I help consumer brands make more profit per customer using AI analytics

Situation: Ahead of a disappointing earnings call, the company lacked a clear view of which categories, sub-brands, and countries offered the highest future value and was still treating a declining sub-brand as its flagship growth engine.

What I did: Built a single market map combining category growth and share by sub-brand and country, revealing a hidden-gem category that was growing fast and had structurally higher LTV due to high repeat purchase behavior.

Result: Gave the global leadership team a clear, data‑backed “where to play & how to win” strategy, refocusing investment toward the high potential category to capture a nine-figure profit opportunity over the next 3 years.

Situation: When Covid hit, the business faced huge uncertainty around how shifts in consumer behavior across its categories would affect demand and its ability to hit 5‑year shareholder return targets.

What I did: Built an integrated P&L and macro‑economic scenario model for the US market that linked consumer behavior changes to category demand, forecasted organic growth, and quantified an additional ~$600M in required productivity savings.

Result: The model became a core planning tool for the first year of Covid, guiding 3‑year category investment and inventory decisions and helping leadership capture the $600M in savings while maintaining growth ambitions.

Situation: Donation revenue had been steadily declining for 2 years, and the leadership team did no know why.

What I did: Diagnosed that their problem was over-reliance on generic email blasts with no real segmentation. Identified a hidden segment with over 10x median lifetime donation value. Designed a three‑pronged marketing strategy for priority segments with tailored messaging.

Result: Gave leadership their first comprehensive view of donor segments and a clear marketing plan for what to emphasize with each group to increase responsiveness to campaign emails, average gift size, and donation frequency.

Situation: Their historic core customer base was shrinking, while a fast‑growing segment projected to be a major share of future profit emerged that leadership didn’t understand and didn’t know how to retain.

What I did: Used Bain’s Elements of Value® framework to systematize perceived brand value for this priority demographic, and identify their value drivers and loyalty triggers.

Result: Gave the company a clear, structured view on how to evolve brand positioning, messaging, and service investments to retain this emerging segment and protect long‑term profit per customer as the market shifted.

Situation: Occupancy was dropping as new competitors entered the market, and the owner was planning two expensive multilingual front‑desk hires to “upgrade” the luxury experience without knowing if it would actually drive bookings or referrals.

What I did: Combined booking data with targeted guest surveys to map friction across the customer journey, finding that bathroom investments would result in the biggest lift in repeat stays and referrals, while the front‑desk experience was already one of the strongest parts of the business.

Result: Helped the owner avoid two five‑figure hires and redirect investment into high‑ROI bathroom upgrades, driving better performance and effectively doubling his take‑home pay for the year.

Situation: The brand had recently completed a multi-month project to determine their sub-brand portfolio & channel strategy to maximize profit. However, they lacked a clear, SKU‑level view of optimal pricing, bundling, and retail shelf positioning.

What I did: Prioritizing the “hero SKUs” (high‑sales, high‑margin) in each category, I analyzed each one against competitors to determine the best price and bundle strategy for profit per customer, for each key buying occasion.

Result: Delivered the portfolio view leadership used to shape their Walmart negotiation strategy, with a clear set of shelf and pack/price moves designed to win better placement and maximize profit per customer at retail.