In today’s world, every SaaS company wants its customers to succeed, but the success of the latter entails a clear awareness of the expected value. Analytics can be used to provide such a clear perspective through the analysis of customer data.
There are top 3 types of analysis that can be taken up by the companies:
- Descriptive analysis, giving an overview of the past data.
- Predictive analytics, which is used to predict patterns and patterns of behavior.
- Prescriptive analysis, which suggests out the best actions for any given situation.
Customer success can be improved through these three types of analysis.
Customer service teams are increasingly using this technology for predictive analytics, in order to help them identify customers at risk or willing to buy more. This information is valuable because it allows SaaS companies to engage proactively with customers.
Descriptive analysis of customer data is also valuable within a SaaS enterprise. Apart from predicting customer buying behavior, customer success teams can use this analysis to find out the best ways to better their offerings, processes, and marketing to help customers succeed.
Customer data is available and easily accessible in different systems within a company in CRM, marketing, support, billing, and many other points of entry for customer data.
Combining the details of their customer accounts, their behavior and their comments allow them having a complete picture of their customers. Let’s take a look at some of these popular analysis techniques.
1. Aggregation and segmentation as a starting point
The aggregation and segmentation of customer data is the starting point for any type of analysis. These tools give a complete and quick insight on an account while allowing to test hypotheses. Aggregation picks up a particular set of data and finds out a value out of it.
Segmentation allows you to define a group of customers according to their characteristics. For example, we think of accounts with an RRM of over $ 2,000 or users who have used a particular feature. It is then possible to display the aggregated statistics on the segments, which makes it possible to compare them.
Understanding the factors that affect success across segments can create action plans to directly target customer issues. It is also possible to compare the accounts in order to develop recovery strategies.
Aggregation and segmentation can also help companies understand the profile of successful customers. Focusing acquisition programs on customers who are likely to succeed with a particular solution can help accelerate the growth of a business.
When it comes to using aggregations and segmentation, the challenge is the vast amount of data that needs to be explored. Over the years, teams specializing in customer success will develop insights to speed up the process with customer data, while other analytics will help acquire additional information.
2. Cohorts to plot a particular action
As the name suggests “cohort”, this particular analysis puts the customers in segments based on a particular point in time and not depending on any other type of characteristics.
A common use of cohorts places customers in groups based on the week they have registered. It is then possible to track them over time to monitor changes in customer signups, their retention, or any other level of activity. With the passage of time as the processes and product improves, one would be able to see improvements in the cohorts.
It is also possible to place customers in groups based on the date they took an action and follow them based on this initial action. These features that customers continue to use are likely to provide more value than those that customers rarely use or stop using. Cohort customer data can be combined with segmentation to refine understanding of customer behavior.
It is possible to segment customers by industry, size or industry, for example, to see if certain features are more associated with a particular industry. This makes it possible to identify the functionalities to be promoted to this or that client and makes it easy for product and marketing teams to understand if a customer appreciates a particular feature.
3. Funnel analysis to improve customer steps
The funnel analysis tracks down on the number of customers who complete a sequence of steps and reach a particular goal and find out how long the customers took to get there. For this, it is sufficient to define a series of actions that customers are expected to follow through these steps.
A funnel-based customer analysis involves following users through the sign-up process. By following these steps, you can find out the number of customers to reach different stages, complete those stages and at which stage the major customer drop off occurs.
To get the most out of funnel data analysis, you can start by tracking how many people are completing the signup process and then move onto the follow-up process.
These analyses become very useful for teams that want to understand the problems in their processes or make improvements to their solutions.
4. Regression analysis to analyze correlations
Regression analysis is used to find out the correlation between different factors. Its is useful to find out the trends between unstructured data and draw out conclusions that can help businesses to refine their future strategy.