Customer data analytics is actually the examination of the behavior and information of the customers of a company. It is done so that more profitable customers are identified, attracted and retained. Modern technology has made it possible for sellers to know what their customers want with just a few clicks. All that they have to do is listen.
In today’s article we’re going to talk about customer data analytics in detail. But before you start with this one, why don’t you quickly go over these two articles about calculating the cost of your website redesign and software performance testing.
What are Customer Analytics and Why they are Important?
Customer analytics or customer data analytics is the whole process of gathering and examining customer information to understand their needs, doubts in selecting services and products and sensitivity to price.
Your buyers have access to all the information anytime and anywhere about what to buy, where to buy it and how much to pay for it. This is why we need predictive analytics and data to see how the customers will interact with a certain brand.
The goal of data analytics is understanding the buying behavior and the lifestyle preferences of buyers. The more accurate this information the better the customer experience gets. You’ll need huge amounts of data to draw correct insights or else you’ll fail to come up with the right strategy.
The Four Types of Customer Data
We have categorized the customer data in four categories to show you how different industries use them.
- Product and Service Use Data: manufacturers use this type of data to understand the product and service utilization pattern so that they can come up with a better strategy for the ones that customers don’t buy.
- Transactional Data: You can get this data from retailers and service providers to learn the sales trend. Using this information manufacturers can boost their sales and optimize their marketing strategies.
- Data of Web Behavior: This data comes from the analysis of the webpages you’ve set for your customers and see what they view, scroll through, purchase or return.
- Customer Created Texts Data: More often customers leave reviews about products they liked or disliked either on social media or the review function you’ve set up. Manufacturers can collect this information and understand where they stand and what their customers think about their products/services.
The Four Types of Analytics for Customer Data
Below we’re listing out four types of analytics of customer data. These analytics will help you identify the solution that is better for the issue that you need to solve.
- Descriptive Analytics: This includes finding out what has happened in the past or what is happening now. You do that by examining the past data or real-time data of customers.
- Prescriptive Analytics: This includes deciding on the best possible course of action based on the consequences of the estimated future events using predictive analytics.
- Diagnostic Analytics: This encompasses finding the root cause of a problem in the company by going through the customer related data.
- Predictive Analytics: This involves making forecasts utilizing information from machine learning data mining, real time customer related or historical data.
Top corporates have a very good understanding of their customer analytics. If you have the same ambition and want to take your company to another level then you’ll have to create a better experience for your customers. You can do that by building a customer analytics set. You will see how with time your company turns into a customer-centric organization.