Intent data, or intent data, are complex sets of data that, when treated appropriately, become information capable of predicting the purchasing interests of potential customers, whether they are companies or individuals, in the immediate future or in the short term. The basis for predictive analysis of this data is none other than based on their behavior or searches. Being able to impact a potential when they are at the bottom of the conversion funnel, corresponding to intention, multiplies the response by more than 5 times.
Let's take a practical example: if you are SEAT and you can impact two prospects. The first one, Paco, is interested in cars and the second one, Marta, is not interested in cars, but wants to renew his vehicle because the current one is giving him problems. It is obvious that SEAT will be more interested in Marta, who has a closer and more definite purchase intention in time. We know that Paco likes cars, but no matter how much he is interested in them, he is probably not constantly buying cars. In other words, intention data allows us to focus on those prospects who are more prepared to make a purchase of a specific product or service, and not to waste efforts on those who are not yet ready to do so.
How can we infer that possible purchase intent?
There are multiple avenues behavioral data, searches, correlation patterns or a combination of all of these. If we continue developing the previous example Google Ads offers segmentation of interest in car purchase based on searches; but also if SEAT directly or through a supplier has geolocated users with an app it can detect that need, when the potential customer visits a competitor's dealership or a mechanic shop repeatedly.
It is clear that all intent data is not equally valuable. There are critical moments that can trigger valuable information for multiple companies. For example, if we identify users with the intention of moving home, we identify a very important time when many hiring and purchasing decisions are made. This data on home purchase intentions could be activated by the Tinsa group, through the identification of users who request home valuations. Download E-book "The advantages of using real estate data for marketing and decision making".
In the B2B environment, where the journey to purchase is usually longer and involves several decision-makers, intent data gains special prominence. If DataCentric, you have read this article, downloaded the above Ebook and clicked on an intent data campaign, you would be assigned a sufficient degree of maturity in the conversion funnel to trigger marketing or even commercial actions directly.
Types of Intent Data
There are two main types of Intent Data, First-Party (proprietary data) and Third-Party (modeled and supplied by a third party).
First-Party Intent Data
This is the most powerful because it is built with your own information and has no cost. It can be collected from several sources:
These come from companies that collect data and behaviors from different sources, and aggregate it to use it for advertising purposes. This data does come at a cost, and it is important to be aware of their compliance with privacy laws and to use only reliable providers in terms of their data collection methodologies.
Let's take a practical example: if you are SEAT and you can impact two prospects. The first one, Paco, is interested in cars and the second one, Marta, is not interested in cars, but wants to renew his vehicle because the current one is giving him problems. It is obvious that SEAT will be more interested in Marta, who has a closer and more definite purchase intention in time. We know that Paco likes cars, but no matter how much he is interested in them, he is probably not constantly buying cars. In other words, intention data allows us to focus on those prospects who are more prepared to make a purchase of a specific product or service, and not to waste efforts on those who are not yet ready to do so.
How can we infer that possible purchase intent?
There are multiple avenues behavioral data, searches, correlation patterns or a combination of all of these. If we continue developing the previous example Google Ads offers segmentation of interest in car purchase based on searches; but also if SEAT directly or through a supplier has geolocated users with an app it can detect that need, when the potential customer visits a competitor's dealership or a mechanic shop repeatedly.
It is clear that all intent data is not equally valuable. There are critical moments that can trigger valuable information for multiple companies. For example, if we identify users with the intention of moving home, we identify a very important time when many hiring and purchasing decisions are made. This data on home purchase intentions could be activated by the Tinsa group, through the identification of users who request home valuations. Download E-book "The advantages of using real estate data for marketing and decision making".
In the B2B environment, where the journey to purchase is usually longer and involves several decision-makers, intent data gains special prominence. If DataCentric, you have read this article, downloaded the above Ebook and clicked on an intent data campaign, you would be assigned a sufficient degree of maturity in the conversion funnel to trigger marketing or even commercial actions directly.
Types of Intent Data
There are two main types of Intent Data, First-Party (proprietary data) and Third-Party (modeled and supplied by a third party).
First-Party Intent Data
This is the most powerful because it is built with your own information and has no cost. It can be collected from several sources:
- Declarative data collected in your own CRM
- Behavioral data on web visits, ebook downloads, apps, etc.
- Responses to campaigns
- Information provided from our customer service contact-center.
These come from companies that collect data and behaviors from different sources, and aggregate it to use it for advertising purposes. This data does come at a cost, and it is important to be aware of their compliance with privacy laws and to use only reliable providers in terms of their data collection methodologies.