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Level-up your e-Commerce segmentation

Updated: Jan 10, 2021

Traditionally, there is 4 type of main segmentation - demographic, psychographic, geographic, and behaviour. Today's consumers are engaging with brands through multiple touchpoint - using more than 2 devices to search, to learn, to buy and to watch every day and it all happens online. According to, it is estimated that 95% of purchases will be made online by 2040 — ecommerce is opening the doors of opportunity to businesses. With traditional segmentation, finding right opportunity at the right time becomes challenging and customers are more demanding than ever.

Being relevant and responding in a timely to your customers' action is the key to success, it enables opportunities for cross-channel personas that will boost sales and increase ROI. Well structured data layer, right tool helps marketers easily create multiple micro segments to target the right message to the right customers at right time. However, marketers we still struggle on the point how to differentiate and build segments for e-commerce audience. Depending on your customer data collects, there is tons of attributes you could create segment and it is overwhelming. One of the important rule to remember when you are building a segment, don't get too specific and narrow down. At the end, we are running a campaign and segments need to be big enough at least 100 audience in per segment.

For example, let's say you are creating a segment for your e-comm flower shop, segments like "customer who viewed Valentine's Flower" more actionable than "customers who purchased Rose in last 3 days".

To help you to build effective segment, here I have highlight 3 steps that will guide you:

  1. Align your strategy and objective: You don't want to start building segmentation without aligning with your strategy;

  2. Identify the tactics: Let's say your strategy is to increase ROI by 3% in next 6 months, you would need to list down how to achieve, what type of campaign do you need to run etc.;

  3. Once you planned your tactics, it is much easy to build segmentation as long as you have right data.


As earlier mentioned, you might have collected wealth of data and it has many attributes. It is absolutely necessary to have enough data but finding and focusing on the right one is always take a lot of time. So based on my experience working with e-commerce brands and success rate of the segmentation, I listed example segmentation for your e-commerce audience:

  • High Spender customers - those who spend more than avg. CLV (it's good to specify the timeframe i.e. last 6 months);

  • Economical customers - buying only as much as they need at the moment – one bag of dog food, just a pair of leggings or a bottle of shampoo;

  • First time customers - customer made their first purchase;

  • Indecisive customers - customers who may spend more time to make decision;

  • Dormant customers - who never purchased in last 6 or more months;

  • Coupon Lovers - only purchase with coupon and never pay full price;

  • Inactive customers - customer who purchased more than 6 months;

  • Warm prospects - Never bought and signed in the last 3 months;

  • Loyal customers

  • Marketing Day - Bought only on marketing days - Black Friday, Cyber Monday, Christmas, or Singles Day;

  • Cart abandoners - those who left item in the card;

The segments that related to their spending habit, RFM - Recency, Frequency, Monetary model is commonly used.

Recency - day since last time made a purchase

Frequency - total number of purchases

Monetary - total spent

RFM model numerically ranks a customer in each of these three categories, generally on a scale of 1 to 5 (the higher the number, the better the result). The "best" customer would receive a top score in 3 categories: how recently they've made a purchase, how often they buy, and the size of their purchases.

Thanks to marketing automation tools, that easily helps you to create segments by using RFM model so that marketers can focus on important tasks such as planning and optimising their marketing performance.


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