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Personalization - Refining the Customer Experience with Machine Learning

Personalisierung_Machine Learning
08. 03. 2018

In today's digital saturated world, brand blindness is the norm and consumers are exposed to more advertising and offers than they can process. The consumer therefore expects relevant content. Marketing managers are under pressure to meet these expectations: Competition is fierce, customers are price-sensitive and impatient. In the short attention span of the customer, marketers face the great challenge of reaching consumers with their advertising messages.

The Internet is therefore becoming more and more personal. Personalization has been a central topic for the majority of marketers for some years now. We have reached a point where personalization must take place on an individual level in order to resonate with the audience, whether it is an individualized offer or an individualized advertising message.

Thus, more and more companies are making use of Machine Learning to optimize the customer experience.



Machine Learning is generally the acquisition of knowledge through an artificial intelligent system. By analyzing existing data, a system generates knowledge from experience and develops and optimizes independently.

Machine Learning is also used in many areas of marketing. Companies from all industries can use Machine Learning individually for completely different purposes. Intelligent algorithms analyze the behavior of customers and can thus, tailor offers to suit customers. The customer experience determines the purchase decision and is therefore jointly responsible for the success and failure of companies.



A manual approach to personalization is not feasible because huge amounts of data need to be analyzed and evaluated in real time, which is why companies are increasingly using digital platforms to personalize marketing communications. According to a study by IDC (on behalf of Criteo), 34% of marketers already use technology to personalize advertising and content. Common tactics include emails on birthdays or other personal events, communication of new products based on customer preferences, re-addressing based on individual behavior and retargeting campaigns.

In particular, Machine Learning can significantly supplement the following components of personalization.



Before an online experience, whether it is a visit to an advertiser's website or a touchpoint via an external channel, can be customized and personalized for a consumer, the marketer must understand this very consumer: Who is he? Where is he from? What are his likes and dislikes? What does he care about right now?

In order to understand the customer exactly, large amounts of anonymous data are collected about the user. In addition to his previous touchpoints with the brand or advertiser, this also includes the context in which the advertising is presented to him. This data must be stored centrally and can be combined with other customer data sources (e.g. CRM data).

Marketers must be able to access and respond to this data in real time to reach the customer at the right time with the right advertising message. Machine Learning can help to unite the customer data to scale, to interpret it and to draw conclusions about the persona, e.g. their interests and intentions. With these insights, the advertiser knows when a consumer is receptive to advertising and which content is relevant in each specific phase of the user's journey.



Once the advertiser has an idea of who their customers are and what they are looking for, they can deliver personalized advertising messages in the form of a tailored relevant experience.

As soon as the user comes into contact with an ad of the brand, the algorithms analyze the relevant data to personalize the customer experience. This helps to provide the user with a harmonious customer experience across the entire user journey.



The last critical component of personalization is learning. Each marketing campaign must be reviewed for its success. For example, has the campaign caused an uplift in terms of engagement, conversion rate, etc.?

Machine Learning can help you examine how a campaign performs compared to its performance to date and with your overall marketing objectives. The system can predict what the expected performance range should be, and issue warnings if the predictions are not met.



Machine Learning has already become the norm in many areas. For example, streaming services such as Netflix offer users a specialized genre and media selection, and Spotify creates individual playlists for each listener.

This increases the pressure on marketers to keep up with this standard. The audience is already used to personalized recommendations. However, the oversaturation of digital advertising has led consumers to almost completely ignore ads online. Machine Learning can help to make advertising more relevant, so that the interests and needs of the customer are addressed. Marketers should therefore incorporate Machine Learning into their personalization strategy at an early stage.

Read more about Machine Learning in our blog post "What is Machine Learning and how does it work?".

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