From AI customer journeys and personas

Because life is more complicated than the textbook. Customers do not act in a straight line. They change their minds, get other recommendations, are suddenly short of cash or shift their priorities. This is why customer journeys - the imaginary "journey" of customers from their first awareness of a brand or product to the purchase and recommendation to others - usually take a serpentine or even looping course. The customer journey often ends before the purchase with an abandonment. A purchase is often made, but the loyalty and recommendation phase never follows. The customer journey is different for every customer and takes different turns. Companies are interested in ensuring that customer journeys are as short as possible. And that they are repeated, so that customers remain loyal to the brand. This is where the use of AI on the customer journey can bring a decisive advantage:

Customer data as a key element

An AI customer journey does not change the actual customer journey, but it integrates artificial intelligence at every point of contact a customer may have with the brand/product during the customer journey: on every platform, in-store, online search...

Machine learning and AI enable interactive content, customer service via chatbots and personalized advertisements that are tailored not just to a target group, but personally to each customer. Big data in the form of massive volumes of customer data makes it possible to customize every point of contact and every phase of the customer journey for each individual customer. The key to this is not so much the collection of data - which is not difficult these days - but the correct use of customer data on a large scale. Companies that succeed in this can understand each individual customer journey: Why is someone interested in my product? What problem do they want to solve or what wish do they want to fulfill? What is their motivation? Please note: Satisfying a need is not the same as the motivation to buy a certain product: Someone needs a new hat for a ski vacation or winter in general. EVERY hat, including the one you knit yourself, will keep him or her warm, one perhaps a little better or more comfortable than the other. But not every hat expresses itself: I'm a skier/skater/it-girl/outdoor fan. I pay attention to status/pragmatism/sustainable production/brands with a "good cause"/recycling or fair trade. This is where the motivation to buy one brand or another arises, far beyond the need for warm ears. Emotions should not be underestimated. Companies that know their customers inside out and can take a personalized approach at this point are far ahead of the competition: they can optimize the customer journey so that customers stay with their brand, even if the competitor's product is just as good and possibly even cheaper, or fulfils the same purpose.

Through the customer jungle with AI mapping

AI systems that analyze CRMs, social media data, surveys, customer feedback, ratings, surfing behaviour, purchase history, etc. can create customer journeys that not only reflect real customers and predict their future search and purchase behaviour, but also reflect their sentiment towards a product or brand. In this way, companies can specifically influence customer journeys that have already begun, accelerate them and make them more pleasant for customers. But that's not all: they can also prevent pain points, i.e. ensure that there are no stumbling blocks or disappointments on the customer journey and turn negative (previous) customer experiences into positive ones, e.g. recognizing a previously complicated check-out system as a problem and replacing it with a simple one or offering cheaper shipping than the competition, which may offer the same product at the same price. Furthermore, a customer journey with AI offers the opportunity not only to sell a product, but also to offer up- and cross-selling along the customer journey through precise customer knowledge, to awaken new needs in a targeted manner and thus increase the actual sales volume.

With a traditional customer journey, we can find out where and when customers come into contact with a brand, where they hesitate and when they drop out. An AI customer journey also tells us why they act the way they do and how we can anticipate problems and implement purchase incentives - for each individual customer.

Create AI customer journeys

Admittedly, AI-supported customer journeys require large amounts of data, which poses challenges for smaller companies in particular. data-driven personas in conjunction with AI solves this problem without the need for large research departments. AI buyer personas use internal data (customer surveys, usage data, click rates, complaint management, CRM data, call and chat logs, etc.) in combination with external data (social media, mentions, online reviews, specialist forums, general market research data, etc.) and relevant data from existing and potential new customers to create a detailed profile. With data-based AI personas, even small companies can target a tight marketing budget where it will bring the most profit - currently and projected into the future. And with the data-based personas from the Persona Institute, you can even chat with them yourself to ask them about their preferences and pain points.