Wellthy Automates Data-Driven Alerts to Capture $80,000 in Revenue

Results

• Established a single source of truth using Snowflake
• Captured an additional $80,000 in revenue from two alerts
• Saved 27 hours of manual data work using Hightouch, leading to huge cost savings
• Built over 300 models and 2,500 tests using dbt

The Challenge

Wellthy works with some of the best-known companies in the world and their employees, offering caregiving support to thousands of unique caregiving situations and millions of sponsored lives. The company also works with leading health plans and their members so delivering a consistent and smooth experience is a constant operational challenge. For Wellthy, member eligibility is changing every single day, so it’s vital that these changes are surfaced to the appropriate teams.

Inaccurate data can completely derail the onboarding process, leading to fewer signups, conversions, and lost revenue. In order to combat this problem, the Care Team was spending hours hopping between systems trying to manually reconcile users with the necessary information, creating significant operational inefficiencies.

With no centralized view of members and minimal resources to model, transform, and track member data, Wellthy needed to:

• Build out a new data platform and establish a single source of truth
• Implement a robust data modeling framework to rectify and enrich member data from many sources
• Create automated alerting around themember onboarding process and experience

To solve the problem, the company turned to Snowflake, dbt, and Hightouch to address each step simply with the least operational cost and fastest time-to-value.

The Solution

Snowflake

To solve for building a new source of truth, Wellthy implemented Snowflake. Before Snowflake, the data team was operating out of an application Postgres database. This was causing the team to run into both concurrency and scalability issues because production databases simply aren’t built to handle analytical workloads. Additionally, it was very difficult to pull in data from other sources.

Since adopting Snowflake, the data team has been able to easily and reliably pull data in from any source to aggregate and merge it into one clear, coherent member record. Wellthy no longer has to spend time manually scaling resources for various analytics jobs thanks to Snowflake’s fully managed platform and auto-scaling capabilities.

dbt

The second step was to build a robust data modeling framework without complex processes or manual builds. Enter dbt Cloud.

To fully unlock the value of Snowflake and better serve the needs of the business, the data team needed a reliable and scalable way to automate, orchestrate and manage all of their data models within Snowflake.

This quest quickly led the data team to implement and standardize dbt across the entire organization. Since adopting dbt, the data team has built over 325 models and 2,500 different automated tests.

Hightouch

Once Wellthy had their data centralized and modeled, Hightouch unlocked activation and alerting. With so many dbt models running in the background of Snowflake, Wellthy is constantly trying to identify possible exceptions to these models that are indicative of operational issues. However, there was added complexity because Wellthy needed to identify these exceptions without outright canceling or erroring existing dbt jobs that were running. The data team had built a ton of tests in the transformation layer, and stopping these dbt jobs would interrupt downstream data flows, and ultimately operational reporting.

In search of a solution, Wellthy quickly landed on Hightouch to power two core use cases:

• Exception-based alerting: User and account-level data is constantly changing for Wellthy, and delivering a good member experience means proactively identifying potential problems before they affect the member The Care Team needs to know when an account or user is missing key information because this can halt the entire onboarding process.
• Look-forward alerting: Wellthy works with Fortune 100 companies, so it’s important to notify internal teams of recent changes to client files. The Client Success team needs to know when company X increases (or decreases) the number of eligible users from 1,000 to 2,000, so that they can proactively confirm this new number, rule out upload errors, can allocate appropriate resources.

Previously, identifying and rectifying these eligibility issues could take as much as six hours to pull data on individual users manually–and in many cases, key stakeholders were even forced to contact the client or member directly. With Hightouch, this manual process is eliminated, saving Wellthy over 27 hours per month and generating huge cost savings because key employees can now redirect their efforts to more important tasks.

Wellthy has just scratched the surface with alerting, but the company has already been able to capture $80,000 in annual lost revenue from these two alerts. The data team is looking to implement this at a wider scale to proactively identify even more problems.

What’s Next?

As Wellthy continues to build out a modern data stack, the company is focused on activating the rich member data living in Snowflake. Wellthy is in the process of using Hightouch to move data out of Snowflake and enrich various downstream business applications like HubSpot and Salesforce to power hyper-personalization and ensure that every business team is working off of the same view of the member.