Toptal cuts costs 90% for CPG leader with forecasting model and ad spend algorithm.
The company needed to scale its internal ML platform for faster, more efficient revenue insights and found a high-impact solution through Toptal.
Client
A leading global food and beverage company with a portfolio of iconic brands spanning snacks, drinks, and nutrition products.
Employees
Revenue
Industry
Consumer Packaged GoodsDelivered Services
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Schedule a CallChallenge
A global consumer goods leader set out to improve the speed, cost-efficiency, and accuracy of its revenue forecasting platform. The existing system required significant manual oversight and could only deliver insights quarterly, limiting its impact on day-to-day decision-making.
Solution
Smarter forecasting at scale
Toptal placed a senior machine learning engineer to develop a second-generation revenue forecasting model. The new solution dramatically reduced cloud compute costs and minimized the need for human intervention while increasing model accuracy and reliability.
Portfolio-wide optimization
Beyond forecasting, the engineer created an advanced algorithm to optimize ad spend across the company’s full product portfolio. This marked a leap forward from the previous brand-by-brand optimization model, allowing for more strategic investment decisions.
Outcome
10x cost reduction and daily insights
The new forecasting model slashed operational costs by 90% and accelerated delivery of insights from quarterly to daily. It also enabled the client to reduce reliance on large ops teams, freeing up resources for more strategic initiatives.
Smarter, broader ad-spend decisions
The portfolio-wide optimization algorithm helped the client make more efficient use of marketing dollars, enhancing return on investment and supporting sustainable growth across product lines.
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