Segmenting Your Customer Data
According to marketing guru Professor Malcolm McDonald “Correct needs-based segmentation is the bedrock of commercial success”.
Customer segmentation is a way of arranging your customers into distinct sub groups that typically have separate needs. Standard customer segmentation can be completed by hand, however this typically lacks accuracy and precision, and takes a large amount of time to complete.
Here we’ll look at using artificial intelligence (AI) and machine learning within the segmentation process to get the best out of your resources and data, and help you achieve your business goals. These ‘segments’ can be as simple as dividing your customers by demographics such as gender and age; however, for B2B and larger B2C businesses, customers could be split by more complex variables such as their past behaviours and buying personas.
By being able to target specific groups of people with specific interests, you have a greater ability to cross and upsell your products. When sending personalised content to a customer not only will they have an increased probability of buying your product, but they also develop greater brand loyalty towards your business as the customer feels more appreciated and valued.
Segmentation Using AI & Machine Learning
The extent to which you are able to segment your customers comes down to the amount of available data you have on them. If all you have are names and email addresses, then at best you may be able to segment though gender. The likelihood is this is not the case, and you have adequate amounts of data available, but you don’t know how to use it.
You can attempt to tackle this problem by hand labelling your entire customer database, which for smaller businesses may be a viable option. However, once you start to grow as a business and develop more customers, this may become increasingly difficult. This is where machine learning can come into play. With the aid of bespoke software, your business will be able to identify target groups and even re-label customers that have been incorrectly segmented in a significantly reduced time with much greater accuracy.
Customer Segmentation Software Processes
When segmenting using software, the entire development comprises of four processes; pre-processing, modelling, evaluation and transformation.
Pre-processing – Initial cleaning and transforming of your data is required to make the process function. A ‘gold standard’ training set will also need to be identified for later use.
Modelling – Algorithms would be run to identify what variables are important to your segmentation. These are then scaled in order of importance and applied to the ‘gold standard’ training set so the model can understand what properties are common for your segments.
Evaluation – Using a confusion matrix, previously incorrectly identified contacts can be identified; this data can also be used to identify the accuracy of the model. When the data set contains unbalanced data across segments a statistical coefficient can be implemented to account for class imbalance.
Transformation – Output data is finally achieved. You’ll now have your customers segmented in accordance with your ‘gold standard’ training set.
Reaping the Rewards
Once you’ve managed to segment your customers, it’s time for selective targeting. However, just because you’ve now grouped your customers doesn’t mean you’ll automatically lead to success. You’ll still need interesting, personalised content delivered through different approaches to get the greatest return on investment.
If you believe that customer segmentation may be of interest to you get in contact with us.
You may also be interested in bespoke software development.