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The in-depth back story behind market segmentation in SA will feature as two articles in this Ebony+Ivory blog series for marketers.

Below is a useful summary of key points.

“Post-Amps landscape: play it as it lies” takes us on a journey through the life of Amps (“All Media and Product Survey”) from its first release in 1975 to the April 2016 final release — covering four decades in which its data was the backbone of marketing and advertising strategy as well as media planning in SA.

  • Released on a rolling basis about every six months, Amps’ comprehensive demographic database profiled adult consumers (15+ years) nationally and was overlaid with audience data covering all forms of traditional media, which made it indispensable to media strategists.

  • As well as audience data, Amps provided wide-ranging insights on consumer behaviour, life stage, attitudes and lifestyle, with multiple socioeconomic indicators ranging from motor vehicle and home ownership to activities, interests and retail shopping behaviour.
  • The survey data was supplemented by the “Television Audience Measurement Survey” (Tams), often referred to as people meters, and the “Radio Audience Measurement Survey” (Rams), known as radio diaries.

  • Amps included purchasing and consumption information on a wide range of product categories, brands and retail outlets.

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In the post-Amps age, there are multiple surveys from which to consolidate media plans and marketing strategies (see the full list in Ebony+Ivory blog). This leaves marketers and media professionals asking, “How do we navigate our way through this multi-methodology multi-source data pool to create a collective meta-analysis for market and media segmentation?” 

The third article “LSM to SEM: Finding the golden cord” explains the transition from living standards measure (LSM) to socioeconomic measure (SEM).

Series co-author Gordon Muller says that at a functional level SEM represents the most recent iteration of a national socio-economic segmentation model and it fulfils the same macro-segmentation function as LSM.

  • SEM uses 14 variables to compute various data points and clusters. Nine of the 14 variables deployed as predictors in SEM are totally consistent with Amps LSM.

  • However comparatively, for instance, SEM is not as heavily dependent on households’ durables and technology items, as the historical LSM had been.

  • Compared with Amps LSM, there is a stronger focus on fixed structural items in SEM, such as household building materials and access to amenities such as water and toilets.

  • Other lifestyle indicators, such as home security systems and ownership of a motor vehicle, are built into the segmentation model.

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Did you miss the Ebony+Ivory series introduction?
Click here >>>

This article was paid for by Ebony+Ivory.


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