This link is meant just for you at Talentfoot — a few early, outside thoughts on the CEO search, put together on my own time and shared privately rather than posted anywhere public.
On paper, the fit is operational: I've run commercial P&Ls, led high-SKU and marketplace businesses, owned fitment-led product strategy, and built AI tools that make commercial teams faster without replacing their judgment.
But the part that matters most for a company like this is how I lead. I start by understanding before deciding. I treat people as people, not headcount, and I spend real effort connecting each person's work to why it matters to the whole business. Through mergers and integrations, that approach earned the highest employee NPS of any department, with zero involuntary exits.
A fifty-year-old, family-built company being handed to an outside CEO needs someone who can grow it and be trusted with the people and culture that made it worth buying. That's the combination I'd bring.
The role description points to a company with a real moat: a niche technical category, a long-tenured team, a service model customers trust, and enough channel complexity to create meaningful upside for the right operator. Four parts of my own background line up closely with that.
At International Lighting, I scaled a strategy of selling the same underlying part across many listings based on exact application, channel, and customer context — cross-matching discontinued products to alternatives OEMs no longer supported. That maps closely to replacement-engine and repower decisions.
At TAKKT Foodservices, I owned a $100MM eCommerce P&L inside a $300MM organization and led digital integration across a 700K+ SKU catalog and every marketplace channel.
I've built and led teams through mergers and integrations, aligning sales, eCommerce, operations, and merchandising behind one direction — earning the highest employee NPS of any department through a merger, with zero involuntary exits. In a company that runs on long-tenured people, that's the part I'd protect first.
At RSG, I personally built AI-enabled pricing and sales-support tools that improved decision quality and gave the existing team more reach, without replacing human judgment.
From the outside, I wouldn't start by assuming anything is broken. I'd start by protecting what looks most valuable and building systems around it. These are guesses, worth confirming with the team.
The public site repeatedly points customers back to expert human help for fitment confidence — "We don't hide behind chatbots." In a category where a wrong shaft size can mean a $900 return, that's a strategic asset, not a cost.
The public URL footprint includes thousands of model and application pages, which suggests real latent demand around exact equipment compatibility.
Direct eCommerce, a serious eBay presence, phone sales, a dealer program, and a Powersports adjacency already underway. Most of the pieces for growth already exist.
Based on a crawled sample of roughly 6,500 public pages (out of a larger footprint the sitemap suggests is far bigger), plus a look at the sitemap itself. These are patterns, not final conclusions — the kind of thing I'd validate against analytics, conversion data, call logs, returns, and the team's own priorities before acting on any of them.
The sitemap exposes several thousand replacement-engine application pages. In a spot check of 100 of them, nearly all shared the same generic title and meta description, and none showed structured product data (JSON-LD). These are pages that could be answering exact fitment questions and winning that search traffic.
Turn those pages into answer-ready fitment pages: unique titles, short direct answers, known constraints, recommended paths, call-to-verify rules, and structured data — done at scale with AI drafting and expert review.
The crawl flagged title-length and meta-description issues across a large share of the sampled pages. That's common in a technical catalog — but it points to a dual-audience opportunity: keep the exact spec strings for pros while adding plain-language fitment confidence for less technical buyers.
Use AI-assisted drafting and expert review to build product-page patterns that keep technical precision while improving scannability, click-through, and conversion confidence. Section 04 shows what that looks like — including how the exact spec string stays protected for search.
Product pages appear to carry Product schema, but the high-value FAQ, category, blog, and replacement-engine pages look less structured. For a fitment business, the answer format matters: what fits, what doesn't, what must be measured, and when to call. The blog already publishes genuinely good guidance — the plumbing around it just isn't set up to get full credit for it.
Build a reusable fitment Q&A library and expose it across pages, internal sales tools, and AI-assisted support with citations and clear confidence thresholds.
The crawl surfaced a meaningful number of slow-loading pages and missing alt text across the sample. None of these are glamorous fixes, but at catalog scale they add up — and they improve the funnel that already exists rather than requiring anything new.
Prioritize templates and high-traffic page types first: image alt rules, page-speed triage, metadata templates, and noindex/sitemap cleanup for utility pages.
Adopting something like this would be a real decision for a company that's run on trusted people and phone relationships for fifty years. I'm not suggesting it should happen on day one, or at all, without the team's own read on it — this is just meant to show what's technically possible right now.
This is an example of how I'd approach the after-hours gap in Observation 03: an assistant that answers what customers actually ask, drawing only on the team's own public guides and catalog pages, in the team's own voice.
Ask it about repowering a John Deere 318. It asks whether your Onan is a B-series or P-series, since that determines the flywheel adapter. When it doesn't know something, it hands you to a human rather than guessing.
A production version would be grounded in the live catalog, real-time inventory, and order history, with after-hours conversations logged for the morning queue.
Picking up the dual-audience question from Observation 02. Same product, same spec block preserved for pros — plus a plain-language layer to test with less technical buyers. Toggle between the live listing and the rewrite.
Title is the description — quick to parse if you already know the spec. Worth asking whether it's just as quick for a homeowner, or whether it sends them to YouTube first.
Six directions that seem worth exploring, roughly ordered by how quickly they could show results. None of this is a committed plan — the real sequencing would depend on what I learn once I'm actually inside the business.
There are thousands of "dead engine, good machine" searches — every discontinued Onan, Tecumseh, and legacy Briggs is a keyword cluster with buyers behind it. Scale what the blog already does by hand: AI-drafted, expert-approved guides mapped to kit pages, published on a weekly cadence.
Why it seems worth exploring: the expertise is already in the building; only the throughput is missing.
Worth noting: the catalog already sells oil change kits and tune-up kits — that's a real signal someone here is already thinking in this direction. Every engine sold has a known maintenance schedule; wiring purchase history to that existing kit catalog turns it into automated, engine-specific reminders instead of a product line customers have to remember to shop for on their own.
Why it seems worth exploring: the building blocks already exist; this is mostly about connecting them to what a customer already bought.
The after-hours assistant (Section 04), plus agent-assist for the phone team — instant cross-reference lookup, order history, and fitment data on the rep's screen mid-call. Coverage grows, call times drop, and the humans stay the heroes.
Why it seems worth exploring: it's the JD's named priority, and I've already built these systems in production.
The dealer program and the landscaper customer base deserve a real B2B motion: negotiated pricing tiers, fleet equipment garages, net terms, and a dedicated inside-sales cadence for repair shops that buy every week.
Why it seems worth exploring: repeat commercial buyers are typically the highest-value segment in this category, and the phone-sales DNA here already seems built to serve them well.
The eBay muscle is proven. Extend deliberately — Amazon and Walmart where margins survive, protected by the fitment expertise generalists can't match. In parallel, drop-ship relationships widen the catalog without widening the warehouse.
Why it seems worth exploring: channel-by-channel P&L discipline is the playbook I've run twice at scale.
If it's of interest to ownership: Powersports already looks like an early bet — worth an honest look at how it's performing before deciding whether to expand it or step back. Beyond that, fragmented regional parts distributors and niche fitment-data assets could be natural bolt-ons for this infrastructure and customer base.
Why it seems worth exploring: the customer base likely trusts SEW with more than just engines.
The first operating agenda should be humble: listen first, map the business, then move on the few levers that improve growth without damaging the service model. Everything above is an outside guess — the real plan starts with understanding what the board and the family actually want.
Meet family stakeholders, long-tenured employees, sales and support, vendors, dealers, and top customers. Understand the mandate — what ownership and the board are optimizing for. Map channel economics, fitment pain, returns, inventory, and support demand.
Define channel roles, select the highest-value fitment and content opportunities, identify marketplace and dealer levers, and choose one internal AI-support pilot the team actually wants. Nothing here requires new headcount or new technology to start.
Stand up a KPI rhythm across revenue, margin, conversion, support, returns, content health, and employee adoption. Agree on a 6-month plan with the family and ownership, then reassess with real results before committing further out.