Bid Management Goals
There are generally four different goals advertisers try to achieve in an account:
- Increase branding by driving lots of impressions while staying under a target CPM.
- Increase site traffic by driving lots of clicks while staying under a target CPC.
- Increase leads by driving conversions while staying below a maximum CPA.
- Increase sales by driving conversions with a positive ROI.
Each of these goals requires a different bid strategy. It’s fine to mix and match goals in an account, but you can’t have multiple goals for one item — that pits different strategies against each other and prevents the individual strategies from delivering the desired outcome.
Another important consideration is that, for conversion-driven strategies (numbers 3 and 4 on the list above), there are two different ways to judge performance. The first is to try and maximize the revenue, the second is to maximize profitability. For the former, you will consider the average performance of the CPA or ROAS, whereas for the latter you will need to look at the incremental cost of each additional click you buy.
I’ll explain this more in the section about incremental cost-per-click. The important thing for now is to understand there are two options that require differences in how bids will be managed.
Next, consider that ROAS (return on ad spend, or conversion value / cost) can be measured in many different ways because it relies on the advertiser importing the value into AdWords through conversion tracking, Google Analytics goals, or offline conversion import. Some advertisers will submit a value that reflects the profit they make on the items sold whereas others will import the total revenue produced by the sale.
How this works, mathematically
Weighted averages are an average resulting from the multiplication of each criterion by a weight reflecting its importance. For our purposes, the criteria will correlate to various SEM metrics (CTR, ROI, CVR, etc.).
If we had three criteria (X₁, X₂, and X₃) and wanted to weight each .5, .3, and .2, respectively, the weighted average equation would look like this:
WA = .5(X₁) + .3(X₂) + .2(X₃)
What this equation is doing is assigning importance to the criteria through weights. Each criterion must be measured on a similar scale in order to compare apples to apples – in other words, you cannot directly compare CTR to ROI. You can include as many criteria as you want, but the sum of the weights must equal 1. It is up to you to set the relative importance of the various criteria through weights. The weights should reflect the relative value, going from most important to least important. The final outcome will allow us to rank specific dimensions to see which are the overall best and worst performers.
How this works in practice
Let’s assume we are trying to optimize bid adjustments for an ad schedule (this method will work for all other dimension-specific bid adjustments such as device, location, placements, etc.). Let’s also assume that our client’s KPIs are prioritized as such: 1) maximize conversion volume, 2) minimize CPA, and 3) maximize CTR. Below is a snapshot of a fictitious data set for campaign performance by time of day and day of week:
The first step is to measure our 3 criteria (conversions, CPA, and CTR) on similar numerical scales. 0-100 works well as it is easiest to calculate (where 0 is the worst performance and 100 is the best).
CTR – since all of the CTRs above are less than 1%, we can use that as a benchmark. Working with the first row:
CTR Score = (0.0054/.01)*100 = 54
Conversions – Since all of the conversions are less than 10, we can use that as the benchmark. Again working with the first row:
Conversion Score = (2/10)*100 = 20
CPA – This is a bit tricky considering that a higher score should go to a lower CPA. Thus we will use the inverse. All of the CPAs are less than 400, so we will use that as the benchmark.
CPA Score = (1-(274.52/400))*100 = 31.37
Step two is to calculate the overall score with the aforementioned weighted average equation. You will need play around with the weights according to your KPI priorities. This will require a deep understanding of your account, knowledge of KPI goal prioritization, and common-sense intuition. If the final bid adjustments do not seem quite right, try tinkering with the weights. For this example’s purpose, we will use the following weights: .5 for conversions, .3 for CPA, and .2 for CTR.
Overall score = .5(20) + .3(31.37) + .2(54) = 30.21
Step three is to repeat the first two steps for the remaining rows of data:
Step 4 is to calculate bid adjustments. In order to do this, we need to compare the overall scores to a benchmark score. The average of the overall scores is a good benchmark to use, especially for dimensions with a lot of subsets (such as ad schedules). This will tend to result in an equal distribution of positive and negative bid adjustments. If you want to skew your bid adjustments in either direction, adjust the benchmark to above or below the average. Unlike ad schedules, dimensions with fewer subsets, such as device (desktop, mobile, tablet), will tend to need a lower-than-average benchmark. Play around with the benchmark and use your best judgment – do not forget what is and is not good performance.
The average of the entire data set’s scores in our example is = 35.84 (not all data shown in snapshots above). The bid adjustment calculation for the first row is:
Bid adjustment = ((30.22/35.84)-1)*100 = -15.69%
Because most search engines require whole numbers, we will round to -16%.
If you were working on other dimensions, you should be done! Apply your bid adjustments.
Ad schedules require a little more work as each day of the week can be segmented into six time blocks. To calculate the entire ad schedule, I set consistent time blocks for each day to fill a 24/7 schedule and averaged the bid adjustments of each block.
Overall, multiple criteria weighted averages allow us to factor numerous metrics when judging performance. This is particularly useful when making optimizations such as bid adjustments because it produces a holistic view of overall performance in one simple number. The most powerful aspect of this methodology is that it can be applied to everything: campaigns, ad groups, keywords, ad schedules, locations, devices, etc. Keep in mind that there are limited opportunities to experiment with your weights as there are many other factors that will affect account performance. Additionally, the bid adjustment calculation is a comparison relative to the benchmark you choose. If, for example, your account is performing great across all three devices (desktop, mobile, tablet), you will want to set a low benchmark so that each device has a positive bid adjustment.