TigerEye’s Tracy Young on Enterprise AI, Top-Down Pricing, Mid-Market Mirages, and More
4 MIN READ
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Updated on August 8, 2025
In this episode of Second Acts, Chargebee CEO Krish Subramanian sits down with Tracy Young, co-founder of TigerEye and former CEO of PlanGrid, to discuss what it takes to build a winning B2B SaaS company in the days of AI dominating headlines.
Krish and Tracy explore how second-time founders tackle pricing strategy, organizational design, and the messy middle of the mid-market—with sharper instincts and fewer illusions.
Prefer to listen? → Catch the full episode here
Why second-time SaaS founders make smarter bets
PlanGrid is a vertical SaaS success story. Founded in 2011, it scaled past $100M ARR and was sold to Autodesk for $875M in 2018.
But when it came time to build TigerEye, Tracy didn’t rush.
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We dissected every single minute of the ten years we ran PlanGrid,” she says. “What that gave us was a long list of things we felt we did wrong, and a long list of things we thought we did right.
Her new company, TigerEye, is horizontal, AI-native, and designed with those lessons in mind—from GTM motion to org structures.
“There are still at least 10 good startup ideas in every enterprise software category,” Tracy says. “And the only people who would have the audacity and experience to go after them are second-time founders.”
Why evolving your SaaS pricing model is harder—but more critical—than changing your price
At PlanGrid, seat- and storage-based pricing delivered 130–140% NRR year after year.
“We had a beautiful land-and-expand strategy,” Tracy explains. “But at the same time, one of our competitors was charging a single price per project with unlimited seats. It was incredibly attractive to buyers.”
PlanGrid’s user-first, best-in-class pricing strategy was surpassed by a competitor’s more top-down approach. As Tracy notes, changing pricing models is much harder than changing price points.
“…the reason it’s hard is because you have to look at what percentage of your current revenue that might not renew because of the new pricing. And you always want to protect every dollar of revenue.”
Even if your metrics look great, your pricing model might still be leaking deals—because it’s too complex, too demanding for buyers, even when loved by users, or misaligned with how the product spreads in your category.
Takeaway for RevOps teams: ARPA and NRR tell you what you earned—but not how buyers feel. Go deeper by tracking:
- Where deals slow down due to pricing structure (e.g. license thresholds, approval layers)
- How usage spreads inside accounts—or stalls out as shelfware
- What pricing model lowers perceived risk for your target buyers
Strong monetization metrics can still mask hidden friction. Look for signals of buyer confidence and scalable adoption.
The three distinct flavors of the mid-market segment
Who actually sits in your mid-market segment?
“When we say mid-market—who are you servicing? Enterprise? SMB? Because in the middle are usually a lot of people who are not open to change,” Tracy says.
Tracy breaks the segment down further: mid-market enterprise, mid-market SMB, and what she calls “no man’s land”—a space where companies are too small for enterprise sales but too change-resistant for agile SaaS adoption.
This sharper segmentation helps TigerEye navigate go-to-market tradeoffs and avoid getting stuck chasing deals that won’t move.
How TigerEye does unscaleable things (fast and cheap) to drive customer adoption
TigerEye is rethinking more than just its product. It’s rethinking how a company operates.
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We can do things that would have taken a 6-week engagement with McKinsey—in 30 seconds.
By building its own tailored AI models, TigerEye enables fast, personalized answers for its customers—without the overhead of manual research or consulting bandwidth.
Pricing, segmentation, and building AI-native insights
Hear the full conversation with Tracy Young on:
- Why Tracy founded TigerEye
- How TigerEye’s pricing is centered on simplicity
- Why the classical definition of mid-market fails startups
- How AI economics will change in the next couple of years
- And more!
