There are broadly three ways for a business to grow and defend its current revenue stream: by acquiring new customers, by cross or up-selling to existing customers, and by improving customer retention. All three have a cost associated with them and the businesses are interested in the ROI on their investments. Acquiring new customers may cause anywhere between five to fifteen times more than selling to an installed customer-base. For a consumer facing business, their ability to set up robust processes that predict their consumers’ propensity to churn well in advance, and with enough time to run retention campaigns, and stop critical consumer segments from leaving, makes sound business sense and essential to building a robust business.
Companies have been employing machine-learning techniques on their data to find patterns that signal their customers’ propensity to churn. Historically, companies worked with analytics consulting companies that specialized in developing churn prediction models for specific industries and functions. These consulting companies used established processes to develop churn prediction scorecards for each significant consumer segment in their consumer base. Here are some example steps:
First, ETL & Data wrangling: the efficacy of predictive models is based on the datasets used for machine learning (model development).
Second, Feature engineering: the process of defining customer attributes through historical information about them, followed by identifying predictor features (attributes with highest impact on churn behavior) through statistical algorithms and numerical methods.
Third, Model development: This is usually followed by defining time frames (input data window, output/prediction window), and model training and validation. The models and churn propensity scores thus developed are used to identify future churn propensity based on recent customer behavior.
Fourth, Deployment: These inputs are plugged into churn retention campaigns for specific customer segments.
In this traditional paradigm, churn prediction and management was only accessible to mid-to-large size companies, those that could build data science teams or hire analytics consulting companies. This traditional model of churn management is no longer relevant today for two reasons.
First: new age SAAS prediction services like ours, ScoreData’s ScoreFast™ are bringing down the infrastructure investment and upfront costs substantially, abstracting away the science of churn propensity prediction- making it easier to use for the business managers, all of it contributing to make churn prediction and management accessible to businesses of all sizes.
Second: Cloud, social media, IOT and ubiquitous devices are redefining consumer touch points and the competitive landscape for businesses every day. Companies today are employing smart customer engagement solutions at multiple layers. In this new world, by the time the manually developed churn prediction models get deployed, the underlying assumptions- the indicators of churn behavior, may have already changed. This means your churn prediction models are obsolete by the time they are deployed. Companies need systems that are nimble on their feet, systems that keep up with ever changing business landscape and keep giving superior results.
Let’s try to understand these concepts with the help of some real world examples of churn management in business.
Churn Management for Telecom
First, let’s look at the telecommunication industry. It is one of the earliest adopters of churn management solutions and among the heaviest users today. The landscape of churn prediction and management has gone through a sea change in the last couple of years in this industry on account of big data analytics.
In the Telecom industry, customers (subscribers) are known to frequently switch from one company to another and this voluntary churn has always been a critical business concern. It is a subscription based business model where the majority of revenues come from recurring monthly subscription fees from existing customers.
Although telecom companies have accumulated a lot of domain knowledge about the drivers of churn behavior, they cannot predict (and contain) churn basis these static insights. For example, new plans from competitors are a well known driver of voluntary churn. Companies offer lucrative data and voice packages for new customers but not for existing ones, frequently resulting in customers moving from one company to another to get a better plan.
But subscription plans are very dynamic in nature, where new plans are being launched every day and the whole landscape changes within a matter of months. So you cannot predict future churn based on a competitive plan landscape of today. Moreover mobile phones are no longer just telecommunication devices; subscribers’ needs have a very strong social purpose as well (due to social media, image/ media sharing etc.) and these social attributes are nowhere captured in regular telecom data sets.
The point is you cannot manage churn effectively solely based on the known reasons of churn behavior. And this is where ScoreData has dramatically improved the business problem. What you need is a strategy that allows you to develop predictive models that quantify current churn drivers and keep up with changing landscape of churn behavior at all the times. The model performance necessarily decays over time and you need systems that keep fine-tuning the models whenever performance decays beyond the accepted thresholds.
Churn Management for the Weight/loss management
Let’s look at another industry, and another churn management problem. The “weight loss/management” industry has a big customer churn problem. Consumers subscribe to plans for x months and then discontinue a program even though they may still need to continue the program to experience full benefits from their program. One very important driver of this churn behavior is the difference between expectations and reality. Customers sign up with unrealistic expectations and that often results in disappointments even with modest results (moderate weight loss).
Although this is a well-established phenomena and companies do try to handle expectation management for existing customers there are several other, more important drivers of the churn behavior as well. And these drivers of churn behavior keep changing with time, location and other macro parameters. If the business needs to incorporate hundreds of additional factors to determine which features are really causing churn, you may want to compare and contrast several models with several data sets while experimenting with new signals or external data sets. You need systems that develop churn prediction models, capture these signals from the data, and implement the monitoring on a continuous basis. Systems like these enable businesses to understand the churn behavior of their important customer segments and devise retention strategies.
ScoreFast™, the web based model development and management platform from ScoreData, is built as an enterprise grade churn management system. Its data and model management modules are easy to use, dashboard driven and intuitive. It chooses models for specific use cases after trying out hundreds of algorithms internally and selecting the one with best performance metrics. ScoreFast has collaboration features encouraging sharing and collaboration within large cross functional teams, and access control features that are designed keeping in mind the specific needs of ScoreData’s big enterprise clients. The collaboration features encourage cross functional knowledge sharing and innovation within the companies.
The platform has built in hooks to link raw data feeds into the system and its one push provisioning features mean models once developed and tested can be deployed onto downstream systems with a single push of a button. These features make ScoreFast easy to integrate into existing business processes without any disruption or cost overheads. ScoreFast’s real time self learning module makes sure your model performance never goes below the statistical or business validation thresholds that you setup. As soon as the performance drops below the line, it triggers a retrain- without any human intervention required. This means your churn prediction models are always on top of the game and all relevant signals are taken into consideration while scoring a consumer for their propensity to churn.
ScoreFast has features for advanced users as well: those who want to peek under the hood and customize the models. The platform is not just for the business user, it empowers the data scientist to get into the specifics of model definitions, analyze performance comparisons, and fine tune the models.
ScoreFast is the market leading machine learning and model management platform that is making predictive model development, specifically churn prediction accessible to companies, regardless of their size, with identical predictive power for all. With ScoreFast’s cloud based architecture, and built-for-business-manager interaction designs, the paradigms for churn management are changing very quickly. Companies that respond to these changes and efficiently leverage future ready platforms like ScoreFast for their churn prediction and retention strategies are going to have a substantial competitive edge in the marketplace of today and in future.
- Mudit Chandra and the ScoreData team