The more you know about your users and customers, the better chance you have of deploying effective strategies to personalise the customer experience.
Personal information, behavioural data on preferences and buying habits, interests and psychographic data, as we know, are the basis of an effective profiling strategy, but how much is it possible to know about the users who interact with a brand’s digital and non-digital properties or with its ADS campaigns?
One statistic emerges significantly: in Italy only 1.8% of visitors, on average, choose to disclose their data and leave contact information on a website or eCommerce.
This means that more than 98% of users interact anonymously, an important number that represents a great challenge but also an enormous opportunity. Even when anonymous, in fact, users, on the various occasions of interaction with the brand, provide valuable information (geographic areas from which they connect, devices used, content viewed, etc.) that can still be used to enrich profiles and give rise to subsequent customisation and progressive profiling activities.
It is here that data enrichment processes come into play, which, as the expression itself states, aim to enrich the user profile, whether known or anonymous.
Data enrichment strategy: why is it so important?
The term ‘data enrichment’ refers to the process by which data and information concerning the individual user is enriched, but also updated.
The goal, as can easily be guessed, is to create a complete user profile updated in real time, that unified customer view which, as we know, is the basis of any effective customer experience enhancement strategy.
In the face of the much heralded deprecation of third-party cookies, data enrichment activities have taken center stage especially with regard to anonymous user profiles.
As we know, audiences profiled by means of first-party data represent an important asset for marketers, advertisers and publishers, but their limited size often makes it necessary to adopt different logics in order to achieve relevant numbers in terms of reach and performance.
A data enrichment strategy thus becomes fundamental with regard to both anonymous and known users: in the former case, it allows data and information to be enriched starting from navigation contexts, rather than from probabilistic models, while, in the latter case, it can be deployed with progressive profiling activities through forms, surveys, polls, just to name a few examples.
Going back to the types of data that can be enriched, they essentially concern:
- demographic data;
- geographical information;
- behavioural data;
- transactional data;
- contact data;
- psychographic data.
A data enrichment strategy allows you to get to know your audience in ever greater detail.
All this translates, for brands and companies, not only into the possibility of offering a better customer experience, but above all into the opportunity to implement more effective and timely data-driven strategies.
Data enrichment with Blendee
Blendee’s Marketing Operating System enables effective data enrichment.
This is made possible by the Audience Platform engine and the A.I. Audience Profiling feature.
In particular, the data enrichment activity is based on a three-level incremental system.
First Level
The first level works from the analysis of the reference context in which a user moves. In this case, we are mostly talking about anonymous users for whom the purpose of a data enrichment activity is realised precisely in the possibility of enriching their profile.
By tracking users in media advertising campaigns outside the digital properties of a brand or tracking anonymous users within the digital properties, Blendee’s artificial intelligence algorithms make it possible to detect the context in which the user is located and to associate this user with certain characteristics linked to qualitative and quantitative information derived from the context itself.
In this first level, the Audience Platform engine uses the semantic analysis of content and its classification by means of different taxonomies such as the IAB first and second level taxonomies.
Second Level
The second level adds to the amount of information collected in the first phase, demographic information (gender and age) derived through statistical models that allow us to attribute it to an anonymous user browsing the site through probabilistic models based on different confidence levels.
Terzo Livello
The third level allows us to supplement the previous information with information on known users, data that they have released and that the brand possesses. This is demographic information but also data that may relate to consumption habits or interests. In this case, the behaviour of known users is combined with similar behaviour within anonymous audiences to derive socio-demographic characteristics on the members of these audiences as well.
From data enrichment to audience creation activities, it is a short step: precisely on the basis of the characteristics attributed during a data enrichment activity, Blendee’s A.I. Audience Profiling allows the characteristics to be analysed and on the basis of these to proceed to the creation of different audience types.
Data enrichment is a powerful strategy to gain a competitive advantage and deliver personalised and truly relevant customer experiences.
