Paid search is looking more and more like One-to-One marketing and the latest addition to Google's Adwords arsenal has brought that reality closer to fruition. In today's Search Engine Watch, Greg Habermann gives us a peek under the hood of Google's Interest Category Targeting.
The topic of attribution, which advertisements get credit for a particular conversion event, is often brought up when discussing bid optimization. A question we get frequently asked is if a more “sophisticated” attribution scheme will provide value. The answer is a resounding, unsatisfying “maybe”. There are two major factors to consider with any attribution scheme. The first is “how much does it cost to implement and measure?” The second is “does it provide any benefit?”
As a great soccer player’s wife once said, “So tell me what you want, what you really really want.” She wasn’t talking about paid search bid optimization but marketers should consider, do you want impressions, clicks, leads, sales, revenue, or profit? For some advertisers, any of those will do because their costs per impression, click, lead, sale, unit of revenue, and unit of profit are about the same regardless of their ad spend. That is, each dollar of advertising generates about the same number of impressions, clicks, leads, sales, revenue, and profit regardless of how many dollars they spend (when properly optimized with an application like OptiMine’s). For many advertisers though, this isn’t the case and they must pick what they really really want. Here’s a case in point from retail financial services. For this advertiser, keywords, and competitors, cost per lead is about $33 regardless of how much they spend. Each incremental $33 of spend gets another lead. However cost per sale is about $150 when spending $20,000 a day but $300 when spending $40,000 a day. To understand this, consider a real example from the credit card business a few years ago, before the credit crunch. Back then, if you bid on the paid search phrase “credit cards for people with bad credit,” then you would get a lot of applications (leads). However, you got few sales (new accounts) since the people who search on that phrase rarely had their application approved, thus driving up the cost per sale.
This article is about why we use multivariate linear regression. There are three main reasons: it works really well, it’s been around forever so there are few unexpected behaviors, and it scales like crazy. Assume that predictive analytics is useful for managing paid search max CPC bids. I realize that’s debatable but it has done wonders in other marketing channels including direct mail, newspaper FSI’s, email, and Internet display ads. Predictive analytics, done well, can predict the clicks, cost, and ROI of each keyword for different bids, for a future date, and thus allow us to better pick bids to meet our paid search marketing business constraints and goals. For (a hypothetical) example, we could predict the following where gross profit is profit on merchandise sold before subtracting search engine ad costs, and net profit is after subtracting search engine ad costs.
Welcome to our blog. I’m Rob Cooley, co-founder and CTO. The goal of this blog is to discuss interesting or thought-provoking topics about paid search optimization. I thought I'd start with an initial post about goals.