AI in E-commerce

Given the fierce battle for customers and market share, those online retail companies that succeed better than the competition in meeting the needs of their demanding customers in their B2B online stores in the best possible way have an advantage. This means that AI-based optimization of the customer experience is becoming a criterion that will be even more decisive for success in online retail in the future. This is confirmed by the EHI study "Trends in e-commerce 2023". The top decision-makers surveyed from the German e-commerce consider AI to be one of four megatrends - alongside consolidation, profitability, and sustainability. However, to benefit from AI in e-commerce, companies need vast amounts of personal data that need to be analyzed and made available. This challenge then raises the question: what use cases for AI in e-commerce exist besides the well-known chatbot?

AI in e-commerce for individual product recommendations

Collecting and using as much customer data as possible in compliance with the GDPR forms the basis for increasingly personalized shopping experiences. One obvious use case for AI in e-commerce is customized product recommendations. If every single action - from items purchased to pages visited to ads clicked on - in a B2B online store automatically initiates a corresponding entry in the CRM system, an increasingly comprehensive picture of each individual visitor is created. By drawing on the existing database, this means that AI in e-commerce can - by drawing on the existing database - automatically identify products that users are most likely to be interested in by drawing on the current database. These products can then be displayed not only in a clearly visible area on the homepage as "This might interest you", but also on the detail page of another product. Of course, it is also possible to point out products in the newsletter that the individual subscriber likes.

AI in E-commerce for a Personalized Approach to Customers

The more a retail company knows about its customers, the better it can target each individual with precisely tailored information. Technologies for AI personalization in e-commerce, for example, make it possible to analyze existing customer data in depth, derive certain behaviors from the data, segment customers with similar preferences or other similarities, and thus create finely granular customer groups. On this basis, it is then possible to play out personalized content that goes far beyond tailored product recommendations and individual advertisements. It is even possible to personalize the user interface of a B2B online store in many places, for example, by providing a unique menu and relevant statistics. For example, mechanical engineering companies can be given access to preferred tools, such as an online configurator in which the systems in use have already been imported. This allows users to plan the expansion of a specific machine with little effort. Suitable maintenance instructions can also be made available in a personal support area. In addition, AI personalization in e-commerce ensures that customers are recommended for the fitting spare parts as soon as they report a defect.

Store operators can also utilize the potential of AI-supported image search in customer service. For example, if a drill head is worn out, customers can upload a corresponding photo to the customer portal . Thanks to image recognition, AI in e-commerce identifies the worn component and can offer to supply a replacement part or provide repair instructions to help. The advantage for customers is that they receive quick and uncomplicated support - without having to name the worn part or search for its article number. Customer service could hardly be more convenient.

AI in E-commerce for Automatically Generated Texts and Images

It is a B2B online store equipped with modern technology for generative AI technology; store operators can create target group-specific documentation, product descriptions, landing page texts, and other content based on AI. This is based on high-quality, up-to-date data that is stored in a structured database . In addition, thanks to AI in e-commerce, it is already possible to generate personalized landing pages in real-time. Based on customer history (click paths, pages visited, preferences for product categories, purchases made, etc.) and other relevant data, which can be stored in a customer data platform, for example, AI in e-commerce derives predictions about expected customer behavior (predictive analytics) and dynamically builds the front end in real-time - for each individual person in the B2B online store (hyper-personalization). This includes the automatic website content translation- making international expansion much more accessible.

Artificial intelligence can also be used to create entirely new images and graphics or to modify existing visuals, for example, in the form of new backgrounds or additional color gradients. The aim is to publish visuals in the B2B online store that appeal to users emotionally and thus encourage them to buy.

Sharing your own experience with a B2B online store - whether via social media, directly in the store or via a review platform - is commonplace these days. This presents retail companies with the challenge of filtering out feedback that requires an immediate response - such as negative criticism. An appropriately trained AI recognizes predefined terms, the use of which automatically triggers a specific action - from a note in the CRM system to informing an employee to automatically creating and publishing a response to the feedback received or proactively suggesting an individual solution. Such automated sentiment analyses not only speed up response processes and save costs, but also improve the quality of customer service.

AI in E-commerce for Reliable Fraud Detection

Another important use case for AI e-commerce is fraud detection. An AI monitors every single transaction within a B2B online store and detects abnormal behavior in a matter of seconds. Depending on which deviating behavior patterns the AI detects, this allows conclusions to be drawn about various criminal activities, such as attempts to access data or hack passwords. Supported in this way, online retailers can improve their cyber security high.

Theoretically, these and many other scenarios for AI in e-commerce are already feasible today. However, their practical success stands and falls with the training of the AI. To stay with the example of the worn drill head: In order to deliver correct results, the AI must have been trained with many thousands of images. This means that for it to actually recognize drill head A as such, it not only needs several images of drill head A from different perspectives, but also - for differentiation - of drill head B, C, D and possibly the machines purchased by the customer. Added to this is meta-information that the AI uses to learn which item it is and what exactly a particular image shows. This ultimately enables AI in e-commerce to compare the uploaded image of the drill head with those in the database and display the item with the greatest visual similarity to the uploaded customer image as the result.

Using AI in E-commerce in Line With Demand

In order to use AI in e-commerce, it is by no means necessary to completely replace existing systems. Instead, it is possible to independently implement existing tools for AI in e-commerce and link them with store systems and other sales-relevant applications - from CRM and PIM to service and ticket portals, ERP and other solutions. The ability to centrally consolidate and aggregate relevant customer data from all source systems and enrich it with additional information is crucial - with the aim of generating information relevant to value creation and playing it out in the store system.

Recently, we have been talking about composable commerce (formerly: best of breed) in this context. This involves defining one system as the leading one - usually the store solution - and adding relevant third-party systems with AI components and a powerful database. It is also possible to integrate AI as a service into a B2B online store - as is usually the case with chatbots.

The supreme discipline of building up their own usable database based on existing information from all areas of the company and using this data profitably in e-commerce is currently still too great a challenge for the vast majority of online retail companies.