The personalized targeting of users on digital channels has long been technically possible - but in the past, this usually failed due to a lack of content and data. With the help of AI, this content is now available in any granular personalized form - but the database is still lacking. What can be done about this dilemma?
The first wave of personalization fell short of expectations
Since the early 2000s, there has been talk of personalized targeting of customers and users on the internet. The first software solutions came onto the market around 2010 and made it possible to define user segments - for example, based on previously visited websites and other collected data. This suddenly opened up unimagined opportunities for marketing initiatives to optimize customer journeys.
In reality, however, it quickly became apparent that the target group could not be targeted in a dedicated way due to the granularity of the segments created. The availability of segment-specific content was severely limited and could not be mapped in terms of processes or organization. Content creation was the work of editors, who were unable to adapt the individual content to such small segments due to time and cost constraints. The consequence: half-heartedly implemented data collections in silos without any claim to a truly personalized approach.
Typically, personalization often resulted in fragmented email newsletters in which users received corresponding subsets of available text modules based on defined characteristics.
The status quo: lack of an overall view of the target group
The restrictions on the editorial side described above led to the initial hype of personalized customer communication flattening out. Personalization was not understood and developed as the basis of all digital customer relationships, but was often only used selectively for a few isolated use cases (e.g. sales campaigns). As a result, many companies today still have a number of isolated solutions that collect customer data of varying quality according to different criteria in order, in the best case scenario, to play out dedicated content for roughly structured segments. A consolidated database (customer data platform), in which all data is collected and processed centrally, is missing in most companies.
AI turns the previous situation on its head
AI, especially GenAI, opens up an infinite number of possibilities in content creation in one fell swoop. Texts and images can be generated on the fly in different variations, nuances and tonalities. The more data is available about a user, the more precisely the content generated by GenAI can address that user in their current life or emotional state. Product descriptions can be generated in such a way that the storytelling and imagery have maximum congruence with the preferences of the target group - individually for each user.
In practice, the required database is usually lacking at present due to the history described above: benefit and customer data is fragmented, available in different systems and different qualities and is often inconsistent. In addition, they are not captured with sufficient granularity to enable a 1:1 approach by an AI.
GenAI has thus reversed the dilemma in personalization - while technical possibilities used to significantly exceed what was feasible in terms of content, there is now a lack of a sufficiently granular database to feed what is possible in terms of content.
How far along is GenAI really?
"Wait a minute! That's not true!" some readers will surely be thinking at this point. "GenAI is not ready yet. The texts generated by GenAI cannot be played out without human control. It's too risky, because nobody has an overview of which customers are being addressed and how." And yes, these objections are absolutely justified. We are still at the beginning of this new era. Looking back over the last 24 months, however, it is clear how fast developments in the field of GenAI are and what immense progress has been made in the quality of GenAI content. It is only a matter of time before the quality of the content generated by GenAI is so good that human editing is no longer needed.
The race has started
The challenge for companies now is to consolidate and expand their database in a standardized Customer Data Platform (CDP) in order to create the best possible foundation. The database must be available as soon as GenAI has reached the next level of maturity.
Given the organizational complexity and the existing technical legacy, this is a mammoth task.
The time to act is now - if you want to fully exploit the potential of GenAI and the personalized customer experiences it can deliver in the future.
Despite the challenges mentioned above, the rapid development of Generative AI has greatly accelerated the digital transformation in all industries. Many organizations are now faced with the challenge of recognizing the opportunities offered by artificial intelligence and using them to meet their specific needs. What added value do AI-based solutions bring to your company? We can help you answer this question and find the right solution for you - with a unique approach.
AI tools provided by diva-e can be your jump-start to drive digital transformation through the use of artificial intelligence in your business. We use solutions from B2B tech start-ups that have already proven themselves on the market and implement them seamlessly in the context of the respective customer project.
