Customer Analytics: Types, Tools, and Benefits

Data now delivers crucial insights across business functions, including when it comes to customers. Whether you want to understand how to improve customer engagement or provide more meaningful customer service, customer analytics is the process of using customer data to extract valuable insights and improve success.

In this article, we'll discuss more about customer analytics, including the types of analytics to use depending on what you're trying to discover, and important tools that can help. Afterward, if you're interested in developing your customer analytics skills, consider enrolling in the Google Data Analytics Professional Certificate, where you'll learn how to collect and organize data to draw conclusions, make predictions, and drive informed decision-making.

What is customer analytics?

Customer analytics is the process of using customer data—such as purchase history, geographic location, or demographics—to generate important insights, which can then be used to improve marketing strategies and sales strategies. 

Customer analytics encompasses techniques like customer segmentation, social listening, customer journey mapping, A/B testing, predictive modeling, and data visualization.  

Which professionals use customer analytics?

Depending on the size of an organization, customer analytics could be an integral part of the following roles:

• Data analyst

• Marketing analyst

• Business intelligence specialist

• Product manager

• Customer success manager

• Sales analyst

4 types of customer analytics 

Customer analytics covers a good bit of ground, so it’s useful to consider the different approaches you can take to handling customer data.  

1. Descriptive customer analytics 

Descriptive customer analytics involves gathering historical data to understand how customers have interacted with your brand in the past. Data might include purchasing patterns, website or social engagement, email opens and clicks, and interactions with customer service. These data give you clues about what customers did but not why they did it. 

2. Diagnostic customer analytics 

Diagnostic customer analytics  goes beyond the “what” of customer analytics to find out the “why.” What are the reasons behind what customers do? What is the connection between different factors or events that influence their actions? 

3. Predictive customer analytics 

Predictive customer analytics involves using historical data and machine learning techniques to forecast what customers will do in the future based on what they’ve done in the past. How likely are they to make a purchase? How might they respond to marketing campaigns? What can you do to prevent customer churn? When you can anticipate customers’ needs accurately, you can allocate resources more effectively. 

4. Prescriptive customer analytics 

Prescriptive customer analytics focuses on recommending particular actions that will lead to the marketing or sales results you want, such as more clicks, more purchases, or more engagement. By looking at multiple possible scenarios, prescriptive analytics determines what you should do next, from adjusting your pricing strategy to optimizing marketing messaging.  

7 reasons why customer analytics is important

As consumers become more savvy and look for trustworthy brands, customer analytics is vital to your company's success because it enables you to understand your customers, reach them, and meet their needs. Let’s take a look at some additional benefits of customer segmentation.  

1. Improve customer segmentation.

With more detailed and accurate customer data, you can batch members of your customer base according to their shared characteristics. 

2. Tailor marketing campaigns to different customer segments.

Understanding customers’ preferences, pain points, goals, and where they are in the buyer’s journey can help you deliver the right message at the right time. For example, contacts in the consideration stage may need more information before making a purchase, while your loyal customers may be interested in new product recommendations based on their interests. 

3. Improve customer experience.

Along with tailoring your marketing messaging, customer analytics can help you offer experiences at every customer touchpoint—including your website, social media, email, mobile apps, and physical locations—that resonate deeply with customers. In addition, customer analytics helps you identify touchpoints where customers might disengage so you can optimize the customer journey.  

4. Improve customer engagement. 

Personalized communications and improved customer experiences may inspire customers to participate in your brand’s interactive experiences, such as live events, quizzes, contests, polls, and user-generated content. More engagement can foster a sense of community among customers and make your brand memorable. 

5. Reducing campaign costs.

Customer analytics eliminates a lot of guesswork, so more effective campaigns can mean spending less on paid advertising while still generating leads and meeting marketing goals. 

6. Boost sales.

With the insights you glean from customer analytics methods, you can personalize offers and product recommendations and cross-sell and upsell effectively. 

7. Retain customers. 

Over time, personalized communications and engaging experiences can help you build lasting relationships with customers. These customers may then become loyal and even promote your brand to others. 

Types of customer analytics software 

There are a number of tools you can use to deliver key insights about your customers. Below, we break down the options by functionality and focus.  

Customer experience

Customer experience analytics tools help you manage your company’s interactions with customers throughout the entire buyer’s journey, including conversational marketing, support messaging, integrations with other tools, sales assistance, content creation, and personalization.  

• Zendesk for a complete customer experience solution 

• Adobe Experience Manager for personalizing content and timing its delivery

• Sprout Social for unifying customer touchpoints across different social media platforms 

• Hotjar for examining how customers engage with your site or products

Customer journey 

Similar to customer experience tools, customer journey analytics tools help you market to customers across all channels and optimize the entire journey, from the first touchpoint through making a purchase and beyond. 

• Insider for automating cross-channel journeys and personalizing each customer’s journey 

• Glassbox for reducing digital friction and gaining visibility into all areas of the customer journey   

• Adobe Journey Optimizer for managing cross-channel campaigns and optimizing the entire journey   

• SAS Customer Journey Creation for planning, testing, and executing cross-channel customer journeys

Customer behavior

Customer behavior analytics tools help you examine the actions that customers take on your website or mobile app so that you can improve the UX design and boost conversions. 

• HotJar and FullStory for analyzing heatmaps and recordings of customer activity on your site  

• Usersnap for gathering customer feedback on products and experiences 

• Optimizely for A/B testing different versions of web pages to find out which one performs the best

Customer engagement

Customer engagement analytics tools help you communicate with customers and analyze interactions. Tools span live chat, in-app messaging, email marketing, survey apps, and chatbots.

• Help Scout for optimizing communications, understanding context, and analyzing performance 

• Front for streamlining and personalizing communications at scale, with customer history built in for better context

• Mixmax for empowering the entire revenue team, boost engagement with customers, see what’s working, and close more deals 

Customer service 

Good customer service makes all the difference, leading to higher customer loyalty and even word-of-mouth which keep your customer acquisition costs low. Customer service analytics tools gather insights from support conversations with customers to help you improve interactions, products, and experiences.  

• Kustomer for delivering personalized service, anticipating customer needs, and minimizing support volume  

• Freshworks for setting up AI chatbots, handling support tickets, extracting insights from data, and enabling team collaboration

• CustomerGauge for gathering feedback from enterprise clients at scale, improving support, and getting more upsells and referrals

Customer retention 

Customer retention analytics tools help you figure out why customers stay or leave your business, so that you can improve your ability to keep customers over time.

• Baremetrics for improving your subscription service to reduce cancellations and keep more customers subscribing  

• Yotpo for enabling e-commerce retention marketing and motivating customers with loyalty programs 

• Whatfix for onboarding customers to a digital product with personalized guidance and using data-driven insights to reduce support cost and churn rate 

Note that some of these customer analytics software tools may fall under more than one of the above categories, depending on their features and capabilities. 

How to collect and analyze customer data 

Here are some steps to follow to collect and analyze customer data and drive business success.  

1. Determine your data and business goals.

What specific insights are you looking for from customer data? How will you use this information to make business decisions? For example, if your goal is to retain more customers, you would collect information that gives you clues as to why customers are dropping off. What factors lead to their dissatisfaction? This information could make it easier to improve customer experiences and develop a retention strategy. 

2. Select customer analytics software.

Using the right customer analytics software for your business and goals can speed up processes, provide more insights, and facilitate team collaboration. Using the section above as a starting point, investigate different software programs to find the one that best fits your needs. Consider price, features, ease of use, and the kinds of data each software program measures.  

3. Identify data sources. 

Data sources might include website traffic, social media engagement analytics, customer surveys, purchase details, and customer service desk interactions. Once you ensure data privacy compliance, set up your customer analytics software to begin tracking customer data. 

4. Clean data. 

It’s important to clean data in customer analytics, so that you can gain reliable insights for making business decisions. You can clean data by removing duplicate customer records, correcting errors, standardizing data formats, and improving the categorization. 

5. Identify patterns.

With the help of your software, begin identifying patterns in data such as common customer concerns, cart abandonment trends, email open and click rates among customer segments, and product usage details.

6. Identify learnings and apply them.

Once you’ve found patterns in the data, analyze it to identify your key takeaways. For example, let’s say you discover that most customers whose engagement declines over a defined period of time will churn. Based on this learning, you could create a more robust retention strategy to re-engage customers with personalized communication, targeted product offers, and loyalty programs to lower your churn rates. 

Build your customer analytics skills with Coursera

Build and strengthen your customer analytics and data analytics skills on Coursera.

• Get comfortable with the subject: For an overview of customer analytics, consider the University of Pennsylvania’s Customer Analytics course, part of the larger Business Analytics specialization. You'll learn about descriptive, predictive, and prescriptive analytics, and their application to real-world business practices.

• Build important skills: Consider enrolling in the Google Data Analytics Professional Certificate to gain foundational data skills, including collecting, sorting, and analyzing data.

• Keep advancing: If you've successfully passed the Google Data Analytics Professional Certificate, consider advancing your analytics knowledge with the Google Business Intelligence Professional Certificate, where you'll learn how to design data visualizations that answer business questions.