Building an Open Data Portal for Municipal Government

The Challenge

The company had a good product but was stuck. Customer acquisition costs climbed each quarter, organic growth had flatlined, and the sales team spent too much time on leads that went nowhere. They knew the product worked -- they just had no repeatable way to get it in front of the right people.

Our Approach

Research, Analysis, and Strategic Foundation

• Pulled user behavior data from every touchpoint and found where people were leaving and why
• Benchmarked against five competitors to see what was working in the market and what was not
• Set up a measurement framework tied to actual business outcomes, not vanity metrics

Design, Development, and Implementation

• Built the core platform features, prioritizing the ones users asked for most
• Set up analytics so the team could see what was happening in real time instead of waiting for monthly reports
• Automated the repetitive workflows that were eating 15+ hours per week
• Connected the separate systems into one data pipeline so every team worked from the same numbers

Launch, Optimization, and Scale

• Scaled the campaigns that were working and shut down the ones that were not
• Expanded into two adjacent market segments that early data suggested would convert well
• Signed partnership deals with three complementary companies to reach new audiences
• Built retention programs focused on the first 90 days, where most churn happened

Key Takeaways

1. Shortcuts in the codebase from year one cost us three months of rework in year two. Spending an extra week on architecture early would have saved far more time.
2. Starting in one market segment instead of three kept the budget focused and gave us real data to work with before expanding. The temptation to go broad early is strong, but the results argue against it.
3. Shipping a visible win in the first 60 days changed the internal conversation. Stakeholders who were skeptical about the project became its biggest advocates.
4. Reducing monthly churn by 2 percentage points had the same revenue impact as increasing new signups by 15%. Retention math is unforgiving.
5. Automating the repetitive work freed up time for the decisions that actually required judgment. The key was knowing which decisions to automate and which to keep manual.

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