What is customer analytics?
Customer analytics refers to the process and technology that enable companies to accumulate and study customer data. This data is used to help companies gain a deeper understanding into customer behavior and serves as a basis for decision-making regarding a company’s marketing activities. It features techniques like data visualization, predictive modeling, market segmentation and information management.
Why is customer analytics important?
Companies use the information they obtain through customer or web analytics to make more informed decisions about direct marketing, customer relationship management, and choosing a company’s site location, among other factors.
The process of customer analytics is especially critical for companies today. This is because consumers are more digitally connected than ever before and have 24/7 access to information. This access makes it easier for them to do comparison shopping and make more informed buying decisions, such as where to shop, what to buy and how much to pay. Using customer analytics, your company can gain data-based insights into how customers may behave the next time they interact with your business.
Simply put, the deeper your understanding of your customers' lifestyle preferences and buying habits are, the more accurate your predictions of their future buying behavior will be. This way, you can be prepared and respond accordingly, with the right marketing messages and strategies. Consequently, you’ll increase the likelihood of successfully delivering the most relevant offers that attract, rather than turn away, customers.
A good way to start utilizing customer analytics is to create a website or online store with Wix and use their built in marketing analytics tools. These tools can give you an accurate picture of what is going on behind the scenes of your business.
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What is the goal of customer analytics?
Customer analytics enables companies to do the following:
Contact the right customers with relevant offers and messages, thereby increasing response rates, customer loyalty and ROI.
Target only those customers most likely to respond, thereby reducing campaign costs.
Accurately predict which customers are most likely to leave and develop proactive campaigns to retain them.
Better understand target populations which leads to more effective customer segmentation and more accurate messages being delivered to the right people.