Why customers churn and How to fix it?
As the world has transformed organizations in every industry into a subscription-based business, the most dreaded aspect by every business leader or marketer is customer churn.
Churn rate has a direct relationship with the company’s revenue. While business leaders attribute their losses to the overheads, failed marketing and ad campaigns, extravagant expenses, and failed product experiments, churn escapes the spotlight in these discussions, but that is precisely the place where the spotlight needs to be.
Therefore, identifying the vital factors affecting customer churn in companies and prioritizing these factors help organizations to develop a new solution for preventing customer reduction. This further helps the organization to save the cost and stay competitive.
We can broadly identify three churn types:
Involuntary churn: – this happens when the service provider terminates the relationship with the subscriber or the customer due to the non-payment of the subscription fee or dishonoring the rules of engagement.
Inevitable churn: – this happens due to a shift in service location of subscriber to a different geography or a new market.
Voluntary churn: – this occurs when a subscriber finds a service provider or vendor who offers superior service, quality, value for money, and speed of delivery.
As one can realize, our focus is to identify the reasons for voluntary churn and develop models to address it. Through our experience in helping customers build churn prediction models and literature available from other practitioners, we can identify six key factors which influence churn:
- Subscription or Service Fees: If a price is perceived to be less than the reference price, customers perceive price fairness and transaction value; else, they feel losing value and are prompted to look for cheaper alternatives.
- Switching Cost: When customers switch to competitors, they mostly lose Time, energy, and money even they may be excluded from some benefits and unique features due to being a subscriber of the product/service. But, if the competitor is smart enough to address all of them, it can motivate customers to move.
- Competitors with superior technology: Intense competition in an industry allows customers to switch quickly from one provider to another.
- Service Quality: In service organizations, quality depends on the number of customer expectations answered by the organization, starting with customer onboarding.
- Customer Satisfaction and Delight: Customer Satisfaction or delight has a lot more to do with how well companies deliver on their basic, even plain-vanilla promises than on how dazzling the service experience might be.
- Security Concerns: With a growing number of online products and customer data being online, Security concerns refer to the fear of losing data or personal information that can trigger customer churn.
Everything you need to analyze your churn:
If your company is looking for a solution that will allow you to get a better grip on your churn analysis, you should select one that takes a holistic approach to the various types of potential churn. There are mainly 4 data elements used to consider for any Churn analysis such as:
· Customer Demographic Factors (Age, Gender, Income, Education, Marital Status, Employment, Geographical location, Race or Ethnicity. etc.). Each factor will play a significant role related to customer buying patterns. For example, Age and Gender define the market segments, product preferences, and this will play a vital role in fashion, entertainment industry, Electronic goods, etc. Likewise, Income will play a more significant role in buying decisions, and it is tightly coupled with the product price, offers, discount etc. Also, Race, location will play a different role in the customer perception of a product and service.
· User Behaviour Factors (how a person uses a product/service). There are many factors affecting customer behavior, such as.
o Psychological Factors (Motivation, Perception, Attitude, Beliefs )
o Social & Cultural Factors (Family, Reference Groups, Status)
o Personal Factors (Age, Occupation, Income, lifestyle )
o Economic Factors (Personal/Family income, Credit availability)
· Support Factors (Interactions with the customer support, Response time, Resolution time for each case )
· Contextual/situational Factors – These factors are dynamic and should be changed based on the situation, and that also may vary for each person. Some of the few physical elements which can affect the situational factors are the Layout of a store, Ambiance, etc. Weather conditions, Time of the day/year, etc., are some other situational factors that will affect the customer buying behavior.
Further, this can categorize into various customer segments and, based on the same Data Science team, can create prediction models because this Customer segmentation on top of the above feature categorization will increase the prediction accuracy. Various models can be used for customer attrition like Logistic regression, Decision tree, Random forest, etc.
So, while considering the above factors, it is essential to develop and implement the customer churn prediction solution wisely. It should be integrated with the organization’s data repository, primarily with the Customer relationship management solutions and support systems. Because without accurate data, it will be a hurdle to train the models effectively. Also, the solution should have a repository to store the cleaned data for further analysis. So as a conclusion, with Saxon Global’s deep experience in Machine learning capabilities (Algorithms, Models, Tools, and Analytics) and integration with proper datasets, we can predict the Customer Churn effectively.
Khalil Sheikh is the Executive Vice President of Solutions and strategy at Saxon Global. Under his leadership, Saxon is building ROI driven Data Science AI/ML and recommendation engines for its fortune 1000 customers. Khalil has extensive experience in the Software & IT services industry and in turning around businesses through actionable business intelligence leveraging AI. With more than 28 years of experience across the industry verticals, he has led several successful Data Science & digital transformation journeys for ISV’s and enterprises. He led CTO/CIO forums in the valley and is known for fostering creativity, collaboration, and diversity.