HBS Data Analytics Simulation: Brand Strategy & Market Forecasting
Individual project, 8-year competitive market simulation
Blue was the weakest of four detergent brands, flat at 9-11% market share for four years. Repositioning it on data instead of price took market share to 14.1% and cumulative profit to $242.0M by Year 8.
This was a competitive simulation where four detergent brands fight for the same market over eight years. I inherited Blue, the weakest of the four: stuck at 9-11% market share, priced in an awkward middle where it was neither the cheap option nor the premium one, and giving customers no clear reason to pick it.
Rather than cutting the price, which would have started a race to the bottom against a competitor already undercutting everyone, I used the platform's demand data to find an unoccupied position. The data showed odour elimination was the most in-demand product attribute and pods were the fastest-growing format, so Blue was rebuilt around both, with marketing spend concentrated on the specific regions and customer groups the data favoured instead of spread evenly.
The strategy worked. Market share rose from 11% to 14.1% and cumulative profit more than tripled to $242.0M. But the part of this project I find more valuable is what went wrong alongside it.
In year five, the repositioning worked far better than expected and demand jumped. I'd set production based on the previous year's sales, so we sold out and left roughly $104M of revenue on the table, then took a further hit when customers who couldn't find the product complained publicly and suppressed the following year's demand too.
Going back afterwards and recalculating what better decisions would have earned, roughly $132M of losses across the simulation were avoidable. Almost all of it traced to one root cause: I made each decision on its own, in sequence, when production, pricing, formulation, and targeting all feed into each other and needed to be decided together.
Why it matters
The lesson here wasn't the market share win, it was how the losses happened. Every major mistake came from the same habit: setting this year's number by nudging last year's number, instead of asking what this year's strategy would actually cause. Deciding how much to produce before thinking through what the new marketing and pricing would do to demand is how you end up selling out and losing $104M of revenue you'd already won.
Blue's market share by year. Grey bars are the inherited baseline (Years 1 and 4); teal bars are the years I made the decisions.
Production against demand, Years 5-8. Each year was set from the previous year's number, so the gap flips sign repeatedly. Year 8 also carried 6.0M units of inventory into an unmet 46.3M demand.
Dataset, tools and how it was done+
Dataset: Simulation platform data (Years 1-8, decisions for Years 5-8)
Tools: K-W Vision simulation platform · Scenario analysis · Newsvendor model · Kotler STP & Keller CBBE frameworks
- 8-year market simulation (Years 1-4 inherited, Years 5-8 player-controlled) against 3 competitor brands
- Pre-game analysis of platform data identified odour elimination and Pods formulation as the highest-demand positioning
- Held price at $7.00 through Years 5-7 despite a competitor price war, based on price-bracket demand data ($5-7 bracket 48% larger than $7-9)
- Year 5 production, anchored to Year 4 demand, caused a 14.9M-unit stockout and $104M in foregone revenue
- Post-hoc counterfactual analysis quantified ~$132M in avoidable losses from treating annual decisions sequentially rather than as one integrated problem