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Significance of Dynamic Segmentation in Customer Engagement

Significance of Dynamic Segmentation in Customer Engagement

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'The idea of dividing a market up into homogeneous segments and targeting each with a distinct product and/or message, is now at the heart of marketing theory' - Market Segmentation, Michael J Croft

  

 

The question can even arise why does grouping of customers required and need to share common marketing communication. Traditional marketing communication of sending generalized marketing communication is thing of past. It has been noticed average open rate of emails has been around 15-20%, click rate is only 3-5 %. The quality of communication needs to be improved with proper targeted messaging.

 

Dynamic Segmentation – the new friend for the Marketers

Dynamic Segmentation is one of the most useful weapons data science can offer to marketers for a better customer engagement solution. The blending of results provided by advanced analytical tools and Marketers’ knowledge-base can create the real value add in targeting customers in a meaningful way.

 

Why segmentation is required for marketing campaigns?

There is a saying that no two customers are alike – to reach them – you need to identify them – you need to segment them. In reality, individual customers are part of a group or ‘Segment’, who share similar kind of behavior and pattern. To target customers with proper promotional offer and proper messaging, marketer should segment them properly.

 

Traditional Segmentation

Marketers are using segmentation from long back by grouping them based on static parameters like age, gender etc. The grouping of customers based on a parameter is basically segmenting the customers.

 

Un-supervised Segmentation - Clustering

Using advanced data science techniques, marketers can totally depend on technology and use clustering method to predict the group of customers and the attributes/parameters which are most important in segmentation.

 

Supervised Segmentation – Classification

In Retail, leaving everything to the algorithms to decide is not that wise decision tough. Customers are human beings and predicting their behavior should not be left alone to algorithms.  Here comes the role of classification – where segmentation will be done based on Marketers own defined parameters like recency, frequency, monetary, age, gender etc. In classification technique, the parameters are defined by marketer, based on which the segmentations are done.

 

Dynamic Segmentation

The latest customer segmentation solution demands more. Here, both marketer’s experience and data science predictions are important. Blending them both can create a way to reach the customer with proper target messaging and offers.   In current scenario, customers’ behavior is constantly changing - they are not finite anymore - they are not static. To understand customers with every changing day, retailer has to depend on sound data management principles fed by a constant stream of information that reflects changes in customer aspirations and circumstances. That means that campaigns has to be managed dynamically, in real-time to communicate with customers.  Dynamic segmentation can provide retailer the tool where marketer will be able to use predictive parameters which are true for that particular given point of time (customer’s present price sensitivity level, spending patter, CLV etc.) . At the same time, with dynamic segmentation marketer can blend their own experience to get the most desired outcome to get people most engaged with the brand.

 

*For more information, please visit connectcust.com 

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