First, Second & Third party: The Devil is in the Data
March 3, 2017
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Before we delve deep into this headline, let us briefly understand as to what the above data sources mean:
First Party – Data generated by the self is classified as first-party data. In the context of ad-tech, the data generated by users online belongs to the respective advertisers and are first party data (to be used for themselves). For example, data generated by users while they browse Coverfox belongs to Coverfox and are to be used by Coverfox
Second Party – Data generated by a source but to be exclusively used by another partner is second-party data for the partner buying the data. For example, data generated by Amazingoffer.in if used by Shopclues as part of a specific partnership, then the data is second-party data for Shopclues (which is buying the data as part of strategic tie-up)
Third Party – Data generated by a source, but distributed by an intermediate platform, and is consumed by clients of this platform, is third-party data for the client consuming it. For example, data provided by a partner X and is made available on Vizury platform and is consumed by Vizury clients, is third-party data for Vizury’s platform clients.
The way ad-tech has evolved over the years, value of first party data has been (and continues to be) the most prominent. When a user lands on the website, one gets the perfect chance to understand as to what the user is interested in and then through a series of steps take these users towards the purchase or whatever the end objective is. For example, if a user lands on the Coverfox website means that the user was in some way interested in purchasing some form of an insurance product (Car, Bike, Health, Travel etc.). But for some reason, these portals have only been able to convert a fraction of these visitors into customers.
What should they do next? This is where the ecosystem evolved and complementary marketing partners evolved to help these portals mine their user data and use it to perfection. Some of the tools available to achieve maximum efficiency are:
Call center led conversion (if user dropped a lead)
Remarketing (based on first-party data)
On-site personalization (for next-visit)
and many more. These tools or partners did help improve conversion and engaging users more effectively. They tap into all sorts of user footprints, apply all sorts of algorithms (machine learning, deep learning, AI, predictive modeling etc.) and have definitely created an added level of intelligence, thus creating a user experience.
But at times, there is scope to do something more. What if the questions are,
Can I know something more about my visitor/user, when they land for the first time
Can I influence user basis their external behavior (outside my site)
Can I achieve cross-sell, up-sell basis information which is derived from multiple sources
Some of the above questions have resulted into the concept of second & third-party data. If a portal can get into strategic tie-ups wherein the 2 partners are complementing each other, then such data-exchange (in closed private-view) is what is known as second-party data. For example, insurance portals (Coverfox) can tie-up with travel partners (like MakeMyTrip) and reach out to all the users who have purchased flight tickets and sell them travel insurance. Now this can be done either within the context of MakeMyTrip site or outside on display networks as well by leveraging partner platforms like that of Vizury Engage360.
Another way is where a platform also can get into generic tie-ups with large data-sellers and humanize the information foot-print into actionable insights. Subsequently overlap these insights with client data and help them deliver relevant messaging. For example, basis 3rd party data if Vizury is able to predict that a user is a heavy traveler, next time this user comes to the Coverfox portal for the first time, s/he can directly be taken to the travel insurance section (and not default homepage). Or if this user is found on a banking site, one can personalize an onsite notification, selling them travel related credit-card. Not just this, intelligent platforms can link data-views to first understand if this user already has a credit card or not, or specifically a travel credit card. Only if the user does not have a travel card, will he be shown a travel credit card, else some other travel up-sell product.
What next? Keep thinking and come up with use-cases which can challenge the contours of digital and traditional marketing. And then seek platforms which can help you execute such use-cases, while helping make data available.
Write to us at email@example.com with your thoughts on the devil data.
Nishant Kadian takes care of content marketing for mFaaS. He is passionate about sharing his learning on the ad technologies, mobile ad fraud preventions, and more. Drop him a 'Hello' on LinkedIn or Twitter to start a conversation with him.