We’ve all heard it a thousand times:
you need to understand your customer! Know your customer!
But, with thousands or hundreds of thousands customers, how exactly are you supposed to get to know them all, at scale?
The answer lies within the world of customer analytics. Today, companies have access to more customer data than ever before—but many companies leave it untouched, letting its power for profitability, personalization and prediction go unused.
Not your company though—right?
Implementing a customer analytics strategy doesn’t have to be a mammoth undertaking—you can start small, or even outsource the task entirely to maximize your business value and ROI while reducing the amount of in-house work you have to take on.
In this article, we’ll discuss the most important types of customer analytics to know, how to collect and analyze them, and how you can use your analytics to fuel growth.
What Is Customer Analytics?
Customer analytics (aka consumer analytics or customer data analytics) is the collection and analysis of customer information, data, actions and interests aggregated from across all of your company’s channels, customer interactions and touchpoints, and datasets. These data points are then used to understand, attract and retain the most valuable customers for your business.
Customer analytics helps your team understand and solve problems for your customers. Once you’ve gathered and analyzed customer data, you can:
- better know and understand your customers
- create personalized solutions
- identify your most valuable customers (based on CLV)
- optimize the customer the journey by understanding and predicting customer behavior
- and more
This process almost always starts with historical data to understand what is already happening and create baseline standards and metrics. From there, customer analytics can move into predictive data, allowing your company to create data-driven analyses of what you predict will happen with customer behavior. Predictive analytics allow you to create better solutions and optimize experiences for your customer by proactively understanding what their needs and preferences are, as well as what will influence them.
While customer analytics aren’t always seen as a priority, they’re hugely influential in profits and ROI. Companies who use customer analytics “extensively” are 93% more profitable and see 115% higher ROI than others in their industry who don’t. Of course, just analyzing customer data isn’t enough to bring you these kinds of results—you also need to analyze the right customer data, create a strong data-driven culture and find the right partners.
Which Types of Customer Analytics Are Most Important?
While there’s an almost infinite amount of data you could acquire and analyze from your customers, there’s just four main types of customer analytics to know:
- descriptive analytics
- diagnostic analytics
- predictive analytics
- prescriptive analytics
Within each of these types, you’ll find many various metrics and data points. Which of these you choose to measure should be based on your goals and current customer needs. The most important thing to keep in mind when considering which customer data to track is to ensure you’re using a variety of analytics types in tandem to create a holistic view—don’t just focus on one type.
In addition, it’s essential to prioritize that customer data that allows you to hear from the customer directly, sometimes called voice of the customer metrics. These could include surveys, social media comments, reviews or metrics like CSAT and NPS. The more you can hear from your customers themselves and measure this feedback alongside analysis of consumer behavior, the better you will be able to predict, evaluate and solve for customer needs successfully.
Descriptive analytics give you data on previous or existing consumer behavior. An example would be how many customers return an item after purchasing, or how many customers cancel their subscription after the first month.
Customer journey analytics often fit into this category, as they offer a depiction of the customer’s actions and interactions with your brand throughout the entire customer lifecycle. This includes things like cart abandonment metrics, when customers are most likely to contact your support team, the time from signup to purchase and so on.
Diagnostic analytics give you insight into the psychology of customer behaviors and interactions. Examples of diagnostic analytics would include surveying customers to find out why they returned an item or unsubscribed to your newsletter. Customer experience analytics, like CSAT, often fit into this category, as they can tell you when and why customers are or aren’t satisfied. Voice of the customer analytics such as surveys or product review feedback can also be diagnostic.
While descriptive and diagnostic analytics are based on historical data, predictive analytics—as the name suggests—are forward-looking. They provide insight into expected consumer behavior, generally based on historical trends.
As an example, you might measure how purchases are expected to increase or decline in a given period due to previous customer behavior, trends, seasonality, expected engagement and so on. This type of forecasting is generally seen with customer loyalty and retention metrics, especially customer lifetime value.
Finally, prescriptive analytics demonstrate how customer behavior can be influenced or addressed, again typically based on historical data. It’s forward-looking, but you generally need to have baseline metrics in place before you can start evaluating prescriptive analytics.
An example would be measuring the likelihood of different types of campaigns in increasing purchases. Customer engagement analytics often inform prescriptive analytics strongly, as they provide insight into how customers are engaging with your brand, products and service team.
How to Collect Customer Data
Once you’ve determined the types of analytics and which metrics are most important for your brand goals, you’ll need to determine the best way to gather and organize customer data.
Given the enormous amount of data available to companies today—companies handle 3x as much data today as they did in 2015—there are a wide variety of tools and methods available to collect and organize it.
Internal data may be collected from sources such as:
- CRM databases
- marketing tools
- customer service tools
- purchase history
Companies can also gather external data from third-party sources, social media or other online interactions, or from customers directly, through surveys, reviews and the like. Thankfully, there’s also a number of tools available for collecting, organizing and analyzing customer data. Some of the best include:
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An all-in-one customer analytics tool, Mixpanel is a behavioral analytics platform that allows companies to understand how customers interact with their products and brand. It also offers some forecasting tools for predictive analytics.
Hotjar’s heatmaps show you aggregated customer behavior from interactions with your website or product. These descriptive analytics can help you understand where and why customers get stuck in their customer journey and help you create prescriptive analytics to remove roadblocks and improve conversion rates.
Google Analytics is one of the most popular customer analytics tools, allowing you to see a wide variety of descriptive analytics and metrics related to your website and customer engagement.
For voice of the customer analytics, Brand24 is a great solution. This software solution allows you to monitor brand mentions, customer sentiment, customer conversations online and on social media, and more. Whether you’re looking to build out your prescriptive analytics or dive deeper into customer engagement and voice of the customer analytics, Brand24 is a great tool to have.
How To Analyze Customer Data
Ideally, the descriptive and diagnostic analytics you collect will allow you to analyze and uncover predictive and prescriptive analytics for your customers. However, this process can’t happen without effective customer data analysis. Follow these five best practices for analyzing your customer data effectively.
Organize & clean your data
Having “clean” data is essential when it comes to customer analytics. Erroneous data can skew your results and lead your company to take irrelevant actions. Inspect your data carefully for errors and outliers, as well as any missing values, before analyzing. In addition, you should ensure data is carefully organized and stored in a way that adheres to compliance rules.
When analyzing customer data, it can be helpful to segment your audience before modeling and analyzing your data.
There are a variety of segmentation models—which you choose depends on what information you’re hoping to gain from your customer data, as well as the size of your dataset. For example, you might segment based on:
- demographic data
- geographic data
- behavioral data
- customer value
Your customer segmentation can also align with key personas for your brand, for example, loyal returning customers vs. one-off purchasers who came for a sale.
Data modeling allows you to make representations based on your data that can help with analysis and forecasting. Modeling can take many forms, depending on your objectives:
- Clustering: to compare how data points are similar or different from each other
- Regression: to understand how data points impact or influence each other
- Time series: to chart trends or behavior over time
- Associative: to model relationships between data points
Of course, there are many other models you can use, but these are some of the most common for customer analytics.
Compare quantitative and qualitative data
While quantitative data is easy to measure and analyze, ensure you don’t forget about qualitative data in the process. For a more holistic analysis, though, you need to compare your quantitative data with your qualitative results.
Doing so will give you a more complete picture of the customer journey, as well as the customer experience and recurring trends and roadblocks for customers. Paying close attention to churned customers and high-value customers, as well as any major outliers will help you note best practices as well as weak links to improve. Cluster modeling and associative modeling are often helpful during this stage.
Finally, once you have used your data and analysis to understand historical behavior, you should use it to predict future customer behavior as well. This is the goal of predictive and prescriptive analytics, and once you have a sufficient amount of historical data and analysis in place, you should be able to develop some predictive analytics as well.
For example, if you notice a historical trend of purchases beginning to increase around September 12th and peaking around October 8th, you can further analyze the data to understand which customers and products are driving these sales. From there, you can make predictions about how these customers will behave in the future, and if you can influence their purchases at other times of the year, with new products, or via other factors.
How To Use Customer Analytics To Fuel Growth
Finally, customer analytics don’t mean much for your bottom line unless you take action on them. As you implement a customer analytics strategy, be sure to have a plan for testing predictions and implementing strategies based on the data you find.
Consider these five best practices as a starting point to implement your customer analytics and fuel growth:
Personalize the customer experience based on historical data
Personalization is no longer an optional way for businesses to add value—it’s an expectation for customers. The data around personalization is revealing:
- 62% of customers say they would lose loyalty for a brand if their experiences aren’t personalized, up from 45% in 2021.
- 80% of business leaders say customers who have personalized experiences spend more.
- 49% of customers say they’re more likely to become repeat buyers after a personalized experience with a brand
The takeaway? Personalization is a must—for brand loyalty, for profitability, and for customer retention.
Customer analytics can help make personalization possible. For example, historical customer behavior data, purchase history, and customer journey analytics can help you understand what customers are likely to want and do in the future, allowing you to personalize their experience accordingly.
In addition, customer segmentation can help you deliver more personalized experiences at scale. As you understand how different customer segments behave and what they want, you can deliver targeted content, products and recommendations to new customers who match certain customer qualities and segmentation.
Predict when & why a customer will churn
Diagnostic and predictive analytics can help you reduce churn by understanding when and why customers churn. Are customers dissatisfied, and if so, why? At what stage are customers most likely to churn? Do certain actions increase or reduce customer’s risk of churn?
Understanding the data behind these questions can help you take proactive steps to reduce churn and improve value-adds for customers at key points in the customer journey.
For example: early on, Facebook realized that customers were much more likely to continue using the platform if they created enough friend connections early on in their experience. The golden zone was getting to at least 7 friends in 10 days. After this realization, Facebook doubled down on all of their actions to encourage users to “friend” at least 7 other users within their first 10 days on the platform—and as a result, they grew from 145 million users to over a billion users in just four years.
Analyze the best channels and methods to distribute a product or service
Customer analytics can also be a useful way to analyze the best ways to market or distribute a product or service. For example, companies can use a blend of historical data and ongoing testing to determine prescriptive data, creating a playbook for the future that allows you to run more effective marketing campaigns.
This can be used at any level, from customer service interactions (which interactions are the most effective, and why?) to marketing campaigns (how do social media campaigns impact new product launches?) to customer retention (which trigger emails are most successful in encouraging a repeat purchase?).
Pay careful attention to data directly from the customer
Voice of the customer metrics are often particularly useful when it comes to growth and implementable action plans, but they’re often overlooked. As a result, companies who listen closely to customer desires and preferences—and actually implement their feedback—stand to gain a lot in terms of customer trust, loyalty, affinity, and product-market fit.
Collaborating across teams will make this easier. For example, ask your customer service teams to share common complaints and problems they hear with your product and marketing teams. Can any of these complaints be addressed at the root, or solved for earlier in the customer journey? On the flip side, marketing teams running customer surveys or monitoring social media might give customer service teams an insight into general customer sentiment, allowing them to be more empathetic and understanding of customers’ positions while interacting with them.
Use descriptive analytics to create prescriptive analytics
Descriptive analytics—especially when combined with customer segmentation—can provide you with prescriptive analytics that you can then test and experiment to create successful customer interactions and effectively market new products.
Prescriptive analytics can help you reduce metrics like customer churn rate, call abandonment, cart abandonment and so on. For example:
- If your descriptive analytics show that customers abandon cars once they reach a given page: you can use prescriptive analytics to determine how to reduce effort in the checkout process, or how to improve the page where most customers abandon their cart
- If customers tend to abandon calls after five minutes or more in the queue: you can focus your efforts on reducing average time in queue to four minutes or less.
- If most of your retained customers completed a given action within their free trial period: you can adjust your free trial messaging, trigger emails and goals to align with helping customers complete the given action.
You may need to test and identify multiple potential factors before finding the key factor that is truly prescriptive of customer behavior. However, once you find the key factors and levers, you’ll know exactly how to help your customers achieve their goals and get the most value from your product, creating happy, loyal customers who will return to your brand again and again.
How To Find The Right Customer Analytics Partner
As you’ve seen in our discussion above, the best customer analytics are unhelpful if you don’t have an actionable plan to put your metrics and data into action. However, creating such a plan is often easier said than done.
Not only do you need to have the right tools and ability to collect, measure, organize and analyze the data, you also need to have a team who can create—and execute—an implementation plan. This is why many teams turn to outsourcing to manage customer analytics. An outsourcing partner can analyze, design an implementation plan and even execute it for you as needed.
In addition, outsourcing your customer analytics strategy allows you to ensure you have the right tools and software to collect and analyze all of your customer data, at a cost that fits within your current budget.
However, finding the right outsourcing partner is essential—after all, you need a partner you can trust when it comes to customer data. For a trusted, reliable outsourcing partner for everything customer-related, ROI Solutions is here to help. With decades of experience in CX, ROI Solutions can help your team collect, measure, analyze or implement customer analytics, strengthening customer engagement and loyalty, improving your customer experience and growing your bottom line.
Ready to make customer analytics a priority for your brand? Connect with an expert from ROI Solutions today.