Using Artificial Intelligence to Build Customer Loyalty

Today’s customers expect supreme services and an easy way to achieve their goals. When I go shopping, for example, I expect the store to have all the goods I need, I don't want to wait in line at the checkout, I want the convenience of paying with my smartphone, and so on. It should be the priority of any business today to optimize all of its processes in such a way as to ensure the greatest possible level of customer satisfaction and in this way to increase customer loyalty.

The essence of building loyalty was once beautifully summarized by Walt Disney, who quipped:

“Do what you do so well that they will want to see it again and bring their friends”.

Recent research shows that this is not just a fairytale; companies classified as customer experience (CX) leaders have developed their businesses 14% more than those who have ignored this area of management altogether.[1]

We are now witnessing the growth of technologies that may bring many potential business benefits, but which also pose a number of threats. One of these is artificial intelligence, which delivers three key business advantages. It allows us to look for inter-relationships we might not otherwise be able program into standard predictive models, the performance of these models often improve because of AI's ability to learn by analyzing the results of earlier decisions, and the models themselves let AI analyze huge volumes of unstructured data (making it possible to look into sounds, images or data with which standard analytical models have always struggled).

This new approach to data analysis opens up completely new opportunities for customer personalization, resulting in better matches between products/services in the portfolio and specific client needs. “Look-alike” models that learn from past data are now able to recommend customized discounts, products or services that best meet the needs of an individual customer and do so on the basis of analysis of the customer’s own behavior and that of other buyers with a similar profile. Some large companies have already implemented such technologies successfully, and market research shows that this strategy may lead to a 50% increase in the ultimate conversion rate (or the likelihood that the customer will actually buy the recommended product or service). In addition, AI models continually analyze their own actions and, if these are shown not to work, they will look for more effective new solutions.

Therefore, AI models may tell us how best to customize our offer to target selected customers, based on their previous orders. The process doesn’t rely just on the fact that a given product has been purchased in the first place, but (as in the case of clothes), AI may also be used to analyze the look of a given item and suggest similar clothes from a new collection. AI models may also help plan marketing campaigns and estimate which customers are losing or gaining interest in our products and services.

Chatbots are another area in which the development of AI models has driven rapid growth. Chatbots may be simple decision-making pathways that allow customers to obtain information by choosing predefined queries, but they may also “understand” a natural language, both written (text recognition) and spoken (voice recognition). Today, these are mainly used in customer service.

The reason is that chatbots are much better at adjusting to their interlocutor than humans; they are highly specialized tools that instantly analyze the entire record of past interactions with the customer (including previous orders, refunds and issues), interpret the customer’s current mood, and select the best way to manage the conversation at hand. Additionally, they draw on the experience gained in all previous relations with all customers. Most importantly, a chatbot will never quit its job and take its priceless experience elsewhere. Of course, it would be risky to confront buyers with a chatbot that is not yet advanced enough to do the task, for this would frustrate customers who would refuse any further interaction with a company offering such low-quality service.

Chatbots are also used in another fast-growing sector, known as “conversational commerce”, offering the opportunity to make a purchase as a result of conversation with a specially trained chatbot. In this case, chatbots also guide customers along a path leading to a purchase, suggesting relevant products, changing colors, adapting sizes and so on, all based on talking to a customer. This innovative form of building relationships between customers and providers of products and services ensures an extraordinary degree of personalization from the outset, and makes for much faster, more convenient and successful purchasing decisions that guarantee improved customer satisfaction.

On the other hand, the rapid advances in speech recognition technologies and speech simulators has driven the growing popularity of “virtual assistants”, such as Amazon Alexa or Google Home Pod. VAs have achieved very good sales results and, in the future, their applications may be expected to increase. According to comScore, up to half of all search engine queries will be voice interfaced by 2020.[2] This poses a major threat to all brands as it makes them much more dependent on VA manufacturers. When an “order a large pepperoni pizza” command is entered, it is the VA that will select the pizza delivery service, and the very nature of the interface means the list of potential search results will be much shorter. This will make it even more difficult for brands to reach out to potential customers, while assistant providers will gain much greater control over their users' buying decisions.

Implemented in the interface areas of communication between companies and customers, AI may therefore have a colossal impact on the way customers perceive their interactions with the brand. This will either lead to either greater customer satisfaction and loyalty, or discourage any further relations.

Łukasz Słoniewski
Consulting Director, Comarch

Sources:
[1] Customer Experience Drives Revenue Growth, 2016, Forrester Report, 21.06.2016
[2] Supercharge Consumer Engagement Using Voice Interfaces, Gartner, 13.03.2019