What is Customer Analytics?

Data in any form is considered as the new gold in the 21st century.  Organizations that primarily focus on data-driven approaches have the potential to be ahead of their respective game. One of the important and core objectives of any company would be to maintain a solid and strong relationship with its customers, understanding them and providing them what they want.

Customer Analytics is this field of analytics, where one dives deep into the consumer data and brings about useful insights on their clients. Customer Analytics finds its utmost use in the marketing as well sales departments where the customer data is the key to understand the customer behavior for them chart their marketing as well advertising strategy. Customer Analytics supports business decision-making through targeting specific groups based on income groups, age groups, and customer segmentation as understanding customer groups would help the businesses to create more strong strategies for expansion through the use of various predictive models and forecasting methods. It uses parameters like customer spending patterns, customer reviews of different products, customer churn rate to predict customer behavior. The better the understanding of customer habits, the better the predictive models. A good customer analytics platform makes use of all the interdisciplinary departments of a company such as IT, marketing, sales, customer support, and business analysts.

The primary objective of Customer Analytics is to create a distinct and accurate view of customer behavior and patterns, in order to facilitate decision making on how to attract as well as retain customers, along with answering questions like “what is the most suitable product”, “which customer group to target”, “what communication channels must be used”, at “what price or point of time”, and also “what promotional and advertising strategies to be used, paths are taken by the clients, etc”. Organizations are more reliant on analytics due to the ever-increasing sophistication of the customers in their use of technology such as smartphones. On a broader level, customer analytics helps the organizations to help find how the customer finds their product, which features they like the most, which features they don’t like, and the reasons for leaving whereas, on a smaller scale, It helps organizations to identify the customers as individuals by segmenting them on the basis of region, age, income, ethnicity, etc.

According to McKinsey & Company “Companies that use customer analytics comprehensively report outstripping their competition in terms of profit almost twice as often as companies that do not.” Competitors who make use of tools that gather data from all sources and deliver insights in real-time have a distinct advantage. The use of analytics to know one’s customer has been seen of utmost importance by organizations, especially by the customer eccentric companies. It can increase the revenue of organizations through improved customer experience, customer acquisitions, reducing customer churn, maximizing customer lifetime value, and boosting the return on market value. 

Customer Analytics

Customer Analytics is in itself is the wide term, incorporating a range of departments such as marketing, sales, analytics, operations, and customer support.  Customer Analytics is seen as the one-stop place to all the answers to the customer demands as it incorporates predictive modeling, data visualization, and data management. The stronger the analysis is, the stronger or the better would be the chances of providing a good customer experience.

Customer Analytics requires that companies support or accelerate a data-driven approach with considerable improvement from the senior management. It is pretty evident that the above analytics need a huge amount of data, and with an unprecedented amount of data that is being collected through various channels such as social media and mobile applications at an unprecedented speed, it is imperative that organizations leverage Big Data framework capture, process, analyze and visualize large amounts of real-time data in a limited time frame, hence the term Big Data Analytics consumer analytics. Big Data framework thus enables the usage of unstructured data driven by new communication platforms such as social media reviews, mobile application data, images, videos, etc along with structured data-driven by traditional databases and data warehouses and customer relationship management tools into consideration.  The data then can be subjected to a variety of techniques like statistical modeling, data mining. Optimization methods, data visualizations, and machine learning. Thus Big Data analytics has the potential to change and revolutionize the way organizations use customer data to cater to the fast-changing demands of the customers.

Customer Analytics can be briefly classified into Customer Journey Mapping and Customer Journey Analytics. Customer Journey mapping would include collecting data on customer journey through visualizations whereas Customer Journey analytics being a data-driven approach would try to connect together every touchpoint of a customer’s journey so as to influence and analyze customer journey and thus prioritize the opportunities that impact the business goals. They both differ from each other in their data-driven approach, in the comprehensiveness, the scale of analytics involved, and usage of real-time data, and in presenting actionable insights. The Big Data framework uses a plethora of techniques and tools. A great number of tools such as Enterprise Resource Planning tools, Customer Relationship Management applications, and Customer Data Integration tools must be used concurrently and agreed upon to give in a holistic view of the clients. 

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