Messy problems in. Clear decisions out.

Dealers pay to join the dealer program, and account managers need to show them what they get back. That answer was spread across a dozen sources, and the monthly update took days of manual work with close attention to detail.

I built the first scrappy version in 2018 with Excel, Alteryx and Tableau. In 2024 I took it back and rebuilt it: 4 internal cloud sources plus 8–10 partner feeds, cleaned and joined into one monthly dataset on program use, purchases and ROI for every dealer. I also set up automated national-comparison reports for independent distributors who don't have access to our internal dashboards.

A 3,000–5,000-dealer network in one interactive dashboard. Account, territory and regional managers use it to run their regions and in one-on-one reviews to renew dealers and show them where to improve. The monthly refresh went from days of manual work to about 90% automated.

The old version was a separate table for every source, rebuilt each year, so it could only ever show one year. I restructured everything around a single dealer source of truth and a date key. Every source now lands in one table, and each month's data is appended instead of rebuilt, so the dashboard runs year over year. It works at the month level; managers asked for year-to-date, so that's what it shows, and switching back is easy.

I stepped in during a season of big change: a new pastor and the loss of the youth minister. The church's finances lived in dense system exports few leaders could read. Year-to-date totals looked on track, but that hid problems account by account, and department leaders had no practical view of their own budgets.

I own the monthly reporting cycle between the Finance Committee and the Administrative Council. I built one dataset that feeds three outputs: a narrative council report with flagged items and discussion questions, operational flags for the finance team, and one-page budget sheets for each department leader with personnel detail removed. I also wrote a four-question guide for when to use restricted funds versus the operating budget, to protect donor intent.

Several accounts were past their full-year budget by mid-year. The monthly operating gap was widening and general-fund cash was falling. One ministry's restricted fund was nearly empty while its operating line ran over, and some restricted accounts had gone negative, which I raised as a donor-compliance concern.

Leaders and the congregation now start budget conversations from the same facts. Reporting went from raw exports to a repeatable monthly package. I'm using the leadership change to rebuild the 2027 budget: retiring old account codes, consolidating overlapping accounts and moving from position-based to program-based budgeting.

Leadership decisions across 6 brands and 4 business units depended on recurring reports that took hands-on steps every cycle: pulling exports, rerunning steps and checking results. As the need for data grew, there was a real opportunity to make them faster, more reliable and easier to scale.

I mapped how every input would show up in the output leaders needed, found the manual steps and failure points, and redesigned the process end to end: automated file collection, repeatable prep, scheduled publishing, and automated data checks I keep adding to as new issues appear. I led my team's move to a more stable platform and, as part of Trane's Dataiku Champions group, taught the team and was their go-to contact while they learned. Tools: Dataiku, Power Automate, Python.

20+ pipelines I build and maintain now feed 15+ leadership and decision dashboards on schedule. The team gets back dozens of hours a month (about 5 a week for me), and some monthly projects went from days of manual work to hands-off runs.

Campaign data lived in separate platforms. GA4, Campaign Manager, Google Ads, Meta, LinkedIn and BrightEdge each told part of the story, and none of them connected to leads or dealers.

Designed the data structures that join those sources into one clean model. Brought in new sources like Google Ads and BrightEdge, automated the prep in Dataiku, then built performance dashboards and trained marketers to use them on their own.

Marketers measure outcomes by tactic and channel without waiting on an analyst, and leadership sees one version of return on marketing spend.

Homeowners use the dealer locator to find someone to call. Distance was a good start, but there was room to put the best-fit dealer first, and the data suggested lots of repeated searching.

Designed a dealer score from customer feedback, response speed, Google reviews and dealer level, so the best-fit dealer shows first in each ZIP code. Then I dug into how people actually use the tool and found the site was logging a search on every page load, so thousands of those repeat searches were page loads, not people.

Simpler ranking logic and accurate reporting. It also set the locator strategy: always show three choices, and when no dealer is available, a general contact card takes the empty spot. That was tested and is live today.

To connect website visits to dealer phone calls, every session gets its own tracking number. We moved from a vendor pool of millions of numbers to our own Twilio setup with only a few thousand that rotate. Call volume dropped hard, and the question was whether we'd lost real customers.

The rotation logic was built by developers; I owned the analysis. I compared call patterns, durations and completion before and after the switch to find out who the "lost" callers actually were.

Total calls fell about 80%, but wins held flat. The lost calls were almost all bots, random dials and people who weren't looking for a dealer, and every step of the funnel now converts better. Average dealer call time went from under 30 seconds to about a minute. It changed how the business defines a healthy call volume.

Most homeowners have no idea what size system they need, and that uncertainty stalls them before they ever call a dealer. The official load calculations need details a shopper doesn't have.

Researched how dealers size systems, then pushed to get real-world housing listing data with known system types. I used machine learning to weight each factor (square footage, ceilings, insulation, windows, orientation, occupants, age) into a points system. Homeowners answer in simple ranges instead of exact numbers. The output was good, better and best options (14, 17 and 20 SEER) with a cost-to-own chart using local weather and energy prices, plus a call-a-dealer button.

The proof of concept became the base developers built the website tool from. I've rebuilt my version as a working calculator on this site.

We ran dozens of tests a year, but campaign traffic was thin, so down-funnel results were sparse and often inconclusive. One test removed a "contact dealer now" card from product category pages. It showed no clear impact either way, so the card came out.

About a month later, leads from that area were way down. I went through the lead data and found that the source had dropped out as expected, but those calls never showed up anywhere else. Visitors weren't finding another path to a dealer.

We brought the card back and lead volume recovered. The lesson I carry: when traffic is thin, keep watching the downstream numbers after anything ships.