How retailers create better customer experiences

Whitepaper: Digital Retail – Engaging Customers with AI Recommendations

Today's customers expect more from digital commerce than just a functional online shop and a wide range of products, as shown by a recent survey conducted by Lufthansa Industry Solutions (LHIND). Comprehensible advice and personalized product recommendations are in demand. Read our whitepaper to find out how you can use chatbots and AI-powered recommendation systems to offer your customers an online shopping experience that leaves nothing to be desired.

Survey: AI-Assisted Guidance and Recommendations Instead of Advertising

Roughly 89% of online customers are satisfied with their shopping experience – according to a recent survey conducted on behalf of LHIND. However, one issue is prompting debate: advertising. Some 44% of survey respondents said they consider current advertising levels excessive; 39% even feel outright “stalked”.

Instead, many customers are looking for genuine advice, which has so far been in short supply online. Chatbots are still rarely used for this purpose, although 60% of users are satisfied with them. Recommendation features are significantly more popular and are frequently used by 38% of users.

 

Specialized AI Solutions for Online Retail

Specialized AI solutions to meet customers’ increased requirements are already available. For example, the SHAPE (Semantic Hybrid Algorithm for Personalized Engagement) recommendation system from LHIND combines a recommendation engine with a digital sales assistant in the form of a language-model chatbot. This means that, rather than simply receiving a product list with recommendations, the customer benefits from personalized guidance based on their personal preferences and purchase history. A clean data structure is critical for this and other systems – and, without it, successful AI applications are impossible.

Brief insight into our whitepaper "Digital Retail – Engaging Customers with AI Recommendations"

 

Free Download of our Whitepaper: “Digital Retail – Engaging Customers with AI Recommendations”

Check out our whitepaper to learn which issues matter most to the retail customers of tomorrow, what role chatbot-based guidance plays in online retail, and how AI-powered recommendation systems turn digital touchpoints into genuine customer experiences.

A Selection of the Insights in our Whitepaper: “Digital Retail – Engaging Customers with AI Recommendations”
  • The products with particularly high need for online advice
  • The current state of customer trust in AI solutions
  • The benefits customers hope to see from AI deployment in online retail
  • How the SHAPE recommendation system elevates personalized advice to a new level
  • The solutions LHIND offers today for digital commerce
Bar chart for the question "When shopping online, how often do you use the following functions when they are offered to you?" Product recommendations such as "You might also like" are used very often by 8%, somewhat often by 30%, somewhat rarely by 43%, and never by 19%. Chatbots or virtual assistants for product queries are used very often by 5%, somewhat often by 15%, somewhat rarely by 44%, and never by 36%.
Product recommendations are used more frequently than chatbots – but both features still have a lot of untapped potential. Greater personalization can help increase acceptance of these solutions.
  • Demand for online advice is rising
    Instead of traditional advertising, customers are increasingly seeking genuine advice when shopping online. Although this task is primarily assigned to chatbots, they are still rarely used at present.
  • AI acceptance depends on age
    Almost half of respondents (47%) in the 60+ age group remain skeptical of AI solutions; this figure drops to just 17% among respondents aged 29 and under. This clearly shows the direction in which online retailers must evolve to reach tomorrow’s high-value customers.
  • Data quality as a success factor
    From customer data and product master data to clear product descriptions and interaction data, AI applications cannot succeed without a clean data structure.