An Upside Down Approach for Successful Location-Based Mobile Advertising

    An Upside Down Approach for Successful Location-Based Mobile Advertising

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    I was reading an article in Adweek about what work agencies recently did for the Google search app, discussing the challenges of location-based mobile ads and the difficulty of pinpointing the exact person with the right type of ad on the fly.

    The agencies discussed their use of “data outside the app to personalize results to particular users”, citing data including weather, time, location, and photos and it later briefly mentioned targeting specific groups of users based on “mini data profiles.”

    This, to me, seems upside down.

    How exactly do weather data, time, and photos deliver personalized results? It’s the data profiles generated, ironically, using signals from apps matched against precise local venue, event, and activity data that provide the insights needed to actually personalize results.

    And the piece went on to discuss the need to scale this type of targeted approach. I would argue the opposite. If location-based mobile ads—or any digital ads for that matter — are being served at scale using data and tactics that aren’t actually personal then consumers will rebel and ad blocking, that has many in the industry rightfully concerned, will only be exacerbated.

    So, I recommend we back up a bit in this context and re-examine what insights brands need from mobile in order to personalize—they need to know the wants and needs of customers based on where they go and what they do as they live their daily lives.

    And as for scale? This begs the old “quality versus quantity” argument and for brands needing to succeed in a hyper-competitive market, quality wins out every time.

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