Amazon’s approach to audience segmentation

Everyone on planet Earth knows Amazon is one of the largest e-commerce market players. Not surprisingly, they have segmented their immense audience. The article I’m linking to tells us about what criteria they used to find specific patterns for precise targeting. Under each segmentation category you will find the criteria and the specific Amazon target segment.

Amazon audience segmentation

In general, this illustrates how a huge marketplace sees you as a customer and adapts its offers to your unique needs.

1. Geographic audience segmentation

  • Region: Amazon covers over 100 countries.
  • Density: both urban and rural areas.

2. Demographic segmentation

  • Age: people aged from 18 y.o.
  • Gender: both males and females.
  • Life cycle stage: single, full-nest, empty-nest or solitary survivor people segmented by classic family life cycle.
  • Occupation: students, specialists and professionals.

3. Behavioral segmentation

  • Loyalty: ‘Hardcore’, ‘softcore ‘switchers’.
  • Benefit: products assortment, convenience competitive prices.
  • Personality: easy-going, determined, ambitious.
  • User status: non-users, potential, first-time, regular or ex-users.

4. Psychographic audience segmentation

  • Social class: working, middle, upper.
  • Lifestyle: resigned, mainstreamer, explorer, struggler, aspirer, reformer.

All in all, they segmented all their customers into the following categories, sorted by conversion simplicity.

Examine this infographics to see their market size, purchasing power and other vital attributes:

how amazon segment its audience

Another, more specific example of Amazon consumer segmentation is finding common groups using classical approach. We described it in our ‘Audience Segmentation: Basics, Examples, Cases’ article.

Amazon AWS

Besides segmenting their own worldwide audience, Amazon Web Services recommends getting acquainted with audiences to anyone who starts a business and wants to know their potential customers. After Amazon got extensive experience in working with different audience segments, it’s team started teaching newbies how to do that. Still, the approach differs slightly from the one they use for defining their own customers groups.

AWS segmentation example for a random Sports app:

1. Demographic audience segmentation

  • Males between 20 and 35 years old.
  • They speak English.
  • They use tablets running iOS 10 and newer.

2. Interest based segmentation

  • Football fans.
  • They often watch live score updates.
  • They prefer to be notified about available events & tickets.

So, if you manage to learn what your clients are interested in, you can create strong audience segments.

Combining interest based data with demographic data opens up new horizons in the audience segmentation process. Amazon gives this example on the AWS website:

  • Male Seattle Seahawks fans between 20 and 35 y.o.
  • Spanish-speakers, football fans living in the US.
  • iPhone users who would like to receive score updates.

3. Engagement based segmentation

You can also keep an eye on how actively customers use your product. Every business should focus on loyal and revenue-generating clients. Finding weak points where customers abandon your product without taking any active actions can boost your success.

This can be tracked with ordinary engagement metrics including:

  • Clients who registered the previous week.
  • Clients who had several sessions per day.
  • Clients who logged in and made a purchase during the last month.

Amazon recommends interacting with dynamic segments and comparing the cohorts of users from week to week. People’s behavior may change for different reasons and it’s vital that you make changes to move with the times.

What can we learn?

Amazon uses in-depth audience segmentation in all its commercial activities. Everyone has experienced how it works while shopping on their website. At least, getting suggestions for books on Audience segmentation, while researching this topic, is handy.

Anyway, what looked simple at first glance, appeared to be more complex and tangled. The huge amount of data they process to offer you a couple of suggestions makes these recommendations data-driven, and consequently, accurate.

This is another example of how services can target their audiences and make profit.

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