Customer Segmentation for eCommerce: Models, Examples, Guidelines

What is customer segmentation?

Broadly speaking, customer segmentation is the process of dividing your customer base into specific groups based on common characteristics. These typically include demographics, purchasing habits, interests, and behaviours. The objective is not merely to analyse which type of customer is more profitable for your business (though this is one crucial outcome) but also to build precise predictions about the purchasing preferences of every segment and devise relevant offers that will maximally appeal to each segment.

How is customer segmentation in eCommerce different?

Almost every business undertakes customer segmentation. Even your local body shop probably segments customers into luxury car drivers who can afford to spend a lot and offers extra add-ons every time they visit, versus frugal drivers who only spend the bare minimum, and if possible, use public transportation. eCommerce has an undeniable advantage in this context. It has access to a plethora of data about its customers. From browsing patterns to purchase history, this data forms the basis for precise and highly detailed customer segmentation. And precision is vital: the more detailed your segments, the better your purchase predictions, and the more relevant your offers will be.

Precision is vital: the more detailed your segments, the better your purchase predictions, and the more relevant your offers will be.

Grasping the power of accurate customer segmentation

If you're unsure whether your business can benefit from customer segmentation, here's a quick overview of the benefits it offers.

Accurate customer segmentation outcomes for Marketing:

For instance, if you run an online tech store, you can segment your customers into 'tech enthusiasts,' 'casual tech users,' 'deal hunters,' and 'Apple brand fans.' By providing each segment with pertinent product recommendations and tailored discounts, your marketing team can create highly personalised marketing campaigns. These typically result in a 10% better conversion rate and a 20% increase in sales (source).

Accurate customer segmentation outcomes for Inventory Management:

If you've been in business before 2023, you know how inventory management has evolved this year. Most eCommerce companies have grappled with excessive inventory and the inability to liquidate stock due to two factors: mistakes in demand predictions and shifts in the price elasticity of demand. In layman's terms, price reductions have stopped being as effective as they once were.

According to Aimondo's research conducted on 500 UK shoppers, personalised discounting can still be an efficient strategy for faster stock turnover. Shoppers who receive discount offers aligned with their purchasing habits are more likely to not only make a purchase but also check that seller's website first when they need to buy something else. As mentioned above, the only viable way to curate personalised deal offers is through accurate customer segmentation.

According to Aimondo's research conducted on 500 UK shoppers, personalised discounting can still be an efficient strategy for faster stock turnover.

Accurate customer segmentation outcomes for Sales Teams (if you have a B2B channel as well)

Many eCommerce companies also have a B2B channel to cutter to big clients. Having a proper customer segmentation practice in place help sales teams tailor their pitch, and craft more accurate B2B pricing workflow emphasising products or services that are more relevant to a particular segment. Also it increases the probability of purchase, as sales team get better insights into what products to upsell, when and how. Not to mention the value of customer segmentation for B2B pricing! This one is hard to overestimate.

The impact of accurate customer segmentation on overall business results

Accurate segmentation leads to improved customer satisfaction, leading to repeat business and increased revenue. As mentioned above, our survey conformed this: the majority of respondents admitted that they would re-purchase and prioritise sellers that are eager to offer personalised experience (based, as we now know, on accurate customer segmentation).

Basic Principles of Customer Segmentation

At its core, customer segmentation is, in essence, an exercise in categorisation. Perhaps you're already applying some form of it by classifying customers based on their spending history, such as through a loyalty program where they're assigned to a tier qualifying them for specific discounts. Maybe you're sorting customers based on their proximity to your warehouse to discern which ones yield a better profit margin because delivering to them doesn't significantly inflate logistics costs.

A more intricate approach to segmentation employs a data analysis method known as K-means clustering. Primarily, K-means and K-median are the two predominant algorithms used to segment data into distinct clusters based on distinct features or attributes, laying the groundwork for today's "personalisation."

K-means Clustering:

•           Often applied to continuous data.

K-median Clustering:

•           Typically used to pinpoint outliers.

Implementing Customer Segmentation Using K-means Clustering

Here's a quick guide:

•                             Data Collection: Amass pertinent customer information.

•                             Data Pre-processing: Refine and pre-process this data.

•                             Determining Number of Clusters (K)

•                             Interpreting Clusters: Delve into each cluster to grasp the distinct attributes of every segment.

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Data Needed for K-means Clustering

•           Customer Demographics like age, gender, and location.

•           Purchasing Behaviour including purchase history, frequency, and average spend.

•           Online Engagement metrics such as click-through rates and time spent on site.

•           Customer Feedback and Reviews

•           Loyalty Program Data (when relevant).

Identifying the correct number of clusters (K) is pivotal. For those unfamiliar with data analytics, consider it akin to discerning distinct groups at a social gathering based on observed commonalities. Various methods, such as the Elbow Method and Davies–Bouldin Index, are employed to determine the optimal K.

Using Advanced Data Analytics Software for Customer Segmentation

For businesses with a limited product range and customer base, tools like Excel spreadsheet templates might suffice. For example, you can use this Excel spreadsheet template that builds 4 and 5 clusters out of 100 wine seller’s customers.

However, for larger datasets, consider hiring a data analyst proficient in Python. This may mean more responsibilities for your tech team. Also, constant customer flow can make dynamic customer segmentation challenging. It could require a dedicated department for data analytics.

An alternate route is outsourcing and leveraging technology. Platforms like Aimondo’s Ai-Price Optimisation & Management can simplify customer segmentation, offering dynamic updates and facilitating personalised pricing and marketing strategies.

Want to learn more about customer segmentation and clustering? Join our Pricing Excellence Free online course!

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