Marketing Optimization for Fortune 500 Bank
The Challenge The bank had a third-party ‘black box’ ad bidding system and they wanted to verify efficacy while improving price-per-click (PPC) bids on search terms in Google Ads Marketplace. The company lacked an accurate way to estimate their marketing attribution per keyword. They relied on qualitative tracking to assess the third-party bidding tool.
Client: Fortune 500 Bank Sector: Finance Vertical: Marketing Optimization Model: Upper Confidence Bound/Epsilon-Greedy
The Case Study
Estimate Keyword Value Since we had a production predictive model, our focus was to identify and improve future results based on existing results. We began by aggregating historical data to estimate the value of each keyword as a benchmark. The bank had stored historical bid data on previous campaigns and determined a correlation keyword attribution.
Model Selection We identified this as a Multi-Armed Bandit (or contextual bandit with keyword estimation data) problem where the problem is defined as choosing to allocate a fixed set of resources between alternative options. In this case, the Estimated Keyword Values were applied against competing campaigns. This approach asserted preference to campaigns that are performing well within target estimations, while deranking variations that would underperform.
Solutions A few approaches were applied to optimize different campaigns. Other models were tested but these resulted in the most immediate improvement: Upper Confidence Bound: This strategy is based on the Optimism in the Face of Uncertainty Principle, and assumes that the unknown mean payoffs of each arm will be as high as possible, based on observable data. Epsilon-Greedy: A randomly chosen campaign was selected a fraction ε of the time. The other times, the arm with the highest known payout is pulled and these were measured in comparison then reinforced.
Banks Customer Lifetime Value Marketing Pay Per Click Predictive Analytics