Learn how customer analytics can uncover customers’ wants and needs, enabling them to design, market, sell, and support their products and services in more effective, personalized ways.
Business activities from marketing to product design to customer support should all be crafted with the customer in mind. But how do companies know what customers actually want?
Today, the answer to that question is becoming more and more clear, but only as companies compile, understand, and act on the data derived from customer touchpoints. Customers interact with a company’s brand, products, and services in so many ways, creating an abundance of data. But that raw data isn’t useful in and of itself. You need customer analytics to turn that data into valuable, actionable insights.
Customer analytics can inform many decisions, from how much money to spend on marketing campaigns to where to improve the sales process. But customers have more choices and options than ever before, making it harder and even more critical for businesses to understand exactly what customers want.
In this piece, we’ll cover what customer analytics is, why it’s important, and how you can leverage it in your organization.
Customer analytics helps you identify and examine patterns in customer behavior. Companies collect vast amounts of data from their customers, but separating the signal from the noise is challenging. Customer analytics makes sense of that complex data, providing valuable insights into customer demographics and preferences. Many companies utilize business intelligence tools to collect, process, and analyze customer data across departments.
Customer analytics uncovers customers’ wants and needs, enabling businesses to develop, market, sell, and support their products and services in more effective, personalized ways.
Customer analytics helps marketing teams understand how well their campaigns resonate with their desired audiences and what levers they can pull to boost customer acquisition and engagement in the most efficient way possible. Further, customer analytics sheds light on the most effective sales strategies to convert customers and what customer success methods increase customer retention, allowing those teams to fine-tune their processes.
But most importantly, customer analytics helps companies create 360-degree customer profiles — the foundation for intelligent customer interaction. Knowing how customers found your company, what motivated them to engage, and what products and services they purchased can highlight opportunities for additional services they may want.
Plus, customer analytics reveals opportunities to optimize your pricing and packaging, enter new markets, or create new products. In fact, when combined with ML and AI, customer analytics can predict what customers may want in the future, giving companies a leg up on their competitors.
Many different metrics fall under the customer analytics umbrella. While this isn’t an exhaustive list, we outline a few of the most common types of customer analytics below.
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.
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.
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.
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.
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.
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.
Customer analytics can feel abstract without examples, so let’s dive into a few to demonstrate how customer analytics works in practice.
One company that leverages customer analytics to the extreme is McDonald’s. Over the years, they’ve amassed a wealth of customer data and used that to their advantage. Today, McDonald’s customizes their drive-thru menu based on time of day, weather, and historical sales data. They also recently launched an app with a robust loyalty program that learns customer preferences and personalizes discount offers tailored to each user’s favorite meals.
In turn, the app functions as another way to harness demographic data, tracking which franchises customers frequent most and any changes they make to their individual profile. All these data points funnel into their marketing and culinary teams, who dream up new menu items and ways to advertise them. These customer analytics techniques have helped McDonald’s grow into one of the most (if not the most) popular quick-serve restaurants.
Another company that is known for its laser focus on customer analytics is Amazon. Amazon tracks browsing data, email data, ad data, review data, sales data, and more to see what products customers prefer. From there, they created a strong recommendation engine so that whenever a user comes to their site looking for something specific, the best possible product appears.
This makes the buying decision much easier, converting more prospects to loyal customers. And once Amazon learns the habits of a typical customer in a certain segment, they use lookalike modeling to find other similar prospects who are likely to turn into high LTV customers. As a result of these customer analytics strategies, Amazon’s growth has skyrocketed over the past decade.
ThoughtSpot’s Modern Analytics Cloud delivers Live Analytics for your modern data stack so you can operationalize customer insights in a self-service manner, anytime, anywhere. ThoughtSpot has cutting-edge AI technology to find new populations to target or forecast how current customers may behave in the future. Not only that, ThoughtSpot also makes it easy for users to build interactive data apps that integrate with other cloud tools like your CRM, ERP, and product analytics software, allowing you to put those customer insights to work right away.
Get started on your customer analytics journey by signing up for a 30-day free trial of ThoughtSpot today.
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