Flexible budgets improve ad spending efficiency on high traffic days
The Power of Flexible Budgets in Adwords
Data Structures and Algorithms
Summary
Advertising platforms sometimes spend more than an advertiser’s daily budget on busy days, as long as the monthly total stays within budget. The authors studied how allowing such daily flexibility affects ad allocation over many days. They found that small daily flexibility can significantly improve spending efficiency, especially as the time period grows long. They designed an algorithm that nearly matches the best possible performance under these flexible budget rules. The findings show that letting advertisers’ daily budgets vary can benefit both advertisers and platforms.
What this means in practice
- •For advertising platform engineers: Optimize daily ad spend limits to improve overall budget use and increase revenue efficiency across months.
- •For digital marketing teams: Adapt campaign budget rules to allow flexible daily spending while ensuring monthly limits, enhancing campaign performance on busy days.
A theory result. No direct application yet.
Authors
Suho Kang, Rajan Udwani
Abstract
Search advertising platforms routinely spend beyond an advertiser's average daily budget on high-traffic days, so long as total spending over the month stays within the monthly budget. Motivated by this practice, we study a $D$-day generalization of the Adwords problem (Mehta et al. 2007), where each advertiser $i$ has a nominal (average) daily budget $B_i$ and a total horizon (monthly) budget $DB_i$. Given a flexibility parameter $δ$, the platform may spend at most $δB_i$ on advertiser $i$ on any single day, subject to the horizon spending limit of $DB_i$. We quantify the power of $δ$-flexible budgets by benchmarking against the inflexible offline optimum, which may spend at most $B_i$ on advertiser $i$ on each day. We show that no amount of flexibility helps direct generalizations of the classical algorithm of Mehta et al. (2007). By contrast, for every fixed $δ$, we design an algorithm whose competitive ratio converges to $1-e^{-δ}$ as $D\to\infty$, and we show that this is asymptotically optimal. Perhaps surprisingly, this matches the optimal competitive ratio in a more permissive setting where the algorithm receives a fresh spending limit of $δB_i$ each day and may spend up to $δD B_i$ over the horizon. Along the way, we characterize the exact optimal competitive ratio for every pair $(D,δ)$ on high-traffic instances, where the offline benchmark exhausts every advertiser's budget on every day.