5 min read
Obviously, every company wants happy customers who are getting the most out of the product or service the company has to offer. But how do you know if your company has them or how to make them even happier? Customer success analytics is evaluating and interpreting customers’ behaviors and interactions with your company. Using the different types of customer success analytics will let you know not only how happy your customers are, but what things you can do to make them even happier.
Customer success means making sure that your customers are getting what they want from your product, service, and company. A customer success team can be involved with customer interactions at every stage in their journey with your company, including when customers are deciding to purchase, setup and use the product or service, as well as ongoing customer support.
It can sometimes be hard to clearly distinguish between marketing, sales, and customer success teams in terms of what they do and how they all perform separate but important tasks for a business. A good way to think about it is that marketing let’s potential customers know you exist, sales converts them to customers, and customer success keeps them happy once they’re in.
Analytics in tech fields in general refer to the science-based process of finding and sharing meaningful patterns in data - essentially turning data into decision-informing insights. When we apply this to customer success it means collecting the raw data about customers’ interactions with and satisfaction about your company, then studying that data in a way that helps improve those things going forward.
The collecting of data about your customer’s experience with your company is metrics, whereas the analysis of this data is analytics. Customer success metrics for SaaS companies are also often called key performance indicators (KPI) and can include:
customer churn rate
average revenue per user
net promoter score
These metrics and more can be collected in a number of different ways, like:
customer satisfaction surveys
email open rates
number of minutes spent on each page of your company's site
Now that we know what metrics we might be working with, let’s look at the importance of customer analytics and which one’s make the biggest difference. There are 4 main types of analytics: descriptive, diagnostic, predictive, and prescriptive. Each one is needed to get to the next one in the chain, and ultimately you need all of them to get the final result you’re trying to achieve with these analytics.
Descriptive - what customers have already done
Diagnostic - why they did it
Predictive - what they might do in the future based on past behavior
Prescriptive - what can be done to change how they act next time
Let’s look at an example to better understand how all these pieces fit together to get the final result of improving customer’s satisfaction with your company. Let’s say you have developed an app that let’s employees coordinate delivery food orders for lunch meetings easily. It allows every employee to vote on where they would like to order food from, automatically chooses the most popular option, then prompts them to place their order so all the food will be delivered at the same time. You have raw data from employers using your app and you're ready to analyze.
Descriptive analysis shows 50% of a company’s employees weren’t voting for a restaurant in the allotted time.
Diagnostic analysis shows that it was because they were not receiving alerts in time.
Predictive analysis indicates that more or louder alerts will help.
Prescriptive analysis recommends making sound alerts automatic with app download and adding an additional alert would increase employee engagement.
This same process can be used in an unlimited number of ways to inform a customer success team about all elements of how a customer interacts with a company. Everything from addressing billing issues, developing a loyalty plan, renewing contracts, and more.
The last, but arguably most important, step in this process is actually using the results to create changes in the company. Gathering the right information and analyzing it perfectly will not improve anything for your customers if it isn’t acted on. Having a mechanism in place for using the actionable customer data from the results of your analysis will ultimately determine how useful this analysis is for improving what your company is doing.
Empowering your customer support team to not only help individual customers with problems as they arise, but to make changes to the company that will reduce future problems and improve customer happiness is the real goal of customer support analytics. This can and should apply to many areas within the company, everything from rewarding customer loyalty, to learning about which type of customers are most profitable, to setting marketing strategies and getting referrals.
Without happy customers, your company’s future is dreary. That’s why your customer support team needs great tools to do their jobs well. At Planhat, we empower all your teams to work together to drive better outcomes to your customers. Our platform provides data collection, ease of workflow, security, automation, integration, and presentation building to make customer support easy! Sign up for a free demo and see the difference for yourself.
In G2 Summer Report 2022 Planhat has been named a leader in five categories: Customer Data Platform, Client Onboarding Software, Customer Success Software, Client Portal and Customer Revenue Optimization.
Gurprem Sagoo, Planhat, has been awarded The Customer Success Manager of the year
Behind every loyal and growing customer base is a solid analytics process, which has 6 key steps.
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