Every SaaS company wants its customers to be successful, but customer success requires a clear understanding of the expected value. Analytics can provide such a perspective through the analysis of customer data.
There are three types of analysis for the company:
- Descriptive analysisThe following table provides an overview of the historical data.
- Predictive Analysiswhich predicts trends and patterns of behaviour.
- Prescriptive analysiswhich focuses on the best action for a given situation.
Client success can be improved through all three types of analysis.
Customer success teams are increasingly turning to technology for predictive analytics, helping them identify customers at risk or willing to buy more. This information is valuable because They enable SaaS companies to proactively engage with customers..
Descriptive analysis of customer data is also valuable within a SaaS company. Beyond predicting customer behavior, customer success teams can identify the best ways to improve their products, and their marketing process to contribute to customer success.
Customer data resides in a variety of systems within a company in CRM, marketing, support, billing and many other entry points for customer data.
Combine details of his account, behaviour and comments provides a complete picture of the customer. Customer analysis uses this rich data to identify how companies can improve their products. Let’s take a look at some of these popular analysis techniques.
Aggregation and segmentation as a starting point
The aggregation and segmentation of customer data is the starting point for any analysis. These tools provide a complete and rapid understanding of an account while allowing hypotheses to be tested. Aggregation takes a set of data to extract a unique value.. For example, it could be the number of unique users in the last 30 days or the frequency of use of a feature during the week. Viewed over time, this customer data shows a trend or changes in customer behavior.
Segmentation allows you to define a group of customers according to their characteristics. Examples include accounts with an ARM greater than $2,000 or users who have used a particular feature. It is then possible toDisplay aggregated statistics on segments, allowing you to compare them..
Understanding the factors that affect success within different segments can help create action plans to directly target customer issues. It is also possible to compare accounts in order to develop recovery strategies.
Aggregation and segmentation can also be used to help companies understand the profile of successful customers. Focusing acquisition programs on customers who are likely to be successful with a particular solution can accelerate a company’s growth.
When it comes to using aggregation and segmentation, the challenge is the large amount of data that needs to be explored. Over the years, customer success teams will develop insights to speed up the process using customer data, while further analysis will provide additional information.
Cohorts to plot a particular action
Cohort analysis segmented differently than some characteristics. A common use of cohorts places clients in groups. They can then be tracked over time to monitor changes in the customer retentionthe health score or the level of activity. As the product improves, it may be possible to see that the cohorts show improvements.
It is also possible to place customers in groups according to the date on which they have carried out an action and follow them up on the basis of this initial action. These features that customers continue to use are likely to provide more value than those that customers rarely or no longer use. Cohort customer data can be combined with segmentation to refine the understanding of customer behavior. Customers can be segmented by sector, size or industry, for example, to see if certain features are more associated with a particular sector. This makes it possible toIdentify the features to be promoted to a particular customer. and helps product and marketing teams understand whether a customer likes a particular feature.
Funnel analysis to improve customer steps
Funnel analysis focuses on the number of clients who completed a series of steps and how long it took them to do so; and. To do this, it is sufficient to define a series of actions that are expected from customers to follow them through these steps.
Funnel client analysis involves tracking users through the registration processes. By following these different steps, it is possible to see the percentage of customers who complete each step, complete each step and where significant problems occur.
To get the most out of funnel analysis of customer data, it is possible to start by tracking the number of people who complete the registration process. Funnel analysis is very useful for teams wanting to understand or improve their solutions. It is also valuable for teams that can interact with foresight. If customers have problems with a part of the product, they can better guide them through the different steps with the help of training.
Regression analysis to analyze correlations
Regression analysis identifies the correlation between the different metrics. For example, it is possible to run a regression for check the correlation between connection frequency and revenues. The results may show an increase in revenue with increased connection frequency or a decrease in revenue with increased connection. or find no correlation between income and frequency of connection. Regression is a useful way to identify trends from unstructured data. By identifying the correlation between time and a metric, it is possible to determine whether that metric has increased or decreased.
Amazon uses customer data to open a bookstore
In short, the analysis of customer data helps customer success teams identify product improvements and new marketing processes. Analysis of customer data can therefore enable SaaS companies to significantly improve their businesses.