Streaming Services: Increase Your Subscriber Revenue Using This One Metric

The media and entertainment industry is feeling the pinch. An uncertain economic outlook is causing consumers to think twice about their next purchase at the same time as the “streaming bubble” is popping, forcing the industry to course correct and shore up revenue. 

Competition for subscribers and viewership is incredibly fierce. Entertainment giants and incumbents are creating high-quality, diverse content at nearly breakneck speeds, feeding consumers’ appetites in an effort to maintain and grow their viewership. Not surprisingly, a recent Salesforce survey cites increased competition as the No. 1 challenge facing the industry. 

Given the market conditions, media and entertainment companies are looking to optimize revenue streams in new and innovative ways. Essential to optimizing revenue streams is first having detailed information about current revenue streams. Especially for subscription-based businesses like streaming, a key metric is ARPU or average revenue per user. 

ARPU measures the average amount of revenue generated per customer. To calculate it, you simply divide your revenue by the number of subscribers over the time period you want to measure, such as a week, month, or year. ARPU is most often measured on a monthly level.

Using ARPU not only allows companies to more effectively understand subscriber-generated revenue, but makes it possible to analyze subscriber behaviors and identify opportunities to upsell services and promote and develop new offerings. However, to gain a more advanced understanding of this metric and its underlying drivers, it can be helpful to leverage data from multiple sources and use data science techniques to segment customers. 

To make the most of the metric, companies can leverage the following best practices:

1. Collect data from multiple sources: To effectively calculate ARPU, companies need to collect data on the revenue generated by their subscription business, as well as data on the number of customers or subscribers. This data may come from a variety of sources, including a billing system, CRM software, website analytics tools, and customer surveys. It may also be beneficial to collect demographic and behavioral data on subscribers to aid in segmentation.

2. Clean and prepare the data: Once the necessary data has been collected, data scientists need to clean and prepare it for analysis. This may involve removing duplicates or errors, normalizing data across sources, and transforming data into a format that can be easily analyzed.

3. Intelligently segment customers with ML : With the data cleaned and prepared, customers can then be segmented using data science. This may involve clustering customers based on shared attributes, such as demographics or usage patterns, or using machine learning algorithms to predict customer behavior.

4. Calculate ARPU for each segment: After customers are segmented, ARPU can be calculated for each segment. This will provide a more granular view of how different types of customers contribute to the organization’s overall revenue. Companies may also want to calculate other metrics, such as churn rate or customer lifetime value, for each segment to gain further insights.

5. Analyze and visualize the data: With ARPU data and customer segments in hand, marketing intelligence teams can begin to analyze and visualize the data to gain deeper insights into your subscription business. This may involve creating dashboards or reports that show how ARPU varies by customer segment, or using statistical tests to identify significant differences between segments.

At its core, ARPU provides organizations a nuanced understanding of how different types of customers or subscribers contribute to revenue streams. By analyzing and visualizing this data, media and entertainment companies can make more informed decisions about pricing, marketing, customer-retention strategies, and more. In today’s highly competitive market, it’s a powerful tool and one that opens the door to effectively leveraging data to power better, faster business decisions.

To learn more about effective data science to drive subscriber revenue, check out our Subscriber 360 Best Practices for Data Science ebook.