How to reduce churn with data engineering

How to reduce churn with data engineering

Customer Retention System schematics

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Reducing churn is among the most common and hardest to impact KPIs subscription-based businesses face.

Churn, or customer attrition, significantly impacts revenue and growth prospects. If put into numbers, subscription services usually have higher customer acquisition costs than monthly subscription price. That means that higher churn rates can easily make even customer acquisition unprofitable.

For example, if the monthly cost of a subscription service is $15, and the cost of acquisition is $90, it takes 6 months for a company to reach net zero with a newly acquired customer. If a customer churns in month 4, the company actually loses at least $30 on that customer. Any associated costs, like support or cost of operations make that loss even higher.

It makes sense for any business to take the matter of churn seriously and deal with it. The most common actions taken include customer support training, releasing features that could stick with customers more or sending out surveys to detect potential churn factors.

With new developments in data science, you can take more proactive and, for customers, less disruptive actions to lower churn. 

Riding on sophisticated data engineering practices and the evolution of data architecture, businesses are leveraging comprehensive data lakehouses to gain actionable insights and proactively address churn. Let’s delve into how data engineering, particularly through data lakehouses, is revolutionizing churn management.

The Role of Data Engineering

Data engineering plays a transformative role in churn management by harnessing data to gain valuable insights. Traditionally, detecting churn reasons is overly simplified by correlating churn to CRM entries prior to canceling service. While that approach may drive some insights, it doesn’t take into account the complexity behind people’s decision to churn. According to Bain&Company, events priming the customer to churn can sometimes even be years apart.

Reasons for oversimplification are many, but partially, it’s a result of technical complexities behind analyzing customers’ behavior and interactions throughout the entirety of a lifecycle. Data science and engineering make it viable to get a much more accurate and holistic image of causes of churn. With solutions like data lakehouse, companies can continuously draw insights from consumers’ behavior and in real time, spot events that can trigger churn. For example, in Deegloo, we developed customer segmentation data models for an insurance company. That enabled our client to focus their effort into reducing churn among the base of customers most likely to stay, effectively optimizing the effort needed to reduce overall churn. At the same time, it also provided insights on the best channel of communication for each particular customer, further scaling down the efforts needed to impact end results.

Data Lakehouses: Unifying Data for Actionable Insights

One of the technological advancements making it easier to use customer data for various purposes is a data lakehouse. A data lakehouse integrates structured and unstructured data from various sources into a unified repository. It’s an approach that enables businesses to collect and store vast amounts of raw data cost-effectively while maintaining data integrity and accessibility. Considering companies using subscription models, like insurances or telecoms collect vast amounts of data about the customers, markets, online and offline behavior and transactions, data lakehouses help in unifying all of it into a digestible user story.  Combining the scalability of data lakes with the structured querying capabilities of data warehouses, data lakehouses become a powerful platform for churn analytics.

Data consolidation

Speaking of unifying data, data consolidation is both, crucial for accurate churn prediction and a starting point to get any other feature implemented as it merges disparate data sources into a single, unified view. This comprehensive dataset provides deeper insights into customer behaviors and patterns, enhancing predictive models’ accuracy. By eliminating data silos, businesses can more effectively identify at-risk customers, enabling timely intervention with personalized retention strategies. Ultimately, data consolidation fosters better decision-making and helps reduce churn, boosting customer loyalty and profitability.

Enhanced Customer 360 view

Data engineering significantly enhances customer 360 profiles by integrating and analyzing diverse data sources, providing a comprehensive view of customer behaviors and preferences. This understanding enables more accurate churn prediction, allowing businesses to identify at-risk customers proactively. By leveraging data engineering techniques, companies can tailor personalized retention strategies, improving customer satisfaction and loyalty, ultimately reducing churn rates and increasing long-term profitability.

Customer segmentation

Improved view of individual customers makes it viable to also push towards better segmentation. Using larger number of data points enables smarter customer segmentation and a more personalised approach in marketing and sales. Using models like UPLIFT helps identify the customer segment that is the most likely to either upsell or in case of churn, most likely to stay, significantly improving customer retention results with a decreased sales effort. With data engineering, the traditional ways of customer segmentation relying mostly on demographics can be brought to another level by utilising real-time behavioral data from both, internal and external systems making segments more relevant and enabling fully personalized offers.

In conclusion, data science and engineering are transforming how businesses combat customer churn. By leveraging comprehensive data sets and advanced analytics, businesses can proactively identify churn indicators, personalize retention strategies, and ultimately enhance customer satisfaction and loyalty. As organizations continue to invest in data infrastructure and analytics capabilities, the battle against churn will increasingly be fought—and won—through the strategic application of data engineering and willingness to adapt business practices to the insights data generates.

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