Within the product ad landscape, Product Listing Ads (PLAs) and Dynamic Product Ads (DPAs) have consistently stood out as two structured data-driven ad units that have successfully increased profitability for the savvy retail marketer. However, there are times when key stakeholders who are constrained by financial, technological, or structural resources might ask marketers if it is worth running exclusively on one type of ad unit/marketing platform over the other. To find the best fit, marketers need to get at the real value behind the performance and strategy from PLAs and DPAs, which in turn requires a clear understanding of the use cases for both types of ad units and the marketing platforms they run on. Two platforms that retailers see success on – Google Shopping for PLAs and Facebook for DPAs – best demonstrate the differences between these two types of ad units and why they are so effective for so many retailers.
PLAs versus DPAs or PLAs and DPAs?
When considering investing in PLAs and DPAs, it is crucial that marketers fully understand the competitive advantage of “good” structured data and their capability for producing it. Structured data, usually passed as a feed file or through an API, is the most crucial element for triggering and sending compelling product ads to potential online consumers. In this, the PLA and DPA platforms are similar since they share a standardized set of feed specifications and structured data that’s used throughout the industry.
As similar as PLAs and DPAs are, however, it is the stark differences between how to activate product ad units on Google and Facebook that create a competitive difference between PLAs and DPAs.
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Feed Delivery |
Feeds are delivered to a Google Database (Google Merchant Center). |
Feeds are delivered to a Facebook Database (Facebook Business Manager). |
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Data Quality & Accuracy |
Google Merchant Center (GMC) uses a crawler to compare product pages and compare data quality/accuracy. |
Facebook uses a site pixel to compare data quality/accuracy within the feed and on a retailer’s website. |
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Platform Features and Segmentation Tools |
Marketers can annotate layers of data to further extend relevant information to consumers like merchant promotions, product ratings, and local inventory annotations. They can also use remarketing lists to cross-sell and up-sell products. |
Cross-selling, down-selling, and up-selling use the Facebook pixel and power editor platform to remarket products to consumers who have visited a retailer’s site. |
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Customer Journey Function |
Google PLAs become a tool that converts upper funnel search queries and product purchase intent into measureable revenue. |
Facebook DPAs become a tool that converts website actions and product purchase intent into measureable revenue. |
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Standardized Best Practices |
Best practices exist regarding how to structure and optimize the feed, detailed policy guidelines, and established testing guidelines. |
Best practices are not yet established or documented. |
The data below, which is from an iProspect client who focuses on athletic apparel, illustrates the performance differences between PLAs and DPAs with respect to their activity in the upper and lower funnels.
When comparing PLAs and DPAs, the most striking difference is that PLAs outperformed DPAs across KPIs (impressions, clicks, orders, conversion rate, and revenue). Regardless of month or seasonality, CPCs for DPAs average 69% lower than PLAs, which translated to competitive click volumes post-holiday. This implies that there is less competition entering Facebook’s DPA auction and an opportunity for marketers to find a competitive advantage on the Facebook platform.
The lack of competition for DPAs led to post-holiday success when DPAs seemed to resonate well with consumers. Typically, we see the PLA auction become less competitive in January. However, in this case study, January investment for Google PLAs was still 64% higher than January investment against Facebook DPAs, and DPAs continued to accrue competitive revenue relative to level of investment. These efficiencies were not just seen exclusively in January. Examining all the data uncovered that DPAs delivered 76% higher CTRs than PLAs. This higher CTR achieved a 7% increase in AOV and resulted in an average of 16% higher ROAS.
Different Platforms and Funnel Lead to Differing Performance
When thinking about PLAs vs. DPAs, it is important to consider attribution in relation to strategy. While Facebook DPAs outperformed Google PLAs in CTR, AOV, and ROI for this particular client, the major gains were achieved in a last-click attribution environment. In short, PLA searches could have resulted in Facebook DPA conversions because of the many paths customers take in their online journey. Marketers should be cognizant of the role of each of these channels plays in driving customer acquisition. The social elements on Facebook encourage users to collaboratively shop with their peers, circulate content, and advise on product information. In contrast, the search and intent elements that drive Google facilitate product comparison and filtering based on price, features, or other criteria.
If PLAs assisted in DPA conversions, the question remains – how much of an assist did DPAs, or was the performance a merit to the power that brands have to connect with their customers on Facebook?
While this particular comparison of PLAs and DPAs purposely explored the differences between Google’s and Facebook’s product ad offerings, it is important to call out that results will vary based on client, vertical, and audience demographics. A more accurate test would compare Google’s DPA offering (dynamic remarketing), which leverages the same data from PLAs, to Facebook’s DPA offering.
The most important takeaway from this analysis is not that one platform drives more sales than the other, but rather that PLAs and DPAs complement one another, meaning that marketers should invest in both platforms. If invested in both platforms, marketers can maximize revenue by creating multiple opportunities to capture consumers at different points along their personal purchase journey from awareness to purchase.
Eric Kanner, Manager, Paid Social, and Matil Mangual, Lead, Paid Search, also contributed to this blog post.
