way we shop. The advent of online stores has given many consumers the opportunity to buy almost anything with just a few clicks. With growing competition in e-commerce, it is not only attractive prices that count today, but also the ability to anticipate users' needs. This is where machine learning in e-commerce comes in handy. How can this technology be used wisely, and is it worth implementing at all? We will take a closer look at this phenomenon in this article.

Machine learning in e-commerce – predicting customer behaviour and personalising offers

Although it might seem that we have already achieved everything technologically in the field of sales, machine learning in e-commerce is giving the world of online sales a new lease of life.

Machine learning involves the use of algorithms that learn based on customer behaviour data. This makes it easy to personalise the shop's offering and introduce more automation.

What does this actually mean in practice? First and foremost, online shops no longer just analyse consumer interactions that have already taken place, but are able to predict what will happen next.  The machine learns which products we are specifically interested in, what we like to add and what we later remove from the basket, and even why we abandon it altogether. .

How machine learning in e-commerce can help you get to know your customers better can be divided into several areas:

• customer segmentation,  
• shopping basket analysis,
• product recommendations,  
• demand forecasting,
• price optimisation.

This solution will help you tailor your offer to the customer's needs, taking into account their individual purchasing behaviour.  This has an impact on conversion and user satisfaction. . Importantly, the algorithms learn independently. They are quickly able to recognise certain purchasing patterns, enabling rapid business decisions to be made based on hard, real data.

Machine learning in e-commerce supports not only marketing activities, but also logistics. If  integrate analytical systems with the logistics platform , you can react quickly to changing market conditions. In online sales, this is a very valuable skill.

How  machine  Does e-commerce learning assist in analysing customer behaviour?

Machine learning in e-commerce enables in-depth analysis of customer behaviour by collecting and interpreting vast amounts of data. Algorithm  closely monitors what the user does on the website : checks the time spent on it and how much of it was devoted to viewing the product, looks at which recommended products the user clicked on and when the purchase was finalised or the transaction was completely abandoned.

By analysing this data, it will be easier for you to understand which of your products are truly attractive, what actually works in terms of marketing, and what sometimes causes you to lose customers. .

Behavioural analysis also allows for user segmentation. It can  segment them based on their preferences and purchase history With this information at your disposal, you can create personalised offers tailored to specific groups, which increases the likelihood of a purchase.

After a certain period of time, the algorithm is also able to  predict customer behaviour , such as when they will be ready to make a purchase, what products might potentially interest them, and whether there is a risk of them abandoning their shopping basket.

Thanks to machine learning in e-commerce, data analysis happens in real time, allowing you to respond to changes in customer behaviour almost immediately. For example, if the machine notices a sudden increase in demand for a specific product as a result of its analysis, you can quickly increase its exposure in recommendations and modify the promotion.

In this context, API integration with logistics systems such as InPost allows for the automation of shipment creation and stock status updates. This allows you to respond efficiently to demand forecasts and personalise your shop's offering as much as possible.

Personalisation of the offer thanks to  machine  learning in e-commerce

The most visible effect of implementing machine learning in e-commerce is maximum personalisation of the offer. Learning algorithms take into account data from many sources; they examine past purchases and current customer behaviour. On this basis, they create personalised  product recommendations This ensures that each of your customers receives an offer tailored to their preferences and purchasing habits.

Personalisation takes many forms . This could be displaying products similar to those the customer has previously viewed, recommending new items in the shop based on previous interests, or even creating dynamic prices and promotions. The result? The customer feels that the shop understands their needs. This has an impact on loyalty and engagement with the brand.

However, remember that for everything to work properly, a good algorithm must be accompanied by appropriate marketing communication. By introducing machine learning in your shop, you will obtain customer segmentation and learn about their communication preferences. This way, you will find out what works best for them: newsletters, push notifications or social media ads. 

Examples of successful implementation  machine  learning in e-commerce

The implementation of machine learning in e-commerce can be found in both large global chains and smaller online shops. One example is Amazon. Amazon effectively utilises the data it collects and uses it to recommend products "for you". All this is based on your purchase history and the products you have viewed.

Real-time recommendations have also found their place in fashion. The Heuritech platform is a great example of this. It is an innovative system that analyses over 3 million photos posted on social media every day and generates objective data on the current state of the fashion market. 

The algorithm excels at detecting over 2,000 product features: it recognises patterns, colours, fabrics, small details and even entire product lines. Heuritech studies the behaviour of different consumers; its data is based on influencers, celebrities and ordinary consumers.

How does this help fashion businesses? With access to this data, shops can efficiently predict which products will be popular in the coming seasons, which colours and patterns will be in vogue, and how to personalise their offerings for specific customer groups.

Is it worth introducing?  machine  learning for your shop?

Machine learning in e-commerce is not just a tool for data analysis. It is a way to significantly increase sales and improve your customers' experience. Algorithms that learn from available data will allow you to  predict user behaviour, tailor your offer to them, automate marketing and logistics processes and ultimately make the best possible business decisions.

If you decide to integrate data with a system such as InPost API, you will gain shipment automation and efficient stock updates, which supports quick response to demand forecasts. . You can also tailor the shop's offer more precisely to the needs of your customers. 

The implementation of machine learning is beneficial not only for large chains, but also for smaller shops. It will help you increase sales, gain user loyalty, and manage operational processes more efficiently. Implementing such analysis will allow you to better compete with larger players. You can efficiently optimise your marketing and logistics activities, minimising the risk of product unavailability and increasing conversion.