Customer segmentation is how businesses divide their audience into smaller subsets or different groups of customers, based on common characteristics like demographics, psychographics and behavior.
It’s one of the most important marketing concepts for business owners and marketers to understand.
Studies have shown that marketing campaigns sent to segmented customers can result in a 200% increase in conversions.
But here’s the kicker…73% of ecommerce store owners don’t use it!
That’s why we’ve written this article. Today, we’re going to cover:
- What are the benefits of customer segmentation?
- Type of customers segmentation:
- How to do customer segmentation
What are the benefits of customer segmentation?
In short, better, more effective marketing campaigns.
Evergage surveyed brands that used customer segmentation in their campaigns and found that:
- 64% reported an improvement in customer experience.
- 63% reported increased conversion rates.
- 55% reported an increase in visitor engagement.
And that’s not all. Let’s take a look at four benefits of customer segmentation…
Create relevant messages for your audiences
It’s easier to create targeted messaging that resonates with your audience when you don’t have to talk to all of them at the same time.
For example, if you own an online pet store, you wouldn’t want to send all pet owners the same message – a snake owner isn’t going to have the same interest in taking their pet for a walk that a dog owner would!
When you make your marketing more relevant and less one-size-fits-all, you create a better customer experience. And that means more loyal customers, better retention, less churn and higher revenue!
Choose the best marketing channels
Using the same example as above, many snake owners hang out on the sSNAKESs.com forum (yes, this is an actual site and yes, it has the greatest URL ever created), so the best way to reach them may be a marketing partnership.
But dog owners love Instagram (#dogstagram!), so it might be best to whip up a paid social media campaign in Facebook’s Ads Manager.
Sell more products
Let’s be frank here, the main reason marketers obsess over customer segmentation is because it’s a money-maker.
When Lenovo segmented their customers into ‘profiles’ based on household data, like what media they consumed and where they shopped, they were able to increase their conversion rate by 40% and revenue per visitor by 25%
It can help you identify opportunities for upselling and cross-selling, test different pricing options and even give you ideas for new product development that the particular segment is desperate for.
Identify profitable customers
As a business owner, you have limited resources – there are only so many hours in the day and your marketing budget isn’t exactly infinite.
Some customers may be worth $10 to you, others may have a Customer Lifetime Value of $1,000. So don’t make the mistake of spending an equal amount of time and money on each and every customer.
Find your best customers by segmenting your customer base. Then develop a marketing strategy that concentrates on your most profitable segments to build customer loyalty and increase Customer Lifetime Value.
Types of customer segmentation
There are four types of customer segmentation:
Demographic data segmentation groups customers using traits like:
- Job title
Demographic data is important to know because it impacts how businesses market to each customer segment.
Take a fashion retailer considering an email campaign, for example. They’re likely to showcase different products depending on whether customers are male or female, how old they are and how much disposable income they have.
Here’s a great example from Loft, who have segmented by job title to reach teachers.
They’ve chosen school-appropriate clothing like a blazer and trousers, and given a discount when a valid Teacher ID card is used at checkout.
Google Analytics records a lot of demographic data, but you can improve your data using survey tools or looking through U.S. Census Bureau reports.
Geographic data segmentation groups customers by location.
Customers can be segmented by:
- Where they work, live or vacation
- Continent, country, state, city or ZIP code
- Climate and season
- Type of area (e.g. rural, suburban or urban)
Geographic segmentation is important because customers in different locations typically have different needs or characteristics.
For example, a footwear company will probably sell more flip flops in a warm place like Los Angeles than in New York.
Take a look at how McDonald’s advertises in different countries.
In their Saudi Arabian ad, they reference that it’s considered rude for a child to eat before an elder, something that wouldn’t necessarily resonate with audiences in other countries.
Psychographic segmentation groups customers based on their psychological characteristics, like:
- Social status
Psychographic data is important because it looks at the why rather than the what.
If you sell home furnishings, you’ll likely have more success by targeting based on psychographic data like whether they enjoy decorating their home than something like gender or age alone.
An outdoor brand would cater differently to a young family of campers than someone who views themselves as a ‘rugged adventurer’. They’d use different terminology, marketing messages and products.
Patagonia does a great job of segmenting customers by their interests and activities, like surfing, climbing, trail running, mountain biking and many more.
Behavioral segmentation groups customers based on their buying behavior or stage in the customer journey, e.g. high-spenders, discount shoppers or customers at risk of churning.
Behavioral segments are some of the most powerful targeting options available to ecommerce merchants, which is why it’s what we focus on at Segments.
Behavioral segmentation is so important because rather than using predictions about customers (e.g. because they’re interested in the outdoors, they might like our range of waterproof jackets), businesses can use customers’ actual behavior to understand what stage of the customer journey they’re currently in.
Instead of relying on demographic and psychographic profiles, our software analyzes your customer data set and automatically generates different segments for you, as well as any other custom segments you want to create.
How to do customer segmentation
Segmentation begins by gathering customer data. Ecommerce stores will generally have access to information like:
- Customer name.
- Email address.
- Number of orders.
- Products bought.
- Marketing channel source.
- Discounts used.
- Emails opened/clicked.
But an online store is unlikely to have other information like job title or marital status in their CRM, unless they gather it using another method.
While large businesses have had greater access to segmentation and big data analysis in the past, the new wave of analytics apps means even a small business or startup can segment their customer base.
Here are some of the most common ways to segment your customers and potential customers:
- Active new sign-ups
- Churned customers
- High Customer Lifetime Value (CLV) customers
- Loyal customers
- Most likely to churn
- One-time purchasers
- Price-conscious customers
If you’re not using a tool like Segments to automatically generate your segments (and we think you definitely should be, especially with our free 14-day trial!), you can use the RFM model to do it yourself.
The RFM model of customer segmentation
Check out our article on RFM analysis for an in-depth explanation, but here are the basics…
RFM stands for Recency, Frequency and Monetary.
- Recency – how recently they last purchased. The assumption is that the more recently they bought, the more engaged they are and the more likely they are to make another purchase.
- Frequency – how many purchases they’ve made over the given time period. The more orders they’ve made, the more valuable they are to you.
- Monetary – how much they’ve spent. The higher that figure is, the higher their value.
RFM assigns a value to each customer based on how they score in these categories.
The highest value customer will be someone who has just made a purchase (Recency), has bought many times before (Frequency), and has a high Average Order Value (Monetary.)
Check out this article for step-by-step instructions to do your own RFM analysis.
Now let’s take a look at those important segments we identified earlier and work through the more complicated ones.
For each segment, we’ll give our definition and instructions to create it manually.
Active new sign-ups
Definition: Customers who have subscribed to your email recently, but haven’t bought anything yet.
Method: Create a segment in your email provider that selects for new email subscribers where number of orders = 0.
Next steps: Subscribers are most engaged within 48 hours of signing-up. Create a welcome sequence that includes details like your brand values, how customers can engage with you, customer service options, and a selection of your best-selling products.
Definition: Customers who have not purchased for more than double the average days between purchases for their buyer segment.
Method: Pull your customer data from whichever ecommerce platform you use (e.g. Shopify), then work out the average number of days between purchases. You can do this by picking a sample of your data and counting the number of days that elapse between each individual customer making their first order and second order, and then taking an average of those results. Once someone hasn’t bought for double that figure, they’ve churned.
Next steps: Create a win-back campaign to bring these customers back. Try re-engaging them by asking them to complete a micro-conversion, like reading your latest blog or social media post, and then move to a discount ladder if that doesn’t work. Don’t be afraid to offer deep discounts at this point, as otherwise it’s unlikely these customers will shop with you again.
High Customer Lifetime Value (CLV) customers
Definition: The total amount of money a customer is predicted to spend over the entire duration of their relationship. You can divide these into three parts (high, medium and low CLV) for ease of use.
Method: Segment your customers using RFM analysis and then calculate Customer Value. Estimate the Average Customer Lifespan and multiply that by Customer Value. For step-by-step instructions, check out our ecommerce guide to Customer Lifetime Value (CLV).
Next steps: Spend most of your time on your high CLV customers and trial using this segment as the basis of your Lookalike Audiences on Facebook. When Growth Cave started using high-value customers to build their LLAs, they saw a 44% improvement in ROI over their previous all-customer LLAs.
Definition: Customers with three or more purchases.
Method: Finally, a simple one! Pull your data and look for customers who made three or more orders in a recent time period. What time frame you choose will depend on your industry – for example, a furniture retailer should be looking over a longer time period than a fashion or cosmetics store.
Next steps: We’re not trying to change the behavior of customers in this segment, we’re trying to understand them, in order to use those learnings on our other customers.
Look for answers to these three questions:
- Which products did they buy and in what order?
- What marketing channels brought them to your online store?
- Which marketing campaign converted them?
Once you have that information, double down on whatever worked.
Most likely to churn
Definition: Customers who have not bought during the average days between purchases, but who not yet churned (double the average days between purchases.)
Method: Calculate how many days the average customer waits between purchases. Anyone who hasn’t purchased after this milestone has been reached is likely to churn.
Next steps: Use marketing channels like email, social media and direct mail to incentivise them to complete another purchase before they churn completely.
Definition: Customers with exactly one purchase.
Method: Pull data from your online store and create a filter in the ‘number of orders’ column for ‘= 1’.
Next steps: Incentivize them to make a second purchase (email automation is your best friend here.) Segments users can use the ‘Product Analysis’ functionality to see which products are normally bought as a second purchase and use those in their email campaigns.
Definition: Customers who only buy low-priced items or buy when using discounts.
Method: Analyze your order data to find customers who have a low average order value or who have only purchased using discounts (Shopify’s ‘Sales by discount’ report is helpful here.)
Next steps: Consider the impact these customers are having on your business, particularly if you offer free shipping. Which marketing channels or campaigns are attracting these customers? Do these customers take up too much time from a customer service perspective? Are they right for your business or would you prefer to attract a different customer segment?
Final thoughts on customer segmentation
Today, we’ve covered why customer segmentation is so important, how to segment your customers manually (and how Segments can do it for you) and the most important segments for any online store owner.
If you want to understand other important ecommerce concepts to take your store to the next level, then check out our guides below!
Other ecommerce guides
Want to learn how to understand and improve other important metrics and benchmarks for your online store?
Read our in-depth ecommerce guides here:
- Average Order Value
- Cohort Analysis
- Customer Churn
- Customer Lifetime Value
- Customer Retention
- RFM Analysis
- Shopify Analytics