Megaventory Blog - Online Inventory Management Software, Order fulfillment and Control System
This is the company blog for megaventory, an online software that helps small businesses that buy, sell and manufacture physical products to manage sales, purchasing, manufacturing and inventory. We blog about new features and updates but also about enterprise software, small businesses, cloud computing and the industry in general.

Thursday, January 4, 2018

Why Customers Leave: Megaventory’s Study on Churn

The main ambition for most – if not all companies – is to reach more people and grow their client base. In the mind of many, it’s simple mathematics.

More customers means more revenue. Seems reasonable, right?

Well, not exactly.

According to various studies, it is actually more profitable and cost-effective to retain and satisfy existing customers, rather than constantly trying to attract new ones. Nevertheless, many companies tend to relax when they see their rate of new acquisitions growing up and neglect to pamper their older customers.

If you are guilty of this mistake and your company is in the SaaS industry, this mini e-book is for you!

Why Customers Leave?

In recent literature, there is a variety of reasons why paying customers might cut their ties with your business. The most common ones can be summarized in the following list:
  1. Bad customer service
  2. Poor on-boarding experience
  3. Missing feature
  4. Usability issues
  5. Bugs or other technical issues
  6. Unexpected Inconveniencies
  7. No Loyalty Programs
  8. Customer-related issues
When a customer stops using a subscription service that they are subscribed to, it is generally referred to as “churning” or “churn”.

A similar term is churn rate, which refers to the proportion of subscribers who leave a supplier during a given time period. In other words, churn rate is the number of the customers you lost over the number of customers you initially started with.

But why calculating churn is important?

Churn is a possible indicator of overall customer dissatisfaction, cheaper and/or better alternatives, more successful marketing by competitions, or reasons having to do with the customer lifecycle.
As resources and time are limited, knowing the reasons of churn, i.e. where you fall behind, you can allocate your team members and funds where it will matter the most.

Megaventory’s Case Study

But what new does this case study add?

Most studies about churn tend to focus on how to predict and identify the customers who are about to churn and what (mainly marketing) actions can prevent the churn.

Our approach is slightly different: we asked ourselves first “why do people stop using a product?” Starting from there we created a series of metrics to investigate the possible reasons that lead our former customers to stop using Megaventory and to understand which of them are actually statistically important.

In other words, we wanted to find out what seems to have the biggest impact on churn.

It can be summarized in the following metrics:

  1. Time to cancellation
  2. Pricing plan and number of users
  3. Use of features: cards and reports
  4. Last subscription package
  5. Missing feature
  6. Reported bug or technical issue
  7. Demographic factors

Addressing these 7 key metrics we hope to be able to:

  • predict possible churners better and be able to take proactive measures to retain valuable customers, and 
  • understand what needs improvement on the product (features, usability, on-boarding, payments options, etc)

As many SaaS companies have a similar business model, checking our Megaventory’s case study might be beneficial for the SaaS entrepreneur, founder, or developer that wants to reduce the number of canceled subscriptions. Note that only one of the possible reasons is Megaventory-specific (Cards and Reports – no 3) – the rest are easily applicable to other SaaS companies too.

Even if the metrics don’t directly correspond to your business model and other industry details, it may still be educational to see how we did our statistical analysis and be inspired for your own analyses.

Click the button below to start reading Megaventory's Case Study:

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