“Achieving the Gold Standard in Paid-Search Bid Optimization” was the first white paper published by OptiMine and remains the most widely distributed. One of the reasons for its popularity is the way it distills the complex world of bid optimization methodologies into an easy-to-understand guide. Whether you are a paid-search veteran, or just starting out, the Gold Standard white paper will help you segment and understand the differences in approach and results among the various optimization techniques. Today we embark on a series of posts that excerpt Achieving the Gold Standard. We begin with an overview of paid search.
Paid-search advertisers have a range of choices when selecting a keyword-bidding approach: rules-based bidding and three different versions of model-based solutions. This paper will analyze the differences in these bidding techniques and explain why global keyword-level bid optimization delivers performance gains of 25 percent to 200 percent more than what can be achieved with the other bidding approaches.
Google’s advertising model is both simple and fiendishly complex. In most cases, paid-search advertisers bid on keywords daily to locate their text ads in the most advantageous positions on Google’s search engine. The bid, combined with Google’s Quality Score—a formula Google has developed that factors in an ad’s quality, relevance and historical click-through performance—determines an ad’s
In effect, advertisers have to predict the future cost and value of every single keyword in their search engine marketing campaigns. It’s not a trivial task. Large paid-search advertisers may need to optimize tens of thousands to millions of keywords to meet a variety of different campaign objectives. Not all keywords have the same weight, of course. Head terms, or keywords that trigger a large number of clicks and conversions, are usually the focus of most SEM efforts. Tail terms, or keywords that might receive just a few clicks per day or week, receive less attention. But regardless of where a keyword lies on the distribution, if its bid is too low, sales are lost. If the bid is too high, more money is spent on that keyword than its potential return or value merits. Achieving the best performance takes automation, science and domain knowledge.
Over the past few years, search engine management consultants and digital marketing platform vendors have developed a variety of products and services to help advertisers optimize their paid-search spend. In most cases, the efforts involve some combination of human analysis and model-based bid optimization software, sometimes called an autobidder. Technology is essential because the scale of the challenge easily outstrips human analysis alone, especially when it comes to tail terms, which have little click history and which make up the vast majority of keywords.
A common perception among paid-search advertisers is that model-based bid optimization software is something of a black-box technology that is only appropriate for tail terms and that advertisers should “manually optimize the important head terms of an account.” This perception is based on some of the early solutions in the marketplace, which did indeed keep their inner workings secret and typically delivered only modest short-term performance gains that tended to taper off over time.
Today, however, bid optimization technology has developed to the point that it can maximize financial performance across all keywords while being transparent and meeting critical business constraints – for example, maximizing profit while providing some minimum order volume, or maximizing revenue while maintaining a minimum return on ad spend (ROAS). For advertisers managing a large number of keywords, some form of model-based bid optimization technique is virtually essential. But success in using bid optimization software requires understanding the advantages and limitations of the different modelbased techniques and the recognition that there are no set-and-forget solutions. And bid optimization still requires some degree of human intervention from analysts with domain knowledge and an understanding of the business.
The next excerpt will look at the differences between rules-based and model-based optimization methods and, within models, how local optimization differs from global optimization. “Achieving the Gold Standard in Paid Search Bid Optimization” can be downloaded now.