Customer analytics can be simplified by using technology like Data Science, Artificial Intelligence, and machine learning to understand the patterns of customers.
Understanding customers might sound easy, but it is not. And those who understand their customers and their patterns get an undue advantage over the ones who don’t.
In this digital world, when the customer has infinite access to the information he needs before deciding to buy, it becomes critical to know the buying behavior and understand the triggers that impact the overall buying process.
Multiple use cases are covered as subsets to Customer Analytics, I am going to throw some light below on the most considered frameworks in the space:
Cost of Customer Acquisition:
One of the first and easiest things to increase profitability is to reduce the overall cost. One such cost is the Cost of Customer acquisition. If simply put, CAC is calculated by dividing the total money spent on acquiring customers by the total number of customers that were acquired in that period when that money was spent.
By knowing the cost of customer acquisition from each channel individually, you get to understand what is working for you and what is not, that helps you plan where to keep burning money and where to stop immediately.
In today’s time, Hyper customization is the new normal, and that becomes possible with the help of customer segmentation as it allows companies to engage and communicate with their customers in a tailored and customized way.
For accurate and better segmentation, there are three most used approaches, RFM Segmentation (Recency, frequency, and Monetary), Cluster Analysis for Market Segmentation
And Market Segmentation using Machine Learning. Companies combine these methods to create a dynamic approach to improvise the accuracy of customer segmentation.
Customer Retention and Churn Analysis:
For Business to excel. It needs to keep onboarding new customers while retaining the old ones.
Customer Churn Analysis helps in finding out the customers we are losing and why. Tracking Customer Churn helps in identifying the churn rate and knowing the reasons why the customer is churning out so the pivots can be done accordingly to retain the customers.
Mostly, churn analytics helps in tracking individual user events that reveal the user’s journey to know exactly when the user churned out.
Customer Journey Analysis:
Having visibility in the complete customer journey touchpoints like purchase history, product usage, preferences, and shopping cart abandonment helps in providing a better customer experience. It also allows you to understand the customer’s interactions with your brand right from their initial research about your offerings to the actual purchase.
Customer Text and Thoughts Analysis.
In many Industries, till today, the customer is considered king. And hence, what he says and thinks matters a lot. Now, with online access to feedback, it’s a lot easier and simpler for users to talk about their good or bad experiences. And this information needs to be tracked and should be brought into consideration to maintain or improvise the experience of the customer.
The idea is simple, know what they like or dislike and accordingly adjust your campaigns to target them with the right offers.
Natural Language processing techniques can be used to analyze text data from customer reviews, customer complaints, social media posts, surveys, and other interactions to know what customers think about your business and allows the companies to respond appropriately.
Today, in this Data Driven world, it’s very important to leverage the information you have about the customer to understand what they want, approach them with the right campaign based on their preferences, reduce the churn rate and customer acquisition cost to enhance the customer experience and improve the overall Profitability of the business.
Views expressed above are the author’s own.
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