The new Bid Simulator, which you’ll see in the keywords tab, forecasts how many clicks and impressions you’ll see at different bid levels. It only shows the simulator for some of the keywords– not sure what logic is used to choose which ones. It certainly isn’t search volume, since some of the lowest volume terms in our campaigns have the Bid Simulator icon.
Important to note that the Google AdWords Bid Simulator doesn’t predict the future— rather, it estimates what would have happened in the last week had everything else stayed the same except for your bid. Google explains it here.
In this first screenshot, you see that we’d get nearly the same traffic at any bid price for this keyword. Note that the estimates impressions is the same. By bidding higher, we move to a better position. We are currently bidding $3 a click to get 63 clicks, but if we drop our bids to $1.01 (a third the price), we get only 3 clicks less (a 5% reduction). Thus, a 200% bid drop for only a 5% click volume drop– for you economics students out there, that’s significantly inelastic.
Why? At some point, you’re already in first position, so bidding higher won’t matter. Google’s AdWords bidding auction, as clearly explained by Hal Varian (Google’s Chief Economist and the author of my undergrad Econ textbooks) in this video, shows that our price is based upon an increment of the next highest ranked bidder and your Quality Score. P1 = B2Q2/Q1. In other words, the price you pay to be in position 1 (P1) is the AdRank of the advertiser in position 2 (Bid of Advertiser 2 x the Quality Score of Advertiser 2)– then divided by your Quality Score.
On high volume, highly competitive terms, you would expect to see a more gradual fall-off in this bid curve. Normally, you’ll get hit with the double whammy of more clicks at a higher cost per click— if clicks and CPC are both increasing by 50%, then you’re hit with an overall cost increase of 1.5 squared, which is 225%.
What I think Google may have neglected to include in their Bid Simulator is the impact of sitelinks in position 1, which give a tremendous boost to position 1 advertisers. This estimation would be hard to do, given that the feature is not in wide release– though BlitzMetrics is fortunate enough to have enough accounts that we have a few of them with this beta feature enabled.
Here is an example of a Bid Simulator shown when there is hardly any data— on a tail term with phrase match on. Were there enough ad data, we’d be able to calculate the Incremental Cost per Click (ICC). Don’t make fun– the ICC is the term that Google uses to describe this concept– namely, the additional price you pay for incremental clicks, measured by the change in cost divided by the change in clicks. If you’re bidding up, your ICC is significantly higher than your average CPC, which averages in all lower click costs.
Overall, I find the AdWords Bid Simulator partially helpful. Looking at average position I believe is nearly as good, since Google won’t tell you the bids and Quality Scores of the other advertisers anyway. It also doesn’t appear to take into account the effects of negative keywords, dayparting, geo-targeting, and other settings (at least based on their internal PowerPoint showing the actual data used versus estimated for Bid Simulator.
The next step for Google is to make recommendations on how to increase profit based on the simulation. If I raise my bid to get more traffic, then I’m also decreasing my profit per click. Google should tell me what bid makes the most of this trade-off. Currently, all Google’s recommendations seem to be to increase bids, add keywords, and increase budget, so not sure if they’re going to do this any time soon.
However, it is true that if you use Conversion Optimizer, that you can’t use Bid Simulator and that using Conversion Optimizer is effectively maximizing profit if you know the right CPA target. Love to hear anyone’s experiences here with Bid Simulator, ICC, Conversion Optimizer, and other estimation tools.adwords bid simulator, adwords forecasting, google adwords bid management, ppc elasticity curves, predicting PPC traffic