The power of hyper-personalization: how to use customer data more intelligently

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The more personal data you have, the higher the level of personalization you can achieve. We have the techniques and the technology for that. So it is possible. But that does not mean that all companies put it into practice. Most still do not get much further than “Customers who bought this product also bought…” and “Often bought together”. That is a shame for two reasons: it no longer goes far enough in this digital age and it is an unnecessary missed opportunity.


Traditional forms of personalization are increasingly making way for 'hyper-personalization'. New digital technologies, the rise of omni-channeling and ever-increasing customer expectations are driving hyper-personalization in virtually all industries, from retail to finance, travel and hotel, healthcare, telecom and media. Here, it is customer-specific dialogues that deliver success.

What is hyper-personalization?
Hyper-personalization is based on additional customer dimensions. These relate to the activity, interest, opinion, attitude, values ​​and behavior of the (potential) customer. The information is collected from various sources, including social media and research. The deeper, richer customer profile obtained in this way is mapped argentina telegram number list onto the entire product or service portfolio of your company. Two techniques are used to achieve hyper-personalization: “Attribute Analysis” and “Event Sequence Analysis”. Incidentally, it is not a matter of choosing between the two: they can be used separately, but preferably simultaneously. A brief explanation of both.

Attribute Analysis
Attribute Analysis uses mapping to describe the customer, using demographic, physical, psychological, functional, occupational, and aspirational attributes. Each attribute of the customer is mapped to a product, service, or piece of communication. This data helps make recommendations that go beyond related products and services. Instead, they dig through the customer's needs to make suggestions.

Mrs. de Vries as an example


An example using the imaginary Mrs. De Vries, who is known in the CRM systems to live in Amsterdam and is a primary school teacher. On Facebook, we can read that she does not read newspapers, but gets her information mainly from television. She is crazy about traditional dinners and is very concerned with her health. There is also data about her shopping behavior. Mrs. De Vries does her shopping at different supermarkets, but she is brand loyal. An average shopping visit lasts 63 minutes, and she spends an average of 45 euros per visit. And she loves bargains: she always stays the longest at the offers.

This wealth of personal information is clustered and poured into models – such as a Bayesian Probability Model and a regression analysis. Based on this, you get the core, a statistical model, that can be used to 'hyper-personalize' the digital contact with Mrs. de Vries.

Event Sequence Analysis
Event Sequence Analysis observes the successive 'events' in the customer journey, in order to understand what are the leading factors for a certain positive action (e.g. a purchase) or a negative action (e.g. regretting a purchase). For an e-tailer, the following events could be relevant: the way of logging in (e.g. via a Facebook or Google login, which provides quite rich details about the customer), the search terms used (e.g.: fridge, buy house, LED lights), adding items to the so-called 'wish list', 'liking' the Facebook page of a product or service, crowdsourcing by means of a post on Facebook (e.g. Apple vs Samsung smartphone), and so on.

Each event offers its own opportunity for hyper-personalization. This can be used for offers via email, a chat bot, a direct offer via the web page, or offering discount coupons for certain products, and showing interesting items.

Different interactions at different touchpoints
Event Sequencing is a logical method that does not require a special technical platform, a specialized data mining tool, or even Excel – although that is easy. It is about capturing the events in the customer lifetime. These events differ per industry, and can be different interactions at different touchpoints: a customer making a purchase, liking the company’s Facebook page, walking into a store and buying something there, liking your Facebook page, clicking on an email sent to them and accepting an offer – these can all be events.

Track and map data
Suppose a company has defined 10 events, then it can be tracked per customer which events he goes through and in what order. And in this way map the path that each customer takes. Then it becomes possible to discover the most common path. Then the marketer can try to influence the next choice of the customer per touchpoint - namely that of the most traveled path.



If many customers like your Facebook page, you can adjust the content of the page accordingly, so that the next event follows. Suppose that after liking almost always follows a purchase, you can make that extra attractive.

How do you make the transition?
In principle, all companies can make the transition to hyper-personalization. It does not require special IT platforms; any data warehouse or big data ecosystem is good enough. Collect the data, combine it with each other and, based on a number of basic analyses, a sharp customer profile can be drawn up. It is necessary to master the necessary techniques and methods. Three specific competencies are required for this.

Three competencies
The first is knowledge of data science. A data scientist has deep statistical skills and can look at very large amounts of data, understand the data, and turn that data into insights.

The second is data engineering. That is the technical side of data. For example, a data engineer can extract data from social media by applying a technique such as data scraping . The engineer then puts that data in a kind of data warehouse in the format that is needed to combine it with traditional personalization data (history, transaction data, demographic characteristics) from other sources.

The third is industry knowledge. Understanding the market in which the company operates, the company's position and approach, and the way in which customers make a purchase.