Research/Blog
Machine Learning Predictions for Subscription Business Models
- November 6, 2017
- Posted by: CellStrat Editor
- Category: Artificial Intelligence Big Data Bots Deep Learning Machine Learning
The subscription business model is a business model where a customer must pay a subscription price to have access to a product or service. The model was pioneered by magazines and newspapers, but is now used by many business and websites. This model is also rapidly proliferating among the e-commerce giants like Nearbuy, Myntra, Amazon etc. Machine learning can help marketers of subscription e-commerce business by providing predictive insights.
Subscription business requires lot of money for acquiring users from social media, referral programs etc. If the users don’t maintain their subscription for at least few cycles, it can be highly detrimental to the sustainable growth of the company. The biggest challenge to subscription business model is retaining its subscribers.
Machine learning can play a crucial role in identifying potential subscribers. Given all the information about users at sign-up, a classifier model can learn which features are associated with long-term customers and casual ones. For example, there are two persons A and B, Person A was acquired from Google, aged 31 years, resides in Mumbai and own an iPhone while person B is from some rural area and acquired through Facebook via a PC. In this case person A is more capable and potential to be a subscriber for a given service. This segmentation on acquisition can be a powerful way to deploy or test various strategies. Understanding that locations, registration sources, and personas are your cash cows, is one of the foundations of building a strong business model.
Another challenge to subscription business model is predicting which customer is about to cancel the subscription as associated cost with each subscription is very high and when a subscription is paused or cancelled it bring sudden loss in revenue. Online behavior, subscription parameters, and product responses from users can be used to determine how well they are doing in the subscription cycle. Some features can be created and passed into a custom clustering model to identify endangered subscription and can be used for targeted marketing campaigns.
Machine learning can be extremely beneficial in predicting customer lifetime value (CLV). Once a predictive system is put in place, it’s important to analyse the acquisition sources, locations, and demographics with high or low CLVs. A newfound application of the CLV prediction is to leverage the High CLV segment to create “lookalike” Facebook audiences. This can lead to better customer acquisitions and reduced costs to achieve greater lifetime value for users. The outcome is better acquisition results, higher retention rates, and longer subscription cycles.
Differentiating between passive and engaged subscriber is important requirement of subscription business model. Machine learning can be used to segment users into active and passive categories thus helping businesses keeping their revenues revved up all the time.