Understanding the Complexity of Data Segmentation

Understanding the Complexity of Data Segmentation

Published On November 12, 2013 -   by

Data Segmentation allows you to break your dataset or database into smaller groups of data which share similar characteristics. A typical example where data segmentation is very prominent is in Marketing, where the marketing team acquires data about customers and segments customer data based on various characteristics like age, gender, interests, etc.

Data Segmentation can be very complex. In order break down the complexity, here is a simple guide to help you get started on segmenting your data and analysing your results.

  • Choosing the right objectives
  • Having the right data
  • Applying different segmentation models
  • Using segmentation effectively

First and foremost, it is important to be clear about your objectives ”“ What are you trying to achieve by segmenting your data? What is your business outcome or goal? Once you have a clearly defined objective, your next step is to check if you have the correct data. Correct data means that you have the right set of information, obtained during the right time.  For instance, if you want to understand the level of traffic to your website during different times of the day, it is not enough if you only have information about the traffic from 8 AM to 8 PM.

Now that you have the correct data and know your objective, you can move to the next phase of deciding the appropriate segmentation model. There are several segmentation models to choose from. Some of the key models are illustrated here.

Profile Based Segmentation: This model is related to segmenting customer data. If you have collected information about your customers like Name, Age, City, etc., you can apply profile based segmentation. One example is to segment your customers by age groups of say < 18 years, 18 ”“ 25, 26 ”“ 35, etc. When it comes to determining the range of age, you have to go back to your business objective to see how best to segment the data based on age. Another example is to segment by demography, i.e., by the city or area or country from where your customers come from.

Behavioral Segmentation:  This model is used to segment data based on behavior. For example, if you have a website and want to understand who your most prominent users are, you can use behavioral segmentation to classify your users as ‘visited the site in the last 1 month’, ‘visited the site in the last 6 months’, ‘visited more than a year back’ etc.

Lifestyle Segmentation: This is a deeper form of segmentation that needs in-depth data about customers.  This method is used in cases where you want to understand your customer’s interests, their needs, etc. For example, if you have social data about your customers, you can use this model to understand what customers like what kind of things and target your customers appropriately.

You can spend all the time and effort in performing complex data segmentation, but at the end of the day, if you are not going to use these data segments effectively, there is no added value. Segmentation is mainly used for understanding your business deeply, launching campaigns designed for specific customers, etc. Using the data segments goes hand in hand with the first step of choosing the right objective. It is essential to utilize the segmented data properly.

– Data Czar @ DEO

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