Sure, we all know that average position is not always a wholly reliable metric, just as achieving a higher position does not necessarily equate into more traffic. But nevertheless, we often strive to achieve the highest position. In the interest of questioning this process, I decided to look at a small data set from one of our existing campaigns to illustrate how decisions change based on the manner in which we group data. For this case, I grouped the campaign traffic based on position. This grouping method would tell us how many impressions and clicks our campaign got at position 1-3, 3-5 and so on, and at what cost. This table looks good at first glance, but what does it tell us? It tells us that positions 1-3 gave us the maximum traffic and cost more. But if we really want to compare the performance based on positions, then we need to look at CTR and CPC. Let’s take a look: According to this table, whenever ads in the campaign showed up at Position 5 or lower, I actually had a higher CTR and lower CPC. Strange, but true. According to this data, we should look only at showing our 5+, right? It’s still too early to make that decision. Lets first look at percentage of traffic (clicks): This makes things a little clearer. I spent 78% of my cost on ads appearing in positions 1-3 which gave me 64% of my traffic. This isn’t too efficient, but it’s still driving a large volume of traffic. But traffic in Positions 3-5 also seem to be doing well in terms of CPC, so how do we decide what the ideal position is for this campaign? That, in my opinion, would depend on the goal of the campaign. If the aim is drive more traffic, and in return awareness, then CTR is the metric we want to optimize. The above analysis was done for a brand advertiser for a small sample size, but a similar analysis could be run for performance based campaigns, in which conversion data could provide further insight. For advertisers running performance/conversion campaigns, there are several possible scenarios. For example, clicks from a higher position could result in more conversions per click, in which case a higher position would be positive. But if the conversion rate is found to be fairly uniform across positions, then it might make sense to go for the least CPC position. Of course, this is assuming that we have enough impressions at the lower position. If you are a marketer who regularly sets bids based on position to maximize coverage and/or conversions, this type of analysis can provide further insights into which position is best for a specific campaign. With this information you can start improving the efficiency of your campaign- rather than chasing the ‘holy’ position 1.