Global direct-to-consumer slashes membership churn
Customer churn prediction
A direct-to-consumer food and beverage service suddenly faced unexpected customer churn for its subscription-based beverage program. While churn is easy to observe as a declining number of customers, it can be difficult to identify the causes of churn. This service faced precisely this challenge. In addition, they wanted to predict which customers would likely churn in the future. This task proved an excellent match for Pecan’s Predictive analytics uses data, statistics, and machine learning techniques to build mathematical models that can generate predictions about things likely to happen in the future…. platform, with a In the context of machine learning, a model is a specific instance or example of an algorithm that has been created based on a particular… built and generating actionable churn predictions in days to empower the team’s rapid response.
Industry: Direct-to-consumer subscription-based beverages
Company Size: Size: 7,000+ employees
Solution: Subscription churn (customer retention)
Platform Use Case: Customer Churn prediction involves building a predictive model based on past customer data. That model will help identify patterns in customer behavior that correlate with churn,…
Data Stack: SQL Server
overall reduction in churn
of would-be churn detected in advance
campaigns made from predictions
Customer churn proves tough to address with conventional methods
As a provider of subscription-based beverage plans with globally distributed customers, this company noticed an unexpected rise in subscription churn but couldn’t detect why it was happening. To try to deal with the problem, the company initially engaged in ineffective and generic re-engagement campaigns that didn’t reduce the churn. Even worse, the campaigns’ lack of targeting caused a negative customer experience and yielded poor campaign ROI.
In addition to determining why customers churn and who was likely to churn in the future, the company wanted to effectively A prediction is the ultimate goal of a predictive model. In Pecan, a prediction is often tied to a specific customer. After learning from data… high-risk customers as far as 3 months in advance.
The company’s data was complex, accounting for 48 different beverage lines and multiple data sources, which required a solution that could connect to and combine them all.
Traditional data science techniques might have offered options, but the unexpected increase in churn meant this team needed a quick solution — not the six months to a year it might take a Data science combines statistics, computer science, scientific methods, and business knowledge to analyze, model, and predict using data. The data science toolkit can be used… team to develop a In the context of machine learning, a model is a specific instance or example of an algorithm that has been created based on a particular… by hand.
Automatically prep and combine diverse data in existing systems
With the Pecan platform, this company automatically connected to and combined data across all the beverage lines and data sources. This data represented marketing campaigns, distribution data, CRM activities, promotions, and more. Pecan’s versatile suite of connectors made it possible to add Predictive analytics uses data, statistics, and machine learning techniques to build mathematical models that can generate predictions about things likely to happen in the future…. into their workflows without disruption, integrating rapidly and seamlessly into their existing systems, and working with data no matter its source.
Smashing the timelines of traditional data science
In just days, the client had a fully trained Churn prediction involves building a predictive model based on past customer data. That model will help identify patterns in customer behavior that correlate with churn,… In the context of machine learning, a model is a specific instance or example of an algorithm that has been created based on a particular…. The model identified which customers were most likely to churn and also allowed the company to pinpoint and prioritize VIP subscribers. In addition, the granular customer-level predictions provided by Pecan offered specific direction for the marketing team to use in outreach to each subscriber.
Proactive strategy with predictions, not retroactive diagnosis with historical data
Pecan’s model enabled the marketing team to develop a preemptive retention strategy by segment, time, and customer value, and feed these predictions directly to their CRM. Instead of waiting to see customers churn and retroactively trying to diagnose the problem, the team could see into their customers’ future. This foresight gave them the opportunity to proactively plan the best way to try to retain those customers likely to end their subscriptions.
85% of churn detected and treated in 1, 3, and 6 month campaigns
Using the Pecan platform, the marketing team identified 85% of customer churn in advance. Armed with that foresight, the marketing team created focused campaigns 1, 3, and 6 months in advance, resulting in vastly improved customer retention and lifetime value.
Overall, the company saw an 11% overall reduction in subscription churn, reducing the surge in churn they were experiencing by 39%. Additionally, among VIP customers, the company lowered the overall churn rate by 20%.
Rapid ROI on Pecan’s churn predictions
This beverage marketing team successfully addressed the mysterious churn problem with proactive campaigns informed by precise predictions. Traditional BI and manual Data science combines statistics, computer science, scientific methods, and business knowledge to analyze, model, and predict using data. The data science toolkit can be used… methods would have struggled to find a timely, effective solution to this dilemma. Fortunately, Pecan’s platform was ideally suited to quickly identify potential churn and enable future-driven action — keeping their beverage subscriptions and business flowing.