Performing a manual customer segmentation analysis is better than none.
But in a world where automated tools are everywhere and help us improve the way we’re doing business, why should customer segmentation analysis be an exception?
Using automation for this process is as important as sending a confirmation email after a client placed an order or asking for feedback from your customers. At the same time, the manual approach has multiple shortcomings.
What is customer segmentation analysis?
Customer segmentation analysis is a process that helps you measure the performance of your customer groups and evaluate the effectiveness of your segmentation technique.
Regular customer segmentation analysis helps you:
- Monitor how well each segment is performing compared to the goals you’ve set at the segment level and at the company level;
- Identify the most valuable segments and focus on them;
- Improve the Ideal Customer Profile based on your most valuable and loyal customers;
- Manage problematic or underperforming customer segments;
- Adjust your messages so you can find the best approach for each segment;
- Find gaps in your segmentation technique and decide if you need to change your approach or get more granular.
Analyzing segmentation is as important as analyzing the results of any other process in your eCommerce store.
How would you know if a paid campaign was successful if you don’t evaluate results?
How would you know what customers think about their experiences if you don’t ask for their feedback?
How would you know what product to push if you don’t know your top-performing products?
For an effective customer segmentation analysis, you need to check these four steps:
- Identify or make a list of all of the customer segments in your CRM, such as RFM segments;
- Define the criteria for your analysis such as Customer Lifetime Value, Net Promoter Score, Order Return, Segment Growth;
- Analyze each group’s performance by looking at KPIs;
- Formulate your conclusions and suggest an action plan.
Although we’re talking about analyzing data that is totally in your control, there are multiple reasons why performing customer segmentation analysis manually is not recommended for eCommerce businesses.
The Importance of Customer Segmentation Analysis
With Customer Segmentation, you can create relevant campaigns, push better products, and even optimise your entire market positioning.
Yet, without analysing your segmentation data, the whole act of segmentation becomes futile; the analysis is the next natural step of segmentation, as it provides you with the actionable insights you need to move the needle toward the right direction.
Segmentation Analysis also helps you personalise the customer journey from A to Z. Think about the creatives you’re using in your Ads, the products you push, the post-purchase treatment. All can be tweaked to satisfy the needs of various market segments.
It’s interesting to note that the importance of customer segmentation grows exponentially as your business grows. With more customers joining your customer base, it’s becoming more challenging to understand what customer success means and how to achieve it.
Benefits of Customer Segmentation Analysis
In short, Customer Segmentation Analysis provides the vision you lacked to deliver better customer experiences and contributes greatly to customer loyalty.
For example, if you deploy need based segmentation and you group customers according to their needs and problems, you can be of better service to them.
It’s one thing to deliver a universal solution, and a whole different feat to deliver custom solutions that empower your customers according to their evolution in the customer journey.
No matter the type of customer segmentation you use, the insights coming from the analysis can push your business forward.
Other note-worthy benefits of Segmentation Analysis include (but are not limited to):
- Better targeting
A customer segmentation model like the RFM segmentation highlights those customer clusters who are bringing you the most revenue.
Obviously, you’d want to focus the better part of you efforts into acquiring more customers similar to your power segments. So you’ll create audiences that resemble power customers, thus targeting more efficiently.
- Better ROI
Following the previous idea, you can understand why segmentation brings better ROI. You can either base your audiences on the attributes of a power segmented customer, or try and get everyone in the acquisition pool.
However, not all customers will spend the same in your store.
For example, if you’re running a sports eCommerce, your gym rats will spend more on a monthly basis on equipment, supplements and clothing, while a newbie will only buy the bare minimum worth of products.
It’s in your best interest to target the type of customers who are interested in spending more – and who will generate higher ROI per conversion.
- Better engagement
Customer engagement is a great indicator of the effectiveness of all your marketing and CX efforts. With segmentation analysis you can get all the customer data you need, so you can drive even more engagement: use the findings to customise content for each of your customer segments.
The idea is simple: instead of going out with generic messages to please the masses, you’re actually creating hyper-targeted content, that reaches those interested in what you have to say.
Great engagement is mostly helpful when you need customer feedback on your products or services. Some marketers are finding it difficult to convince their customers to share feedback. However, with great engagement you have more chances of getting valuable insights from your customer base.
Why is manual customer segmentation analysis bad for your eCommerce store?
You are wasting precious resources
If you want to measure your segmentation performance and effectiveness, you need to analyze customer segments regularly.
It takes a lot of time, effort and concentration to perform this analysis manually. Just think about all of the customer segments you’ve defined and the fact that you need to export customer data for all of them to evaluate each KPI in your analysis criteria.
After you aggregated all this data manually, you have to extract insights and conclusions from your analysis. That alone takes a lot of time and should be performed with full focus, not after hours spent preparing spreadsheets.
You are prone to errors
The more data you need to aggregate, the more you are prone to errors. You certainly don’t want to make decisions based on incorrect data.
Automated alternatives for eCommerce businesses are becoming more accessible, so don’t waste your time on manual work and focus on tasks that cannot (yet or ever) be replaced by an automated tool.
Spend less time on aggregating data and more on important decisions for your eCommerce growth, like defining the campaign that will reactive high-value dormant customers that you shouldn’t (and cannot afford to) lose.
It becomes obsolete when it isn’t performed regularly
In eCommerce, things are changing fast and that can feel like both a blessing and a curse. When it comes to analyzing eCommerce customer segments, it’s clear that you cannot make decisions based on reports generated three months ago.
Let’s say that in the analysis made three months ago, one of the main conclusions was to reactivate a segment of high-value customers that haven’t bought from you in a while. If the campaign was successful, it generated a change in customer distribution that will only be reflected when performing another segmentation analysis.
You’re not acting on time
Because it involves so many resources, you cannot perform it as often as possible when using an automated customer segmentation analysis tool.
It means that you might miss opportunities for your e-store or you might identify the source of a problem later than you would if you automated the entire process.
For example, you might have noticed that there are many order returns for a popular product among one of your high-value customers. Suppose you have to go through a manual process to aggregate data and make correlations between variables. In that case, you won’t address the complaints in time and lose trust from valuable clients.
It’s harder to maintain consistency and traceability
When you’re analyzing customer segmentation manually, you’re more tempted to make changes in your approach than you are when you’re using an automated solution.
One of the crucial steps in your customer segmentation analysis is to define your analysis criteria. Sticking to that set of criteria is also very important for observing evolutions in your segments over time.
This is not possible if you are inconsistent and change the reference system over and over again. It is as bad as changing segmentation techniques without taking time to observe what results can be obtained by choosing, let’s say, behavioral segmentation.
An example of customer segmentation analysis – manual versus automated
How to conduct customer segmentation analysis
You can change the way you’re segmenting your customer base depending on your end goals.
Ecommerce businesses are using all types of segmentation models – demographic, behavioral, psychographic etc.
Let’s say that your most important goal for the next 12 months is to improve customer retention rate, so you choose RFM segmentation, a model used by many eCommerce companies that share the same goal.
By using RFM segmentation, you define your segments based on three factors:
- Recency – when was the last time the customer purchased something from your store;
- Frequency – how often does that customer buy from you;
- Monetary Value – how much does that customer usually spend in your e-store.
This segmentation model’s advantage is that it is based 100% on first-party data, so it’s easy to gather data.
You can perform RFM analysis:
- Manually – by exporting your customer database in a spreadsheet and ensuring that you have gathered values for all RFM variables – Recency, Frequency and Monetary Value.
- Automatically – by using a tool like Reveal that performs automatic RFM segmentation and generates reports that allow you to analyze your RFM groups from multiple angles such as Revenue vs Margin by RFM group or Net Promoter Score by RFM group.
Drive meaningful change in your ecommerce
Monitor & improve what matters.
Regardless of your approach, you have to set the scale and scores for each variable based on your eCommerce store’s particularities, such as the industry you’re part of or your customer lifetime cycle.
For both manual and automated RFM analysis, you can go with the default customer segments or you can group and name them your way as long as your representation makes sense for your business in the long term.
If you don’t know how you should group your customers based on their RFM scores, we suggest you take a look at the RFM segments we recommend as default to our clients using the RFM segmentation features in our tool named Reveal.
You’ll learn how to address each RFM group in our guide on Customer Value Optimization, so take a look at all segments, from the most valuable ones to the most harmful for your online store.
When you’re using an automated tool for RFM analysis, that is all the manual work you need to do. As your segmentation analysis tool is connected to your store’s analytics, your segments are updated in real-time and you can go straight to analyzing reports in search of insights for your next steps.
You can perform an analysis as often as you need and have an accurate representation of how well you’re addressing your customers.
If you choose manual RFM analysis, you will face all the shortcomings of any manual segmentation analysis. It’s a good starting point if you are not sure you want to use RFM segmentation for your e-store and you want to see how you could segment the customer base.
But then again, you shouldn’t waste precious resources on manual work, so you could try a free version of a customer segmentation analysis tool to figure out if it is the right approach for your business.
If improving customer retention and lifetime value are on top of your list, you should definitely try RFM analysis for your e-store. We recommend you start a free trial with Reveal, our customer value optimization tool, which allows you to perform automated RFM segmentation and analysis, along with many other features designed for eCommerce companies.
Optimize your Customer Lifetime Value strategy
Get more valuable customers for a more profitable e-store.
Frequently Asked Questions about Customer Segmentation Analysis
Customer Segmentation is the act of dividing your customer base into into sub-group based on shared characteristics. One example of Customer Segmentation is the Recency, Frequency, Monetary (RFM) segmentation. This segmentation allows you to group your customers depending on how recent they bought, how frequently they shop from you, and the monetary value of their purchases.
The strategy of customer segmentation is the criteria through which you’re segmenting your customers. For example, you can choose demographic criteria, need-based criteria, their loyalty towards your brand, or even the value of their purchases.
Customer Segmentation is the act of dividing your customer base into into sub-group based on shared characteristics. The four types of segmentation are Demographic, Psychographic Geographic, and Behavioural.
To conduct customer segmentation, you must first decide on the criteria you’re going to use. Then, you can use a tool to divide all customers into specific sub-groups, based on your criteria. All customers who share specific characteristics go in their respective group. For the analysis part, you have to identify their needs of each group and asses how your brand meets those needs. Then you should link your products with customer needs and prioritise meeting the needs of best customer segments. In the end, you should develop specific strategies to deliver the value expected by your customers.
