Eight periods. Four firms. One graded metric: the share-price index. This is a quantitative retrospective of how Team M — starting with the smallest war chest in the industry — ended the simulation with three times the market value of its nearest rival, told through the simulation's own data.
MarkStrat is the strategy simulation used in MBA and capstone marketing programs: student-run firms compete in a simulated consumer-electronics category (Sonites), making granular decisions every period — R&D feature targets, price points, production plans, advertising budgets and messaging, channel staffing. The market clears everyone's moves simultaneously, then publishes the results.
Our course grade was tied to the Share Price Index, and SPI is relative: one firm's dominance can mathematically lock the others out of an A. I served as CEO of Team M, and I took the zero-sum framing literally.
Sonite demand grew from 746,000 units in Period 0 to 2.4 million by Period 8 — and midway through the game an entirely new category, Vodites, opened from zero and reached 900,000 units.
Growth that fast forgives nobody: a firm that merely holds its ground is shrinking in relative terms, and SPI is a relative metric.
Starting endowments were deliberately unequal, the professor's nod to real market conditions. Team M opened with $10.9M in earnings — the lowest in the industry, 22% behind Scorpion — and the smallest next-period budget.
In MarkStrat, budget is performance-linked: each period's allowance is a function of the previous period's results. A weak start compounds. So does a strong one — which meant the early periods would decide who got to play offense for the rest of the game.
By Period 8 our marketing budget was $25.3M — the largest in the industry — while Rumble, who out-earned us at the start, was allocated $9.0M.
Every curve on this page is downstream of this one: budget is the compounding variable that turns good periods into permanent advantages.
MarkStrat sells market research the way the real world does — à la carte and expensive. Consumer surveys, panel data, semantic scales, competitive ad estimates, market forecasts, advertising and commercial-team experiments: ten studies per market, per period.
I bought all of them, every period, from the first turn onward — $6.1M cumulative, spent while we had the least money to spare. Each period's bundle ran ~40 pages of tables. Most teams, by their own admission, didn't read them.
Semantic-scale studies report each segment's ideal product on a 1–7 scale, attribute by attribute. If the Savers' ideal price scored 2.0, we set our marketing sliders to 1.9 — betting rivals would sit on the published ideal or ignore it entirely.
The experiment studies quantified the marginal return of the next dollar of advertising or the next salesperson before we spent it. We built Excel calculators to convert report values straight into R&D inputs.
We plotted every brand on the two dimensions the semantic scales ranked most important to consumers: price and processing power. Period 1 told a clean story: our budget brand MOST sat usefully close to the Savers' ideal, but MOVE was stranded in no-man's-land — and Turtle already owned the premium corner.
The decision: R&D MOVE toward the Shoppers — an under-served segment the forecasts said would grow.
R&D in MarkStrat takes a full period to complete and another to reach shelves, so a repositioning decision is a bet on where the market will be, not where it is. While MOVE's project was in flight we held pricing discipline on MOST and kept buying the studies that told us the Shoppers were still coming.
By Period 5 the capture was complete: MOVE sat on the Shopper ideal, converting that proximity into a 70%+ purchase-intent lead in the segment. MOST, meanwhile, fought Scorpion's SOLO and SOFT over the Savers — a fight we were losing on positioning but winning on awareness and budget.
Our standing rule was to never let two of our own products converge — cannibalization is competition you pay for twice. At close, MOVE held the Shopper territory nearly alone, MOST held a defensible Saver position, and no Moose product competed with another.
Drag through periods above with the scroll — every dot is the simulation's actual multidimensional-scaling output, not an artist's sketch.
Competitive-advertising estimates let us watch every rival's budget. As the flywheel turned, our ad spend grew to $15.9M by Period 8 — the industry's largest, 2.5× Rumble's. Brand awareness tracked spend almost mechanically: more money, more recall.
Awareness across our brands climbed steadily — yet awareness is a necessary condition, not a sufficient one. Turtle's Vodite brand would later hold an awareness lead over ours the entire time we were taking its market.
Purchase intent — the share of consumers naming your brand as their first choice — is driven by how close your product and messaging sit to a segment's ideal. This is what the semantic-scale discipline bought us: MOVE's intent among Shoppers reached 70% by the final period.
Ad experiments then told us exactly how hard to press the advantage: spend was scaled where measured marginal return was highest.
Planned production consistently landed short: order 1,200K units, receive 960K. Worse, in the growth periods we sold everything we made — Periods 3 through 6 ended with zero MOVE inventory, which looks efficient and is actually a silent ledger of lost sales.
Zero inventory isn't efficiency. It's unmeasured demand you handed to a competitor.
Holding cost in MarkStrat is 8% of transfer cost — pennies against a product with healthy margins. Once we ran the numbers, the policy flipped: deliberately overshoot production and treat leftover inventory as cheap insurance against stockouts.
The worst case proved the point: when a MOST refresh obsoleted 357K units in Period 8, the disposal loss was $3.2M — painful, but a fraction of what serial stockouts on MOVE would have cost at its margins.
We tracked net contribution per brand in real time, and allocated resources toward brands whose contribution was flattening — using the experiment studies to check that another dollar would actually move the needle before committing it.
MOVE's line tells the story of the whole strategy: $3.9M → $87.5M across the game.
A commercial-team experiment in Period 3 projected significant unmet revenue in MOVE's channels. We expanded the sales force accordingly — and MOVE's contribution jumped 4.5× in a single period.
And note the pink line that enters at Period 6: MEME reached $128.8M of contribution within three periods of launch — more than half the company's profit by the end. That story is next.
The Vodite category launched with three segments — Innovators, Adopters, Followers — and a forecast we trusted: the market's center of gravity would migrate hard toward Followers, who grew from 7K to 677K units by the end.
Entering costs two full periods (R&D, then launch) and a fortune we didn't yet have. So we watched, saved, and bought the Vodite reports while others paid the tuition of going first.
Rumble pioneered with REF in Period 3 — positioned poorly — and was crushed to a 29% share the moment Turtle's TEPETE landed near the Followers' ideal in Period 4. By Period 5, TEPETE held 100% purchase intent: it was, briefly, the only game in town.
Watching two rivals spend money mapping the market for us was the cheapest research we never paid for.
We refused to launch a compromise product and patch it later — with a low budget, an extra R&D cycle post-launch would burn two periods of income. MEME arrived needing no correction, placed between the Adopters and Followers ideals precisely as the majority tipped from one to the other.
First period on the market: 72% unit share. Our budget doubled the following period.
TEPETE's purchase intent collapsed from 100% to 22% in the period MEME arrived — while TEPETE still out-scored MEME on brand awareness. Consumers knew Turtle's product; they wanted ours.
A follow-up R&D project shifted MEME onto the Followers' ideal as that segment became the market, and we closed at 84% unit share. Being first matters less than being right.
Team M's earnings before taxes grew 20.8× over the game — against 1.7× for Rumble, 2.7× for Scorpion, and 3.4× for Turtle. From the industry's smallest starting position, we ended with more profit than the other three firms combined, twice over.
Plot our EBT on a logarithmic scale and it is nearly a ruler line: an exponential fit gives R² = 0.97 — 47% compound growth per period, sustained through eight periods of active competitor counter-moves. Rumble's equivalent fit: R² = 0.06.
Consistency is the signature of a strategy. Noise is the signature of guessing.
Three times Turtle, four times Scorpion, six times Rumble. On the metric the course was graded on — the metric that was zero-sum by construction — Team M didn't just earn the A. We set the curve.
Looking back across the eight periods, our playbook separates cleanly into rules we never broke and reflexes we retuned every period.