Keyword Bid Optimization] Paid-search bid optimization comes in two flavors: rules-based and model-based. Within the broad realm of model-based optimization, you’ll find three common methods: global cluster-level modeling, local keyword-level modeling and global keyword-level modeling. Each has pros and cons and each offers various degrees of performance improvement. If you’re not certain what method you’re using currently, or are considering changing what you’re doing, read on for an overview of these different optimization approaches and their relative strengths and weaknesses.
Global keyword-level is the gold standard for paid search bid optimization] Global keyword-level is the pinnacle of paid search bid optimization. Nothing can beat it for driving performance improvement. Not rules-based, not local optimization and not global cluster-level optimization. The reasons for global keyword-level's superiority are many.
Large and complex paid search programs have the same problems as their smaller, less complex relatives, but the grander scale makes those problems more difficult to solve. This edition of the Industrial Strength column on Search Engine Land offers a few tips on how to manage the issues and how to add incremental improvements to a mature PPC campaign. We're talking ad groups, tools and the ever-present issue of bid optimization.
If you want a great summary of auto-bidding technology used for paid search bid optimization read this article (plug warning: the interviewee is Rob Cooley, OptiMine's CTO).
The obvious answer to the question posed in the headline is: Yes. After all, the ad in the top position is more likely to be clicked and, therefore, drive more business in the direction of the company holding the coveted spot. To reinforce that notion, Scott Smigler contributed an article to the practical ecommerce comparing top position click thru and conversion rates with those of side ads.
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.