Intentionist data, the new holy grail of programmatic advertising?
With the advent of AdTech (Programmatic Purchasing, Data Management Platform, Data Onboarding), brands now know their customers intimately and know how to analyze their past purchases.
But when it comes to buying behavior, what was true yesterday may not be true tomorrow.
In this context, the next major challenge for brands is to effectively anticipate future consumer purchases. This is where intentional data brings all its value by helping brands better understand the needs of their future customers, identify the predictive signals of an imminent purchase and be the first to respond to them.
Two types of intentional data
What exactly do we mean by “Intentionist Data”? It is above all necessary to distinguish between two categories of intentionist data, each of them having its specificities:
Internal intentionist data are captured by the brands themselves. These are specific actions carried out by visitors to the brand’s digital universe: viewing a product page, downloading, requesting information, subscribing to a newsletter, etc. Belonging to the data category “First party” advertisers, this data allows brands to identify people, existing customers or prospects, who have expressed an interest in their products or services at a given time.
External intentionist data come from third parties which, through the service they offer their users, structurally attract an uninterrupted flow of profiles in the purchasing phase. Comparators, and to some extent search engines, are among the main providers of these purchase intent signals. Unlike internal intentionist data, this external intentionist data concerns all people at the advanced stage of their purchasing decision, often when there is still time to influence this decision.
Many use cases
Beyond the simple use of this data in the context of prospecting campaigns, this intentional data delivers all its power when it is crossed with customer data (CRM database, loyalty programs and cards, transactions, etc.). In this way, we can deploy an extremely powerful arsenal of digital marketing actions allowing in particular to:
Identify existing customers who are actually in the purchasing phase and in particular among them those presenting the greatest risk of churn because they are not active in the brand’s universe but have triggered signals of intention purchase on third-party platforms.
Address and allocate budgets in a differentiated way according to the value of prospects and customers. By prioritizing certain profiles, or conversely, by excluding some (“negative targeting”) to optimize performance.
Trigger automated actions to engage the different types of prospects with the right messages, the right offers, at the best time in their purchasing decision cycle.
Evaluate the quality of intentionist data
To sort through the intentional data available on the market, especially when it comes from third parties, brands must use their best judgment before choosing their partners on the subject.
In the selection criteria, we will mainly retain the data source (know exactly where these intentionist profiles come from), their granularity (qualify the intention on specific products and brands), their intensity (classify the degree of purchase intention according to user actions) and their freshness (data in flow and in real time). Finally, the notion of “reach”, dear to the online advertising professions, should be addressed with caution since in the world of purchase intention, quantity does not often rhyme with quality.
Limits and precautions
To fully exploit the potential of this new type of data, advertisers will need to be careful to avoid several pitfalls.
Among these, the “Big Brother” effect which, through too advanced a customization, could give users a feeling of intrusiveness.
Another imperative is to rethink the logic of ad hoc campaigns in favor of a common thread approach, more suited to the flow of purchase intentions and the development of activation scenarios adapted to the different types of audience.
In the same logic, digital levers must be decompartmentalised in order to select the most effective levers according to the different audiences, their uses and their maturity in the purchasing cycle.
Of course, advertisers and their partners must ensure strict compliance with legislation on personal data, in particular on the subjects of data aggregation and the anonymization of profiles.
Digital marketing expert for over 20 years, Eric Cholet is the president and co-founder of Marketshot, a company specializing in the detection of purchase intentions and editor of the comparator Choose.com. He previously co-founded the consulting firm Equancy and acquired a solid experience in digital marketing within several agencies in charge of the digital budgets of major advertisers.
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