What makes your customers tick? This might sound like a simple question, but every business knows it’s not. On the contrary, you need robust customer analytics to understand complex customer behavior – especially in a digital world where customers go through multiple stages and are willing to conduct a whole load of research to determine what to buy and when.
Customer analytics (or consumer analytics for B2C) is the process of collecting and analyzing behavioral customer data across a range of channels, devices, and interactions. These analytics give you the insight necessary to form strategies, products, and services that your customers will want to engage with.
For all types of customer analytics, you may need to use techniques like data collection and segmentation, modeling, data visualization, and more.
Want to learn more? Then read on to see the importance and overarching categories of analytics, as well as six types of customer analytics that can work well for your business.
Some business benefits of the different types of customer analytics are:
That’s because data tells you what you need to do to achieve each and every revenue and growth goal — from how to encourage customer engagement to how to promote customer loyalty. You can’t improve what you can’t measure, as the saying goes.
And this is especially because consumers are more informed and selective than ever. To truly understand them, and their needs, you need the right data.
It’s no coincidence that successful companies are heavily data-driven. According to McKinsey, companies making intensive use of customer analytics are 2.6 times more likely to have a significantly higher ROI and three times as likely to generate above-average revenue growth than competitors.
Once you begin tracking and analyzing customer data, you can use them to answer questions about customer behavior and make important business decisions. For example, you can identify ways to:
One thing to consider is, given the many different types of analytics (that we’ll see below), you’ll need buy-in from multiple teams and data from a range of disparate sources to complete the full picture. To do this, you can use the unified customer view that customer experience platforms offer.
Find out why omnichannel views and flexibility is the best approach for customer experience analytics.
Here are the four categories of analytics with customer analytics examples:
The different kinds of customer analytics always fall under one of these four categories.
This type of customer analytics focuses on understanding the customer’s interactions with your brand – from their initial research on your product or service to the actual purchase and beyond.
That’s why they may involve a mix of data points from different interactions. For example, organic and non-organic traffic to your product pages contains insight about the initial stages of the customer journey: research and information gathering. Shopping cart abandonment rate can tell you how many customers leave their shopping cart before completing a purchase.
Think about what metrics would help you evaluate the particular customer journey steps you’ve identified as important to your business.
Learn how to map the buyer's journey from the awareness stage to beyond purchase.
Customer experience analytics shed light on how your customers feel when they interact with your brand. An important aspect to these metrics is to do with customer support (e.g. time to resolution) and customer onboarding (e.g. user adoption and time to value). If you’re using a platform to track support tickets or email and live chat, you’ll probably have easy access to customer support metrics.
Another part of customer experience is CSAT scores. In other words, how satisfied your customers are with your services (think diagnostic analytics). CSAT surveys are easily administered via email or via software like Delighted and ChurnZero after important events (such as training or purchasing). CSAT surveys help you, in part, evaluate the quality of your customer onboarding, too.
You may also find analytics like customer effort scores useful to gain insight into customer experience.
Pay attention to qualitative data as well. For example, if a customer sends you a complaint email, reply appropriately and then note down the complaint (even an excel would do). If you continue to get similar complaints, report on them to discuss actions.
Customer engagement analytics may be divided into two categories: analytics for engagement with your product/service and analytics for engagement with your brand (e.g. web analytics).
Customer success teams may track user engagement with your product (e.g. usage metrics), but engagement marketing is also common: investigating and influencing the relationship of your brand with interested customers.
For example, you could segment your website visitors, and see how they interact with your content and calls-to-action, and their navigation paths, and then target them with personalized ads or email content based on that. Email marketing metrics, like click rates and click through rates, as well as social media engagement, can give you insight into how to increase customer engagement, too.
Bear in mind that customer engagement is relevant to every stage of the customer journey, so it’s natural the two types of customer analytics will overlap.
This type of analytics measures how loyal your customers are. How many of your buyers are repeat customers? What percentage of your customers churn? These metrics tell you whether your customers like you more than other similar businesses.
Perhaps the most popular way to measure loyalty is the NPS (Net Promoter Score) survey. It’s the tried and tested “would you recommend us to a friend” question. If you’re thinking of implementing NPS surveys, check out our Ultimate Guide to NPS.
Also, customer churn and customer retention are metrics that could point to big problems down the line. Combine them with customer experience metrics to find ways to create proactive customer retention programs.
In a broad sense, customer lifetime overlaps with customer journey and customer experience. But, an important additional metric in this type of analytics is the Customer Lifetime Value (CLTV). This metric shows you how much revenue you can expect from a single customer throughout the entire business relationship.
The way to calculate this metric may be different depending on your business – sometimes, bringing in a consultant may work better in identifying the right formula for your company. But, a simple way to calculate this metric is by multiplying your average retention rate by the average number of purchases, and then multiplying the product by the average deal total.
Of course, there are more complex (and therefore more accurate) ways to calculate this metric. Also, segmenting this metric based on type of customer helps you see which ones are more valuable, and should be the audience of your more expensive marketing campaigns.
You can use CLTV in different ways to inform your decisions. For example, if you see it decline, that signals an issue with repeat customers. If it’s lower than what you spend on acquisition and marketing campaigns, then you’re probably spending too much without getting enough back.
The voice of the customer is a self-explanatory idea: it’s what your customer says that’s relevant to your business. With these analytics, you capture customer opinions, preferences, and expectations.
Voice of the customer analytics also refers to CSAT and NPS surveys, social media posts and interactions, and really anything that lets you listen to your customers’ thoughts. Take a structured approach to surveying customers by following best practices like asking the right questions, digging into demographics, and choosing the right medium.
Here are some tools that can help you gather and report on customer data:
Acquire is a customer engagement platform that helps you keep interaction history with customers in one place, so you can have data from multiple channels (including live chat, website, social media, chatbot, and more) at your fingertips. It also keeps data of your team’s actions (e.g. time to respond to customer questions) to identify better ways to serve customers.
Kissmetrics helps you understand user behavior across your website (e.g. looking into which elements bring more conversions).
IBM Watson Customer experience analytics helps you visualize and analyze the customer journey.
Google needs no introduction; GA has a robust array of tools to measure traffic, behavior, and attribution on your website.
Hootsuite analytics help you monitor your social media performance and get valuable insights for future campaigns.
Hotjar helps you drill down to web visitor behavior by giving you tools like heatmaps, recordings, and more.
If you want the best possible results from your data, make sure you:
How do you use customer analytics? What benefits have you seen? Let us know in a comment below.
Nikoletta is a Content Specialist at Acquire. She's a writer and editor with an avid interest in data, tech, communication, and the customer journey.