Six types of customer analytics and when to use them

According to Gartner, a customer analytics platform is "a technology-enabled business process that provides near real-time insight into customers, their needs and preferences." In other words, it's a way to track customer behavior so you can better understand what they want and need from your product or service. But there are many different ways to perform customer analytics, each with its advantages and disadvantages. So which one should you use? Here are six types of customer analytics you can consider for your business.

1. Segmentation Analytics

Your customer base is likely made up of several distinct groups that care about different things. Marketing to these folks in the same way doesn’t make sense. Segmenting your audience lets you learn about and cater to your messaging, content, and support specifically to each group.

Segmentation analytics also helps companies discover new clusters forming in their customer base and understand those customers’ behaviors and motivations. Segmentation analytics helps teams find new ways to appeal to and communicate with each segment of your audience, increasing satisfaction and retention KPIs.

2. Customer Acquisition Analytics

Acquiring new customers is an essential component of any business. But it’s easier said than done. Customer acquisition campaigns can be pricey, and it’s hard to know if the new customers you’re attracting are truly a good fit for your products or services. Customer acquisition analytics illustrate how effective your acquisition strategy is.

Within customer acquisition analytics, you might calculate (and try to decrease) your cost per lead. You could also compare the number of qualified leads that result from multiple ad campaigns to determine which one is the strongest. Combining customer engagement analytics with customer acquisition analytics can also highlight opportunities to enhance personalization and refine your go-to-market strategy.

Typical customer acquisition metrics: cost per lead, conversion rate, and time to close.

3. Customer Engagement Analytics

Customers interact with a brand in many ways, including using different products and services, consuming social media, clicking on ads, reading email newsletters, and responding to NPS surveys. Customer engagement analytics measures the level of that interaction, revealing what campaigns resonate with customers the most, what challenges customers are facing in terms of product adoption, and how customers feel your services could improve. This feedback is extremely valuable for marketing, product, and even sales and customer success teams. Plus, mapping out the customer journey end-to-end can give companies ideas of where to add more personalized touches and where to streamline the experience.

Typical customer engagement metrics: customer engagement score, feature usage, bounce rate, and session time.

4. Customer Satisfaction Analytics

Customers leave satisfaction breadcrumbs everywhere一on review sites, social media, customer support surveys, in-app polls, and more. Customer satisfaction analytics aims to find ways to measure and increase customers’ perceived value and, in turn, brand loyalty. Customer satisfaction analytics use both qualitative and quantitative methods to paint a holistic picture of how customers feel about your products or services. Companies should monitor customer satisfaction metrics all the time, but especially after releasing new products or features, making drastic changes to their branding, or adjusting their support structure.

Typical customer satisfaction metrics: CSAT, CSE, and NPS scores.

5. Customer Churn Analytics

Customer churn analytics show how long the average customer’s relationship is with a company. A high rate of churn means customers are canceling their subscriptions, returning products, or aren’t continuing to buy new products or services. Companies pay close attention to customer churn analytics because they significantly impact their bottom line.

Every department keeps a close eye on churn, so when an increase happens, people immediately jump in to investigate why it’s happening. Typically, rising churn rates indicate product or customer support issues. By identifying these problems early, companies can act fast, addressing the root cause of churn and brainstorming remediation efforts, whether those are new customer loyalty programs they can implement, changes in pricing, and revising customer service policies. When customer churn analytics are low, companies can pinpoint what’s going well to strengthen retention.

Typical churn KPI metrics: net revenue retention, churn rate, or renewal rate.

6. Customer Lifetime Value Analytics

Not all customers are repeat buyers. Some customers buy your product or service once to test it out but never come back. Others make a purchase every week. Understanding the difference is key to financial success.

Customer lifetime value analytics shows how much the average customer spends (value) over their tenure (lifetime) with your brand. In other words, customer lifetime value analytics indicate which types of customers are the biggest bang for your buck. Customer lifetime value analytics help you focus your acquisition, retargeting, and retention campaigns on the right customers, empowering you to budget wiser and grow your profits quicker.

Typical LTV metrics: average LTV, CAC, and repeat purchase rate.

Analyze your customers like never before

The six types of customer analytics we’ve outlined are a great place to start when it comes to understanding your customers. But, as always, the key is in turning data into insights and then taking action on those insights. ThoughtSpot can help you do just that. With ThoughtSpot, you can quickly uncover hidden trends and correlations in your data to make better decisions about where to focus your efforts - and turn more prospects into happy customers. Sign up for a free trial today and see how easy it is to get started with analytics that matters most to you and your business.