How Project Broadcast Scaled Analytics Operations to Increase Customer Lifetime Value (LTV) by $667,000 with Rivery

Jake Dempsey’s company Project Broadcast, a text messaging platform designed to help businesses engage with their customers through automated SMS and MMS campaigns had built a significant customer base. Still, like many fast-growing tech companies, they faced challenges in analyzing their product usage data effectively. All their product data was sitting in MongoDB. While they had valuable customer insights within reach, the company needed help to harness the power of this data for meaningful analytics. 

To solve this, Jake’s team partnered with Datateer, an analytics and engineering consultant firm, and hired a full-time data scientist. Together, they aimed to create a foundational analytics pipeline. 

Challenge: Building a Scalable Analytics System

As the company scaled, it became clear that to drive growth further, they needed to understand their users better—everything from subscription trends to churn rates, to user behavior, and messaging interactions. However, the team was unable to unlock the insights hidden within their data due to the complexity of the task. MongoDB, while an excellent NoSQL database, was not easy to extract analytical data from.

They implemented Meltano, an open-source data integration tool, to facilitate the movement of data from MongoDB to Snowflake, a powerful cloud data platform, for storage. On top of that, Datateer managed all the data modeling using dbt, allowing the team to transform raw data into insights. They then leveraged Sigma to visualize the data, offering the organization a first real look into product usage data, offering insights into how customers were interacting with the platform. With this setup, Jake’s team could now monitor key metrics such as subscriptions, churn, and user behavior. 

Seeing the immediate value that data analytics brought to their business, Jake’s team realized they needed to scale this operation even further. The insights they gained helped drive strategic decisions across departments, from product development to marketing. However, as they relied more heavily on data, the limitations of their current setup began to surface. 

For one, Meltano—while functional—required significant infrastructure management, a burden for Jake’s small data team. The system would break down two to three times a year, forcing the team to spend precious time fixing it instead of working on expanding their data capabilities.

“When something failed we would have to backfill missing data, which was a painful process as there were one to two million records a day which was a lot of data to go back and resync.” – Jake Dempsey, CEO of Project Broadcast.

Given the growing importance of analytics to their business, Jake’s team wanted a solution that would allow them to scale without the headache of maintaining infrastructure. They aimed to bring the entire operation in-house but needed a tool that could handle data ingestion and orchestration, as their data team was using Python for machine learning models. 

Furthermore, with a small team, they couldn’t afford to rely on complex tools that required constant maintenance.

At this point, Jake and his team began evaluating several options. They were looking for a platform that could replace both Meltano for data ingestion and Prefect, which they used for orchestration. After testing various tools, they found that Rivery stood out as the most straightforward solution to meet their requirements.

Solution: Transitioning to Rivery

After evaluating several platforms, the team selected Rivery for its simplicity and comprehensive data management capabilities. Unlike their previous setup with Meltano, Rivery offered a fully managed solution that would streamline data ingestion, orchestration, and transformations—all while ensuring that their small data team could manage the operations efficiently.

With Rivery, the team worked alongside Datateer once again to transition the analytics operation fully under Jake’s ownership. The new architecture involved Rivery for data ingestion and orchestration, dbt Cloud for transformation, and Snowflake for data warehousing, all feeding into Sigma dashboards for real-time data insights. This setup enabled them to handle their analytics needs without worrying about infrastructure breakdowns or the overhead of managing too many tools.

“I wanted a tool that would allow us to easily connect to additional data sources and work with dbt cloud.” – Jake Dempsey

With all this data centralized in Snowflake and transformed using dbt, they created comprehensive dashboards in Sigma that provided real-time insights into customer behavior, marketing effectiveness, and product performance.

“Six months ago we only had one data source flowing into our Sigma dashboards. Since onboarding Rivery, we now have MongoDB, Google Ads, Google Analytics, Facebook Ads, HubSpot data, and Google Sheets all flowing into Sigma dashboards.” – Jake Dempsey

Results: Expanding Data Sources and Leveraging Analytics for Business Impact

The transition to Rivery and a more scalable setup brought immediate results. The company expanded from analyzing just product usage data from MongoDB to integrating eight additional data sources. These included key data from marketing platforms, sales data, and other product-related metrics. By having all this data consolidated and analyzed through Sigma, the company was able to leverage analytics in ways they hadn’t imagined before.

Some of the key outcomes included:

• Increased visibility into product usage: This allowed the team to identify which features customers were engaging with most and which were underutilized.
• Improved customer retention strategies: The team was able to implement more personalized engagement tactics, which helped them retain more customers by addressing churn before it became an issue.
• Better decision-making on product development: With comprehensive data insights, the team could make more informed decisions on which features to prioritize based on customer behavior, leading to more relevant product updates and higher customer satisfaction.

Impact on Customer Lifetime Value (LTV)

One of the most significant impacts of the analytics transformation was the effect on Customer Lifetime Value (LTV). By having more accurate, data-driven insights into customer behavior, Jake’s company could take specific actions to extend the lifespan of their customer relationships. As a result, they reported an LTV increase of $667,000 for their top customers. This was a substantial increase from their previous estimates before having proper analytics in place.

“Rivery is super powerful in the sense that it allows us to make decisions based on data we couldn’t access, which is something we have never been able to do.” – Jake Dempsey

Key factors that contributed to the LTV growth included:

• Reduced churn: With improved customer retention efforts, they could prevent a significant percentage of customers from leaving, directly increasing the average lifetime of each customer.
• Better upsell and cross-sell strategies: By understanding customer usage patterns, the company was able to identify opportunities for upselling additional services or premium product features, which increased the revenue generated from each customer.
• Enhanced customer satisfaction: The ability to analyze and optimize product features based on actual usage data led to higher customer engagement and satisfaction, which further extended customer relationships.

Future Outlook: Continuing to Scale with a Small, Efficient Data Team

By transitioning to Rivery, Project Broadcast successfully scaled its data operations without significantly increasing the size of its data team. The new architecture provided them with a robust, scalable, and easy-to-manage solution that allowed them to focus on generating insights rather than managing infrastructure. 

This transformation has allowed the company to continue growing while maintaining full control over its data, making better-informed decisions, and finding new ways to leverage the power of analytics. Today, they are not only using data to understand their customers better but also to drive innovation and improve the overall efficiency of their business.