Data-Driven Personalization with Artificial Intelligence

Fig. 1: People and their preferences differ — data-driven personalization takes that into account

Imagine walking into a store and finding exactly the products you came for right on the first shelf. And not just once, but every time you shop — even when you arrive with completely new needs, wants, and requirements. You find exactly what you're looking for in that moment. That's precisely the effect online personalization achieves through the use of artificial intelligence.

What Is Personalization?

Digital transformation is reshaping every area of life and work, and the pandemic has only accelerated this shift. Customers are increasingly online. Their browsing and buying behavior is changing, as are their expectations when shopping. They have less time, patience, and interest in searching websites or e-commerce shops for the information and products they want. Instead, they expect their needs and requirements to be anticipated and met.

This is where personalization comes in. For companies, it's a way to deliver contextual messages, offers, and experiences tailored to each individual visitor's profile. Homepages, web designs, or product selections can be presented in a way that's specific to the user, addressing visitors individually — in real time and depending on context such as time of day, marketing channel, and device. Methods like machine learning are a key enabler here. Given the sheer volume of data involved, only automated AI algorithms can act fast enough to deliver the desired results.

Personalization Means Revenue Growth

Two people visit a sporting goods e-shop at the same time — a first-time visitor on her smartphone, female, 20 years old, living in Berlin, and a 45-year-old male visitor from the Ruhr region on desktop, a soccer fan and already a customer. Both arrive via the same marketing channel and are looking for T-shirts, yet in real time they see different landing pages, product selections, and offers, each matched to their profile. Both intuitively find what they're looking for — without having to search for it.

This has a positive effect on their shopping experience and, in turn, on visitor satisfaction. But the investment pays off for e-commerce providers too: results and studies show that more relevant offers and content lead to higher conversion rates and, in turn, higher revenue. Going forward, only companies that understand e-commerce and personalized content go hand in hand will succeed.

What's needed, though, are the right systems to automate the delivery of relevant, personalized experiences — so providers can stand out from the market and win customers.

Automatic Clusters Instead of Manual Target Groups

Considering the many possible dimensions of personalization, it quickly adds up to several hundred different clusters of website visitors, each of whom should be served individually and with the greatest possible chance of conversion.

Visitors are divided into segments — clusters made up of people who share a high degree of similarity in their characteristics. These clusters go far beyond the target groups or personas that were traditionally built by hand. Clusters are many times more granular and flexible. Rather than being predefined manually and statically, they emerge dynamically from the information known about each visitor, such as gender, age, time of day, purchase history, geographic information, device used, and more. In other words, all available information feeds into the cluster formation.

The Machine Keeps Learning

Processing this enormous volume of diverse information, data sources, and datasets in milliseconds, and using it to serve individualized websites, requires high-performance technology capable of handling the process automatically. Evaluating the qualitative information about visitors happens through various statistical methods — rankings, correlation analyses, or probability densities — which can be grouped under the term machine learning.

The condition: the system deployed must be capable of learning. The machine learning algorithm must continuously identify and define buyer groups — in other words, form new clusters and continuously process incoming information. The system learns continuously from visitors' click and purchase behavior and uses these insights to keep optimizing the algorithm automatically.

The decisive advantage over conventional systems is that trends and seasonal effects are also recognized by the algorithm, with decisions adjusted accordingly. Visitors are continuously analyzed, and their contextual information is matched against the best-fitting cluster. Each cluster then automatically receives the product suggestions, designs, and content optimized for that particular buyer group.

Where Does the Data Come From?

There's no shortage of data available for personalization. Information such as search terms, marketing channel, device, gender, or purchase history, combined together, allows for a precise classification of visitors into a buyer group. More often, the challenge lies in evaluating and using the data. This usually happens across different systems and areas of responsibility — without a unified overview. For example, purchase histories exist in CRM systems, campaign results sit in campaign management tools, and landing page performance data lives in tracking systems. In practice, though, all this data and these results are mostly managed and evaluated in isolation, used only to optimize the channel that "generated" them. In other words, the evaluation of a Google campaign is used only to optimize Google campaigns; the evaluation of a sales email is used only to prepare the next mailing. Properly understood and applied, personalization, by contrast, uses data across channels and in combination with each other — creating substantial added value.Fig. 2: Using all available information to personalize an example customer journey

The Difference from Conventional Approaches

Compared to a classic A/B test, the advantages of the new approach — data-driven personalization powered by machine learning algorithms — become clear: The simplest form of testing is the A/B test. Two websites, A and B, are defined, incoming traffic is split evenly between them, and the goal is to see which one achieves a higher conversion rate. After a predefined period, the website with the lower conversion rate is switched off and no longer shown to visitors going forward.

This form of testing carries a high cost in the form of lower conversion rates, since half of all visitors are directed to the worse-performing variant for the duration of the test.

Another drawback of the A/B test is that the variants aren't served based on target audience — instead, they're shown to all visitors purely at random until one variant proves to be the better performer. This approach forgoes the opportunity to further optimize the conversion rate through audience-specific targeting.

AI algorithms, by contrast, independently learn which content works best for which cluster and increasingly serve it to the corresponding visitors. This drastically reduces the initial cost of testing. Within a short time, the algorithm "understands" visitor behavior and delivers close to optimal content to visitors almost every time.

More Relevance = More Revenue

The benefit for e-business operators is obvious: personalization makes for more relevant results on the page, which boosts conversion rates and increases revenue. Experience shows that Acceleraid's personalization solution can quickly deliver double-digit percentage increases in conversion rate.

Beyond the direct impact on a company's online revenue, data-driven personalization also brings further indirect sales benefits across the entire customer lifecycle: stronger customer loyalty through greater satisfaction, deeper insights into customer behavior and reactions, and precise customer segmentation as a basis for future decisions are just a few examples.

Outlook

Data-driven personalization opens up entirely new dimensions for the whole field of customer intelligence and business intelligence — and this is only the beginning of its potential. The Internet of Things is becoming ever more real: more than 30 billion devices are already connected to the internet today, generating enormous volumes of data that can also be put to use for personalization.

The customer is at the center of it all and has access to an almost complete overview of the market. At any given moment, they can tap into a wealth of purchase-relevant information — yet they can barely process that volume of information themselves anymore. They therefore need providers to help select the information and products that are relevant to them. The key to that is personalization.