Customer Analytics: Using Analytics the Right Way!

Customer Analytics: Using Analytics the Right Way!

Customer analytics can be made simpler by leveraging technologies like data science, artificial intelligence, and machine learning to comprehend client trends. Although it may seem simple, understanding clients is not simple. And those who are aware of their clients’ needs and behaviour patterns have an unfair advantage over those who are not. Knowing the buying behaviour and the triggers that impact the buying process is crucial in this digital age because customers have unlimited access to the information they require before making a purchase. Customer Analytics covers a variety of use cases as subsets; we will shed some light on some of the most popular frameworks below:

Cost of Customer Acquisition:

Lowering total costs is the first and most straightforward way to boost profitability. The price of acquiring new customers is one such expense. Simply put, CAC is determined by dividing the total amount spent on customer acquisition by the total number of customers acquired during the period the money was spent. Understanding what is working and what is not for you by knowing the cost of customer acquisition from each channel separately allows you to decide where to continue spending money and where to stop immediately.

Customer Segmentation:

Customer segmentation makes it possible for businesses to engage with and communicates with their customers in a customised and customised fashion. Hyper customisation is the new normal in today’s world. The three most popular methods for accurate and superior segmentation are RFM Segmentation (Recency, frequency, and monetary) and Cluster Analysis for Market Segmentation. And machine learning-based market segmentation. Companies combine these techniques to provide a flexible strategy that improves the precision of client segmentation.

Customer Retention and Churn Analysis:

Maintaining both new customer onboarding and client retention is essential for business success. They are finding out which clients we are losing and why it is much easier with customer churn analysis. By keeping track of customer churn, businesses may determine their churn rate and understand the causes of customer attrition so that appropriate pivots can be made to maintain consumers. Churn analytics primarily assists in tracking certain user events that expose the user’s path to pinpoint the precise moment the user was churned out.

Customer Journey Analysis:

A better customer experience may be delivered by having visibility into all touchpoints of the customer journey, including purchase history, product usage, preferences, and shopping cart abandonment. It also enables you to comprehend how customers connect with your business, from when they first learn about your products to when they make a purchase.

Customer Text and Thoughts Analysis:

The consumer is still seen as king in many industries. Therefore, what he says and thinks is important. People may more readily discuss their positive or negative experiences with online access to feedback. And to preserve or improve the client experience, this information needs to be tracked and taken into account. The concept is straightforward: find out what people like or dislike, then modify your campaigns to target them with relevant offers. Techniques for natural language processing can be used to evaluate text data from customer reviews, customer complaints, social media posts, polls, and other interactions to learn what people think about your organisation and enable businesses to respond appropriately.

Conclusion:

In today’s data-driven world, it’s critical to use the customer information you already have to understand what they want, target them with the right campaign based on their preferences, lower the customer churn rate, lower customer acquisition costs, and improve overall business profitability.