- log in
Posted by Nate Elliott on March 8, 2013
For years, brand marketers have guessed at people’s affinities from the barest of demographic, geographic, and contextual clues. We deduce that Midwestern men prefer pickup trucks and that people watching extreme sports like energy drinks, and then we spend billions advertising to these inferred affinities.
But today, we no longer have to guess. Every day huge numbers of people online tell us what they like. They do this by clicking a ‘like’ button, of course — but there are many other ways people express affinity: talking about things on Twitter and in blogs; reviewing things on Amazon and Yelp; spending time with content on YouTube (and telling us where they’re spending their offline time on Foursquare); and sharing things through both public and private social channels.
People’s rush to post their affinities online recalls another flood of data that began a decade ago: the explosion in online searches. John Battelle once described the data created by search as the “database of intentions,” which I’d define as “a catalogue of people’s needs and desires collected by observing their search behaviors.” In the same way, the result of all these online expressions of “liking” has created the “database of affinity,” which Forrester defines as:
A catalogue of people’s tastes and preferences collected by observing their social behaviors.
It's easy to see why people are excited about this database of affinity. Last year, Google turned the database of intentions in more than $50b in revenue for itself — not to mention countless billions more in sales for marketers. What if the database of affinity is similarly valuable?
But the database of affinity is fundamentally different from the database of intentions in several ways:
- Intent is expressed before a purchase; affinity is expressed after a purchase. Intent and affinity aren’t necessarily commercial in nature — but when they are, they’re expressed at different stages of the customer journey. Our data says people show intent when exploring products just before a purchase, and that they typically show affinity as a form of post-purchase engagement.
- Intent is mostly rational; affinity is mostly emotional. You buy a new car when your old one breaks down or is in danger of losing its resale value; that’s rational. But you prefer BMWs to Kias not because you need heated seats but because they make you happy; that’s emotional.
- Intent data expires; affinity data remains evergreen. A search you did for a flower shop in Ft. Lauderdale three months ago isn’t likely to be relevant today; you’ve filled that need by now. In contrast, the affinity you expressed for sunflowers three months ago is probably still relevant.
The upshot of all these differences? While the database of affinity is reminiscent of the database of intentions, marketers must use this data in a completely different way. Intent data that’s expressed just before purchase and describes a short-term need is perfectly suited to driving direct response for marketers. By contrast, affinity data that’s usually expressed after a purchase and that lasts for years is perfectly suited to supporting brand advertising campaigns.
We think the database of affinity, properly harnessed, can bring the same rigor and discipline to brand advertising that the database of intentions brought to direct response. But that’s going to require a combination of broad data, expert analysis, scalable buying tools, and ad inventory that creates brand impact. And no one company can offer that combination today.
We’re still exploring how the database of affinity will be built — and tracking how Facebook, Google, and dozens of other vendors are working to build it. We’ll publish a report on this topic soon, but in the meantime I hope you’ll join me at my speech on the topic at SXSW:
Sunday, March 10. 5pm. Four Seasons Hotel, San Jacinto Ballroom
Search Forrester's Blogs
The dynamics that will shape the future in the age of the customer »
Planning for innovation and risk in the wake of Brexit »
Forrester's CX Index
Predict how actions to improve CX will affect revenue performance.
Measure the customer experiences that matter most »
- Brandon Verblow (2)
- Brigitte Majewski (1)
- Carlton Doty (6)
- Cliff Condon (5)
- Collin Colburn (1)
- David Truog (2)
- Emily Collins (1)
- Erna Alfred Liousas (12)
- Fatemeh Khatibloo (1)
- James McQuivey (1)
- Jennifer Wise (10)
- Jessica Liu (9)
- Jim Nail (32)
- Joe Stanhope (2)
- Laura Ramos (64)
- Lori Wizdo (1)
- Luca Paderni (11)
- Melissa Parrish (51)
- Michael Barnes (1)
- Peter O'Neill (3)
- Rebecca McAdams (3)
- Richard Joyce (4)
- Rob Brosnan (1)
- Rusty Warner (2)
- Ryan Skinner (41)
- Samantha Merlivat (5)
- Samantha Ngo (2)
- Sarah Sikowitz (6)
- Shar VanBoskirk (119)
- Susan Bidel (6)
- Thomas Husson (141)
- Tina Moffett (7)
- Xiaofeng Wang (39)