In my last post, I discussed how the mismatching of search terms to keywords can dramatically impact bidding decisions and the resulting performance. In this post, I’m going to address another campaign structure issue that impacts the way we bid on low volume keywords.
Structural Problem #2: Grouping Keywords to Support Long-Tail Predictions
The second most common structural issue impacting bidding performance is a lack of statistically significant volume necessary to make accurate predictions. Yes, I’m talking about long-tail keywords. These tend to be the biggest challenge with bid management, and can inhibit bid optimization in a number of ways.
Let me start with the most severe cases I’ve observed and move to the less severe. I recently audited an advertiser’s account that contained over a million keywords. This account was actually low spending, driving about 30,000 clicks per month. At most, only 3% of keywords had a chance to generate a click, and this would only occur if no keyword received more than one click. They had essentially forced every keyword to be long tail. I don’t care if every keyword was perfectly matched to its exact search term counterpart; this is what I call ‘bid management suicide’.
There is no point even bidding at the keyword level anymore. What do we do when a keyword has only received one click in the last 30 days? Most often, nothing. There’s not enough data to make a decision. But doing nothing with your long-tails is also making a big mistake, which leads me to the less severe long-tail structural challenge.
Take a look at the performance (table 1) for similar blue widget phrase match keywords over the last 60 days. The phrase match keyword “enormous blue widget” hasn’t received nearly enough click volume on its own to predict future performance, but it would be a mistake not to increase its bid. We intuitively know that “enormous blue widget’ will likely perform similarly to all of these other keywords. Conversion rates on the other keywords range from 3.8% to 6.8%. So even if “enormous blue widget” converted only as well as the worst performing similar keyword, we would be willing to pay as high as $0.46 to drive a CPA equal to the average of the ad group. Unless there was a lot more volume with horrendous performance prior to this date range, this keyword is severely underbid.
So how do we formalize what we intuitively know about similar keywords to make better decisions on long-tails? Unless you have access to technology performing some pretty complicated natural language analysis and identifying levels of correlation based on token terms within keywords, you’re likely going to rely on campaign structure. If I were deciding what to bid on a keyword that has never received a single click, I would be smart to start with a bid based on the average conversion rate of the ad group with which it resides. For a keyword with 10 clicks, I might consider both the keyword and the ad group conversion rate, weighting more heavily on the ad group rate. For a keyword with 100 clicks, I might do the same but weighting more heavily on the keyword rate.
Now structure has become a major component of my bidding strategy. The segmentation of ‘yellow’ and ‘small’ widget keywords into separate ad groups now serves a purpose beyond ad association. It helps us make better bidding decisions on long-tail keywords. In short, the structural segmentation and grouping of keywords is critical for effective bid management for long-tail keywords.
Thinking Beyond the Bid
To generalize this topic a bit, no component or metric in your campaigns exists in isolation. Your campaign structure, budget caps, geo-targeting segmentation, number of ads in rotation, ad scheduling, conversion tracking, attribution, and a dozen other variables will affect bid optimization success. If your bid optimization process is only looking at the impact of bid levels, you’re destined for failure, or at least sub-optimal performance.
So the next time one of my counterparts from a bid management software company claims that merely plugging your campaigns into their algorithm will magically improve performance by 30% to 50%, remind them that bid management success isn’t just about bidding. Addressing structural issues like keyword/search term matching and the proper grouping of long-tail keywords for supporting data must be done first. Only then can you get the most from bid optimization, regardless of the bidding tools you are using.