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? Using the different types of customer success analytics will let you know not only how happy your customers are, but also what things you can do to make them even happier.
What is Customer Success?
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.
What are Customer Success analytics?
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.
What is the difference between Customer Success analytics and metrics?
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
What analytics are important for Customer Success?
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
What is an example of customer analytics?
How to use customer analytics
Planhat: We can make it happen
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.
Founder
Scaale.io
Jonas is the founder of Scaale.io, a growth partner for B2B tech companies, and brings over a decade of experience across brand, media, and marketing strategy. Previously Director of Brand & Communications at Planhat, he helped shape the company’s global narrative and positioning from the early days. Before that, he ran Make Your Mark, a Stockholm-based agency delivering strategic content for brands like Klarna, Volvo, and Vattenfall. Earlier in his career, Jonas served as Editor in Chief at Aller Media, where he led the digital transformation of Sweden’s iconic lifestyle brand Café.
