What is customer analytics?

Customer analytics describes the processes, tools and strategies companies employ to gather and analyse customer data to inform business decisions.

Every business is different — from the smallest independent family-run shop to the largest multinational conglomerate — companies and organisations cover the full range of industries, products, services and goals. But there is one thing that unifies every business under a common banner: the need for customers.

And along with that, the need to address the needs of customers.

Customers are the lifeblood of your entire business. And whether you operate within the B2B or B2C markets, building your brand and growing your business means meeting the needs of your customers better and with more consistency than your competition. Unfortunately, there’s a difference between understanding the importance of customer satisfaction and knowing how to make it happen.

To understand who their customers are, what challenges they face, what they expect in terms of service and products, and what their overall customer experience entails, successful businesses make customer analytics a top priority.

On the surface, customer analytics is fairly straightforward: using data to better understand your buyers. But there is more to this mandate than meets the eye. Understanding customers means knowing them on a personal and demographic level, grouping them into behaviours and identifying the trends that they follow. In essence, it’s about decoding customer actions so that businesses may gain actionable insight into what strategies can best ensure a favourable customer outcome.

To do this, an effective customer analytics framework will generally consist of three primary processes:

1. Knowing your customers

Before you can even begin collecting customer data, you should have an idea of who your customers are. Customer journey mapping can help you visualise the process the customers go through on their way to making a purchase (and beyond), including all the potential interactions and stages they may encounter. And while additional data can certainly help you flesh out your understanding of the customer journey, creating a preliminary map will provide a clearer picture of what touchpoints are best suited to gathering actionable data.

2. Gathering and analysing customer data

Once you’ve mapped the customer journey, identified the most data-relevant touchpoints and found any gaps where you might be missing out on data-collecting opportunities, the obvious next step is to begin collecting the data itself. Every touchpoint and interaction produces customer data — websites, social media, in-store interactions, emails, app usage, advertising clicks, service and support requests, etc. It can also be beneficial to take a more proactive stance to customer data gathering through surveys and user research studies.

3. Defining your outcomes

Data without a goal is just a waste of time. As such, it is essential to determine what objectives and outcomes you want to gain from your customer analytics strategy. Data analytics outcomes are typically divided into four distinct categories:

  • Prescriptive
    Prescriptive analytics address specific questions and their possible responses.
  • Descriptive
    Descriptive analytics offer insight into past behaviours, answering the question: “What has happened?”
  • Diagnostic
    Diagnostic analytics provide additional insights into past behaviour, answering the question: “Why did it happen?”
  • Predictive
    Predictive analytics look at possible future actions, answering the question: “What will happen?”

What are the benefits of customer analytics? Possible advantages have the potential to affect nearly every aspect of your business — customer facing and otherwise. This is because a deeper understanding of customers and their buying habits allows you to predict the future of your business more accurately. And the more detailed and successful your analysis, the better prepared you will be to address upcoming trends and deliver relevant interactions at the right time to drive business.

More specifically, the benefits of customer analytics include the following:

Graphic showing the benefits of implementing customer analytics.
  • Improved customer retention
    It is always more cost effective to retain an existing customer than it is to attract and nurture a new customer through your pipeline. Customer analytics helps businesses understand their buyers’ pain points, challenges and needs so that they can provide the right resources and support at exactly the right times. This results in more repeat business and fewer clients leaving to investigate their other options.
  • Reduced customer acquisition costs
    Every dollar you spend acquiring customers is a dollar that must be compensated for elsewhere. Customer analytics allows you to focus your finite resources on the channels and strategies that are proven most effective for your audiences, ensuring that the money you spend on acquisition isn’t going to waste.
  • Improved effectiveness of customer service
    Customers are busy people who value their time; the more effort it takes on their part to interact with your customer service solutions, the less satisfied they will be. Customer analytics helps you streamline your service options for improved efficiency and effectiveness, providing more consistently satisfying outcomes for your customers while demanding less effort on their part.
  • Increased revenue
    In nearly every case, the success of a business is tied to its profitability. And your ability to grow your business and generate revenue is tied to your customers. Customer analytics gives you a greater understanding of what your customers want and what it will take to get them to buy — and continue buying for years to come. This goes beyond simply promoting existing products and services; correctly applied, customer analytics can uncover unaddressed needs to inform you of future product and service offerings.

Customer analytics as a process must include three key steps if it is to be effective. These steps help ensure that data is being captured, legitimised and fully examined so that it may be put to effective use. These steps are defined as follows:


As previously stated, every customer interaction is an opportunity to collect relevant customer data. This includes every possible touchpoint, from website analytics detailing what visitors land on your pages and what actions they take, to service calls and social media engagement, to data gathered through customer feedback strategies. The more data you collect, the more complete the picture of your customer base will become.


Although the goal of the data-collection stage should be to gather as much customer data as possible (within legal limits), the purpose of the data-validation stage is to refine that data. Data validation processes help filter the quality customer data from the superfluous, leaving only the information that is accurate, consistent and reliable. Often, proper data validation requires a dedicated team member specifically tasked with reviewing data, backed by an effective data-analytics platform.


Finally, the data is collected and refined and ready for analysis. Proper customer analytics depends heavily on how complete your customer personas are. Customer personas help you know who your ideal buyers are. You can then cross-validate your data with available customer use cases to build a predictive model of how your current and future customers are likely to behave in any given situation. Properly implemented, data analysis helps you identify and define the customer variables that have a direct impact on your business, such as how customers discover your product, what features they prefer, and why they leave.

Data analysis is a multi-faceted process that may include machine learning and AI-enhanced automation solutions to identify patterns and create scalable workflows.

Customer analytics has the potential to improve conversions, reduce customer churn and enhance the operational efficiency of your business. But the effectiveness of customer analytics and the advantages it delivers depend on how you approach it. When adopting a customer analytics strategy, consider the following best practices:

Analyse omnichannel interactions

Today’s customers don’t always stick with the same channel as they interact with your business. Taking an omnichannel approach to data analytics will give you a more accurate picture of how your various groups of customers are connecting with your business, and what their comprehensive customer journeys actually look like.

Understand customer satisfaction level

A key aspect of the customer experience is customer satisfaction — the extent to which customers feel their goals have been achieved by your products and services. Often, making customer satisfaction a part of your customer analytics approach will mean taking a more aggressive stance by reaching out to your customers directly through surveys and other feedback opportunities.

Make predictions and test solutions

The insights gained through customer analytics are not always a sure thing. Get a feel for the accuracy of your analysis by putting it to work. Make predictions, test solutions and document your results. You may be able to locate and resolve issues within your strategy early so that you can refine your approach for increased accuracy.

Engage on the right channels

Your customers want the freedom to interact with your business using the channels that best meet their needs. Being available on these channels is essential, but you also need to be more than available; you need to be active. Use analytics to determine which channels your individual and collective customers frequent, and then prioritise those channels to improve the effectiveness of how you engage with your buyers.

Organise your data

Essential to the analysis process is data organisation. Data should never be siloed or simply dropped into a data warehouse. Instead, analyse how data points connect with one another and group them together to create customer and demographic profiles. Analytics platform-as-a-service (PaaS) solutions can manage much of the heavy lifting in organising and applying your customer data.

Leverage data visualisation

For a professional analyst, numbers on a page may be all the storytelling you need to understand the significance of your data. But what about the other stakeholders within the company? By using data visualisation—charts, graphs and other visual representations—you can present your data in a way that makes sense to marketers, sales teams, executives and others. These stakeholders can then easily act on the patterns and insights your data has uncovered.

Use the correct tools

A comprehensive approach to customer analytics is only possible with the right analytics tools. Advanced AI solutions and business intelligence empowers organisations to see the connections and patterns hidden within massive volumes of customer data. For customer analytics at scale, this is one area where the ‘best practice’ is really an ‘essential practice’.

Every business depends on its customers. And in this increasingly digital society, those customers are always generating data. Customer data offers insight into how customers act and what they value, but with so much data available, creating a customer analytics strategy capable of finding the patterns in the chaos can be extremely difficult. ServiceNow offers the solution.

ServiceNow Performance Analytics puts the power of data in the hands of those who are responsible for delivering successful customer service. Built on the industry-defining Now Platform®, Performance Analytics brings all your analysis tools together in one place, and offers a unified, central location for your business to analyse, report and act on the data in real time. It’s powerful, intuitive and secure, and makes customer analytics a natural fit for any business.

Learn more about how ServiceNow can help you get the most out of your customer data; click here to talk with an expert and see how the right data at the right time can make all the difference.

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