The data science team here at Tapad is constantly working to identify patterns among past converters (converters being people who have taken a desired digital action, e.g. clicking a specific link or button on a client’s website). Part of this research has been around the timing and frequency of website visits, where we learned the importance of lifestyle and intent indicators.
For example, imagine that we are looking for devices belonging to people who are looking to buy a pickup truck. To find the right people, there need to be two ingredients:
Lifestyle - they have interests associated with pickup trucks, e.g. outdoors or construction, and;
Intent - they intend to purchase a vehicle in the near future. In this case, a digital conversion could be something like clicking on a “Find a dealer” link on a car website, which demonstrates their interest in purchasing a pickup truck.
Signs that a device has the right lifestyle or intends to make a purchase are present in the digital browsing behavior of that device. Visits to some websites, such as sites about the outdoors, may reveal that a person has interests (lifestyle) often associated with pickup trucks, whereas others, such as car review sites, demonstrate an intent to buy a vehicle in the near future (intent).
So why is this important? Being able to distinguish devices that have both ingredients from those that only have one (or none) can make engagement strategies more effective. For example:
If a device has the right “lifestyle” and shows an intent to purchase soon, we could serve them a limited time offer to push them through to conversion.
If a device has the right “lifestyle” but shows no sign of intent to purchase, we could serve brand messaging to prime them for conversion farther in the future.
Through our research of lifestyle vs. intent, we’ve able to reveal important timing patterns to partners, providing a fuller picture of cross-device browsing behaviors and insight into what websites to pinpoint and when.
If you’re interested in learning more about the data science and methodology behind this research, read this post from our Engineering blog.