Paid Search: Man Vs. Machine

An often debated question in our industry is “Which is more important: the bid management tools used or the person who does the managing?” Sometimes the argument becomes self-serving, with artisans who manage their programs manually saying it’s all about the person, and tool providers arguing that it’s all about the algorithms, analytic power and automation.

For large, complex, tail-heavy accounts, this is like asking: “Would you rather be operated on by a skilled surgeon using stone tools, or a Neanderthal using the latest equipment?” The first may be preferable, but neither option is attractive.

However, the fact of the matter is not all programs are large, complex and tail-heavy. Indeed, the roots of many disputes in our industry are a product of the vastly different types of challenges and accounts on which we’ve worked. What is most important to some programs is least important to another, hence digging into the man vs. machine debate with a bit more precision may be a worthwhile exercise.

The Human Element

Humans are much better than machines at many elements of the paid search game, including:

  • Keyword selection
  • Match-type and syndication layering
  • Negative association anticipation and selection
  • Landing page selection/testing
  • Copy writing
  • Anticipating seasonal, geographic, and promotional effects;
  • Classification schemes, recognizing and predicting meaningful keyword commonalities
  • Establishing and evaluating success metrics
  • Troubleshooting
  • Nuanced understanding of attribution, the characteristics of channels, and the ways that both last-click attribution and cheesy “assist” counting mechanisms can torque the truth.

The Power of Machines

Machines outstrip humans in some areas of the game as well:

  • Speed, speed, speed. Automation allows a person to move mountains in a hurry.
  • Precision. Programmers may make mistakes, but machines execute solid programs pretty much flawlessly.
  • Data crunching. We have a number of folks on staff who can do pretty high-level math—present company excluded—but those aren’t uniformly available skills. Machines and software packages can execute enormously complex calculations across massive arrays of ads and engines and do so hourly without making a mistake.
  • Pattern recognition. Humans are key for providing flagging mechanisms for algorithms to watch, but the machines are often better than humans at identifying signals in the data and separating them from statistical noise.

It’s worth noting that not all paid search automation software is created equal. I once heard a computer described as “the fastest, most diligent, but stupidest clerk you’ll ever meet.” Indeed, James Zolman was spot on when he pointed out that many so-called bid management platforms simply execute rudimentary rules set by humans (for example, “if over efficiency target by X then decrease bids by Y,” or “bid this collection of keywords to position 6″). More valuable software with real algorithmic power is a rarer breed.

Of course, not all paid search managers are equally good at all facets of the game either. Smarter, better trained analysts will do the human work better than the average Joe.

Quality software allows smart human users to do tremendous volumes of work, quickly and flawlessly, and takes away much of the routine scut work from the plate of the paid search manager. This has the important side-effect of making their job much more interesting, allowing them to do more of the brain work that humans do better than machines, and improving retention rates of paid search managers. The better the machinery, the better caliber of paid search manager you’re likely to attract and retain.

However, both the human manager and the software used cost money. These costs can exceed their value depending on a number of factors.

How much money can be spent wisely on search? If everything is handled perfectly, what’s the benefit? If the answer is “not much” because the market niche is too small, having both brilliant software and top notch analysts on the case may be too expensive.

Is the tail material? If 95% of a paid search program is in the top 100 keywords, then the value of automation is greatly reduced. As the value of the tail increases the value of automation and smart algorithms increases.

Is there much traffic volume? Sophisticated algorithms require a certain volume of traffic before they become more valuable than intuitive judgment. If the data is too sparse (regardless of CPC) smart algorithms simply don’t help much.

Is there much ongoing maintenance to be done? The ongoing value of human oversight may be reduced if the product/service offerings are static, if there isn’t much seasonality, if there aren’t many promotional changes to implement, if geography is irrelevant, etc.

Are there many anomalies to address? The fewer exceptions to the rule there are due to seasonality, events, the news, competitive pricing, inventory shortages, etc. the more accurate will be the assumptions of a good bid management system and the less value there is in smart human data analysis and anticipatory bidding.

For large, complex, dynamic programs, both sophisticated technology and a sophisticated manager(s) are essential to generate the best results. However, as the above implies, in other cases what matters most depends entirely on the context.

The essential element in almost any case is having the core program build out properly from the ground up, and this is only done well by humans. For tiny accounts, the cost of that build out may even be prohibitive, and Google Boost may be the right solution for those folks. But for anyone spending real money investing in the smart skilled human to do the work well is the one piece that no amount of hardware or software sophistication can replace.

Beyond that build out, whether the algorithm or the person is more important, or whether either can be effective without the other, will depend entirely on context.

Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.

Related Topics: Channel: SEM | Paid Search Column


About The Author: is Co-Founder and Chief Marketing Scientist of RKG, a technology and service leader in paid search, SEO, performance display, social media, and the science of online marketing. He also writes for the RKG Blog. Follow him on Twitter at @georgemichie1.

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  • Stuart Draper

    Where is that post where you mention James Zolman so I can check out his article?

  • George Michie

    Sorry about that, Stuart, I meant to link to it. It is here:

  • http://philipjrosen philipjrosen

    Excellent post, George. You really hit on one of the key dilemmas facing paid search managers today. I wanted to add my two-cents by broadening the discussion beyond the two options you mentioned: human or bid-management systems. Our Edge.BI SaaS takes business intelligence(BI) technology and customizes it to the analysis needs of a paid search manager who has to deal with huge amounts of data from many campaigns across multiple channels. The result is not a bid-management system, but rather a highly sophisticated decision-support system the empowers the intelligence, experience and creativity of the PPC manager.

    Our belief is that paid search (and paid advertising in general) cannot and should not be fully automated–only parts of it will be. Frankly, the automated bid management tools have brainwashed us throughout the years to “automate” but in real life, any SEM and/or media manager out there will tell you how much tedious, manual work they still need to do (e.g. using Excel to manipulate and analyze campaign data to come up with actionable insights)

    Next generation SEM tools need to empower the human, not to replace him or her. We believe that at least half the time of and SEM manager should be invested in analysis, and this is what Edge.BI makes possible. It is much more important to know where to put your next advertising dollar than to automate that allocation (again, full automation does not work).

  • George Michie

    Thanks Philip.

    We agree that both are essential elements for maximum performance. We’d argue that the use of databases and stats packages (R, SAS, etc) will go a good bit farther than excel, but whether basic or advanced smart folks can extract value when they have full access to the data. As this post addresses, there are situations where the value extracted may not be worth the cost of extracting it, but for large, dynamic, complex campaigns that’s where the bulk of time should be spent.

  • modelstyle

    Here a list of tools (not complete, but the bigger ones) (one of the bigger more expensive tools) (more for smaller account (my opinion)) (leading European tool with testing period) (could not test it as no testing period)

    any more interesting post here…


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