Subscription Economies can Use Prediction and Data Analysis to Reduce Customer Churn

A subscription business model is built on the idea that companies can capitalize on the compounding value of customer relationships through subscriptions. In this regard, Software as a Service (SaaS company) has evolved over the last twenty years from infancy to empowering companies with a significant influx of business.

The average amount of time it takes to fully implement a new software product has plummeted from 57 hours ten years ago to 7 hours today thanks to improved technical breakthroughs in the web industry. With an estimated 11,000 SaaS companies today the flexibility, accessibility, cost-effectiveness, and the wide range of features offered by cloud-based SaaS enterprises are luring more and more organizations.

According to Gartner, SaaS revenue is expected to hit a whopping 151 billion dollars by 2022.

In order to tap some of those revenues and to witness increased growth as a SaaS company, you should understand the importance of using customer churn prediction to your advantage.

The basic layer in any customer churn prediction software is to start analyzing the pain points throughout the customer journey and assessing the ones who are about to leave by observing their behavior.

The importance of customer churn prediction and prevention for subscription economies is commensurate with the growth of the saas company. Statistics show that, on average, 65% of an organization’s business comes from existing customers, and acquiring new ones is 15 times more expensive than retaining existing customers. The problem here lies in the recognition of unhappy customers before they leave so that a nice customer retention strategy can be put into place.

An efficient customer churn prediction model helps to forecast future trends and behaviors and identify previously hidden indicators leveraging artificial intelligence (AI) and machine learning.

The most common customer churn prediction models are based on older statistical and data-mining methods, such as logistic regression and other binary modeling techniques.

These approaches offer some value and can identify a certain percentage of at-risk customers, but they are relatively inaccurate and end up leaving money on the table. In order to predict customer churn, machine learning algorithms are turning out to be most useful especially when working with large amounts of data.

Product managers or customer success managers need to be proactive in the collection of qualitative and quantitative customer data and record customer satisfaction metrics and lagging indicators such as NPS and CSAT that help in customer churn analysis to a certain extent.

Further, the collection of behavioral data like product usage reports and buyer personas will help the CX department with the demographics needed to segment the customer base and in turn, identify which groups are most likely to churn. Consequently, customer success managers create a churn prediction model after analyzing the collected data which is then deployed into usage.

To summarise the process of customer churn prediction modeling Hubspot mentions these five important steps.

  1. Determine your current churn situation.

  2. Survey customers and find out reasons for churn.

  3. Leverage customer behavior data

  4. Segment your customer base.

  5. Activate your customer success team.

To predict customer churn with the right model, data scientists need access to a wide variety of data from feedback and surveys to align the company goals and the product roadmap before making new models.

Designing the training modules for the machines, fine-tuning the models, and selecting the one that works best is a part of building the algorithm, which forms the major part of their work. The product managers then choose the model with the highest accuracy in prediction to deploy that into production.

Anyone that goes in the path of customer churn prediction using python and machine learning must conduct these steps in a structured manner in order to help find a solution or let’s say help the machine in finding a solution on its own. With this in mind let us now look at how to reduce customer churn.

In a situation where businesses are estimated to lose around $1.6 trillion per year due to customer churn, the company that masters a way to reduce customer churn will always be blessed with an increase in profit, improved brand recognition, and customer loyalty growth.