Insurance: Budget Optimization
Budget optimization of media spend in the insurance industry using predictive analytics.
Challenge
A major insurance company needed to optimize their media spend across multiple channels and markets. They wanted to maximize ROI while reducing unnecessary advertising spend.
Solution
We developed a predictive analytics solution using Bayesian machine learning to optimize budget allocation across different marketing channels. The model considers historical performance, seasonal trends, and market conditions to predict the most effective spending strategy.
Each channel has its own response curve: spend buys sales, but with diminishing returns as the channel saturates. The Bayesian model learns these curves with uncertainty, then reallocates budget toward the channels with the highest marginal return, the same sales for less spend.
This work is now productised as Alviss AI, our marketing science SaaS platform, so any team can run the same Bayesian media-mix modelling and budget optimization on their own spend.
Each channel's response curve (spend to sales) with a Bayesian uncertainty band. Dots mark current spend; returns diminish as a channel saturates.
Business Value
Sales budget savings of up to 25% compared to existing spending. The company was able to achieve better results with less budget by reallocating resources to more effective channels.