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User segmentation is like visiting your favorite boutique, where the staff suggests exactly the T-shirts or shoes that match your style. You don’t have to say much – they already know what you’ll like. Imagine your app knows the typical behaviors and preferences of each user, tailoring offers and content to them. The result? Higher conversion and more satisfied customers. It is possible, thanks to user segmentation based on artificial intelligence. Discover strategies that will allow you to use AI to offer solutions better tailored to the users of your mCommerce app. User segmentation and its impact on customer engagement User segmentation means dividing users into groups based on some common factor. It could be, for example, gender, average cart value, or similar needs or behaviors, such as willingness to shop in the evening hours or reaching for a mobile app to place orders. Segmentation enables more accurate personalization, which plays an important role in mCommerce apps. As many as 98% of leaders believe personalization will be a key element supporting business success in the upcoming years (source: The State of Personalization Report 2024 by Twilio Segment). In a McKinsey & Company study, 75% of customers admitted that they feel frustrated when a store does not offer personalization. There is no doubt that the company’s approach to this issue has an impact on its growth, and segmentation can help offer better experiences (UX). It is worth remembering, however, that segmentation does not always have to be associated with personalization. Sometimes, it may, for example, indicate problems related to usability. Let’s say you notice that a segment of users over 65 often abandon their cart soon before the transaction. It is a signal that something is wrong and needs to be investigated. It may be due to problems in the app, e.g., the interface fonts are too small. Popular segmentation strategies We can choose various criteria when dividing customers into groups, depending on our business needs. What are the commonly used segmentation strategies? Demographic segmentation You do this by following demographic data. For example, it can include age, gender, education, or marital status. Segment example: A large group of users of the eCommerce app with toys are mothers of children up to 6 years old. How to use it? Send this group special notifications concerning promotions for toys and accessories adapted to the age of their children. Geographic segmentation This strategy assumes that we divide users by location, such as a country, city, or region. Segment example: The online bookstore sells books in a language specific to one region in your country. Users living in that region will constitute a separate segment. How to use it? Notifications directed to users from this region will be created in their language. It should evoke a positive response among these customers and encourage them to shop. Psychographic segmentation The key is psychological issues such as lifestyle, personality, or approach to values. Segment example: The beauty app has a large group of users who check if the production of cosmetics is environment-friendly and if the products aren’t tested on animals. How to use it? The loyalty program offers customers discounts on online stores with organic food and on the platform to book stays in agrotouristic locations. Behavioral segmentation You are dealing with this type of segmentation when you group users based on their behavior. You can, for example, use their shopping habits, the way they search for product information, the frequency of using promotions, etc. Segment example: A group of users who always shop in the early hours, between 5.00 and 8.00 am, is a separate customer segment. How to use it? They receive notifications earlier than other users to increase the chances that they will react to them and open the app. There are also other types of segmentation, such as based on technology or customer lifetime value. You don’t have to limit yourself to just one type of segmentation – combine them to best suit your business needs. For example, suppose you have an online grocery store. In that case, you can focus on the in-app behavior of people who have recently become parents and are interested in using only eco-friendly products. In this way, you combine behavioral segmentation with demographics and psychographics. Segmentation done wrong. What mistakes should you avoid? It’s not always nice and easy. It’s important to remember that segmentation doesn’t bring benefits every time. The results depend on whether you manage to carry it out correctly. What should you pay attention to when creating user groups? Unknown purpose of segmentation Before you start dividing users into segments, determine why you are doing it. What goals do you achieve this way? What problems do you want to solve? Establishing the purpose of segmentation allows you to determine what data and information you need. If you skip this step, you may be spending your budget and resources tracking metrics that won’t lead you anywhere. Examples of goals: I want to increase customer engagement by 10%. It means I need to get to know the user segment in the engaged group better. Thanks to this, I will learn what factors influence their behavior in the app and encourage them to take action. I want to reduce the number of abandoned carts, so it is worth looking at the group of users who abandon them, analyzing their path, and determining what causes them to leave the app without making purchases. Lack of established priorities Too much of anything is good for nothing. This saying also applies to data. If you create too many segments and try to satisfy all groups simultaneously, you may end up with unhappy customers. Consider which customer groups are the largest, the most profitable, or which have the potential to become so. Choose priority segments and keep their needs in mind when planning new activities. No updates Do you remember the last time your team developed customer segments? How sure are you that nothing has changed since then? Perhaps there is a significant increase in the group of engaged users who don’t buy anything? Or maybe a segment of people who used to respond to notifications decreased as they lost interest in them? It is worth regularly checking whether the segments identified earlier still constitute such a large percentage of your users and whether other groups have emerged that are worth addressing. Improper selection of segments Sometimes, teams attach too much importance to the age of users or the size of the cities in which they live and forget about behavior analysis. Meanwhile, it often turns out that age does not significantly impact the way the app is used or the choice of payment or delivery methods. Preferences and shopping behavior are more important – such segments are often a source of valuable data. Therefore, it is essential to define the criteria for selecting segments correctly. Benefits of customer segmentation with AI Since we can use artificial intelligence for customer segmentation, we can access new possibilities. What are the benefits of using AI? Customer segmentation is more accurate and faster, even when it includes multiple data sources. The algorithm can analyze a large amount of information and notice patterns that are difficult to spot in any other way (e.g., non-obvious user segments). Greater personalization: Thanks to more accurate segmentation, your messages and promotional offers will better match user preferences, which should increase sales. Help with data interpretation: Artificial intelligence helps to understand how the acquired information translates into business operations. Predicting behavior: AI analyzes user behavior and, based on this, draws conclusions regarding, for example, predicted user actions. With this knowledge, you can counteract cart abandonment and adjust the offer or advertising strategy in advance to encourage the user to make a purchase. Automatic improvements: AI can use the conclusions from the analysis of segments to automatically optimize activities or formulate recommendations for changes. Improved UX: Brand loyalty should increase because the app will offer users a better experience. Up-to-date and clean data: Artificial intelligence monitors changes in user behavior and preferences and automatically takes them into account when developing customer segments. In addition, it cleanses data, removes duplicates, and detects anomalies. Examples of using AI in the segmentation process Predictive segmentation Artificial intelligence can predict the future. Machine learning (ML) algorithms can predict user behavior based on available data. Example: An AI algorithm finds a segment of users who are unlikely to complete the checkout path and make the transaction. With this knowledge, you can counteract cart abandonment and adjust the offer or promotional strategy in advance to encourage the user to make a purchase. Segmentation based on preferences and needs AI analyzes purchase history, viewed products, search history, and wish lists. It can also take into account cart analysis, for example. This way, it determines which products are often bought together and can suggest them to other customers. Example: A group of users is interested in articles for interior design, often browsing the category of furniture and decorations for the apartment. Such users will certainly be interested in the new furniture collection, so you can send them a notification informing them that it is already available. The app can also offer them special discounts and suggest specific items from their favorite category, complementary to what they have previously purchased. Segmentation based on LTV (lifetime value) Want to focus on the customers who drive sales in your store? AI can analyze data such as purchase history, average cart value, and order frequency. The algorithm will calculate the customer’s lifetime value (LTV) and assign them to the appropriate segment. Example: An AI analysis of a clothing store shows that the preferences of customers belonging to the group with the highest lifetime value are often associated with seasonal novelties. Such potential customers are interested in what’s new in the app. If they receive notifications immediately after the collection appears in the store, they are more interested in buying. Dividing customers based on their lifetime value and knowledge of their preferences allows you to devise solutions that will best meet their needs and encourage them to check the app frequently. On the other hand, you also get data about other, less valuable segments of users and their shopping habits. It can help you create a strategy encouraging them to be more active in the app. Segmentation based on sentiment analysis You can segment users based on their emotions and attitudes towards your services or products. Such segmentation is possible thanks to technology that allows you to process natural language (NLP). The algorithm analyzes user entries, e.g., product questions and reviews. This way, it can determine who is positive, negative, or neutral towards your offer. Example: AI identifies a large group of users who often criticize similar elements related to furniture delivery, and their willingness to make further purchases decreases. If possible, you can improve the elements that cause dissatisfaction in this group or offer them products from a different category that should interest them without causing negative emotions. Integrating AI for user segmentation in your appNeed help implementing AI solutions for your store? Contact us. We will choose the right tools to help you meet your goals and integrate them with your eCommerce platform.Contact us RFM analysis With the help of AI, more complex customer segmentation is also possible, e.g., using RFM analysis. What is this method? It consists of three elements: Recency: This is an indicator that groups users depending on how long it has been since they last made a purchase. The less time has passed, the easier it is to attract customers back because they still remember your store. Frequency: Refers to the frequency with which a customer purchases in the app. Monetary: Indicates how much the user spends on purchases. Within each of these categories, users receive points from 1 to 5. It’s up to you to choose the exact criteria. Thanks to the RFM analysis, you can determine which customers have the greatest value for the store, how often they buy, and at what intervals. Example: 5 points: Customers who made a purchase last week 3 points: Customers who made a purchase last month 1 point: Customers who made a purchase six months ago This view of behavioral analysis, combined with segmentation, allows for better planning of marketing strategies and app development. Summary: app development with AI-powered segmentation With artificial intelligence, customer segmentation can be faster and more accurate. AI considers patterns that are difficult to notice in other ways and allows even advanced behavioral analysis of users. You can improve marketing campaigns, mobile app development strategies, loyalty programs, offers, and more based on such segmentation. Audience segmentation using AI allows a better understanding of customers, personalized marketing, and increased sales. Want to learn how AI can help your store in a mobile app? Contact us!

User Segmentation with AI: How to Improve Sales in Mobile Apps with Artificial Intelligence?

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User Segmentation with AI: How to Improve Sales in Mobile Apps with Artificial Intelligence?

Mobile Apps for Beauty eCommerce: Feature Ideas to Enhance User Experience (UX)

AI for E-Commerce: Mobile App Features that Boost Sales and User Engagement

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Project estimation

Why choose us?