Diagnosing Impression Share in Google Shopping Campaigns

Using Google's Auction Insight Report to Understand Impression Share Relative to Competitors

Roy, at first glance our impression share in our Shopping Campaign is very low (~15-18%) compared to our other campaigns that are much higher, and I’ve always assumed this was a huge opportunity for growth if we could get it right.  If I view the auction insight report it shows me that most of our direct competitors are lower. I have a feeling that there is some component of this that I’m missing and you could really only compare apples to apples if a competitor was bidding on the EXACT same brands and/or keywords.  I was wondering what you would make of this?  Basically is this typical or are we really missing the boat on impression share?

My understanding is that Google is showing you their impressions share across the same eligible impressions that you are being compared against. So, for example, a competitor might have an overall share of 30% for all of their eligible impressions, but only 17.24% of the impressions where you overlap with them.

But you're right to interpret impression share as opportunity. I also use it to detect match rate issues with product feeds. If you had a formatting issue with your MPNs, for example, then Google would report very high impression shares, as no one was competing with you for your typos. A low impression share means that you are matching against the products correctly, and that there are shoppers you're not meeting.

However, simply increasing your bids to try to reach them is a dangerous game. Rather, I focus on shifting money from areas of relatively low performance to those of higher performance. This is done through regularly updating the taxonomy to adapt to available performance data. For example, if a product is doing significantly better than its siblings in a Product Group, then it's subsidizing the rest of them, and once you isolate it, then it can bid much higher, and the rest of the group's bids shift down to compensate. Likewise, if an outlier in a group is underperforming, and soaking up costs, splitting it off frees up the rest of the group to fly a little higher. However, splitting them all out individually drives the sample sizes into the ground, and you end up guessing on almost all of them--that's the genius of Google's current structure.

The long game, big picture strategy is to also work on conversion rates overall. Any time you can boost either AOV or Conversion Rate, it is equivalent to increasing your COS, without affecting margins--as far as the bids are concerned. AOV * COS target * Conversion Rate = target CPC, after all, so Conversion Rate is often the most powerful lever, if the most challenging to actually affect. If you guys aren't using a tool like Visual Website Optimizer, Optimizely, or similar, then you might consider doing so--while most tests are washes, there are plenty of small wins to be had. And Scot Wingo was right in his Bronto keynote--a small change in your mobile conversion rate can have a huge impact, as that shopper segment is always growing, and a small change to a low conversion rate is a larger shift, as a percent.

Have questions?  Drop me a line via the Contact form--I love to talk shop.

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