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Segmentation Models Are Outdated: How to Update Your Marketing Segmentation Practices

by Vignesh Clingam  |  
January 10, 2018

The 2016 US presidential election is perhaps the biggest and most public failure of segmentation models in recent memory. Most models not only predicted Hillary Clinton's victory by a comfortable margin but also ignored the demographic, racial, and geographic segments that predicted a different outcome—and propelled Donald Trump's victory.

Taking that failure as an example, and considering the overall outdated mode of assessment that segmentation models seem to be stuck in, we should admit that segmentation models must evolve.

Especially for businesses, an evolution can result in more effective marketing strategies and more successful marketing performance in our increasingly dynamic marketplace.

Why Segmentation Models Could Use an Update

Since the 1980s, companies have spent a lot of time and effort meticulously segmenting audiences on the basis of demographics and creating personas for marketing teams. However, those methods are increasingly irrelevant, for a few reasons.

  • First, there is no longer a competitive advantage to traditional segmentation. Every company is doing basic recency, frequency, monetary (RFM) segmentation using the same combination of behavioral or demographic overlay data.
  • Second, potential clients change faster than static models. Many segmentation models that are static by design or updated only a few times a year, at best, don't capture consumers' fast-changing behaviors, especially in high-velocity markets such as technology and retail.
  • Third, the models' predictive power doesn't translate to actual return on investment. Traditional segmentation approaches were developed for mass-media buying and are not very effective in an increasingly one-to-one addressable world.

Predicting consumer habits solely by focusing on identifiers such as demographics is misguided. Even formerly successful marketing campaigns based on these techniques are now struggling.

Tesco, for example, famously pioneered combining RFM segmentation with one-to-one targeting at scale in the 1990s through its Clubcard program. The program was aggressively digital for its time, rewarding shoppers for buying healthful food and showing loyalty. But because the model no longer provides a competitive advantage, Tesco's 20 million-member loyalty base is not producing growth today.

Benefits of Switching to Needs-Based Segmentation

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Vignesh Clingam is managing partner and SVP of experience analytics at marketing and advertising agency RAPP. Vignesh believes in the power of data to enrich the human experience.

LinkedIn: Vignesh Clingam

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  • by Dominiq Wed Jan 10, 2018 via web

    Thanks for this great article. I agree that old socio demo segmentation approaches are outdated.

    Another approach (than need base) is "tribe" based i.e finding large group of prospects who are actually connected in social networks around a need, a purpose or other "magnet people".

    Using network signals as part of the segmentation is also great for influencer approach, referrals and virality.

  • by Ayotunde Phillip Thu Jan 11, 2018 via mobile

    Dynamic nature of marketing stipulates no strategic options can sustain market in long run, so, need base segmentation is welcome idea but more consumers behavioural system outlook understanding may assist in more approach to market segmentation.

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