The Subtle Science Of Bidding Part 3: Second Order Effects
In the past two posts of this series (part 1, part 2), I discussed the actual auction mechanism that the search engines deploy, with real life examples with both brand and non- brand keywords. In the final and concluding part of this series, I will discuss what I term as “second order effects” and will focus my attention on brand terms.
Many marketers like to think of keyword performance metrics individually—that each keyword operates on its own and that its performance is a function of its own bid, CPC, impression and ROI tradeoffs. However, the truth is that for effective campaign management, keywords have to be looked at simultaneously to make smart decisions.
Simultaneous keyword management has two parts. First, there is the bid management piece. Assuming I know the exact bid, CPC and performance tradeoff for every keyword, I should look at the tradeoffs of all managed keywords at the same time to make optimal bidding decisions. The outcome of this approach is called portfolio theory, a rigorous mathematical method that guarantees the best outcome for any goal. The focus of this post is on the second order effects: understanding keyword performance tradeoffs that occur due to decisions made on other keywords in the campaign. This is best understood by a real life example.
Consider this Google brand campaign where the bulk of traffic came from three broad matched brand keywords. Clearly, December 6th was a disaster. Not only did impression volume tank from 500,000 impressions to 150,000 but the spend went up from an average of $600 per day to $12,000. What really happened?
The first clue comes from Chart 1 itself. The bulk of impressions came from brand variation 1 on a usual day, but on December 6, its impression volume tanked. The bid, CPC chart gives us more insight as to what happened.
Brand keyword 2 was bid between $2 and $4 on a usual day. However, on December 6th, in an attempt to increase traffic on keyword 2, the advertiser increased the bid on keyword 2 to $7. What this advertiser had not modeled out was the effect of this bid change on the search engine algorithms. The search engine squelched traffic from brand keyword 1 and increased traffic on brand keyword 2. Unfortunately, not only was the overall traffic much lower than before, it came at the cost of a tenfold higher CPC. The net effect was one quarter the average number of impressions (and clicks) at 20x the expense. Note that there was no indication that this would happen. A previous experiment on December 2, where the bid on brand keyword 2 was increased to $5 (as against $7 this time) yielded no performance change.
Key takeaways for the advertiser:
Consider the effect of bidding decisions on keywords simultaneously. As this example showed, looking at keywords in their own silos can have unintended consequences. Further, from the portfolio management perspective, not making simultaneous keyword level bidding decisions leads to suboptimal performance.
Brand keywords have to be actively managed. As I mentioned in my last post, brand keywords need active campaign management. This example involving brand keyword further corroborates the point.
Optimization and Automation are prerequisites for effective management. In an ideal world where search engine algorithms behaved predictably with high transparency, management of small keyword sets involving hundreds of keywords would be relatively simple. However, given the realities of the marketplace and search engines, even small campaigns need sophisticated optimization and automation. The example above involved one brand and three keywords. Could you imagine the complexities of managing 20 or more brands with hundreds of brand variations?
This concludes my series on bid management. I hope you have had as much fun reading my posts as I have had writing them. In the end, as I have tried to decipher the quirks of search engines, I feel like the historian and philosopher, Will Durant when he said:
The most interesting thing in the world is another human being who wonders, suffers and raises the questions that have bothered him to the last day of his life, knowing he will never get the answers.
For more details on portfolio theory, see my whitepaper Algorithms and Optimization.
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