For top customer experience, you need to know your customers - AI helps with that

Will product quality soon be irrelevant? Certainly not. But it is becoming increasingly important what experiences customers have throughout the entire customer journey, from the first time they see the brand or product, through the entire lifecycle, right up to the (hoped-for) follow-up purchase.

Because a current globally conducted market study states that 34 percent of customers who have a bad experience with a brand never come back. But about 40 percent would pay up to 20 percent more for a better customer experience. It's clear what the potential is here - for strong growth or business mega-disaster, as the case may be.

To win customers, then keep them for as long as possible and turn them into real premium customers, companies need to know much more about them than billing address and previous purchases. Whether B2C or B2B, a quasi prophetic and at the same time deep psychological knowledge of the customer and of course of the entire customer relationship is the basic prerequisite for successful marketing. And one of the decisive factors is that this knowledge is available in real time at all points in the company when a customer contact takes place. Because the experience of really being recognized in a contact, with needs, wishes and problems, is unbeatable. In interpersonal contact it can trigger infatuation, in customer contact ... well, almost something like that.

Data is to customer experience what candlelight is to a date

Plush eyes are not the seduction instrument for successful customer contact - but the professional use of marketing data, which only becomes really effective with AI, has a high potential to establish and deepen relationships. AI and the computing power of the cloud are necessary because there is no other way to get to grips with the sheer mass of data that constantly provides information about the situation of customers from a wide variety of sources. And this applies not only to the marketing department, but also to ordering, sales including logistics, service, and so on.

Such data management requires a technologically advanced platform that both adheres to all compliance regulations and provides all the data needed for an outstanding customer experience in real time for every customer contact. Before that, however, this data must be collected. However, it is pointless if it then rots somewhere in the back office. It must be used intelligently. So the data belongs on a central customer intelligence platform. Oracle, for example, offers a platform for using this and other data from third-party providers to deliver better service and thus increase trust in a company.

Best practice example: Daimler Predictive Analytics

In the area of purchase prediction, Daimler's best practice example is worthy of emulation far beyond the automotive industry. "When we first decided to innovate in analytics, we wanted to use it to predict future buyers of entry-level A-Class models," says Daniel Pape, senior project manager at Daimler. "We knew we had to use artificial intelligence to get real value from our data." Entry into the project ran through a combination of data from customer relationship management from the branches on the one hand, and from campaign management on the other.

The analysis team drilled so deep into the data and examined the customers from this dataset so thoroughly that it was able to identify some of them who already owned two S-Class Mercedes and were considering buying another vehicle. This was done with about 100,000 Berlin customer records. An initial round of analysis steamed that number down to 5,000 potential buyers of an A-Class Mercedes. In six weeks, this yielded dozens of potential customers who were most likely to buy an A-Class vehicle.

The Daimler team started with deep mining the data and then used business intelligence (BI) and AI. With the functionalities, they went back to BI to challenge the models. An open solution then made it easy to visualize the results. With this combination, the team was able to sketch a quasi-composite customer who was an S-Class owner and could buy an A-Class Mercedes.

AI permeates customer experience throughout the process

The next big thing for the company is to apply continuous intelligence to its processes. Specifically, this means that Daimler will receive a feedback loop from the AI, including its relationship to customer decisions, so that the algorithm can learn from itself.

The question is not only interesting for car manufacturers: When and under what circumstances is a customer prepared to buy my product X again - or as an up-sell or cross-sell to another product or service? It is clear that anyone who has even halfway reliable answers in their quiver can significantly increase the accuracy of their marketing measures.

But the challenge is not trivial: When companies fail, it's primarily because they don't think or haven't thought long term enough, explained Rob Tarkoff, Executive Vice President Oracle Customer Experience Cloud & Oracle Data Cloud, in a recent interview. "Many may have implemented a CRM system in the short term, a quick fix for an acute problem, but then they didn't connect that to the back-office solutions. Or they've rolled out email marketing without connecting that to their ad tech solution. Companies that think longer term tend to think end-to-end. Despite all the technology trends, one thing has always remained the same: the need to be able to store information securely, manage it efficiently, and trust it for generations. Data is still at the core today - most especially in the Experience Economy - and is the foundation for Artificial Intelligence."

There are enough customer signals, including really useful ones, on which a company can build. Consumers use apps, websites, click on ads, call call centers, participate in surveys, download a whitepaper... The whole thing results in something like a digital body language that can say a lot about a person. All this is mapped in so-called graphs, which serve the AI as a basis for further steps such as purchase recommendations.

This must be exploited - in order to provide existing or potential customers with the top customer experience that will be decisive tomorrow.