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Advanced Product Listing Ad (PLA) Management

Industry-Leading PLA Results Driven by Segmentation of Query Intent

Our industry-leading approach to PLA management has driven remarkable increases in profitability for our clients across Google Shopping, Bing Shopping and Yahoo Gemini Product Ad channels. We credit our success to three components of our methodology: our process, our strategy and our technology.

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Our Process: 

Our process for improving PLA performance begins with the product feed.

Every client’s product feed is subjected to a 77-step data feed optimization process where feed attributes are reviewed and revised for optimal performance. In additional to external revisions, we typically work closely with in-house IT and development teams in order to tackle the challenge of constructing a higher quality product feed from the ground-up.

Once the product feed has been thoroughly scrubbed and meets our standards, next comes the implementation of our unique PLA strategy.

Our Strategy:

Our approach to PLA channel management and optimization utilizes a highly sophisticated level of segmentation in order to overcome the natural weakness of PLA campaign structure: the lack of control over query segmentation. By leveraging our proprietary tool set, we are able to rapidly create PLA campaigns at scale while segmenting ad delivery and media spend by the intent of the shopper’s search.

pla revenue share by query type

Above: Upon delivering our marginal ROI analysis to a prospective client, we found that the vast majority (88%) of PLA revenue was driven by low-funnel queries. However, this retailer was allocating the majority of their ad spend to high-funnel queries, and failing to saturate market demand on the more valuable low-funnel terms.

Beginning with the previous 12 months of data, we put search queries through an automated classification process, identifying them as either high-funnel, mid-funnel, low-funnel or irrelevant searches. After human review and approval, each of these query segments are isolated within the PLA structure, allowing for optimal distribution of bids and budget and, ultimately, improved profitability.

Our Technology:

Systematic Query Classification and Optimization: In order to accurately segment PLAs by the intent of a user’s search, we have developed a customizable tool that automatically classifies all search queries retroactively. Through active learning, we are able to improve the accuracy of this process further by predicting queries that have not yet triggered an ad to serve, but express similar levels of intent to purchase – even accounting for variations such as misspellings.

Bid and Budget Optimization: Omnitail leverages real-time search data and competitive intelligence to optimize bids and budgets across product and query segments. Our bid optimization process allows for the customization of lookback windows in order to determine proper bids with confidence, ensuring accuracy. By harvesting data from the client’s product feed, analytics platform, ad engine and marketing database, we are able to forecast the incremental profit that will be driven from every bid adjustment, maximizing PLA contribution.

Every client relationship begins with a marginal ROI analysis. This analysis will forecast and quantify the incremental profit that our PLA strategy is expected to yield for your business. If you would like us to run this free analysis for you, please fill out the form at the bottom of this page and we will be in touch.

PLA Operating Profit

Above: After hiring Omnitail to manage their PLA channel, this retailer saw explosive growth in profit as well as accelerated M/M revenue growth – a result of being able to distribute their ad spend more effectively across query and product combinations.

Want to learn more about our PLA strategy? Read our article on using keywords in Google Shopping/PLAs.