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Keyword bidding is no easy task. There are several tools and strategies each with their own pros and cons. This is the first in a series of five articles where my colleagues and I will discuss a few bid concepts and bid strategies. We will also address their associated trade-offs to help you choose the best fit for your business.

Before discussing the bid strategies, we will review two key concepts:

1. Key Performance Indicators (KPI) Positional Impact

2. Keyword Level vs. Portfolio Level Analysis

Key Performance Indicators (KPI) Positional Impact

KPIs, such as CPCs, differ across positions. Using Microsoft Advertising Intelligence monetization data, you can see metrics for the average advertiser for the keyword “computers” across the Bing marketplace.

*download Microsoft Advertising Intelligence

Based on the graphs, “computers” tend to have higher volume, bids, and CPCs in better positions, but the slope of each curve differs. In this example, the result is:

· Average position 1 bids ~2.9 times more than position 6¹

· Average position 1 pays ~2.2 times more than position 6

· Average position 1 gains ~13.5 times more clicks than position 6

If you want to increase volume, positions 1-3 will get you a substantial increase in volume without needing to pay a proportionally equal increase.

Collecting and logging this data via Microsoft Advertising Intelligence and your own testing of keywords can improve your analysis.

Factoring in Conversion Rate²

Knowing the basic search KPIs across positions is usually not enough to make a good bidding decision. We also need to factor in conversion data.

Imagine you bid .62 on “computers” with a cost per acquisition (CPA) ³ goal of . In this example, your performance report shows:

Position	Clicks	CPC	Spend	Acquisition	CPA
4	208	.15	9.17	8	.90

 

You are above your CPA goal at .90 CPA and need to improve. Using the charts below as guidance, you can see how conversion rates across positions affect your bidding decision. Unlike the basic search KPIs, you cannot retrieve conversion data from our tools. You will need to collect this data through testing because the conversion rates will be different for your set of keywords.

The charts below illustrate how to analyze this hypothetical problem with different conversion rate scenarios.

Scenario A: Flat Conversion Rate

 

Scenario A uses findings from “An Empirical Study of Search Engine Advertising Effectiveness” which concludes that conversion rates are flat across positions for certain engines. The above charts show that with a flat conversion rate curve, the CPA and CPC curve have the same shape, so CPC acts as a proxy for CPA.

Let’s apply a flat conversion rate assumption to our bid problem. Recall your CPA for “computers” is too high. This data indicates that CPC for “computers” falls with position, so you should bid down to lower your position, which will lower your CPC and CPA.

There is strong evidence that support conversion rate being flat across positions. If you assume a flat conversion rate curve, this can simplify bidding for other cases as a number of keywords’ CPC tend to fall with position, so CPA falls with position for them too.

Scenario B: Linear Conversion Rate

Also, there are articles that suggest you will find a different result for specific groups of keywords. Scenario B is based on a finding from the “The Atlas Rank Report II: How Search Engine Rank Impacts Conversions” article that suggests conversion rate increases with position for low volume keywords. If we apply this assumption to “computers”, the CPA for positions 1-3 is within our CPA goal, and so we should consider bidding up to those positions.

Ultimately, I recommend testing your keywords to understand your landscape. Hopefully this helps illustrate how to analyze your data to make a more informed bidding decision.

Next time we will discuss keyword vs. portfolio level analysis.

Footnotes
1. Calculation: Pos1_Bid /Pos6_Bid
2. Conversion and Acquisition is used interchangeably
3. CPA = Cost/Acquisitions = CPC/Acquisition Rate