Companies increasingly focus on automation in customer service and after-sales, meaning that past actions by users can be machine-evaluated in order to derive probabilities for future actions, since they must be resource-saving and, above all, can no longer process the huge amount of service requests personally that are coming from different devices of the customers. Depending on the complexity, individual measures can be triggered automatically: e.g. the chat window opens at the right time, emails arrive at the right moment with the appropriate content - or customer care picks up the listener personally if the customer has a problem. Certainly, personal conversations remain indispensable. The process behind it leverages predictive analytics and, through its use, enables the creation of a good customer relationship, as available capacity

A comprehensive database is a must

Data driven marketing has become a trend that helps companies to gather more information from their customer behavior and to draw conclusions from relevant evaluations for ongoing and upcoming campaigns. Questions raised are, for instance, “How long is a particular channel used?”, “What is the actual opening rate of the newsletter and which parts of the website are clicked by whom how often?” When this anonymized information is combined with one another, patterns emerge that use predictive analytics to create probabilities from which recommendations for the design of the digital customer journey can be derived. Different behavioral patterns and user preferences are captured by profiling and aggregated into user profiles. Major players such as Amazon or Zalando show how to make predictions about the wishes of customers, their buying behavior, or even the probability of termination with the help of targeted analysis of the collected data and outlined user segments.

Increase user relevance with AI & ML

A learning, data-driven Artificial Intelligence (AI) is the basis of all these optimizations. If all data on inventories and customers are brought together without contradiction in a central database, AI can read them out automatically.

Links between different marketing channels are becoming interesting, for instance, when emails or chatbot windows are getting activated automatically by certain triggers. In addition, data is always collected when a user accesses lead magnets such as white papers and webinar on a website, where he/she leaves personal data, or even repeatedly calls up certain areas of the website such as the help area. Actually, it is not necessary that data is left via a contact form, but also information obtained from cookies allow patterns of user behavior to be analyzed. Pre-defined scenarios enable the AI to recognize these triggers in real time, responding to customer needs by offering customers the most attractive and accurate offerings.

The same applies to advertising measures that pick-up users with individualized relevant content for their current needs, certainly saving the advertiser a lot of money that way. A particularly precise target group approach by segmentation is noteworthy, because such targeted campaigns reach the right recipients and banner circuits pay off optimally.

Consider clever customer-oriented approaches

Predictive analytics is not just about user orientation. Data and algorithms provide the customer with a more personal and satisfying experience as they are picked up with personalized offerings at a point where they are likely to be ready for action anyway - for which the campaign will then be the deciding factor. However, the conclusions derived from these findings must be able to influence the assortment design in purchasing as well as the preparation for the customer discussion in sales. The art will be channeling marketing appropriately via email or pop-up, voucher or video, with or without sound.

By Daniela La Marca