Rocket Mortgage

Prospecting Marketing Intelligence

Senior Product Owner, Data Intelligence•2020 – 2023

I shifted into a new team in the department and served as product manager for a prospective marketing initiative. This involved procuring a new source of key data from a major data vendor warehouse, then thoroughly vetting the data to ensure it was actually what was promised. We used this data to stand up a new marketing and sales pipeline designed to identify and convert net new customers who had not previously entered the ecosystem.

The task:

Systematically and continuously refine the data source to focus on the highest-converting segments
Operationalize the workflow into a net new marketing campaign executed on a monthly basis
Coordinate with Sales to activate the newly identified segments
Track and report the success of the campaign
Maintain a positive P&L

The approach:

Meet with the vendor weekly to refine inputs and tighten what we were buying and how we were using it
Set up a 6-month roadmap to improve execution of the newly acquired datasets
Automate intake and sales/marketing operationalization of the datasets
Run ongoing experiments to identify improvements — both on the data side and the sales execution side
Share learnings with the broader department so improvements could scale across teams

The challenge:

When a vendor can "offer you the world," it can be tough to identify exactly what you need for success while staying within budget — refining our scope and requirements was a constant effort
This workflow became a primary source of leads for a sales channel, which meant decisions I made directly impacted other people's paychecks. That adds pressure to keep improving every step of the funnel
With a dataset this large, iteration can take days instead of hours. In a hot market (like the post-COVID low interest rate environment), days can mean millions of dollars

Learnings:

When working with Sales, your decisions directly impact their paycheck — act accordingly and be respectful when fielding criticism
At this scale, even small changes can have big downstream effects, so you have to think through impacts carefully
It's easy to get lost splitting hairs when you have a ton of data — keep a lean mindset and don't over-analyze at the start
Experimentation is always useful; adding and removing variables creates valuable signal for conversion performance and budget impact