Customer retention is a crucial success metric as a Software-as-a-service (SaaS) business.
Businesses in the SaaS industry face much competition because customers are free to choose from various providers within the same product category. As a business owner, you may be wary of losing customers as the cost of acquiring new customers far outweighs the cost of retaining them.
As a SaaS business, how can you achieve your business goals despite customers and companies' tendency to drift apart? Even a small scale churn can significantly affect your business. Customer churn analysis provides a wealth of knowledge your business can use to its advantage. Fortunately, one of the most effective ways of identifying and combating churn is predictive analysis using machine learning.
In this article, you'll learn how to;
- See churn rate as an opportunity for future influence using artificial intelligence (AI).
- Identify the three ways churn rate analysis increases ROI
- Explain the past and predict the future
The churn rate, also known as the rate of attrition or customer churn, is the rate at which customers stop doing business with a company. It is most commonly expressed as the percentage of service subscribers who discontinue their subscriptions within a given period.
Research has shown that the cost of customer acquisition is 5-25 times more than the cost of retaining one. Hence, it is advisable to retain subscribing customers' efforts rather than expend resources on getting new ones. But there's the question, how do you recognize which customer category to direct your retention efforts?
ML Models, Customer Segmentation and Churn Reduction
How do you differentiate between premium clients and average clients? It will be a misuse of resources to put so much effort into persuading a customer who is prone to leaving. But it's even more tragic not to put that effort into a client who has the potential to stay.
The solution to this dilemma is customer churn modeling. With machine learning models, you create customer retention marketing campaigns that will further reduce customer churn risk by targeting customers with more potential to stay and increase loyalty. This will help your business to assign resources where they will yield the most profit.
To do this, you need to get the data about your clients in one place and begin modeling. Typically, businesses have information about customers spread across different departments. Data Integration is merging this data from various sources to be used for building predictive models. Data Integration allows you to create a unified view of all your data sources and write queries to create insightful visualizations, predictive modelling and generate actionable insights in real-time. Many companies aiming to stay ahead in their respective industries embrace big data with all its potential and challenges.
It is understandable that only big companies have been able to utilize data analysis tools in the past. Owing to factors such as the huge cost of setting up a data analysis department and perhaps a lack of enough knowledge in the area, many SaaS businesses have not enjoyed the advantage data analysis gives. However, with the advent of automated data analysis tools, where with an affordable subscription fee, businesses can now get access to all the data insights they want, this challenge is solved.
Using these platforms will help you save time, effort and then the huge upfront cost associated with processing data traditionally.
3 Ways Churn Rate Analysis will Increase Your ROI
Why are we making all this fuss about predicting churn? How does this exactly affect the bottom line?
- Improves targeting: Results from analyzing churn models are crucial for marketers to determine where to focus their efforts. Once a potential deviating customer segment is identified, your marketing team can concentrate their efforts on this group, offering incentives like targeted discounts, applying new tariffs, updating existing pricing, special promotions, free training, etc. Remember losing customers who could have stayed if the right efforts were invested is a tragic experience.
- Encourages investors: Investors are another factor to consider for reducing the rate of losing customers. A consistently high customer churn rate may depict instability of the business, hence scaring away potential investors.
- Boosts customer retention: As said at the beginning of the article, it is more expensive to acquire a new customer than to keep one. As much as possible, you want the efforts targeted at gaining new customers to be worthwhile, and not that you keep losing your clients after you've invested so much in ads, training sales personnel and organizing promotions only to gain customers that will leave after a short while.
According to research done by Frederick Reichheld of Bain & Company, retaining customers by 5% increases the profit margin by 25-95%. These are many reasons to be interested in keeping your churn levels low.
Explaining the Past, Predicting the Future
With the emergence of Artificial Intelligence, businesses can not only determine the rate at which customers leave; they can also draw insights from the information to reduce customer churn. A professional data analysis platform will offer predictive analysis tailored to your businesses’ specific needs. By using the pre-built machine learning models you can identify at-risk customers and properly channel your customer retention efforts.
To begin analysing the data you have on past customer churn; sign up here for a free 15-minute consultation.
This article was written by Eniola Daini. She is a Content Marketing Associate at Voyance HQ, one of Nigeria's leading data analysis/machine learning companies.