Structured Data Drives E-Commerce Growth

The e-commerce industry is experiencing great shifts due to the evolution of ad units across platforms like Google, Amazon, Baidu, and Facebook. We’ve seen platforms like TripAdvisor, Pinterest and Polyvore emerge on the scene, each offering unique ad experiences that allow consumers opportunities to interact with goods and services.

With this e-commerce expansion and growth, revenue opportunities have never been riper. Those who want to remain competitive should draw focus to the drivers of this evolution and its potential impact on the relationships with the aforementioned partners.

One driver is the quest to create ad units that don’t feel like ads. Back-end processes, user interfaces, campaign management functionality, and even analytics features are all undergoing constant optimization, in an effort to deliver a better user experience. From the inception of Froogle to the evolution of Google Product Search, similar traces can be seen across Google’s travel, hospitality, media and financial services ad units.

Each evolution and iteration brings a greater need for supplier-fed metadata that has been “structured” for matching algorithms. These algorithms interpret matching metadata and populate the content of the ad unit. Promotional messaging, ad extensions, customizers and even the headline are now populated by columns of data within meta feeds. Quality metadata within your feeds that’s both linkable and relevant can influence position, visibility, CTR, conversion rate and even ROI.

Other drivers include acquisitions and competitive friction. Facebook’s acquisition of The Find was a means to enhance its shopping ad experience, while Yahoo’s acquiring Polyvore was a means to revitalize ad revenue with a more native shopping experience. Connexity’s acquisition of Become and PriceGrabber, Travelocity and Orbitz by Expedia and Priceline’s Rocketmiles showed further industry consolidation. Each acquisition stems from sites that leverage technology to aggregate millions of structured attributes like product, inventory, location and rates to provide an apples-to-apples comparisons of similar goods and services. Providing this data in a structured format allows platforms to better recommend, suggest and align products and services to consumer demand opportunities.

The quest to capture transactional data places a greater threat on advertisers’ goal of driving conversion and efficiency to their own websites. These ad programs provide matching algorithms with consumers’ historical views, saves and purchases that allow for enhanced segmentation, giving deeper insight into the advertiser’s consumer. Even with the introduction of Google’s Manufacturer Center, unified views of the same product’s search performance can be seen across those that resell the same product.

As these evolutions take place, e-commerce platforms and partners gain better insight to structured metadata that can help close the gap of supply and demand. This closure places greater amounts of friction between advertisers and their e-commerce partners. As marketers, we want to match the right products to the right consumer at the right time, in order to be more personalized.

However, when large e-commerce partners have access to both consumer and supply data, the relationship of the consumer could be up for grabs. An example is closed user groups, similar to Jet. This paid model allows consumers to see aggregated goods by multiple suppliers, giving them the ability to secure the best deal available. This could dramatically change the typical cost structure for advertisers to that of an affiliate, CPA model, by having access to advertiser’s structured data. As the industry continues to evolve, data will continue to blur the lines of many of these relationships.

Data is at the root of many advertising programs offered by today’s search and social platforms. Advertisers looking to find efficiencies across their e-commerce initiatives should pay attention to details within their metadata and identify ways to better structure that data to drive marketing automation, targeting, ad relevancy, and overall e-commerce media effectiveness.

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