Customer analytics 101: How to make the most of your data
How well do you know your customers? Use customer analytics to gain a deeper understanding of your audience so you can make smarter business decisions and improve CX.
Last updated March 23, 2022
Companies are increasingly prioritizing the collection of customer data. According to the Zendesk Customer Experience Trends Report, business leaders increased their investment in customer data management by a whopping 92 percent amid the COVID-19 crisis.
But capturing customer data alone isn’t enough. The information you gather must be relevant. You also need to interpret that data to form insights and take action.
This is where customer analytics comes into play.
Businesses use customer analytics (or consumer analytics) to identify and understand what makes customers tick. Data analysis helps you discover how consumers use your product or service to solve problems; it also reveals the roadblocks they face on the customer journey.
Leverage consumer analytics to anticipate customer needs, and you and your team will be set to provide proactive support and a seamless brand experience.
What is customer analytics?
Customer analytics is the process of gathering and analyzing consumer data to gain meaningful, relevant insights into buying behaviors and preferences.
Companies can collect customer data from a number of touchpoints, including websites, apps, social media, and surveys. From there, team members can analyze the data and compile a report—either 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.
There are four types of customer analytics:
1. Descriptive analytics
Descriptive analytics involves collecting and assessing data about past customer actions. While this type of consumer behavior data helps you understand what has happened, it doesn’t tell you why it happened.
Say a significant number of customers give their support interactions a low rating in customer satisfaction (CSAT) surveys. Descriptive analytics would highlight this trend. But it wouldn’t necessarily tell you why you’re receiving low scores.
2. Diagnostic analytics
Diagnostic analytics examines data to determine the cause of trends—it reveals why customers act the way they do. For example, diagnostic analytics can explain why customers gave you a poor CSAT score.
You can gather this information by using an open-ended survey question or by reading reviews and social media comments. After studying the data, you might learn long resolution times are the problem.
3. Predictive analytics
Predictive analytics forecasts what your customers are likely to do based on historical data. This can help your support team anticipate customer needs and identify patterns, and as a result, deliver a better experience.
It could be something like this: By using data to determine when a customer buys a particular product, a business can predict when that customer might need the product again and send a targeted email to them. 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 answers the question, “What should we do?” by recommending a course of action based on historical data. It provides ideas on how you can achieve certain outcomes. For example, you can plan to reduce resolution time by 20 percent to increase customer retention by 50 percent.
Why consumer analytics is important
Consumer analytics provides full visibility into customer behavior. You can learn how people discover and use your products or services and how they interact with your support team.
By tracking and analyzing CX metrics like Net Promoter Score® (NPS) and CSAT score, you can see where you’re excelling and where you need to improve. Qualitative customer data, such as social media comments or responses to open-ended survey questions, can point to specific reasons why customers are satisfied or dissatisfied.
By tracking and analyzing CX metrics…you can see where you’re excelling and where you need to improve.
These critical insights allow businesses to make the changes needed to achieve customer happiness, increase customer loyalty, and reduce churn.
Imagine a retail company sees customers are searching for a particular product that’s out of stock. The retailer can work to quickly restock the product so they don’t lose customers to their competitors. Or, say a support manager notices customers are complaining about long wait times. They can add more self-service options or hire more staff to resolve that issue.
In both cases, customer analytics can be used to expose gaps in the customer experience. The marketing platform Mailchimp, for instance, uses consumer data analytics to understand trends in their support tickets. By listening to customers and looking at the big picture, the Mailchimp team knows just the right features to roll out to boost customer satisfaction.
How customer data analytics works
The customer analytics process involves data collection, cleaning, and analysis. These three steps are time-consuming when done manually. We recommend using a customer service software solution—like Zendesk—that integrates with a customer data platform (CDP). This will make the gathering, processing, and summarizing of customer data more efficient and secure.
Capturing customer data
So, where can you find consumer data? If you use omnichannel customer service software, you already have a source of critical data. This type of software houses relevant information like customers’ names, addresses, previous support tickets, and purchase history.
You can also use your customer service software (or a CDP) to gather data from various sources—such as social media, websites, and mobile apps. The system can then create customer profiles to help you see all the key information at a glance.
When using a CDP, you’ll need to create a tracking plan: a living document that outlines the metrics you want to track, why you want to track them, and where they will be tracked. This helps to standardize the data, which makes it easier to sort and analyze. Creating a tracking plan usually requires knowledge of SQL, but Zendesk integrations like Segment don’t require you to write code.
You can also carry out customer surveys. Use CSAT or NPS surveys to collect customer feedback on product and service interactions. Be sure to add open-ended questions so you can gather qualitative data, too.
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.
Cleaning and categorizing data
The next step is organizing and cleaning the data you’ve captured. This will help ensure you make accurate assessments. The analytics tools in your data platform will clear any errors by removing irrelevant and duplicate data. Once you’ve implemented your tracking plan, the CDP will place data sets in categories in the central database.
Use your platform to assess the data and identify patterns. CDPs utilize machine learning to sort through data and surface trends.
Say you want to find out what factors influence customer engagement for your streaming service. The CDP will analyze data from your app, website analytics platform, email marketing platform, and customer support tool to track specific actions that encourage subscribers to use the service. You might discover that your customized movie suggestions in emails are driving more clicks than the in-app recommendations by the algorithm.
After you’ve gathered, organized, and analyzed your data, it’s time to share the findings with the team. Use data visualization mediums like graphs, bar charts, and dashboards to show patterns, highlight trends, or tell a story.
Turn consumer analytics insights into action
Now that you understand your customers better, what’s next?
Use what you’ve learned to surpass their expectations. Do your customers want faster answers? Integrate self-service options into your support channels for on-demand support. Do your customers want a new feature or product? Communicate their suggestions and concerns to the product team. Do they prefer social messaging over email? Engage with customers on their favorite channels at the right time.
Businesses that proactively improve the customer experience will strengthen their connections with buyers and drive loyalty, profitability, and growth.