---
title: "The RevOps Poker Game"
episode: 32
podcast: "The LeanScale Podcast"
publisher: "LeanScale"
guest: "Spencer Hodgson"
guest_title: "Revenue Operations Leader"
date_published: 2025-10-28
date_modified: 2026-07-22
duration: 00:35:05
word_count: 5955
topics: ["revenue-operations", "gtm-strategy", "forecasting", "sales-compensation"]
canonical_url: https://leanscale-knowledge-hub.netlify.app/podcast/spencer-hodgson-revops-poker-expected-value/
source: "LeanScale Podcast Knowledge Hub — https://leanscale-knowledge-hub.netlify.app"
license: "Free to quote and cite with attribution to The LeanScale Podcast."
---

# The RevOps Poker Game

_Spencer Hodgson on betting on channels and reps with expected value_

**Episode 32 · The LeanScale Podcast**  
Spencer Hodgson, Revenue Operations Leader · Hosted by Anthony Enrico  
Published October 28, 2025 · Updated July 22, 2026 · 00:35:05  
Canonical: https://leanscale-knowledge-hub.netlify.app/podcast/spencer-hodgson-revops-poker-expected-value/

**Topics:** Revenue Operations · GTM Strategy · Forecasting · Sales Compensation


## Executive summary

RevOps drowns in data — which reps are performing, which channels are paying off, which segment to bet the year on — and almost no framework tells you how to weigh those calls against each other. Spencer Hodgson, whom host Anthony Enrico calls one of the smartest RevOps leaders he's met, borrows one from an unlikely place: the professional poker table. Inspired by Annie Duke's 'Thinking in Bets,' Spencer runs go-to-market decisions through expected value (EV) — probability times value, summed across outcomes — so that every choice becomes a good bet or a bad bet rather than a win or a loss.

The first half is a clean primer. Spencer walks the lottery: a $2 ticket has deeply negative expected value most of the time, until a jackpot crosses roughly $750M and the math flips positive. He does the same with a poker pot — call $100 into a $300 pot to win $400 and you have 25% pot odds, so you should call any time you believe your hand wins more than 25% of the time, even though you expect to lose the individual hand three times out of four. The lesson that carries into business: a losing outcome can still be the right decision, and Kaizen — his company's continuous-improvement value — means grading the decision, not just the result.

Then he brings it to RevOps, where operators actually have the conversion rates the poker player only estimates. Take the perennial 'should we move up-market' question. With enterprise at a 20% win rate on a $10K ACV, SMB at 40% on $5K, and mid-market at 30% on $7,500, the per-deal expected value is identical for enterprise and SMB ($2,000) and highest for mid-market ($2,250) — so equal EV does not mean equal investment. Layer in rep cost, deal volume, and marketing spend and the picture inverts: at 50 SMB deals versus 10 enterprise deals, with a $100K SMB rep and a $250K enterprise rep, SMB nets about +$40K while enterprise runs roughly −$180K. The 'obvious' up-market move is the losing bet.

The back half is about operationalizing it. Spencer has built the model into both Salesforce and HubSpot — custom properties, workflows, and calculated fields that surface a 'white space' score so a rep can filter to the highest-expected-value deals without ever seeing the math. Anthony connects it to LeanScale's TAM and territory work: weight territories by expected conversion, not gross dollar value, and a $5M territory converting at 50% beats a $10M territory converting at 1%. The same logic can pressure-test quotas — if an assignment is 10–20% off the modeled expectation, fine; if it's 5X off, something is wrong. Both agree the real edge is that business, unlike poker or chess, is never a solved game: you fill the unknowns with judgment, then measure whether your assumptions held and adjust next quarter.

Spencer's origin story lands the human point. A former seventh- and eighth-grade math teacher at a Catholic classical school in Alaska and a summer tour director, he lost the touring gig to COVID, moved to Tulsa, and took an entry-level SDR job — where he became obsessed with exporting his call data into Excel to prove he was the best rep, and quietly worked his way into sales operations. Who should listen: RevOps and GTM operators buried in dashboards, sales and revenue leaders making segment, territory, and quota bets, and founders weighing whether a move is a good bet even when the odds look long.


## Key takeaways

1. **Expected value reframes every decision as a good bet or a bad bet** — EV — the probability of each outcome times its value, summed — lets you evaluate a choice on its own merits rather than on whether it happened to pay off. A $2 lottery ticket is a bad bet until the jackpot is large enough that low probability times huge value beats the near-certain $2 loss.
   _Why it matters:_ Stop grading decisions by outcome. Score the bet: was the expected value positive given what you knew? That single shift de-emotionalizes hard RevOps and go-to-market calls.
   _For:_ RevOps Leaders, Revenue Executives, Founders

2. **A loss can be the right call — and a win can be the wrong one** — Not winning the lottery, a poker hand, or a deal doesn't mean you decided badly. Spencer's discipline (rooted in Kaizen) is to keep asking 'was I making the right decision based on the estimated value?' regardless of how a single instance resolved.
   _Why it matters:_ Build a culture that reviews decision quality, not just results. Punishing good bets that lost — or rewarding bad bets that won — teaches the org exactly the wrong lessons.
   _For:_ Revenue Executives, Sales Leaders, Founders

3. **RevOps has the conversion rates poker players only estimate** — In a startup bet you have to leap on assumed probabilities; in RevOps you have actual win rates, ACVs, and funnel conversion. That makes expected value far more tractable — dial rate, pickup rate, meeting rate, deal rate, and win rate are all real, measurable probabilities.
   _Why it matters:_ Treat your funnel data as the inputs to an EV model. RevOps is one of the rare seats where the math is grounded in observed probabilities rather than gut feel.
   _For:_ RevOps Leaders, Revenue Executives

4. **Equal expected value does not mean equal investment** — In Spencer's example, enterprise (20% win, $10K ACV) and SMB (40% win, $5K ACV) produce the identical $2,000 per-deal EV, while mid-market (30% win, $7,500 ACV) edges ahead at $2,250. Per-deal EV alone can't decide where to put money — it only tells you the bets are comparable.
   _Why it matters:_ Never stop at per-deal EV. It's the starting line, not the answer; you still have to layer in volume, cost, and deal count before you know where to invest.
   _For:_ RevOps Leaders, Sales Leaders

5. **Moving up-market is a bet, not a status upgrade — and often EV-negative** — Almost every company Anthony works with wants to move up-market. But once you weight the same segments by deal volume and rep cost — 50 SMB deals with a $100K rep versus 10 enterprise deals with a $250K rep — SMB nets roughly +$40K and enterprise roughly −$180K. The prestige move is the losing hand at that scale.
   _Why it matters:_ Run the full expected-value math before investing in an up-market motion. What you expect to gain has to clear what a $250K enterprise rep and the associated marketing cost you when you lose 80% of the time.
   _For:_ Founders, Revenue Executives, Sales Leaders

6. **Real EV includes the cost of losing, not just the upside of winning** — The naive version pretends you 'spend nothing' on the deals you lose. In reality you carry rep salary, marketing collateral, and opportunity cost on every miss. Apply the loss rate to those costs (0.8 × $250K enterprise rep = $200K expected loss) and the total value flips.
   _Why it matters:_ Fold fully-loaded cost and loss rate into the model. An investment that looks great on gross ACV can be deeply negative once you price in what failure actually costs.
   _For:_ RevOps Leaders, Revenue Executives, Founders

7. **Build the model into the CRM as a 'white space' score reps can act on** — Theory doesn't help a rep who just wants to win and make money. Spencer has built EV into both Salesforce and HubSpot using custom properties, a couple of workflows, and calculated fields that output a 'white space' number — set-it-and-forget-it math that quietly tells reps which deals are worth the most.
   _Why it matters:_ Operationalize EV where the work happens. A rep should be able to filter open deals by expected value at quarter-end without doing — or even seeing — the underlying math.
   _For:_ RevOps Leaders, Sales Leaders

8. **Weight territories and TAM by expected conversion, not gross dollars** — LeanScale routinely loads TAM and account valuation into the CRM to build territories, but usually on gross dollar value. Add expected conversion and it gets more honest: a $5M territory expected to convert at 50% beats a $10M territory expected to convert at 1%, even though the $10M looks bigger on paper.
   _Why it matters:_ Layer expected conversion into territory design. It changes which territories are actually the strong ones and gives reps a defensible prioritization of prospects and open deals.
   _For:_ RevOps Leaders, Sales Leaders, Revenue Executives

9. **Use expected value as a data point for quota-setting** — If you trust the data, you can build quotas from the expected value of the accounts inside a territory. Culturally, teams still want reps pushing past the past — but EV is a sanity check: 10–20% off expectation may be fine, 5X off means you should reevaluate or have explicit reasons you expect win rate or ACV to jump.
   _Why it matters:_ Don't set quota on wishful thinking. Use modeled EV as a reality anchor, and demand a stated reason whenever the number departs meaningfully from what the data supports.
   _For:_ Revenue Executives, RevOps Leaders, Sales Leaders

10. **Business is an unsolved game — fill the unknowns with judgment, then measure** — Poker and chess are 'solved' in the sense that the information or the optimal move can be computed. Business isn't: you can't see the opponent's hand, so you have to estimate what adding a second enterprise rep or new collateral does to win rate and ACV. The operators with better intuitions win more.
   _Why it matters:_ Put explicit placeholders on the data you don't have, run the calculation, then check whether those assumptions held. Judgment plus measurement beats either alone.
   _For:_ Revenue Executives, RevOps Leaders, Founders

11. **Kaizen: keep honing the inputs and the model every cycle** — Expected value isn't a one-time calculation but a continuous-improvement loop. You can zoom in on any input — lift win rate, raise ACV by pointing reps at better-fit SMB accounts, replicate what top reps do (using tools like Gong) — and re-run the bet as the numbers move.
   _Why it matters:_ Treat the EV model as living. After each quarter, ask whether the unknowns you guessed came true and adjust the decision-making model for the next quarter or year.
   _For:_ RevOps Leaders, Sales Leaders

12. **Modeling decisions data-point-by-data-point aligns the whole exec team** — When you break a call down into probabilities, values, costs, and counts, you can see exactly what you know and what you're guessing — and so can everyone else. It's far more persuasive than asserting 'enterprise is better, we should move up-market,' and it makes the decision measurable after the fact.
   _Why it matters:_ Use the model as a communication tool, not just a decision tool. Laying out the components gets the executive team on the same page and creates a scorecard you can grade next quarter.
   _For:_ Revenue Executives, RevOps Leaders, Founders


## Frameworks

### Expected (Estimated) Value (00:45)

**Definition:** Sum, across all possible outcomes, of each outcome's probability times its value: EV = P(outcome1) x V(outcome1) + P(outcome2) x V(outcome2) + ... . A positive EV is a good bet; a negative EV is a bad one.

Spencer's core lens for 'all of life' and especially RevOps. The lottery shows how a low-probability, high-value outcome can still net negative — until the jackpot is large enough to flip it positive. Because probability should be proportional to value, the interesting decisions live where value climbs but probability falls and you have to find the break-even.

### Thinking in Bets (Decision Quality vs. Outcome) (04:00)

**Definition:** From Annie Duke's book: judge choices by the quality of the bet given what you knew, not by whether the single outcome was good or bad. A good decision can lose and a bad decision can win.

Anything can be reframed as a good bet or a bad bet rather than a success or a failure. Not winning the lottery, a poker hand, or a deal doesn't mean you decided wrong — which is what lets you focus on improving your assessment of probabilities and values over time.

### Kaizen (Continuous Decision Improvement) (04:44)

**Definition:** A core value of Spencer's company: constant, incremental self-improvement — applied here to decision-making, by reviewing whether a choice had positive expected value regardless of how it turned out.

Even after a loss, the question is 'was I making the right decision based on the estimated value?' Kaizen turns EV from a one-off calculation into a repeating loop of refining inputs, assumptions, and the model each cycle.

### Pot Odds (05:33)

**Definition:** The ratio of the amount you must call to the total pot you stand to win, expressed as the minimum win probability that justifies calling. Call $100 into a pot that becomes $400 and you have 25% pot odds.

You should call any time you believe your hand wins more than the pot-odds threshold — 26% clears a 25% requirement — even though you expect your opponent to have the better hand up to 74% of the time. It's the cleanest illustration that a bet can be correct while the individual outcome is likely to lose.

### EV-Weighted Segment Selection (Enterprise vs. Mid-Market vs. SMB) (10:29)

**Definition:** Model each go-to-market segment as a bet: win rate (probability) times ACV (value) gives per-deal EV; then layer in deal volume, fully-loaded rep cost, and marketing cost to get the true expected value of investing in that segment.

Spencer uses circles for probabilities and squares for values on a whiteboard. Per-deal EV can be identical across segments (enterprise and SMB both at $2,000) yet the right investment differs once you add volume and cost — in his example SMB nets about +$40K while enterprise runs about -$180K, making the popular up-market move the worse bet.

### White Space Scoring in the CRM (27:18)

**Definition:** A calculated field in Salesforce or HubSpot — built from custom properties and workflows — that outputs how much a given account or open deal is 'worth' by expected value, so reps can filter to the highest-EV deals.

The rep-facing version drops salaries and other costs to stay simple. It's set-it-and-forget-it math that hums in the background and tells sellers which deals to press near quarter-end, without asking them to understand the EV formula behind it.

### EV-Weighted Territories, TAM & Quotas (27:59)

**Definition:** Build territories and quotas from expected value — TAM and account valuation weighted by expected conversion rate — rather than gross dollar value.

A $5M territory converting at 50% is a better bet than a $10M territory converting at 1%, even though the $10M looks bigger. The same expected-value data can pressure-test quota assignments: 10-20% off expectation is tolerable, 5X off signals the quota (or the assumptions behind it) needs to be reexamined.


## Quotes

_Speakers inferred from an undiarized transcript — verify before attributing._

> "Basically applying game theory in a way to all of life, we can try to figure out: what would I stand to gain from any one decision?"
>
> — Spencer Hodgson, The LeanScale Podcast Ep. 32 (00:45)

> "Everything can be thought of as a good bet or a bad bet. Not, did I succeed or did I fail? Because not winning the lottery or a certain game of poker doesn't mean that you made the wrong decisions."
>
> — Spencer Hodgson, The LeanScale Podcast Ep. 32 (04:00)

> "Just because you failed, we should be asking ourselves: was I making the right decision based on the estimated value?"
>
> — Spencer Hodgson, The LeanScale Podcast Ep. 32 (04:44)

> "As long as you suspect that your hand is better than winning more than 25% of the time, it makes sense to call."
>
> — Spencer Hodgson, The LeanScale Podcast Ep. 32 (06:25)

> "Being okay with 'I lost, but I made the right call.' If you knew you had a 50% chance to win the billion dollar lottery, you should go buy a $2 ticket. Even if you lose, it was a good decision to do it."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 32 (08:00)

> "In some ways we have it really good in RevOps, because we have the data. It's not quite the same as when you're deciding to start a business — there, you kind of have to jump, you have to leap."
>
> — Spencer Hodgson, The LeanScale Podcast Ep. 32 (09:09)

> "Oftentimes the goal is to move up market, but we need to know and understand what do we expect to gain before we invest all of this time and energy."
>
> — Spencer Hodgson, The LeanScale Podcast Ep. 32 (11:16)

> "What's interesting about this example is it's told us that the mid market is the route to go."
>
> — Spencer Hodgson, The LeanScale Podcast Ep. 32 (16:13)

> "You have positive 40 and negative 180 on the enterprise. So taking that line of mathematical thinking, SMB market might actually be a better bet for you to invest in."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 32 (20:16)

> "This is where poker and other games, they're solved — you have all of the data on the table. This is where business gets a little bit more in the gray area."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 32 (21:14)

> "The people who make the better intuitions about this stuff are going to succeed more and their companies are going to do better."
>
> — Spencer Hodgson, The LeanScale Podcast Ep. 32 (22:01)

> "When you break it down data point by data point, you model out potential decisions into the future. Then you can clearly see what you know and what you don't know — it makes it so much easier to make a call and communicate it to the rest of your executive team."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 32 (23:21)

> "If you can get it into Salesforce, HubSpot, whatever you're using, and show them: these are the deals that are worth the most to you — you're going to make more money, you're going to be more successful, and your boss is going to be happy."
>
> — Spencer Hodgson, The LeanScale Podcast Ep. 32 (25:54)

> "On paper, without bringing in estimated value, you'd say the $10 million territory is better. But when you look at what we actually expect to achieve, the $5 million territory is better."
>
> — Anthony Enrico, The LeanScale Podcast Ep. 32 (28:42)

> "It was as an SDR that I started applying some of this probability stuff to my life. I was obsessed with knowing: am I the best SDR? And I just kind of worked my way into sales operations and I've been loving it ever since."
>
> — Spencer Hodgson, The LeanScale Podcast Ep. 32 (31:59)


## Practical advice by role

### RevOps Leaders

- Turn your funnel into an expected-value model: dial rate, pickup rate, meeting rate, deal rate, and win rate are all real probabilities you can multiply against ACV to compare bets.
- Never stop at per-deal EV — layer in deal volume, fully-loaded rep cost, and marketing spend before recommending where to invest; per-deal EV only tells you the bets are comparable.
- Operationalize it in the CRM: build custom properties, workflows, and calculated fields that output a 'white space' score so reps can filter to the highest-EV deals without doing the math.
- Re-run the model each cycle (Kaizen). Check whether the unknowns you estimated came true, and adjust win-rate or ACV assumptions for the next quarter.

### Sales Leaders

- Weight territories by expected conversion, not gross dollars — a $5M territory at 50% conversion beats a $10M territory at 1%.
- Give reps a prioritization they'll actually use: surface the highest-expected-value open deals so they know which ones to press near quarter-end.
- Replicate what works: when certain reps or account types convert better, study the play (using tools like Gong) and point the team at the higher-EV motions and segments.

### Revenue Executives

- Grade decisions by expected value, not outcome — reward good bets that lost and scrutinize bad bets that won, or you'll teach the org the wrong lessons.
- Pressure-test quotas against modeled EV: 10-20% off expectation is fine, but 5X off means either the number or your assumptions need a hard second look.
- Model big calls data-point-by-data-point so the whole leadership team can see what's known versus guessed — it's more persuasive and creates a scorecard you can grade later.

### Founders

- Evaluate a move as a bet: even a 10% chance of success can be a good bet if the value is large enough and the downside is small — many side hustles are near-impossible to make EV-negative.
- Before chasing prestige (like moving up-market), run the full EV: what a $250K enterprise rep costs you when you lose 80% of the time can turn an attractive gross ACV into a deeply negative bet.
- Business is never a solved game — put explicit placeholders on the data you don't have, decide, then measure whether those assumptions held and improve the model.


## Operations takeaways

### Revenue operations

- **Data is the advantage.** Unlike a founder's leap, RevOps has real win rates and conversion data — the probabilities an EV model needs are already in your funnel.
- **Per-deal EV is the start, not the answer.** Compare segments on probability times value, then layer volume, rep cost, and marketing spend to find the true expected value of an investment.
- **Cost of losing counts.** Apply the loss rate to fully-loaded costs (0.8 x $250K rep = $200K expected loss); a great-looking gross ACV can be a deeply negative bet.
- **Ship it into the CRM.** Custom properties, workflows, and calculated fields turn EV into a rep-facing 'white space' score — set-it-and-forget-it math they can filter on.
- **Kaizen the model.** Re-run the calculation each cycle; check whether estimated unknowns held and refine win-rate and ACV assumptions for next quarter.
- **EV as an alignment tool.** Breaking a call into probabilities, values, and costs shows the exec team what's known versus guessed and creates a scorecard to grade later.

### Pipeline & marketing ops

- **Model the whole funnel.** Dial rate, pickup rate, meeting rate, deal rate, and win rate are all probabilities you can chain to estimate the expected value of a channel or motion.
- **Volume changes the bet.** Two segments with identical per-deal EV can diverge sharply once you factor in how many deals each can actually produce.
- **Territories by expected conversion.** Weight TAM and account valuation by expected conversion rate, not gross dollars, so reps prioritize the territories and deals most likely to pay off.
- **Replicate the winners.** Study high-converting reps and account types (via tools like Gong), then point the team at the higher-EV plays to lift win rate and ACV.


## Metrics mentioned

| Value | Metric | Context |
| --- | --- | --- |
| ~$750M jackpot | Lottery ticket EV flip point | A $2 ticket has deeply negative expected value most of the time until the jackpot reaches roughly $750M, where low probability times huge value tips EV positive (e.g., to ~$2.20). |
| 25% call threshold | Poker pot odds | Calling $100 into a pot that becomes $400 gives 25% pot odds — you should call any time you believe your hand wins more than 25% of the time. |
| ~10% past 5 years | Small-business survival | Only about 10% of businesses make it past their first five years — long odds that can still be a good bet given a large enough potential outcome and small downside. |
| Enterprise 20% / Mid-market 30% / SMB 40% | Segment win rates | Illustrative win (conversion) rates used as the probability input in the expected-value segment model. |
| Enterprise $10K / Mid-market $7.5K / SMB $5K | Segment ACVs | Illustrative ACVs used as the value input; enterprise and SMB yield the same $2,000 per-deal EV, mid-market the highest at $2,250. |
| SMB $100K / Enterprise $250K | Rep cost by segment | Fully-loaded rep salaries layered into the model; the enterprise rep's higher cost, applied against an 80% loss rate, is what flips the up-market bet negative. |
| +$40K vs. -$180K | Net EV: SMB vs. enterprise | At 50 SMB deals versus 10 enterprise deals with those rep costs, SMB nets about +$40K while enterprise runs about -$180K — the popular up-market move is the worse bet. |
| 10-20% OK, 5X = reevaluate | Quota tolerance vs. modeled EV | Expected value as a quota sanity check: an assignment 10-20% off expectation is acceptable, but 5X off means the quota or its assumptions need reexamining. |
| $10M at 1% vs. $5M at 50% | Territory comparison | Weighted by expected conversion, a $5M territory converting at 50% beats a $10M territory converting at 1%, even though the $10M looks bigger on gross dollars. |


## Entities mentioned

- **LeanScale** (company) — Anthony's firm; he describes how LeanScale loads TAM and account valuation into Salesforce/HubSpot to build territories, and how layering expected conversion in would make territory design more honest. · https://leanscale-knowledge-hub.netlify.app/company/leanscale/
- **Spencer Hodgson** (person, guest) — RevOps leader who applies expected value and Annie Duke's 'Thinking in Bets' to go-to-market decisions; ex-Alaska math teacher and tour director turned SDR turned sales-ops operator. · https://leanscale-knowledge-hub.netlify.app/guest/spencer-hodgson/
- **Anthony Enrico** (person, host) — Co-founder of LeanScale and host of The LeanScale Podcast. · https://leanscale-knowledge-hub.netlify.app/guest/anthony-enrico/
- **Salesforce** (tool, CRM) — One of the two CRMs Spencer has built (or would build) the expected-value / white-space model into; also the system LeanScale loads TAM and territories into.
- **HubSpot** (tool, CRM) — The CRM Spencer has actually implemented EV in — via custom properties, a couple of workflows, and calculated fields that output a 'white space' score for reps to filter on.
- **Lucidchart** (tool, Diagramming / Visual Collaboration) — The diagramming tool Anthony references for sketching out the expected-value model — circles for probabilities, squares for values — before it goes into a CRM.
- **Microsoft Excel** (tool, Productivity / Spreadsheet) — Where Spencer, as a new SDR, obsessively exported his call data to compute conversion rates and prove he was the best rep — the origin of his move into sales operations.
- **Gong** (tool, Revenue Intelligence) — Cited as an example tool for figuring out what top reps are doing that succeeds, so the winning motion can be replicated to lift win rate.


## FAQ

**Q: What is expected value (EV) and how does it apply to RevOps?**

A: Expected value is the sum, across all possible outcomes, of each outcome's probability times its value. In RevOps you plug in real numbers — win rates and ACVs, or funnel conversion rates — to score a decision as a good bet (positive EV) or a bad bet (negative EV). It lets operators compare messy choices like which segment, channel, or deal to invest in on a common, math-backed footing instead of gut feel.

**Q: Does a losing outcome mean you made a bad decision?**

A: No. Drawing on Annie Duke's 'Thinking in Bets,' Spencer Hodgson argues that not winning the lottery, a poker hand, or a deal doesn't mean you decided wrong. You judge the quality of the bet given what you knew — the expected value — not the single outcome. A good decision can lose and a bad decision can win, so the discipline is to grade decisions, not just results.

**Q: How do you use expected value to decide whether to move up-market?**

A: Model each segment as a bet: win rate times ACV gives per-deal expected value, then layer in deal volume, fully-loaded rep cost, and marketing spend. In Spencer's example, enterprise and SMB have the same $2,000 per-deal EV, but once you add a $250K enterprise rep against an 80% loss rate and realistic deal counts, enterprise runs about -$180K while SMB nets about +$40K. The popular up-market move can be the worse bet.

**Q: Why isn't per-deal expected value enough to decide where to invest?**

A: Because equal per-deal EV doesn't mean equal investment. Two segments can produce the identical expected value per deal yet differ sharply once you account for how many deals each can realistically produce and what the reps and marketing cost. You have to layer volume, cost, and loss rate on top of per-deal EV before you know which bet actually makes money.

**Q: How do you build expected value into Salesforce or HubSpot?**

A: Spencer has implemented it in both using custom properties, a couple of workflows, and calculated fields that do the math automatically and output a 'white space' score — how much an account or open deal is worth by expected value. The rep-facing version drops salaries and other costs to stay simple, so a seller can just filter open deals by expected value near quarter-end without ever seeing the formula.

**Q: How does expected value change how you design territories and TAM?**

A: Instead of weighting territories by gross dollar value, weight them by expected conversion. A $5 million territory expected to convert at 50% is a better bet than a $10 million territory expected to convert at 1%, even though the larger one looks better on paper. Adding expected conversion to TAM and account valuation gives a more honest view of which territories are actually strong.

**Q: Can expected value be used to set sales quotas?**

A: Yes, as a data point. If you trust the data, you can build quotas from the expected value of the accounts in a territory and use it as a sanity check on assigned numbers. Roughly 10-20% off the modeled expectation is tolerable, but if a quota is 5X off, you should reevaluate it — or have explicit, stated reasons you expect win rate or ACV to rise.

**Q: Why is business a harder EV problem than poker or chess?**

A: Poker and chess are effectively 'solved' games — the optimal move can be computed or the odds are known. Business isn't: you can't see the competitor's hand, so you have to estimate what a second enterprise rep or new collateral does to win rate and ACV. You fill the unknowns with judgment, run the calculation, then measure whether your assumptions held — and the operators with better intuitions win more.


## Timeline

- **00:00** — Intro: Spencer Hodgson and Thinking in Bets
- **00:45** — What is expected (estimated) value?
- **02:09** — The math: probability x value (the lottery)
- **04:00** — Good bet vs. good outcome, and Kaizen
- **04:44** — Pot odds: a poker example
- **07:08** — Starting a business as an EV bet
- **09:09** — Applying EV to go-to-market and RevOps
- **10:29** — ICP example: enterprise vs. mid-market vs. SMB
- **14:47** — Layering in pipeline volume
- **16:13** — Factoring in rep costs
- **17:40** — Deal counts, profit, and the full EV
- **20:00** — SMB +$40K vs. enterprise -$180K
- **22:01** — Business as an unsolved game: intuition and judgment
- **23:21** — Modeling decisions to align the exec team
- **25:05** — Putting EV into Salesforce and HubSpot
- **27:59** — TAM, territories, and quotas with EV
- **30:36** — Spencer's background: Alaska, teaching, and poker
- **34:02** — Where to connect with Spencer


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## Full transcript

_Machine-transcribed and not diarized; speaker attribution is inferred._  
_Transcript only, as a separate file: https://leanscale-knowledge-hub.netlify.app/podcast/spencer-hodgson-revops-poker-expected-value/transcript.md_

### 00:00 — Intro: Spencer Hodgson and Thinking in Bets

**[0:00]** (logo whooshes) - Today we have Spencer Hodgson, one of the smartest RevOps leaders I've come in contact with and really excited to go into what we're talking about today. So I think something that everybody in RevOps experiences, there is just an absolute inundation of data and so much to sift through and so many metrics to go through and so many decisions to make, which reps are performing well, which marketing channels are performing well, which customer success reps are doing well, and it's really tough to go through all of the data and know how to make sense of it. And I love how you've taken a play out of Annie Duke's book, "Thinking in Bets,"

### 00:45 — What is expected (estimated) value?

**[0:45]** and taking a professional poker player's mindset to RevOps. Can you explain to us how you're using the concept of estimated value, what it is in the first place, and how you apply it to the RevOps space? - Yeah, for sure, thanks. So I love this concept. I'm gonna call it estimated value. It's also expected value. It's known as a couple of different things, but basically applying game theory in a way to all of life, we can try to figure out what would I stand to gain from any one decision? And this, essentially, like in mathematics, they said that the probability, needed to be proportional to the value. So as the value goes up of what I'm expecting to get,

**[1:32]** if the probability is going up as well, that's gonna mean that I'm gonna have a higher result. But potentially, like for lower probabilities, it starts to get murky and you start to wonder, if my value is getting higher and higher, but my probability is going lower and lower, where does it break even? Like where am I making a good bet? And so this is what I've gotten really passionate about, trying to figure out how to optimize decision making in multiple spots in life to bring about that value. So I'd love to just dive into what expected value or estimated value is, if that's all right. - Yeah, that's perfect. I think it's been a little bit

### 02:09 — The math: probability x value (the lottery)

**[2:09]** since I've taken a math class. So if we can get a refresher, I'd appreciate it. - For sure, yeah. So this is estimated value here where you take the probability of the first result times the value of the first result, plus the probability of the second result times the value of the second result, and so on and so forth. So if you were to be, let's say we'll just stick with the lottery example, and you know that you're gonna be spending $2 on the lottery ticket, and your probability of success is extremely, extremely slim. So your chances of losing $2 is very high, but your chances of gaining however many million dollars,

**[2:49]** let's call that the first probability, the probability is very low, but the value is very high. So what you can do is you can look at all of the different probabilities, the probability of winning the first, you know, the jackpot, the probability of winning, you know, one of the other kind of side bets where you maybe don't get the special ball, but you get all of the others, and that's a different, you know, maybe just a single million dollars or something like that. You can add up all of the probabilities times the meta values, and then you'll get the estimated value of your bet, which most of the time for the lottery is gonna be less than $2.

**[3:22]** You expect to lose your $2 most of the time. You expect to win some large number, very small percentage of the time. And that means that, you know, like let's say your estimated value on the lottery would be most of the time negative 150, something like that, is for a $2 ticket. But when that lottery gets to a certain amount, once you're into that, I think the math ends up being in the 750 millions or so, the probability starts to outweigh the fact that you're gonna lose. So, although you expect to lose your $2, eventually it starts to make money as a bet, where the estimated value becomes, let's say $2.20 or something like that,

### 04:00 — Good bet vs. good outcome, and Kaizen

**[4:00]** because now you're in the positives, and that is a good bet. So, where I think this gets really interesting is, you can think of it as, everything can be thought of as a good bet or a bad bet. Not, did I succeed or did I fail? Because not winning the lottery or not winning a certain game of poker or something like that, doesn't mean that you made the wrong decisions. And so this is where I think it gets really important and interesting as well is, how do you focus on your own decision making and your assessment of the probabilities and your chances of, you know, maximizing XYZ value? And then how do you turn that into,

### 04:44 — Pot odds: a poker example

**[4:44]** you know, positive estimated values as much as possible? So, and then this gets to a principle that my company is one of our core values. It's something we're really passionate about called Kaizen, where you're constantly trying to hone in on self-improvement. So, just because you failed, we should be asking ourselves, am I still, was I making the right decision based on the estimated value? So, let's go into one more example with poker, where I think it can get pretty concrete. So, if I am in a pot that is for $200 and it's just me and one other player and that player bets $100, now the pot's up to 300 and I have a decision. Do I call? Do I fold? Do I raise?

**[5:33]** And if I am trying to think in bets, right? I'm trying to maximize my value here. I, what I need to ask myself is, if I make this $100 bet, what do I stand to gain and what's my probability that I gain it, right? So, this pot, once I call, if I were to call, would be worth 400, the 200 initial, the 100 from the opponent and then the 100 from me. And then that 100 is what I'm calling. So, my $100 is at stake in order to make $400 total. And I then can use that to say, I've basically got a 25% pot odds here. Like, I need to think that I'm going to win this hand more than 25% of the time in order to have a good bet.

**[6:25]** So, if I believe that my hand wins 26% of the time, I should call. And that's where it can get really interesting with like I'm saying these decisions. You can expect to lose. Like, you expect your opponent to have a better hand than you, 76 or not 76 actually, than you wouldn't call. You expect them to have the better hand, 74% of the time or less, and you would still call. And so, this is where I think it can tease out some of the gray areas. As long as your hand is better than winning 25% of the time, which it can get into a lot more math there, but as long as you suspect that your hand is better winning more than 25% of the time, it makes sense to call.

### 07:08 — Starting a business as an EV bet

**[7:08]** - I think that makes a lot of sense. And also just in terms of decision making is the most important part. I think of another example too of maybe starting a business. So, the majority of businesses go out of business. So, I think only 10% make it past the first five years of being in business. So, the odds from a percentage basis are against you. However, the potential financial outcome is definitely worth it if you make it past that, and then you could have some potential exit or create something that can create some scale and continued on wealth beyond that. So, I think rather than just thinking of it as,

**[7:50]** oh, well, 90% chance of failure, it means I shouldn't do it. It's, you have to put the value in it to say, okay, well, if that 10% chance hits though, it's well worth the investment. And if you do it enough times in the long run, then you'll be successful. So-- - Absolutely. - So, I think bringing that layer in in decision making is really important. And being okay with I lost, but I made the right call. Extreme example, if you knew you had a 50% chance to win the billion dollar lottery, you should go buy a $2 ticket. Even if you lose, it was a good decision to do it. - Yep, absolutely, yeah. And I think with your example of starting a company,

**[8:34]** it's a really good one as well for estimated value because you can also think, what do I stand to lose if this company doesn't work out? Like I've wasted, I don't know, let's say five years of my life. I've spent things like I have opportunity costs I could have been spending elsewhere. I've spent some number of dollars of my own money, some amount of relationships trying to fundraise XYZ. But what's the real cost? Because certain things too are, they could be very positive estimated value if you're, you don't stand to lose very much by making this bet, right? Like, and with some businesses, you can start up a side hustle at very little cost to yourself.

### 09:09 — Applying EV to go-to-market and RevOps

**[9:09]** And it could be an absolutely good bet because it's almost impossible to go to negative estimated value. It might just not pay off as much as you desire and you decide to go with something different for your time later on. - Right, right. And managing the upside and downside risks is just a really different perspective and way of thinking when you're approaching decision-making in business and in life in general. So taking this and tying it to a go-to-market context, bringing it back to RevOps, how do you use estimated value to make decisions in a go-to-market context? - Yeah, great question. So I think in some ways we have it really good in RevOps

**[9:50]** because we have the data, like you said, everybody's talking about data, it's out there. It's not quite the same as like when you're deciding to start a business or not. Like you kind of have to jump, you have to leap. - Yeah, you have to make assumptions on those percentages, right, you have to, it's not given. It's not like a school math problem where you say, hey, you have a 50% chance of this business being successful. You have to use some judgment in it. But you're right, in RevOps, you have the actual conversion rates. - Yeah, this is what I love about it and why I love applying it here. So I'm gonna walk through a little bit

**[10:22]** of how I would go about thinking about a specific example.

### 10:29 — ICP example: enterprise vs. mid-market vs. SMB

**[10:29]** So if we think about something like an ICP, I'm gonna use circles to represent probability and I'll use squares to represent value.

**[10:41]** Let's just say that I have a decision to make, right? Is my company gonna go and kind of strategically place themselves and invest more time, energy, marketing dollars, hire new reps in the enterprise market? Let's just, we'll do this one. This top will be enterprise.

**[11:05]** Am I gonna invest more time in the mid-market or SMB?

**[11:16]** And it's a tough, scary thing for a company to decide, everybody wants to move up market. We're all thinking, I mean, actually many companies. - Usually, I'd say many times out of 10. Companies we work with want to move up market. - Exactly, oftentimes the goal is to move up market, but we need to know and understand what do we expect to gain before we invest all of this time and energy. And so this can help make that decision. So for what you would do is you would take your existing win rate or your conversion rate or whatever you're measuring. Like if you're doing, let's say cold calling, you could say, what's my chances of them?

**[11:57]** Once I dial, how often do they pick up? Once they pick up, how often do they book a meeting with me? Once they book a meeting, how often do they become a deal? Once they become a deal, how often do they win and so forth? So you could look, all of those are probabilities that we could kind of factor in here, but I'm gonna just take the kind of the closing motion of, once we've created a deal, how often do we win that deal, right? And so like, let's say that my enterprise win rate is, I don't know, 20%, my mid market win rate is 30%

**[12:35]** and my SMB market win rate is 40%, right? So it's easier to win the smaller deals potentially in this example, but I stand to gain more from my, the ACV of those deals. So the amount of this ACV is where it all comes down, right? Because what I've said right now isn't enough to decide, does it make sense for me to move up market or not? Because it depends on how good the ACV is for the enterprise company and how much worse it is for SMB. And it could be that actually mid market is my best way to go. And it all will depend on my, the ACVs. So in this fictional example, if I were to make this a, let me just think of easy math.

**[13:31]** Let's do this at 10K, is the ACV. And we'll do this one as 5K.

**[13:44]** And we'll do this one at halfway 7,500.

**[13:51]** So right now, what I can do is I would do 0.2 times 10 to figure out what my expected first value is. We're kind of back in this probabilities here, right? So we've got the probability of success times for the first result, times the value. And so I can do that and I can figure out what my probability is for, or my estimated value is for probability one enterprise and same and so on and so forth. But what I attempted to do here is these values should be equal, right? Because my ACV of 10K times 0.2 is gonna get me the same as my ACV of mid market.

**[14:40]** So this is 2000, I'm gonna just use this.

### 14:47 — Layering in pipeline volume

**[14:47]** And then this is 2000. - When the ACV and conversion rates are the same, and so you're coming up with a similar expected value, would you layer in something like volume of pipeline you think you can produce in either one of these channels? - Yeah, so that is a super important thing to factor in here, which would be how do I, kind of like a scientist, right? How as I influence my like subject material whatever it is that I'm studying, if my being there is somehow manipulating the behavior of the experiment, then it gets very messy. So, but I would think you would, we would try to make an educated guess here. And the good news is we still have a better,

**[15:31]** it's, in my opinion, a little bit easier than you're in the company example where you're just like, what would it be like, is this industry something that they need a new upstart like me to start in? Because we might have some better indications of when we had two enterprise reps instead of one, how did that affect our win rates and ACVs? When we put in some new marketing collateral and did that change anything? So you do have to kind of guess at what me spending and deciding between these two, if the estimated values are the same, you have to try to figure out is investing more in one or the other valuable at all, or do I keep it the same?

### 16:13 — Factoring in rep costs

**[16:13]** And you're gonna, you will have to make some educated guesses to figure out what, where to proceed. But I think what's interesting about this example is it's told us that the mid market is the route to go. And this just happens to do with what I, the numbers that I decided on. The enterprise and SMB expected values are the same, and then the mid market is higher. Now, the other thing that we haven't mentioned yet is the costs like an enterprise rep probably costs more than an SMB rep. And a mid market rep might be in between those. So you can also factor that in because you're not just gaining a $10,000 ACV. You are technically like with this value,

**[17:00]** it should factor in the total value that you make doing it. So it's not as simple as either we get nothing, we spend nothing on the 80% of time we lose with the enterprise. And we spend nothing on the 60% of the time that we lose with mid market, I mean, sorry, SMB. We could look at the costs too. And it could be that the marketing collateral is a lot more expensive to put together. And that could mean, you know, that it's more helpful to stay with SMB. - Maybe we put some numbers to this example. So let's say, let's just use the rep cost. There's other costs that probably come in. But let's say the rep cost, the SMB rep is gonna be like 100K.

### 17:40 — Deal counts, profit, and the full EV

**[17:40]** And then the enterprise rep is gonna be 250K. So would you take that 0.6% loss rate applied to the salary of the rep? So like, okay, we expect to lose 60K. And then would you take the 20 or the 80% loss rate expected in enterprise and apply it to the 250K to see, okay, now take the positives and the minuses and let's see which one is a better decision to make. - Yes, yeah, exactly. And I would also wanna layer in here like the count of deals expected, right? So that's another thing that we haven't accounted for yet. But if we can put in like the count of deals, right now I gave this example so that a single deal

**[18:39]** at the current rates makes the same kind of expected amount. And this does help me understand like how the bets are. Should I be betting more on one or another? But where I can get interesting in what you had mentioned is it could be that at the volume that we're doing, we can't make the profit to pay for that salary. You know what I mean? - Right, right. So if this is like currently we're doing 50 deals at SMB and we're doing like 10 or something like that here, then on average we're making 2K 10 times for enterprise and we're making 2K 50 times. This is all close one times here that I'm thinking at least for mid market.

**[19:33]** And that can kind of tell us profit wise, do we have enough money to make the reps kind of paying for themselves to make it a bet that we can continue to make? Even though these two bets are estimated value, expected value wise, they're the same bet. Does that kind of make sense? - Yeah, yeah, but if you carry it through, so if you say 2K times 50, that's gonna be 100K.

### 20:00 — SMB +$40K vs. enterprise -$180K

**[20:00]** - Exactly, yep. - And then you expect to lose 40K. So your like final expected value if you bring this all in would be plus 40K. And then on the top, it's 20K

**[20:16]** and then let's get 0.2 times or 0.8 times 250.

**[20:25]** You'd expect to lose 200, so you're negative 180. So in this example with all these variables, you have positive 40 and then you have negative 180 on the enterprise if you're comparing SMB and enterprise. So taking that like line of mathematical thinking to this, SMB market might actually be a better bet for you to invest in. - Exactly, yep, exactly. Like the bet itself again is the same, but in order to figure out that the other costs associated like you said, like these salaries, if the market has to pay an enterprise rep 250K, then our company going up market might not be worth it because we can't really afford to have an enterprise rep

**[21:14]** unless they're also able to be increasing that something like this. If I'm a company trying to decide between these three options, it could be that I've never really hired a true enterprise rep before. So I'm trying to compare data for what it looked like when a mid market or an SMB rep was trying to win an enterprise deal at what I considered an enterprise deals ACV. So it could be that they could command 250K, that's why we pay them more, but it could also be that for my company, this isn't a good bet. - Well, and I think this is where poker and other games, they're solved and meaning you have all of the data on the table.

### 22:01 — Business as an unsolved game: intuition and judgment

**[22:01]** This is where business gets a little bit more in the gray area. And I think this is where really good leaders will have to make some judgments on what they expect. So, okay, the mid market rep, maybe it was producing that. I think if I pay someone 250 or I get that level of talent, this is what I think it'll affect on conversion rates and potential ACVs. So you're gonna have to put in some placeholders for that data in order to run this calculation and then make a decision. - Absolutely, yeah. And just one quick thing too is even in poker and most games, you still have to do that because your opponent's hand is not visible to you.

**[22:41]** They are betting in such a way or they're acting in such a way that they're representing a certain hand. But no matter what, you have to use your intuition for games like poker. For certain games like chess, there's no point discussing this because there is just a best move that a computer can figure out. But like you said, in business, like we have to, the people who make the better intuitions about this stuff are going to succeed more and their companies are going to do better. - But I don't think a lot of people are even framing their thinking this way. And I think this really helps. Like when you break it down data point by data point,

### 23:21 — Modeling decisions to align the exec team

**[23:21]** you model out potential decisions into the future. You model out potentially what you think will happen if you make decision A, B, or C. And then you can clearly see what you know and what you don't know. And then make educated guesses on some of those components that you don't know. It makes it so much easier to make a call, feel confident about the call. Also communicate it to the rest of your executive team. But when you're just saying, hey, we should go out market, enterprise is better. Why? But if you break it down this way, then I think it helps get everybody on the same page to see exactly where you can win and then you can measure it.

**[24:06]** Hey, after we made these decisions, are those things I didn't know about actually holding true or do we need to adjust our decision-making model for the next quarter or the next year? - Exactly, yeah. And I think too, what I love about this framework is you could hone in onto any of these pieces and really try to figure out is there something we can improve here?

**[24:28]** Obviously, you could potentially have 100% win rate if you lowered the ACV. If you're just like, we'll give you this service for free but companies aren't gonna do that. But you can try to figure out how do I increase my conversion rate? How do I win more deals? What actions do I think could happen? Maybe I have some data around it already that rep-wise, maybe certain reps are winning more and I can use, I don't know, Gong or other tools to figure out what are they doing that's succeeding. And then I can try to replicate that but we can also do the same on deals and we can figure out are certain types of companies better fits for our service within the SMB

### 25:05 — Putting EV into Salesforce and HubSpot

**[25:05]** and therefore we can have higher ACVs if we kind of pointed our reps toward those and so on and so forth. So I think what's really cool is you can hone in to all of this. And so as you, like you said, as we start to be more aware of this in our thinking, we can capitalize on it and use it for a framework for almost everything that we do. - Very cool, very cool. I love conceptually and I love seeing this on Lucidchart and on paper, how do you take something like this and put it into a system? Do you take this concept of estimated value and get this into Salesforce, get this into HubSpot? How do you take it from paper to in the systems you're using every day?

**[25:54]** - Yeah, great question. So we've been talking super theoretically but this isn't that helpful to a rep to be honest, right? They kind of don't care. They wanna win more, they wanna make money, they wanna succeed, they want the company to do well but if you can get it into the system, Salesforce, HubSpot, whatever you're using and have it show them, these are the deals that are the worth the most to you. These are where if you maximize your chances here by investing your time more on certain types of deals or something like that, you're going to make more money. You're gonna be more successful and your boss is gonna be happy.

**[26:35]** So I absolutely, I've built it into both systems and it can be kind of involved but the idea is once it's done, it's just a set it and forget it sort of thing that it's a calculated formula that's kind of just working and humming and telling them what deals to focus on. It can't quite get it the why very well because it's just a big math formula although what I would use for reps wouldn't involve salaries and some of the other costs we expect to make 'cause it gets a little too complicated but you can absolutely use these and build these into HubSpot and Salesforce. The way that I've done it in HubSpot involves kind of a workflow

**[27:18]** and a couple of workflows, some custom properties and then some calculated fields which do all the math so that it can just display what I call white space, kind of how much a company is kind of worth to me and then I can when I'm trying to look through my account set or my open deals that are closing this quarter and I'm trying to decide which ones do I need to really press near the end of the quarter, I can filter by that and say show me the ones that are the most likely to bring me the most that have the highest expected value. - I think that's really interesting and something that we do often for our customers is we'll do a TAM analysis.

### 27:59 — TAM, territories, and quotas with EV

**[27:59]** We'll get the TAM into Salesforce or HubSpot. We will put an account valuation and they will use that to build territories. Now, something that we haven't really incorporated is throwing in the conversion rate, expected conversion rate of that particular segment to come up with that expected value and then looking at territories that way. We normally just look at the gross dollar value but I think that's a much more impactful and honest way to look at a territory. Like hey, we carved you out of $10 million territory but we only expect 1% of this to potentially convert versus like we carved out a $5 million territory and we expect 50% of it to convert.

**[28:42]** On paper, without bringing in estimated value, you'd say oh, the $10 million territory is better but when you look at hey, what we actually expect to achieve, the $5 million territory is better. So I just think it's a really interesting way to think about building territories, setting TAM and then having a rep look at a set of prospects and deals and think which one should I go after and prioritize first. - Yeah and I mean, on top of that, imagine I haven't been at a company where I could convince them to fully do this but if you're confident in the data and you have it set up in such a way, you could build quotas based on the territories

**[29:19]** that they have and the expected value within those accounts. - Yeah, probably culturally. People want the sales team pushing regardless of what happened in the past, like the past is the past but I do think it would help before you assign their quotas to use that as a data point to see are you close? Is it fair? - Yeah, exactly. - And if you're 10, 20% off, maybe that's okay but if you're like 5X off of what you expect, then maybe you should reevaluate. - Yeah, exactly and or at least you should have some reasonings that you expect the win rate or the ACV to increase. If you just increase it and say it'd be nice to,

**[29:56]** if we could increase the value by 20% with this subset, but it would be nice, we have no reasons really that we expect it to, then you're setting unreasonable goals but yeah, I definitely think it's another data point that can be used for things like quota, territories, everything. - Well, Spencer, this is a super interesting approach that you take, really unique. I haven't really heard of any RevOps professionals looking at their go-to-market data the way you are. What's your background? Did you end up in a professional poker team? Or how did you end up running RevOps this way? What's the story? - Yeah, that's a great question.

### 30:36 — Spencer's background: Alaska, teaching, and poker

**[30:36]** So, no, I only, I casually like poker but I actually got passionate about poker because of my passion for estimated value. I actually was a teacher up at a Catholic classical school in Alaska and I would teach in the winters and then I was a tour director up in Alaska in the summers. So I would take people around on my bus all throughout Alaska. I'd stay with them for seven days and kind of tell them where to eat, what to do. And it was a really, it was a good gig with both jobs. But when COVID hit, I couldn't tour direct anymore. That kind of ended all the Alaskan tourism for kind of multiple summers in a row. So I needed kind of a life change.

**[31:18]** And I already had been, I taught like seventh and eighth grade math. And I had been passionate about probability even then and kind of teaching it to the students. And I love that kind of stuff because I love board games basically. And so even then I had kind of started learning and thinking about expected value. And I then once I got into the business world, so tourism ended, I needed a change. And I basically jumped at anything I could do instead. So my wife and I moved down to Tulsa, Oklahoma to be closer to her family for more of a support network. And then I took a job cold calling kind of just the bottom of the barrel, sorry, SDRs.

**[31:59]** But yeah, I had to start somewhere. I could, you know, did anything. I would do anything to kind of make ends meet. And it was as an SDR that I started applying some of this probability stuff that I was aware of to my life. And this was maybe kind of weird, but my boss had given me access to export data and things like that. Maybe you should or shouldn't have done that. But I was able to and was obsessed with trying to export my data into Excel and then figure out if I'm the best. I wanted to be the best SDR that we had, which to be honest is like a very important skill in an SDR because you have to have something driving you to keep getting those no's.

**[32:44]** But hopefully expected value is helping realize that too. Because you can get hung up on 95% of the time and that can still be a very good bet, right? But so I was obsessed with knowing am I the best SDR? Like, and I was really trying to figure out like what were everybody else's conversion rates? What were they getting once they had booked a meeting? Did it result in a deal? I just really wanted to know. And so I started doing more and more calculating and my boss just started realizing that I was passionate about this and he just gave me more and more ability to kind of keep taking things off his plate and essentially just kind of,

**[33:18]** I kind of worked my way into sales operations and I've just been loving it ever since. - That's amazing. Well, I'm sure the math teacher role definitely helped with a lot of the RevOps work for sure. Alaska is beautiful, by the way. I absolutely love the state. I spent, before I was in tech, I was in healthcare and I actually spent a couple months working along the Aliaskan pipeline doing medical equipment deployment. So I spent a lot of time in the state and it's just stunning. So I'm sure some of those tours were very interesting. - Oh yeah, it's definitely worth a visit. - For sure. Well, Spencer, this has been awesome. I really appreciate it.

### 34:02 — Where to connect with Spencer

**[34:02]** Before we wrap up, what's the best way if people want to learn more about your approach or just get connected with you? What's the best way to get in touch? - Yeah, so I like to be really active on LinkedIn. I connect with everybody. If anybody sends something over, just linkedin.com/SpencerHodgson. I post things there. You can DM me if you want. I'm happy to chat, especially nerdy numbers. Maybe this is weird, but I put, I have estimated value in there on my about section. And if SDRs or people were to have mentioned that, I would take a meeting way more frequently. So if people are interested in this kind of stuff, hit me up. Happy to chat.

**[34:41]** Happy to walk through on a more one-to-one basis, like how I built it in HubSpot, how I would go about building it in Salesforce if I were doing it again. 'Cause I'm not on Salesforce right now, that sort of stuff. - Love it, love it. Well, thank you so much. I appreciate the education. Love the approach. And hope to have you back soon. - For sure, thank you, Anthony. - Thank you.


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