Customer Segmentation Analysis: Why Manual Falls Short

Customer segmentation analysis tells you how each group of customers performs and whether your segmentation still works. Doing it manually is better than not doing it at all. But when automated tools already send your order confirmations and collect your customer feedback, there is little reason to keep this analysis in a spreadsheet.

This article explains the analysis in four steps, then makes the case against the manual approach: five shortcomings that cost eCommerce teams time, accuracy and timing. It ends with an RFM example of manual versus automated analysis, using Omniconvert Commander (formerly Nexus), the AI eCommerce growth engine, for the automated side.

What is customer segmentation analysis?

Customer segmentation splits your customers into groups that share traits. The customer segmentation models guide covers the ways to do that. The analysis asks a different question: now that you have the groups, how is each one doing? Regular customer segmentation analysis helps you:

• Monitor how each segment performs against the goals you set at segment level and at company level.
• Identify your most valuable segments and focus on them.
• Improve your ideal customer profile based on your most valuable and loyal customers.
• Manage problem or underperforming segments.
• Adjust your messages to find the best approach for each segment.
• Find gaps in your segmentation technique and decide if you need a new approach or more granular groups.

Analyzing segmentation is as important as analyzing any other process in your store. You would not call a paid campaign a success without checking its results, or push a product without knowing your top sellers. Segments are no different.

How to do customer segmentation analysis in 4 steps

1. List your customer segments

   Identify every segment in your CRM, such as the groups an RFM segmentation tool creates.
2. Define your analysis criteria

   Choose the KPIs you will compare: customer lifetime value, Net Promoter Score (which you can collect with on-site surveys from Omniconvert), order returns and segment growth.
3. Analyze each group's performance

   Compare every segment on the same KPIs, and against its own results from the last analysis.
4. Write conclusions and an action plan

   For each segment, decide what to do next: grow it, fix it, win it back or leave it alone.

The data in these steps is entirely in your control. Even so, there are good reasons not to do customer segmentation analysis by hand in an eCommerce business, as the next sections show.

Why customer segmentation analysis matters

Segmentation lets you create relevant campaigns, push the right products and even sharpen your market positioning. The analysis is the next natural step: it tells you which of those moves to make. It also lets you personalize the whole customer journey, from the creatives in your ads to the products you push and the post-purchase treatment, for various market segments. Customers expect it: 71% of consumers expect companies to deliver personalized interactions, and 76% get frustrated when that does not happen [McKinsey & Company, 2021].

The analysis matters more as you grow. Each new customer makes it harder to see what customer success means and how to achieve it. If you use needs-based segmentation, for example, the analysis shows which groups your products serve well and which ones need a custom solution. Whatever type of segmentation you use, three benefits stand out.

Better targeting

A customer segmentation model like RFM segmentation shows which customer clusters bring you the most revenue. You want more customers like your power segments, so you create audiences that resemble them and target more efficiently.

Better ROI

Not all customers spend the same in your store. In a sports store, regular gym-goers spend every month on equipment, supplements and clothing, while a beginner buys the bare minimum. Audiences built on the traits of your high-spending segment bring a higher return per conversion than an audience that tries to reach everyone.

Better engagement

Segmentation analysis gives you the customer data you need to customize content for each segment, instead of sending generic messages to everyone. Engaged customers are also more willing to share customer feedback on your products and service, which many marketers find hard to collect.

Manual customer segmentation analysis: 5 shortcomings

Manual customer segmentation is defined as scoring, grouping and analyzing customers yourself, usually by exporting data from your store or CRM into spreadsheets. It is a good way to learn. As a routine, it falls short in five ways.

1. You waste precious resources

To measure segmentation performance, you need to analyze segments regularly. Done by hand, that means exporting customer data for every segment and calculating every KPI in your criteria. Only after you aggregate all that data can you start the real work: finding insights and conclusions. That work needs full focus, not the tail end of hours spent preparing spreadsheets.

2. You are prone to errors

The more data you aggregate by hand, the more errors creep in, and you do not want to make decisions on incorrect data. Automated alternatives are now accessible to eCommerce businesses of every size. Spend less time aggregating data and more on decisions for your eCommerce growth, such as the campaign that will reactivate high-value dormant customers you cannot afford to lose.

3. It goes out of date between runs

eCommerce changes fast. When you analyze your customer segments and their RFM scores, you cannot base decisions on a report from three months ago. Say your last analysis told you to reactivate a segment of high-value customers who had not bought in a while. If the campaign worked, it changed how customers are distributed across segments, and a manual process shows that change only at the next run.

4. You do not act on time

Because a manual analysis takes so many resources, you run it less often than an automated tool would. You miss opportunities, and you find problems later. Say many customers in a high-value segment return a popular product. If you must aggregate data and correlate variables by hand before you see it, you answer the complaints late and lose the trust of valuable customers.

5. It is harder to keep consistency and traceability

A crucial step in the analysis is to define your criteria, and sticking to them is what lets you see how segments evolve. Manual work tempts you to change the approach each time, and a changing reference system makes runs impossible to compare. It is as bad as switching segmentation techniques before you see what one of them, such as behavioral segmentation, can do.

Here is a failure practitioners hit often. A team builds its first RFM segments in a spreadsheet, likes the result, and schedules the analysis "every quarter". The second run slips because it takes a full week. By the third, someone has changed the score thresholds to make the segments look more even. The "Champions" segment now holds different customers than it did six months earlier, the trend charts compare two different definitions, and a win-back budget goes to customers who never left.

Manual vs automated segmentation: how they compare

The customer segmentation tools roundup compares the software options, from analytics suites to dedicated RFM platforms.

Example: manual vs automated RFM customer segmentation analysis

You can segment by demographics, behavior, psychographics and more, depending on your goal. Say your most important goal for the next 12 months is a higher customer retention rate. Many eCommerce companies with that goal choose RFM segmentation, which defines segments by three factors:

• Recency: when the customer last bought from your store.
• Frequency: how often the customer buys from you.
• Monetary value: how much the customer usually spends.

The model relies entirely on first-party data, so the data is easy to gather. You can run an RFM analysis in two ways:

• Manually: export your customer database to a spreadsheet and make sure you have values for all three RFM variables.
• Automatically: use a tool such as Omniconvert Commander customer intelligence, which performs RFM segmentation automatically and reports on your RFM groups from several angles, such as customer lifetime value or Net Promoter Score by segment.

Either way, you set the scale and scores for each variable to fit your store, such as your industry and your customer life cycle. You can keep the default segments or group and name them your way, as long as the result makes sense for your business in the long term. If you are not sure how to group customers by RFM score, start with the default RFM segments in Omniconvert Commander. The customer value optimization methodology explains how to address each group, from the most valuable to the most harmful for your store.

With an automated tool for RFM scoring, that setup is all the manual work you do. The tool is connected to your store data, so segments stay current and you go straight to the reports. You can analyze as often as you need, with an accurate picture of how well you serve each group.

Manual RFM analysis brings every shortcoming above. It is still a good starting point if you are not sure RFM suits your store and you want to see how you could segment the customer base more finely. Once you know it works, a free version or trial of a segmentation tool is a cheaper test than another week of spreadsheets.

From customer segmentation analysis to results

Each action plan from your analysis is a hypothesis: a new offer for at-risk customers, a different message for first-time buyers, a new landing page for a lookalike audience. Testing tells you which ones work. AliveCor used Omniconvert to run a structured A/B testing program and achieved a 21 percent lift in conversion rate, a 5 percent lift in revenue per visitor, and 94 percent statistical relevance across their experiments [Omniconvert, AliveCor case study]. The same discipline applies to segment campaigns: change one thing, measure it, and keep what wins.

If improving customer retention and lifetime value are at the top of your list, try RFM analysis for your store. Omniconvert Commander groups customers into RFM segments, calculates True Profit per order and per customer, and pushes your segments directly to Meta Ads, Google Ads and Klaviyo. More retention guides are on the Omniconvert blog.

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