Types of customer analytics

Customer journey has become more complicated than ever before. Customer analytics processes customer behaviour data and provides meaningful information using predictive analytics and market segmentation. With the help of these insights, businesses can improve their customer experience as well as marketing strategies. 

This article briefly describes the three types of customer analytics and how they are crucial for businesses. 

Customer experience analytics 

Customer experience analytics is a form of descriptive analytics that helps in driving revenue. CX analytics helps brands find out “what happened” during the customer journey. Considered the standard type of customer analytics, customer experience analytics illustrates raw data in an easily explainable and understandable format. 

With the help of customer experience analytics, customer service managers can keep pace with the emerging market trends and optimize their strategies accordingly. Also, insights from customer experience analytics help in understanding how well a brand’s support department is functioning, subsequently enabling the firm to optimize its departmental budgeting and improve its customer support operations. 

Customer journey analytics 

Customer journey is becoming more and more complex over time. Hence, having an overview of the customer’s journey is highly important to provide better customer experience. To get a clear picture of the customer’s journey, brands must study their customers’ purchase history, product usage, and have visibility into matters like product returns, abandoned shopping carts, etc. This also includes opened outbound mails, CSAT ratings, customer support conversations, social media comments, etc. Using customer journey analytics on such data, brands can foresee customer behaviours and trends. 

Also, using customer journey analytics, managers can spot those patterns that are currently bringing success to their business. Even more so, this customer data helps in filling information gaps that customer experience analytics might miss. 

Customer retention analytics 

According to the stats published in Zendesk’s Customer Experience trends report, 74% of customers are loyal to a particular company or brand. Customer loyalty directly impacts customer retention. Truth be told, there is an underlying inextricable bond between customer loyalty, customer retention, and customer effort. Customer Effort or Customer Effort Score refers to the amount of effort a customer uses during a support scenario. The logic is pretty straightforward here – the higher the customer effort, the poorer the customer experience; the poorer the customer experience, the higher the customer churn.  

Customer retention analytics falls under the prescriptive analytics category of data analytics and helps businesses in identifying factors that drive customer churn. This, in turn, enables businesses to improve their campaigns, products, and support services. Further, prescriptive customer retention analytics simplifies upselling and cross-selling to existing customers, which is far more profitable and easier to retain customers than relying on new ones. 

Why businesses should bother about customer behaviour 

Humans are unpredictable. Hence, businesses are intimidated when it comes to the use of customer behaviour analytics to predict their customer actions and gain actionable insights to optimize their sales and support strategies while adjusting their product roadmap according to changing market dynamics. As today, big data is invaluable for the growth of a business, brands are striving hard to harness this data to differentiate themselves in today’s hyper-competitive world. Armed with the right customer analytics strategy and data, companies can drive business value and revenue in the current customer-centric business landscape.