What is Customer Analytics?

Every customer interaction leaves valuable insights behind. These insights can take your business in different directions, but the goal is to uncover the most valuable opportunities for growth. To do that, you'll need the right strategy and tools to guide you. 

Simply put, the trail is your customer data, the treasure box is the insights, and the map and key refer to customer analytics. 

Customer data will not do much on its own, but it will do wonders for you with the right tools and techniques. This is why customer analytics is not about unearthing data — it’s more about working with the insights. 

Hence, we’ve put together this guide on customer analytics. By the end of this article, you’ll understand what customer analytics is about, its benefits, use cases, and best tips for implementing or improving customer analytics. 

What is customer analytics?

Customer analytics is the use of tools, technologies, and methods to analyze customer data to obtain insights into customers' behaviors, interactions, preferences, and more. These insights will, in turn, influence product updates, marketing campaigns, and business decisions.

For instance, in the book Predictive Marketing by Omer Artun and Dominique Levin, the authors recommend a framework for allocating marketing budget using customer analytics. This includes:

• Creating separate plans to invest in acquisition, retention and reactivation.
• Differentiating your spending on high, medium, and low.
• Finding the products that bring in the highest lifetime value customers
• Finding the channels that bring in the highest lifetime value customers.

Thanks to the existence of different endpoints, there has been an increase in data volume. You can now access customer data through surveys, interviews, feedback, in-app analytics, market research, etc. 

Our point? Customer data is not hard to come by, and its use case can be found in almost every industry or business type. What matters is how well you can manipulate it to get the insights you need. 

Now, speaking of customer data…

Types of customer data 

These are the four primary types of customer data:

• Descriptive data: This refers to data obtained from customer’s basic information. It’s essential for developing various customer personas, including demographics and information about a customer’s preferences toward the company and its market. For B2B customers, this could also mean firmographic. Such data include customer names, postal and email addresses, gender identity, race, and age.  
• Behavioral data: This relates to data on how customers use your product, whether they shop online or offline, what device they use, and their purchase history, among other things. 
• Interactive data: This pertains to online data. It captures the path taken by a user interacting with your organization. Sources of this data include website visits, click-through rates, social media engagements, and more. It can also be used offline to capture a shopper's routes in a retail store.
• Attitudinal data: This tells you how your customers feel. You can get this data from focus groups, reviews, and surveys because these are ways to source your customers' opinions, quantify their sentiment, and see how it is evolving. Attitudinal data is the most complicated to analyze because it cannot be evaluated with hard figures, and the information will differ from person to person. 

These four data types can create a holistic picture of the customer and business environment.

How does customer analytics work?

In general, customer analytics is built around enterprise data collection. Fully digital companies (such as cloud-based businesses or mobile app publishers) can generate this data easier than, say, a brick-and-mortar grocery store.

Customer analytics often starts by automatically collecting data using technologies that capture basic demographics and locations associated with a website visitor or app user.

Then, as users navigate a website, logs capture their route, how much time they spend on a page, whether they abandon a shopping cart, and more. This fine-grained behavioral data can be used to determine a customer's trouble with the website, technical incompatibilities, or programming bugs.

Another way to capture customer data is to poll users directly about their interests or complaints with the organization. Customers may also willingly and proactively provide data to the organization through emails, social media posts, or third-party product reviews.

After a certain amount of data is collected, some or all this information can be combined to create a very detailed picture of an individual customer and a broad portrait of the business’s customers. At this point, you look for similarities, anomalies, and trends in the data and compile the insights you gain to help make informed decisions.

Benefits of customer analytics

Customer analytics significantly boosts:

• Marketing
• Product adoption/growth
• Sales
• Business decisions

Here are its benefits:

Facilitates segmentation and personalization

Since data is key to delivering personalized experiences, customer analytics ensures access to the right data and insights. These insights will then dictate the customer segments you need to create, the kind of products and services you need to promote, and the messaging to use when marketing to them. 

Alternatively, you can use behavioral data to send personalized messages to your customers to encourage them to take specific actions, such as completing a purchase or updating their subscription.

Improves customer satisfaction

Acting on customer feedback and insights keeps customers satisfied and loyal to your brand. Techniques powered by customer analytics include optimizing your offerings to meet customers' needs, providing better support, and making the customer journey more seamless. 

Influences product development

Customer analytics facilitates product updates by enabling you to understand and act on customers’ reactions to previous updates. This leads to faster feedback and a higher Return On Investment (ROI) on every upgrade.

Lowers operating costs

Customer analytics can help avoid resource waste through unnecessary business investment. 

Strategies for reducing operating costs supported by customer analytics include:

• Data-driven marketing campaigns
• Better pricing models
• Predictive churn analysis

Unlock competitive advantage

Analytics enables you to turn customers into brand advocates, increase your sales performance, and stay lean on business costs, all of which you’ll get an edge over others in your industry. 

Types of customer analytics

Like regular data analytics, there are four types of customer analytics, which include:

Descriptive analytics

This type of analytics deals with historical data as it analyzes your customer’s past actions to expose past events or occurrences.

For example:

• Sales reports
• Customer demographics insights
• Website traffic
• Conversion rates
• Customer satisfaction ratings

Descriptive analytics explains the what but not the why; hence, it’s just surface-level but still helpful for knowing your customers.

Diagnostic analytics

Diagnostic analytics is used to investigate or understand customers' actions. It explains the factors that affect their actions. For this type of analytics, you’ll need to work with interactive data from as many sources as possible.  

Predictive analytics

Just as the name suggests, this analytics type forecasts customers' future actions using historical data, machine learning, and statistical modeling. It answers questions like:

• How likely is a customer to repurchase a product?
• When is the best time to introduce a new feature?
• What kind of product or service can you upsell to a customer?

It allows you to take proactive measures in customer relations and sales and impacts customer retention, satisfaction, and acquisition.

Prescriptive analytics

Prescriptive analytics recommends the best action to achieve desired marketing, product development, or sales results. It works by analyzing historical or interactive data.

For instance, it can suggest solutions for managing recurring customer queries or adding a new pricing tier to capture a particular segment of customers. 

Some common uses of prescriptive analytics include healthcare, banking & financial services, sales & marketing, and security & IT operations.

(Related reading: predictive vs prescriptive analytics.)

Applications of CA

Typical applications of customer analytics are found in the following areas: 

Customer retention

Discover your best-performing channels for turning customers into brand loyalists or investigate whether your marketing campaigns resonate with customers or turn them away. Customer analytics can provide some prescriptive answers and help organizations better understand their advertising expenses.

Customer engagement

Deliver relevant content that keeps your customers engaged and willing to refer your business through analytics. This means creating content that delights them, enlightens them about your products and services, or celebrates their contribution to your business’s growth. 

In addition, you can develop new ways to engage with customers, increasing the time spent on a website, in an app, or a store, ultimately increasing revenue.

Customer journey mapping

Product analytics helps create a customer journey map that elevates the customer experience. Examples of such analytics are the information you get from identifying all the customer touchpoints within and outside the product, tracking feature adoption, and monitoring the signs of customer churn. 

Customer service

Deliver quality customer service by documenting and investigating customer queries, acting on them immediately, and proactively resolving issues that could lead to repetitive queries through predictive analytics.  

Business growth

Forecast your future revenue by calculating your customer’s lifetime value and brand sentiments through predictive analytics. In addition, you can tell the best time to pivot to a new business model, adopt a new pricing strategy, or target a new customer segment through insights from prescriptive analytics. 

Customer acquisition

Improve your chances of getting customers by identifying your ideal customers, optimizing your sales process, and improving your conversion rates. 

Customer analytics can help answer questions like:

• How much is a new customer worth?
• How much should you spend to acquire one?
• What is needed for better customer retention?

Customer analytics tools and software

Most organizations already use some form of customer analytics, even if they don’t actively know it. Free, off-the-shelf tools like Google Analytics are extremely popular, giving you a base level of insight into customer segments and how customers interact with your website.

Since the essential tool is free, it provides a convenient, no-risk gateway for organizations wishing to dip their toe into customer analytics tools — at least as it relates to the web. Your CRM tool is another natural source of customer data that you’re already using.

There are several standalone options for customer analytics, but the market is relatively fragmented, feature sets can vary widely, and not all tools are directly comparable. This list represents the most commonly used and best-reviewed customer analytics tools.

• Adobe Analytics: A broader business intelligence tool for analyzing the web-based customer journey.
• Glassbox: A platform designed to analyze behavior during individual web and mobile app sessions.
• Kissmetrics: A web analytics platform with separate products for tracking SaaS and e-commerce operations.
• Mixpanel: This self-described product analytics tool has dozens of connections to third-party add-ons to enhance functionality.
• Whatfix: A no-code solution that enables organizations to analyze user behavior and track product usage with custom event and user action tracking. 

Best practices for turning customer data into insights

It is understood that you have the tools and a system for capturing and analyzing customers' data, but that is not enough to make customer analytics work for you. So, consider implementing all these for the better results:

Define your goals

Establish what you hope to achieve with every insight you garner. This will dictate the kind of data you should focus on gathering, whether behavioral data, descriptive data, or the like. 

Sometimes, you may need more than one data type to achieve an objective, so it’s at this point you establish what that will look like. 

Segment your customers

Segmenting your customers into groups allows for judicious use of time and resources, produces the best insights, and prevents you from feeling overwhelmed by the volume of data.

Distribute your insights

Distributing your insights to other people, like management and teammates, will benefit from your insights. When distributing insights, be mindful of the format, channels, and language you use. You should share them as summaries highlighting the most critical areas to focus on and explaining how the insights relate to the business’s goals. 

Also, be open to feedback, such as suggestions for improving the report or requests for further explanation. 

Centralize your data

Having a single platform for storing and tracking customer data makes it easy for all stakeholders to access and monitor the insights generated. 

(Related reading: customer data management.)

Challenges of customer data analytics

Despite your best intentions, the following factors can hinder you from getting the best output from customer analytics:

Privacy concerns

Over the years, customer analytics has become more challenging for organizations. This is thanks to recurring cases of data misuse, which has reduced customers’ trust, fear of cyber-attacks, and the existence of various regulations. 

Data quality issues

Since having the right data is vital for customer analytics, another concern is the quality of available data.

This concern manifests in different ways:

• Inconsistent data sources and formats
• Data collection errors
• Incomplete customer information

These issues affect the quality of insights from working with customers’ data and can lead to poor decisions. 

Implementation cost

Like every other technology-dependent venture, implementing customer analytics is hindered by the non-existence of a budget for adopting the necessary tools and the lack of technical talent to implement and maintain the technologies. 

This limits the adoption of customer analytics to large organizations with the budget and skills to handle it. 

Storage cost is also a consideration. You’ll need to use different storage platforms, especially cloud-based ones, which have varying costs depending on the volume of data you’re working with. 

Know your customers to grow your business

Customers today demand personalized, high-quality services and products, and it is easier than ever for them to defect to competitors if you don’t meet those demands. In return, you get their loyalty, money, time, and data, which are elements that will skyrocket your business to the next stage. 

Start small by exploring your existing endpoints and implementing the tools and best practices shared in this piece to extract insights that hold the key to your business’ growth.