How To Correlate Data Analysis and Segmentation?

Usually, massive data analytics becomes approachable in a few clusters with similar observations within each group. Likewise, in the case of customer data, although we have data from millions of customers, still all the customers may only constitute a few segments. 

Customers stay the same within each segment but can vary across segments. We may often need to analyze each component separately, as the chances are that they can behave differently. That implies variable components may have different behavioral patterns and product choices as well. 

While considering and identifying segments in the data, we can use statistical techniques known as Clustering techniques. Keeping in mind how we define “similarities” and “differences” between the data observations, we can find out segmentation solutions. 

Also, these solutions can be defined mathematically using distance metrics. The correlation between clustering and segmentation is it establishes the distance metrics within observations.

Perhaps, data analytics projects need a perfect balance between intuition, experimentation, and considering results and patterns and not just facts and figures. 

Let’s dive more into Clustering and Segmentation in the following steps-

Step 1: Verify data metrics

As many of the statistical clustering methods require metric data to decide on better data. All data should be in numbers, and these numbers have an actual numerical meaning. 

Key Execution Checkpoints Shown in This Diagram

• Binds correlate data analysis and segmentation activity from your repeat customers, VIP buyers, and retention managers directly to your CRM and order ledger
• Enforces automated return-window holds and duplicate checks before your product operations manager releases budget
• Tracks roadmap items influenced or shipped from council input and member participation per session and poll with exportable finance reconciliation logs

Building on the figure above, connecting step 2: scale the data directly to your order ledger eliminates manual spreadsheet reconciliation for your team.

It can simplify the process as one needs to define distances between observations, and usually, distances happen only with the help of metric data. 

Step 2: Scale the data

Though it’s an optional step to do, it is better if done. Most often, having some variables with a different range of scales can create issues like results driven by some large values or more. 

We need to ensure whether or not to standardize the data with the help of raw attributes, all for the sake of avoiding problems. 

Step 3: Determine segmentation variables

It is critical to select which variables will be used for clustering, as it can significantly impact the clustering solution. Therefore, it is necessary to opt carefully for the variables for clustering. 

Recent research gives us an idea of what variables may differentiate customers and partners, assets, products, or areas. In this context, a lot of technical knowledge, creativity, and iterations are needed. 

Step 4: Explain similarity measure

Indeed, the goal of clustering and segmentation is to classify observations according to their similarities.

We must have a better understanding of any of the statements as ‘similar.’ If there is no similarity observed, no statistical method will discover the answers to the queries. 

Step 5: Look for pair-wise distances

When we have gathered similar observations, the next step is to analyze data through visualizing—for instance, individual attributes and the pairwise distances among the statements. 

If there are multiple segments in our data, few of these plots will show variations, with the peaks being the potential segments. 

Step 6: Methods and number of segments

We can use the Kmeans clustering method and hierarchical clustering method. Both techniques require that we have decided on how to estimate the similarity or distance between the observations. 

Describing how these methods work is a difficult task to do. The main difference between both is that Hierarchical Clustering does not require the user to define how many segments to while Kmeans needs it.

Step 7: Interpret the segments

When we have decided on the number of clusters to use, we would like to know more about customers and interpret the segments. 

Data analytics is used to eventually make decisions, which is feasible only when we are comfortable (enough) with our understanding of the analytics results, including our ability to interpret them.

Step 8: Robust analysis

A proficient segmentation process needs robustness of our decisions across many “good” clustering practices used. Many significant approaches by NextBee can follow the process outline as follows – 

• Different distance metrics
• Variations of the original segmentation attributes 
• Different number of clusters 
• Segmentation methods

Segmentation is an iterative process with many differentiators like data, methods, several clusters, and profile generation. The process goes on until a satisfying solution comes up.

Conclusion

We have noticed that customers are similar within each segment but different across segments. We may often want to analyze each element separately as they may behave differently. Like different market segments may have other product preferences and behavioral patterns.

If you wish to perform a comprehensive Data Analysis and Segmentation for your company and elevate the opportunity to grow your business, feel free to consult NextBee.

We have a team of expert Data Analysts who will help you climb the ladder of success and attain specific business goals.