4 Must-Know Facts On Product Listing Ad Mechanics
Google’s Product Listing Ads (PLAs) have rapidly gained market share since being introduced last year, and PLAs are now well established as an integral part of retail marketers’ pay-per-click (PPC) strategies. This rapid growth has been fueled in part by the improved performance of PLAs over traditional text ads. However, the increased spend has also […]
Google’s Product Listing Ads (PLAs) have rapidly gained market share since being introduced last year, and PLAs are now well established as an integral part of retail marketers’ pay-per-click (PPC) strategies.
This rapid growth has been fueled in part by the improved performance of PLAs over traditional text ads. However, the increased spend has also resulted in competitive inflation, where CPCs for PLAs now match or exceed that of text ads.
Despite their growing importance as a marketing channel, PLAs remain frustratingly opaque when compared to traditional, paid-search text ads. Part of this opacity is due to key metrics such as average position and impression share not being exposed for PLAs, leaving marketers with no easy method to gauge the scalability of their campaigns.
Tracking PLA Market Share & Position
Until recently, the mechanics behind how bids, average position, impression share, and click-through rate (CTR) on PLAs interact with each other has remained a mystery to marketers.
However, competitor and trademark tracking tool The Search Monitor recently added PLA-tracking functionality, allowing advertisers to monitor the relative position and market share of their product ads with respect to competition.
Using a provided demo account, I tracked impression results across more than 180 product categories for 6 weeks, collecting data on when and where each ad was displayed. Combining this with bid and CTR data from Adobe Media Optimizer allowed me to glean some crucial insights into how PLAs behave.
Findings On PLA Mechanics
Below are the findings from tracking impression results, collecting details on ad display data and then combining this with bid and CTR data.
1. Impression Share Increases With Higher Bid
This result is not surprising, as impression share has always been influenced by bid for traditional paid search ads. PLAs are no different: doubling the bid resulted in the impression share being doubled as well. However, impression share for PLAs has a somewhat different implication than from text ads, as more than one product from the same advertiser can be displayed for any given impression.
2. “Position” Improves With Higher Bid
Due to auction mechanics, a higher bid results in improved position for text ads. Despite its non-sequential display format, this rule also holds true for PLAs. Every doubling of the bid improved average position by 0.7, where position within a PLA block is defined from top to bottom, left to right (where the top left ad is highest and bottom right is lowest). The reason for the relatively small impact on position is due to the limited number of positions available per impression. But this finding actually does not matter in terms of performance, as explained in below.
3. CTR Is Not Impacted By Position!
I found that ad position, as defined above, does not significantly impact CTR for any given query. Given that PLAs are displayed as blocks instead of ordered lists, this result makes sense. It also somewhat validates Google’s decision to stop showing the average position metric for PLAs. This is also the cause for what may appear as a counter-intuitive effect at the product target level:
4. CTR Decreases With Higher Bid!
For text ads, the loss of relevance resulting from pushing bids too high only manifests itself as a drop in CTR at high positions for broad match keywords. For PLAs, position does not impact CTR, leaving only the negative relevancy effect: I found that doubling the bid decreased CTR by an average of 4%!
Implications For Your PLA Strategy
Below are some tips for how marketers can use these findings to further optimize their PLA campaigns.
Tip #1: Monitor the impact of bid changes on impression volume and CTR to ensure you are not bidding too high.
With the exception of short-term flooding tactics, it is best to keep the bids just high enough to capture the desired volume and impression share, but not so high as to inflate CPCs and negatively impact CTR. With the possibility of more than one ad per impression being displayed for a single advertiser, internal cannibalization is also of particular concern for PLAs.
Tip #2: Monitor impression volume or use a PLA tracking tool in order to assess market share.
With neither position nor impression share data being available for PLAs, the only methods that marketers have to gauge campaign scalability are to either use fluctuations in impression volume as an indicator, or to make use of a tracking tool such as The Search Monitor to track impression share and position.
Tip #3: Position can still be used as an indicator of scalability.
Though position within the ad block does not impact CTR for PLAs, higher bid ads are still ranked higher (close to the top left). This means that positional information, whether collected manually or via a tool, can be used to estimate how much headroom there is for any given product target.