Hyperpersonal and the interest graph vs. hyperlocal
Hyperlocal is a strategy that defines perceived primary audience information desire as emanating geographically. In reality, it is also a strategy rooted in the concept that advertisers can more successfully sell their products by refining their targeting to where their potential customers live.
These assumptions inherently morph into the same blunt force marketing silos we call demographics. Is a female aged 25-54 with children making over $100k a year more likely to buy your product? Or should you instead be targeting a 33 year old married woman with a five year old, a six year old who loves Macy’s, but never sets foot in Target?
Is a person in this age of e-commerce and instant mobile price checks likely to buy a product simply because it is physically near them? Likewise, does that person necessarily even care about the community they live in?
Most of America does not resemble the dense, tight knit communities that exist in the North Eastern metropolises and the Pacific Northwest. A preponderance of gated communities, sprawling suburbs and far flung exurbs dot the landscape, with nary more community than an eight lane road flanked by strip malls and chain restaurants.
Hyperlocal as a driving force requires community, and community is not equally present across this land. So while that approach may thrive in some places, in others it will not be as impactful for one reason or another and can only be a small part of the overall plan.
What then should a local publisher do to turn itself into a utility for a local audience and local advertisers alike? With the rich data sets provided through social media API’s, local media companies need to tap into the interest graph and begin a full throttle jump into creating hyperpersonal experiences for their audiences.
A typical local news consumer may live in a specific town, but be specifically interested in a certain type of crime, as well as golfing. Instead of surrounding a crime story with generically related content, those interests can be harnessed to surface relevant content to that user based on actions users actually take. So they may read a crime story, but be served golf and science content based on what they have read in the past, identified as interests based on their social profiles, or purchased on an e-commerce site.
The social graph can recommend what others in your social group are reading, but the interest graph is the key to serving up customized and engaging content that will have a higher likelihood of being consumed.
Interests and habits match up more neatly with advertising categories and consumer action than simple geography and age. Harnessing the data and effectively merging it with a smart content strategy is the key to driving readership and conversion rates.
