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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.

More importantly, it is wise to first understand the multitude of reasons why customers would want to leave a company which in turn points to the direction of customer satisfaction feedback playing a major role in aiding companies to reduce churn rate. Collecting customer feedback regularly and analyzing it goes a long way with a huge return on investment in the direction of a good customer retention program.


Being consistent in collecting and analyzing feedback is the best way to measure customer satisfaction which in turn increases the scope for improvement. When it comes to collecting feedback, being on time plays a major role in setting the foundation for customer feedback analytics.


The best way is to collect real-time feedback through in-app, live-chat, and chat-bots. Real-time feedback collection would be a 2-way conversation through which customer nuances tap the emotional and nonverbal part of the communication and help in the identification of customers who do not openly communicate can be identified with trained personnel having a keen eye.


This helps in arriving at the solution in a quick and efficient manner. Personalizing this experience will be even more effective as this will lead to more genuine feedback.


Collection of feedback is itself an art and by looking keenly into what exactly you are measuring will provide additional insights on the quality and relevance of them. Normal feedback that you receive will just be a rating or a review from the customer but oftentimes the reason for the review can not be captured. If you look into what you are measuring today, they are likely all lagging indicators which are easily measurable, meaning they measure current performance. The magical morsels of insight that can light your path to improvement are leading indicators which link cause and effect and also show you how to produce the desired results. Leading indicators are difficult to identify, difficult to measure, dynamic and vary from industry to industry.


Further, it is not sufficient to only collect feedback. Scrutinizing it with precision is equally important. Oftentimes it is hard for humans to analyze such huge data let alone being precise when doing so. “People are more difficult to work with than machines.


And when you break a person, he can't be fixed.”― Rick Riordan. This is where a customer feedback analytics tool and a SaaS company like Pyoneer come into the picture. It helps in the reduction and prevention of churn by using AI and machine learning to help companies to analyze the huge amount of feedback and also simplify the process of collecting the leading indicators for your company.


Primarily, predictive analytics tools and predictive analytics techniques must focus on streamlining and categorizing a huge number of customers into segments based on the type of feedback like product, features, etc. This is where data analysis comes in handy with the feedback analytics software that you use.


Segmenting customers into different groups can allow you to find out how each segment interacts with your product or brand. Additionally, you can look at each sub-group and focus on gaining customer insights. Consequently, the next step would depend on the strategy of the customer success or product manager based on the product roadmap and company-specific servicing strategy to improve churn prediction and increase customer retention rate.


The trick here is to figure out how to do segmentation and this is where SaaS products utilizing customer feedback analytics can make your life easier. Analyzing customers’ lifestyle, demographics, SaaS product purchased, types of customers, the purchase value, and the frequency of purchase, and the right type of data depending on your industry segment allows you to discover the type of customers who drive most of your revenues. In this way, you can predict customer churn for future days.


With the advancements in technology and improved processes in AI and machine learning, reducing customer churn should be a child’s play if done correctly. The skill of a craftsman is as good as his tools and companies that use good customer feedback analytics software is equivalent to a great craftsman only if that software uses data analysis to extract insights from customer feedback.


In brief, the efficient and impactful use of prediction and data analysis can help companies, especially in the day and age of new business models like the subscription economies, to reduce customer churn and consistently help in customer retention and churn management. With the evolution of SaaS and its proliferated growth, it is more important to invest in a set of tools that not only help you grow but also put you on the edge.

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