Abandoned shopping baskets at checkout?

What role does Artificial Intelligence and Behavioural Economics play in optimising the user experience? In this article, you will learn on which basis the behavioural scientist Thilo Pfrang and the AI expert Valon Xhafa developed solutions based on behavioural economics and AI that support users in their purchasing decisions in a custom-fit manner and based on their individual behaviour.

eCommerce: a fast-growing sector with unsolved problems

For behamics founders Thilo Pfrang and Valon Xhafa, eCommerce is a fast-growing sector with many unsolved problems. The behamics founders' goal was to use behavioural science to create more convincing online communication for customers in eCommerce.

For example, one problem that concerns many e-commerce customers is that although many consumers select and purchase products online, a considerable number of them then return the goods. This is a clear signal that the choice is often not made online, but at home, and that customers are unsure.

Searching for and selecting the right product: the central eCommerce process often doesn't run smoothly

The likelihood is high that the customer will not only realise at home that he has made the wrong choice, but will already be reckoning with it while shopping. The question for retailers is, of course, how it comes about that the customer orders the product despite his doubts.

For us, the answer lies in the strategic design of the customer journey. It is noticeable that the main focus is often on optimising the checkout and the post checkout (after sales). According to the common definition, the checkout process starts when the customer places the product in the shopping basket. If a largely smooth user experience can therefore be expected from this point on, the problems must occur beforehand, when searching for and finding the right product.

Intelligent mapping of customer expectations to product features

If the customer is looking for his desired product, the customer's expectations must be optimally mapped to the product features. The underlying process needs intelligent support for the correct interpretation of the data. 

For behamics founders Xhafa and Pfrang, the best solution was to draw on the possibilities of AI. The behamics AI aims at scientifically based behavioural appeals to intelligently control online buying behaviour, thus achieving higher conversion and lower return rates. Combining insights from permanently running predictive models in a self-learning system based on clickstream data enables not only changing behaviour, but predicting it.

The application of AI algorithms to customer and behavioural data can not only favour the selection of more suitable product suggestions, but also improve the customer experience by dynamically playing out the right psychological mechanism in the right situation and help customers to make a decision in the first place. It is precisely this advantage that we want to use for the optimisation of the customer journey. It must be possible to access a wide range of information and produce reliable results in real time - to align these processes at the retailer, the extensive experience and know-how of our KPS User Experience Team is the right foundation.

In stationary retail, individual advice from competent sales staff guides the purchase decision. In the online medium, the situation-dependent provision of relevant information replaces the human factor and takes over the function of personal advice.

An individually purchase-motivating user experience is based on

• detailed and informative text and image material,
• insights into practice through the communication of opinions and experiences of other users,
• the integration of elements of live commerce such as video streaming,
• the possibility to clarify open questions in chat dialogues,
• the placement of targeted behavioural triggers at the right time in the right context. 

Using personalisation in a multi-layered way with KPS and behamics

Together with behamics, we use the synergy effects of our competences to enable the multi-layered personalisation of the customer journey in user-centred concepts and design solutions.

In the continuation of this article, you will learn what contribution AI makes in the placement of nudges or in live chat and why certain product labels are a help for the customer.