Customer Segmentation Made Easy with Netcore’s Smart Segments

Given the large volumes of customer data generated across multiple channels today, marketers go through a lot of pain trying to identify customer segments. This step is crucial if you want to maximise your ROI on your marketing spends.

So in a nutshell, here’s what you need to do: Build on traditional demographic and behavioural segmentation.

Data-driven smart segmentation empowers you to:

  • Identify hidden patterns in customer data
  • Mine large unintelligible data sets, and create relevant customer segments based on different customer characteristics, activities, locations, browsing, and purchase choices made over time etc.
  • Accurately isolate customer segments using advanced pattern discovery and machine learning, to help solve pre-identified industry specific use cases
  • Answer tricky marketing questions efficiently by giving you data-driven insights
  • Leverage the power of Artificial Intelligence and Machine Learning as you build custom segments for more in-depth customer level analysis

Watch our masterclass video on how Artificial Intelligence and Machine Learning is transforming marketing.

As you can see, smart data-driven segmentation makes all the difference to your marketing strategy. Try Netcore Smartech’s powerful segmentation engine: Smart Segments. 

Let’s recall the STP (Segmentation – Targeting – Positioning) framework to understand how smart segments can give it even greater firepower:

Customer Smart Segment

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Let’s take an example of how Smart Segments can be applied to the e-commerce/retail industry:

You are marketing a special edition of footwear, by executing a shoe promotion campaign on ecommerce platforms:

Your problem statement would be:

Which registered users should I target for this campaign for this exclusive range of shoes?’

The generic segmentation conditions that you might look at would be:

  • Purchases made over the past 30 days
  • Shoe purchases over the past 6 months
  • Average customer spends over the past 6 months greater than INR 1,000
  • Cost of shoes purchased > INR 6,000, on an average

Most experienced marketers, however, will tell you that this is not enough. It would help you a great deal to find out the number of days from your customer’s last purchase, number of days from the last time the customer bought shoes in particular. Intelligent data mining will have to be done in the background to help you come up with the appropriate cut-offs.

Basically, you will need to understand your customer’s journey better, and do this at scale.

Smart Segments will help you with a list of pre-identified domain-specific parameters, to provide relevant insights around the more frequent and common questions you are likely to face.

Watch this on-demand webinar with Jaimit Doshi on how to use data-driven personalisation to create exceptional customer experiences.

The table below gives you a glimpse into the kind of sample insights – essential to the above segmentation illustration – that Smart Segments can provide based on pre-identified domain-specific parameters:

analytics-insights

 

Now can you easily tailor your marketing strategy especially your offers and content to more precisely target these two customers who show very different behaviour. Remember, accurate customer segmentation lays the foundation for the creation of the most effective multi-channel campaigns in the future.

You too can unlock the true potential of Smart Segments for your brand with Netcore Smartech today!

 
Debapriya Das


I am currently working as Lead - Machine Learning at Netcore, and have a rich experience into analytics, deep learning, DWBI, solutioning, consulting, and project management. I have spent a significant amount of time in my career, building solutions and strategies for pricing, loss mitigation, risk scoring etc in the Insurance industry. Most of the other problems that I have worked on have been pretty spread out and abstract, ranging from problems which can be solved by traditional statistical/machine learning models to practical image recognition and video intelligence.

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