I'm Chase. Teams bring me a question, and I work out what their data can say about it.
Senior Data Asset Manager at Trane Technologies, near Charlotte, NC
I've been at Trane and a Trane venture since 2017, mostly in marketing and dealer analytics. The work tends to start the same way: someone needs an answer, the pieces of it live in a dozen places, and I design a way to bring them together that keeps working after I've moved on to the next thing.
Right now that's 20+ pipelines for 6 brands and 4 business units, the 15+ dashboards they feed, and a program ROI report covering 3,000+ dealers.
Where I've worked
Click any bar to open that role. I started in marketing, moved into analytics, led a team, and came back to Trane to run marketing data for the Americas.
How I work
Four habits, each with a project where it mattered. Pick one to open that project.
Projects you can try
The real versions run on company data I can't share, so I rebuilt each one here in a simpler form with made-up numbers. Everything below is clickable. The first four are where I'd start.
Showing dealers what the program is worth
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 network of 3,000+ dealers 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.
Pick a dealer. Unused benefits show their estimated value, which is where the improvement conversation starts. made-up dealers
Most of the dashboard's sources started as manual pulls and emailed files. Rebuilding with Dataiku, Python and Power Automate moved most of them into scheduled, connected pipelines.
Illustrative mix of sources, not an exact count. What the levels mean
A clear picture of a church's finances
The church's numbers lived in dense accounting exports. Leaders knew money was tight, but there was no shared picture of why, or how long the reserves would last.
Turned the exports into a simple monthly report and a three-chart briefing for church leadership: who's attending, where the money goes, and what happens if nothing changes.
Everyone now works from the same facts. Spending runs under budget, but giving doesn't cover it, so the church draws on its reserves every month. At the current gap, those reserves last about 7 years.
I can't control attendance, but I can make every dollar clearer. For the 2027 budget I'm leading an overhaul: a cleaner set of account codes, a real approval process, budgets built around programs instead of staff positions, and training so staff and volunteers can check their own budgets and file reports themselves.
Reporting that runs itself
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.
Pick how often a report refreshes, then compare the manual cycle with the automated one. example numbers
Every source behind these pipelines gets a grade on my Levels of Automation scale, which is how the team and I decide what to improve next.
One model for every marketing channel
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.
Turn sources on and off. A question lights up once every source it needs is joined in; the tags under each question show what's connected and what's still missing.
Tuning the dealer locator
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.
Click the map to move the homeowner and drag the radius. Results rank by dealer score, and there are always three choices. sample data
Fewer calls, better leads
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.
Same month, one year apart. One dot = 100 calls. Blue dots moved on to the next step; gray dots fell off. rounded, not actual
A BTU calculator for homeowners
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.
Quick rule-of-thumb preview. The full calculator adds range-based questions, good/better/best options and a cost-to-own chart. simplified
Looking past the first result
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.
Enter results from a test to read out lift and significance. Try small numbers to see why low-traffic tests stay inconclusive. example numbers
Two ideas I use to talk about data
A lot of data work is getting people on the same page about what they have and how much to trust it. These are two things I came up with to make that conversation easier. Borrow either one.
T&F metrics
Short for thoughts and feelings.
A lot of marketing still runs on what I call T&F metrics. They're loosely based on real data: a dashboard number here, a gut call there, and a story that connects them.
That's not a knock on marketers. Some programs are hard to measure, and wanting an ROI number for a campaign doesn't mean the data can give you one with confidence. But the bar is moving. Marketing leaders are increasingly expected to show the data behind their programs, and the results they hoped for aren't enough.
T&F won't disappear, and it shouldn't. Experience counts for a lot. It just shouldn't be the whole story.
- Decide how you'll measure a program before it launches, not after.
- Be honest about confidence. "We think" and "we know" are different claims.
- When something can't be measured cleanly, say so.
- Put clean inputs ahead of clever models.
Levels of Automation
A shared scale for "how good is this data source?"
When I started my current role, most of marketing didn't have a way to talk about data sources beyond "it works" or "it's a pain." Borrowing from the levels used for self-driving cars, I built a 0–5 scale that grades every source by how much a person still has to do to use it. It gave the team a common language for where each source stands, what the next step is and where to invest.
A source's level is the lowest level it fully meets on all four questions. The weakest step sets the grade.
Tools, and when I picked them up
Each bar shows when I picked up a skill or tool, lined up against my roles. Hover or tap one to see where it overlaps.
UNC Charlotte: B.S.B.A., Marketing and Organizational Management.
Tableau, Alteryx and Dataiku courses. Member of Trane's Dataiku Champions group. Most of what I know came from building for real team needs.
Off the clock
I like learning by building. This is everything I do when I'm not at work, each one rated by me on five traits and then mapped. Yes, I charted my hobbies. Tap any of them to see its profile.
Got a data problem nobody quite owns? I'd like to hear about it.
I'm looking for data lead and analytics consulting work: being the person a team brings a problem to, who figures out the gap, designs the solution and directs the analysts and engineers who build it. Marketing, supply chain, operations or finance, inside Trane or out.