Customer analytics 101: What it is and how it works for growth
Customer analytics helps businesses deeply understand their audience to make smarter business decisions and improve CX.
By Harry Wray, Director, Customer Experience
Last updated June 15, 2023
Companies are increasingly prioritizing the collection of customer data, but most struggle to put it to use. According to the Zendesk Customer Experience Trends Report 2023, 67 percent of business leaders report seeing disorganized efforts using and sharing customer data. Despite having access to answers regarding customer behavior, many companies are still relying on guesswork.
Customer analytics removes the guesswork, providing insights that enable companies to find a clear course of action. It helps you understand your consumers so you can create a customer journey tailored to their expectations.
Here’s a look at what we’ll cover in this guide:
- What is customer analytics defined as?
- Benefits of customer analytics
- 4 main customer analytics categories
- How to collect and analyze customer data
- What you can do with the results of customer analytics
- Examples of customer data analytics
What is customer analytics defined as?
Customer analytics is the process of collecting and interpreting data from customer interactions to learn about buyers’ needs and expectations.
You can collect customer data from various touchpoints, including websites, apps, social media, and customer feedback surveys. From there, team members can analyze the data and compile a report—manually or with customer analytics software.
These insights give businesses a better understanding of their audience and enable them to develop better products or services. They also help businesses determine the optimal pricing structure, target the right customers with marketing campaigns, increase revenue, and improve the overall customer experience (CX).
Why do customer analytics matter?
Adopting a customer analytics process gives companies access to a wealth of knowledge that would otherwise be untapped if they were to stick to traditional research methods. It reduces the time that is required to collect and analyze data while delivering more actionable takeaways. Failing to capitalize on the benefits of customer analytics will have you chasing after your target market instead of leading the way.
Benefits of customer analytics
Consumer analytics provides a complete view of customer behavior. Tracking and analyzing CX metrics can reveal how people discover and use your products or services, how they interact with your support team, and what they think of your brand. Once you have the data, you can put it to work by improving your operations and the customer experience.
Create personalized experiences
Customer analytics allows companies to create personalized experiences by providing insights into customer behavior, preferences, and needs. Companies can analyze the data and identify patterns and trends that help them understand what customers are looking for and how they interact with their products or services.
Personalization requires more than simply using a customer’s name. If you want to get to know your buyers through their data, here are some questions to guide your approach:
- How can you use customers’ preferences to anticipate what they might want next?
- What do your customers have in common?
- Can you source second- or third-party data to get a complete picture of buyer behavior?
Use this information to personalize the customer experience, from marketing campaigns and product recommendations to customer support interactions. For example, companies can use customer segmentation to identify how customers engage with the website and send personalized emails promoting the products they viewed.
Improve customer engagement
Customer analytics can improve customer engagement by predicting customer behavior and preferences based on past interactions and data. Use the insights to map out the customer journey and the pain points they experience along the way. Resolve those issues, and your customers will likely become more engaged.
Increasing engagement requires diagnosing what’s currently standing in the way:
- Which web pages see the highest and lowest bounce rates?
- Which product features do customers use the most?
- Which social media platforms are your customers using?
A customer data analysis can help you spot trends in your support tickets so you can address recurring issues. It’s important to collect feedback, too. By listening to customers and looking at the big picture, you’ll know the right features to roll out in the future.
Boost customer retention
If you’re successfully personalizing the customer experience and improving customer engagement, customers are more likely to stick around. But customer analytics can also help businesses reduce customer churn.
Analytics can highlight patterns in customer behavior that indicate when a customer is at risk of churning. The company can then take steps to retain them by either fixing an issue or offering loyalty incentives.
If improving customer loyalty is a primary goal for your organization, be on the lookout for these key performance indicators:
- How long are customers waiting for assistance?
- Do complaints center around a common issue?
- Which response will be most effective for retaining their business?
Increase company revenue
Knowing how to analyze customer data opens up new avenues for your company to earn revenue. Attracting new buyers through targeted marketing can grow your customer base. Revenue per customer can increase when your team identifies cross-selling and upselling opportunities. You can optimize your pricing by understanding what a customer may pay.
Increasing revenue might be the end goal, but how effectively you address other business decisions will determine your overall success:
- What is your current customer lifetime value?
- Where are you succeeding or underperforming in customer lifecycle management?
- Are your customer relationships transactional, or are you invested in their success?
4 main customer analytics categories
There are four types of customer analytics: descriptive, diagnostic, predictive, and prescriptive. Understanding the different categories can help you take full advantage of everything they offer.
1. Descriptive analytics
Descriptive analytics involves collecting and assessing data about past customer actions. This type of consumer behavior data helps you understand what has happened but doesn’t explain why.
Say a significant number of customers give their support interactions a low rating in customer satisfaction (CSAT) surveys. Descriptive analytics would highlight this trend without telling you why you received low scores.
2. Diagnostic analytics
Diagnostic analytics helps determine the cause of trends and why customers act a certain way. For example, diagnostic analytics can explain why customers gave you a poor CSAT score.
Use open-ended survey questions or read reviews and social media comments to gather the right information. After studying the data, you might learn that the poor rating stems from long customer service wait times.
3. Predictive analytics
Predictive customer analytics forecasts what your customers will likely do based on historical data. Your support team can then anticipate customer needs and identify patterns, resulting in a better experience.
If a customer routinely buys a product when their inventory runs low, a business can predict when that customer might need the product again and send them a reminder. This can result in higher customer satisfaction, retention, and revenue.
Predictive analytics also enables you to pinpoint at-risk customers and prevent churn before it happens. For instance, you may find that at-risk customers reduce product usage and don’t reach out for support as often. Recognizing these indicators can help you know when to step in.
4. Prescriptive analytics
Prescriptive analytics goes beyond diagnostic and predictive analytics by recommending what you should do in the future. It can suggest a course of action based on historical data or provide ideas on how to achieve certain outcomes.
For example, if reducing customer churn is a primary goal for your company, prescriptive analytics might suggest that a 20 percent reduction in resolution times would lead to a 50 percent increase in customer retention.
How to collect and analyze customer data: 5 best practices
Collecting and analyzing customer analytics can be time-consuming when done manually. A customer service software solution—like Zendesk—that integrates with a customer data platform (CDP) can make the process faster. It makes gathering, processing, and summarizing customer data more efficient and secure.
Establish goals and which tools to use
Gathering customer data is an essential part of any business strategy. It helps companies understand their customers better, improve their products or services, and personalize their marketing efforts. Before you collect customer data, it’s important to establish clear goals to ensure your efforts are focused and effective.
Here are some steps to help you set goals for gathering customer data:
- Define your business objectives: What do you hope to achieve by gathering customer data? Do you want to improve customer retention, increase sales, or refine your marketing efforts?
- Determine how you’ll collect the data: Will you use surveys, social media listening tools, or customer analytics software?
- Set measurable goals: Your goals should align with your business objectives, such as growing revenue, reducing costs, and enhancing the customer or employee experience. For example, if you want to improve customer retention, set a goal to increase customer satisfaction ratings by a certain percentage.
- Create a timeline: Decide when you’ll start collecting data, how often you’ll collect it, and when you’ll review your progress.
Establishing clear goals for gathering customer data helps you collect the information you need to achieve your business objectives. It’s important to regularly review your goals so they remain relevant.
Once you’ve set your goals, this will inform the team to set a clear action plan of identified opportunities to help ensure you will deliver on those goals.
Capture only the data you need
Collecting data for data’s sake should be avoided, as it can create distraction. The metrics you track should be aligned with the goals you established so it’s easier to draw constructive insights.
A customer service software that pulls in omnichannel analytics can be a great data source. This software houses relevant information like customers’ names, addresses, previous support tickets, and purchase history. You can also use surveys to collect customer feedback on product and service interactions to get a mix of qualitative and quantitative data.
Regardless of how you choose to obtain the data, it’s important to prioritize customer transparency. Telling customers what information you’re collecting—and why—will establish trust and give them peace of mind.
Store customer data safely
Safely managing customer data is a top priority for any company that wants to protect its reputation and customers’ privacy. It’s a good business practice, but it’s also essential to comply with data protection regulations.
Here are a few best practices to consider:
- Use secure storage: Store customer data on secure servers with appropriate encryption and access controls in place.
- Implement access controls: Limit access to customer data to only those employees who need it to perform their jobs. Use strong passwords and multi-factor authentication.
- Back up data regularly: This ensures that you can easily restore lost or damaged data.
- Dispose of data securely: When you no longer need the data, dispose of it securely. This can entail physically destroying hard drives or using data-wiping software.
It’s important to regularly review your data storage practices to be sure they remain effective in the face of evolving threats and regulatory requirements.
Clean and categorize the data
Cleaning and categorizing customer data is a crucial step in keeping the data accurate, up-to-date, and useful. The analytics tools in your CDP should handle most of the work, but here are some best practices to follow:
- Identify and correct errors: Use data validation tools to catch mistakes and inconsistencies in the data. This can include spelling errors, duplicate entries, and missing data. Correct any errors you identify.
- Standardize data: Standardize data so it’s consistent across all records. This can involve formatting data into a consistent style or using a standardized set of categories.
- Remove redundancies: Get rid of redundant data that serves no purpose, including removing duplicate entries, consolidating data into a single record, or removing unnecessary data fields.
- Categorize data: Organize data into meaningful categories that you can easily analyze. This can entail grouping data by demographic, behavior, or other meaningful categories.
Look for patterns and actionable takeaways
Numbers alone are unlikely to give you the whole picture, so you must accompany metrics with a narrative that explains what’s going on. Use your CDP to analyze customer data and identify patterns. CDPs utilize machine learning to sort through data and surface trends for you.
While looking for takeaways, be careful when assuming cause and effect based on correlations. Apply a lens of curiosity instead of trying to tell the most compelling story. There are many ways to interpret the same data, so comparing quantitative data points with qualitative data is beneficial in building a broader, more accurate picture.
After analyzing your data, share the findings with your team or the appropriate department. Data visualization mediums like graphs and bar charts can make the information easier to digest and help you tell a story, as opposed to a robotic delivery of facts and figures.
What you can do with the results of customer data analytics
Once you perform your customer data analysis, the next step is to put the insights to work for your company. The benefits of customer analytics apply to sales, marketing, and customer service teams. Here are some ways it can improve performance:
- Improve customer retention: Predictive analytics can use past trends to forecast future behavior. If the data show that a customer is at risk of leaving, your customer service team can work proactively to retain them.
- Reduce operating costs: Consumer analytics help the business identify trends that provide insight to inform operational improvements, such as automations, channel strategy, ticket deflection strategy, and marketing strategy. For example, consumer insights can enable marketing teams to understand customer behaviors and preferences, enabling them to build effective campaigns. The marketing team can then focus resources on the areas where they’ll have the greatest impact and maximize the return on investment.
- Improve revenue generating activities: After you identify buying patterns among your audience, you can send targeted offers that are helpful for the customer and drive upsells and cross-sells.
Examples of customer data analytics
The application of customer data analytics can take many forms depending on your industry and company goals. Look to these examples for how to incorporate analytics into your processes and increase customer engagement, retention, and more.
HotDoc: Tapping into data
HotDoc is an online medical services company that helps patients connect with medical providers. The company’s customer service team saw an uptick in activity in the wake of COVID-19, and the basic data reporting tool they were using couldn’t keep up and failed to provide useful insights.
The company switched to Zendesk for its analytics needs and was quickly able to diagnose and resolve issues. HotDoc uses dashboards to generate monthly reports and measure performance, helping a team of 15 people efficiently field over 4,000 tickets per month.
“By setting up dashboards on Zendesk Explore, we’re actually able to hone in on why they are contacting us and the parts that need the most focus. It was such a powerful lever in getting things moving—a great way for us to show our stakeholders, ‘this is the problem, this is what needs to be fixed.’”
–Kasun Kanangama, CX support team leader at HotDoc
Northmill Bank: Tearing down silos
Northmill Bank uses advancements in tech to bring personalization and transparency to the financial sector. Even though users praised the bank’s customer support, agents struggled to keep up. There was no unified view of customer data, and communication got stretched across four separate email inboxes.
Northmill Bank switched from Freshdesk to Zendesk because it gave the company a connected platform where team members could collaborate and gain a 360-degree view of the customer. The new data insights produced greater team efficiency, helping the bank maintain a 90 percent CSAT score without having to hire more agents.
“Since we no longer have siloed systems, it’s much easier to give agents feedback and understand their workload, because we’re looking at one set of data. That was much more difficult before because it was like comparing apples to oranges when it comes to different channels.”
–Simon Nilsson, chief commercial officer at Northmill Bank
Games24x7: Generating smarter responses
Games24x7 is an online gaming company headquartered in India. It integrates its customer database with its business intelligence software so the team can quickly identify trends in customer queries and resolve issues before they get worse.
The company turns data insights into action by creating automatic responses for as many questions as possible. Meanwhile, standard macros help agents intervene when needed on complex queries. As a result of these automations and triggers, the team is able to resolve 95 percent of tickets within three hours.
“The intent is always to have a seamless experience so that customer queries are answered real-time throughout the day via automation and directing them to the right stakeholders. If a customer asks us a question, we consider what types of automation or quick replies we can create so that the response time is minimal.”
–Nishant Kalgutkar, associate director of customer excellence at Games24x7
Turn consumer analytics insights into action
Use what you learn about your customers to surpass their expectations. If your customers want faster answers, integrate self-service options into the customer portal for on-demand support. If they want a new feature or product, share their comments with the product team. If they prefer social messaging apps over email, engage with them on the right channels at the right time.
Businesses that take the initiative to improve the customer experience can build stronger connections with buyers and get rewarded with greater loyalty and growth.
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