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[Guide] Customer segment analysis: Create Better Offers

Your customers are not identical. They have varying desires, needs, willingness to pay, and risk tolerance.

To succeed, eCommerce stores must be able to create and present relevant, personal offers. In today's guide, we break down a fundamental strategy to understand and engage with customers on a personal level: customer segmentation analysis.

If you'd like to skip straight to the examples, click here. Otherwise, let's start with what customer segmentation analysis is.

What is customer segmentation analysis?

Customer segmentation analysis is a business process that groups customers according to a set of traits. There are four broad categories of customer traits commonly used. They are demographic, psychographic, customer journey, and behavioral traits.

Customer segmentation relies on customer data. Additionally, customer identification capabilities is essential to tie data to customer records. 

Today, eCommerce stores can use customer data platforms to create high fidelity customer segments and power numerous personalization strategies.

4 Basic customer segmentation techniques

There are numerous customer traits used in customer segmentation analysis. However, as an overview, we can broadly group them into four basic types.

1. Demographic customer traits

Demographic segmentation uses demographic  traits to group customers. The most commonly used demographic characteristics include

  • Age: Segment customers based on age brackets or specific ages. Includes birthdays.
  • Gender: Segment customers based on male, female, or other. 
  • Race: Segment customers based on what race the customer is.
  • Ethnicity: Segment customers based on what ethnicity the customer is.
  • Income level: Segment customers based on which income bracket they fall into. 

While demographics can be useful for certain products, this type of segmentation does not do a good job of capturing individual intentions. Instead, it infers intention based on the demographic trait used to group customers.

On balance, this makes demographics less predictive and useful than other types of customer segmentation analysis. 

Above, uses where a customer lives to create personal experiences.

2. Psychographic customer traits

While demographics deal with outwards physical traits, psychographics deal with qualitative qualities about who a customer is. Examples of psychographic segmentation includes

  • Personality
  • Values
  • Interests
  • Lifestyles

3. Customer journey based traits

Customer journey based traits uses pre-defined customer stages as a way to group customers.  Typically, each stage is defined by a set of behaviors, making it a unique form of behavioral segmentation.

We've written extensively on how eCommerce companies can implement customer lifecycle marketing here.

Above, Southwest uses customer journey customer segmentation to trigger personalized messages and provide numerous upsales such as hotel stays.

4. Behavioral customer segmentation

Behavioral segmentation groups customers based on what actions they take. Customer actions reveal more about customers' intent and preferences than any other type of segmentation technique, making behavioral segmentation the most predictive and effective.

Examples of behavioral segmentation includes

  • Onsite engagement metrics: Includes customer behaviors such as first time visiting a site, adding an item to their cart, or viewing product categories or PDP pages.
  • Purchase history: Includes lifetime purchases as well as more nuanced metrics such as most recent purchased products.
  • Failed actions: This includes any actions that you expect a customer to take but they do not. This can include not opening an email to not engaging with a specific offer.

Customer segment analysis examples

1. Use customer life events to trigger retention campaigns (ft. Target)

Often, products are designed around specific life events. One clear example is maternity and baby-related products.

Target offers an excellent example of using behavioral data to place customers in specific life stages. Using past purchase data, they are able to predict the ongoing needs of their customers. 

With this knowledge, marketing teams are able to create specific retention campaigns to drive repeat purchases with relevant offers. In this case, they are able to offer a $10 gift card if you purchase a recurring staple, baby formula.

2. Identify returning customers and create personalized web experiences for them ft.

One of the easiest behavior based customer segments you can implement is new vs returning customers. We showed how important returning visitors are in our study here.

While there are abundant resources on welcome campaigns, there are very few examples of creating personalized experiences for returning customers. offers just that. When you return to their site, they immediately welcome you back and offer their strongest value proposition to sign in - "Sign in to see deals of up to 50% off". 

This helps maximize ability to identify anonymous visitors into known customers and enrich their customer profiles.

3. Create relevant offers to move customers to a new customer segment ft. Starbucks

Our next customer segment analysis example comes from Starbucks. We feature Starbucks often because of how well they execute. 

Here, they present specific offers to non Rewards Members to try and convert them to their loyalty program. Starbucks knows the value of these members, who generate well over half of all Starbucks revenue and serves to help unify data across channels.

Above, the offer is time constrained to a week to manufacture urgency and promises a free drink in exchange for signing up.

4. Geographic customer segmentation is fantastic at content personalization. In fact, we wrote up an entire case study breaking down how they increase conversions.

Here, they use geographic based and behavior based customer segments to fuel their content personalization. 

Next steps...

Customer segmentation analysis is a necessary step to create high converting offers.

Unfortunately, connecting data across channels is a significant hurdle in implementing effective segmentation.

If you would like to see how Barilliance helps hundreds of customers connect their customer data and create relevant, personal offers across channels, request a demo here.

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