Would you like to know more about your customers to improve engagement? Are you able to make them loyal and increase their lifetime value? These are some of the questions that customer analytics can help answer. Customer analytics, also called customer data analytics, is the systematic examination of a company’s customer information and behavior to identify, attract and retain customers.
Here are seven of the top customer analytics techniques (in no particular order) that give you the insights you need to know your customer better.
#1. Customer Profiling
A customer profile is a detailed description of your customers that identifies their multi-channel purchasing behaviors, pain points, psychographic data, and demographic data. A customer profile provides a complete view of a customer for further use in customer analytics and marketing efforts.
For example, personalized marketing uses in-depth knowledge of a customer to tell you the targeted offers that will resonate, the best time and place to connect and the best ways to personalize the customer experience to win more business and drive-up brand loyalty. Personalized marketing leveraging customer profiles can greatly influence customer purchasing behavior. According to Infosys, 78% of customers say they are more likely to purchase from a company that provides them with more targeted offers.
Segmentation divides broad customer or business markets, into sub-groups based on some type of shared characteristic. Micro-Segmentation is a more advanced form of segmentation which groups small numbers of customers into extremely precise segments. Micro-segments allow for highly personalized predictive analysis and marketing action optimization. This customer data analytics technique can uncover relationships among customers and their key purchase drivers while identifying new segments that provide a competitive advantage.
#3. Customer Churn Analysis
Customer churn analysis predicts which current customers are likely to defect based on similarity to prior defectors. Companies often use customer churn analysis because the cost of retaining an existing customer is typically far less than the cost of acquiring a new one.
Churn risk scores help you understand the likelihood of customer churn. Classifying customers likely to churn by behavioral traits enables targeted, personalized, and proactive retention efforts. This prevents churn, while also classifying customers by value, to help you understand which customers you can’t afford to lose.
#4. Next Best Action
Next best action considers the different actions that can be taken for a specific customer and decides on the best one. This technique determines optimal action by analyzing the customer’s past behavior, recent actions, interests and needs in the context of the business’s sales and marketing goals.
Marketers often use next best action in real time to increase purchases, conversions, and sign-ups, by delivering relevant messaging, content, or product or service offers. These depend on the customer’s needs during the interaction and the benefit to the company.
#5. Market Basket Analysis
Market basket analysis, also referred to as affinity analysis, analyzes large data sets, such as purchase history, to reveal products and product groups that customers are likely to purchase together. Identifying relationships between items that people buy provides tremendous opportunity to know your customers better which, in turn, helps you improve customer experience.
Some of the top areas where market basket analysis can have a significant impact include cross-selling, recommendation engines, product placement, affinity promotion, customer behavior targeting, inventory management, store, website traffic, and more.
#6. Customer Lifetime Value
A customer’s lifetime value is the total amount of money a customer is expected to spend with your business during the lifetime of the relationship. Knowing customer lifetime value helps marketers develop strategies to acquire new customers and retain existing ones, while maintaining acceptable profit margins.
Knowing where each customer stands provides many opportunities to elevate marketing. Targeted marketing strategies can turn low-and mid-level customers into higher value ones and incent higher value ones with the right offers and rewards. Customer lifetime value can help you make better choices on the right spend and targets for customer acquisition. And customer lifetime value helps improve demand forecasting to make more intelligent decisions around inventory, staffing, production, and other activities.
#7. Customer Sentiment Analysis
Customer sentiment analysis is the automated process of discovering emotions in online communications to find out how customers feel about your product, brand, or service. This technique is mostly used on textual data from emails, reviews, social media posts, surveys, and more.
The applications of customer sentiment analysis to help businesses gain insights and respond effectively to their customers are numerous. Businesses can improve customer service by better understanding sources of satisfaction and frustration. Insights from customer sentiment analysis can enhance products and services by discovering new features customers want or defects or issues that are causing dissatisfaction. Customer sentiment analysis can also help businesses monitor their brand reputation and optimize marketing strategies by keeping on top of customer opinions about industry trends and new product introductions.
The Foundation to Power Customer Data Analytics
Actian empowers the data-driven enterprise with a Actian Data Platform to make it easy to meet the needs of the most demanding customer analytics techniques. Thousands of forward-thinking organizations around the globe trust Actian to help them solve their toughest customer challenges and to transform how they power their marketing efforts with data.