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What makes your customers tick? This might sound like a simple question, but every business knows it’s not. Human behavior is complex – 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.
That’s why you need customer analytics.
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.
To help you in the process, we explain the importance and overarching categories of analytics and present a list of six types of customer analytics that can work well for your business.
You can’t improve what you can’t measure, as the saying goes.
Think of all the strategies your company can employ to achieve its revenue and growth goals: encouraging customer engagement, promoting customer loyalty, increasing customer satisfaction. Each one of these strategies adds real value to your bottomline.
But, drilling down into what exactly you need to do requires frequent and methodical data analysis. If the data approach was helpful before, it’s nothing less than imperative now – because consumers are more informed and selective than ever. To truly understand them, and their needs, you need the right data. Only then can you offer a better and more personalized service.
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 than competitors and are almost three times as likely to generate above-average turnover (revenue) growth as competitors who evaluate their data only sporadically.
Specifically, among the business benefits of the different types of customer analytics are:
Analytics help you achieve those goals via:
There are four categories of analytics in general. Each of them can be applied to customer analytics. These are:
The different kinds of customer analytics always fall under one of these four categories. Every type has one of two purposes: one, to discover patterns across the entire customer or prospective customer base’s behavior, or two, to understand who each customer persona is (e.g. demographics, background).
That’s how your business can provide a personalized service to your individual customers, but at the same time form services and products to match more general customer needs.
For all types of customer analytics, you may need to use techniques like data collection and segmentation, modeling, data visualization, and more.
Given the many different examples of analytics, you can see that it’s unlikely you’ll capture every single important metric from one department, one method or one software. In fact, you’ll need buy-in from multiple teams and data from a range of disparate sources to complete the full picture – although you can achieve a unified customer view using certain software platforms.
But, the complexity of customer analytics shouldn’t be an argument against using them – just opt for a structured approach and learn exactly what each type of analytics entails. This way you’ll be able to reap all the benefits we mentioned above.
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. Mapping the customer journey is an important first step in collecting the right data.
Then, customer journey analytics 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. While 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.
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 the 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 the customer effort score (how easy it is for customers to interact with your business) useful to gain insight into customer experience.
It’s important to 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, count them and report on them to discuss actions.
Using analytics to improve customer engagement is very good practice. That’s because, if you want to keep someone’s attention, you need to know what they’re interested in – and data is the most effective way to find that out.
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/lifecycle, 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, with the answers categorizing respondents into groups of either passives, promoters, or detractors. If you’re thinking of implementing NPS surveys, check out this guide on how to calculate NPS and these expert tips on how to use NPS to improve customer loyalty.
Customer churn and customer retention are both commonly tracked metrics. These are two of the metrics that could point to big problems down the line if the numbers don’t stack up. Remember, customer retention costs 7x less than customer acquisition, so keeping hold of your customers goes a long way towards ensuring business growth. 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.
CLTV = (Avg retention rate * Avg # of purchases) * Avg deal total
You can also calculate CLTV by multiplying the average customer value by the average customer lifespan, where:
Example: If your customers spend on average $350 per purchase and buy six times a year on average, the avg customer value is $350*6 = $2100. Then, if your churn rate is 10%, the avg customer lifespan is 1/10% = 10 (years). So, your CLTV = $2100*10 = $21000.
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 thus be the audience of your more expensive marketing campaigns.
CLTV deserves its own category because you can use it 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.
So, track your CLTV and focus on acquiring more high-value and repeat customers.
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.
There are many tools you can use for the different types of customer analytics – we’ve mentioned some already, but here are some more you can choose from (and maybe even combine to get better insight):
If you want the best possible results from your data, make sure you: