Amazon and the Effortless 180° Business Model Shift
Amazon has successfully flipped its model. First, by using AWS to fortify themselves as a giant in the cloud computing arena. Second, by becoming a full-fledged advertising juggernaut through their retail media efforts. Google has felt both these moves in almost equal measure.
Amazon’s transition into advertising was – in hind-sight – a not-so-natural shift. They officially launched their retail media platform around 2012 with the belief being that they would attempt to monetize a site that was already driving revenue. As if being the largest online retailer in the world wasn’t enough, this layered a business model atop a pre-existing – and incredibly successful – business model.
They already had consumers and consumer data coming in droves. Why not use that to their advantage and make both their consumer data and their site real estate accessible to brands? Give them the opportunity to access Amazon consumers while making them aware of the opportunity cost of choosing not to access Amazon consumers?
And in that moment, a billion-dollar idea was born.
Google’s Two-Pronged Shift Towards Retail
Google is dealing with a double whammy. They’re fumbling their way through a transition from third-party cookies while their new biggest competitor has developed a platform – out of thin air, no less – that is now stealing budget traditionally earmarked for Google Ads.
So, what’s a trillion-dollar-plus company to do when their backs are against the wall and the odds are stacked against them (a true underdog story)? Well, they’re likely to take a page from another trillion-dollar-plus company (Amazon) and refocus:
- First: A shift towards managed, retail-based services (beyond the cloud offering)
- Second: A rethinking of how they interact with consumers
Both involve heavy reimagining of their flagship search product and how it can be applied to the ever-evolving retail ecosystem.
Google, At Your Service
Google is still the best and brightest when it comes to:
- Building world-beating search functionality
- Understanding consumer behavior and tendencies
As it relates to a slew of recently minted retail services offerings, they’ve leaned heavily on both these strengths. The three products referenced below are products Google was able to retrofit – mostly via technology created for their search engine – into retail-focused, managed service products:
- Cloud Search for Retail – Google’s search functionality packaged for individual use
- Vision API – Google’s Image search technology applied to a retail product catalog
- Recommendations API – the same tech that recommends videos on YouTube
Taking these larger-than-life products and scaling them down to fit unique retail.com environments is no small feat. It speaks to the cutting-edge, yet extremely malleable nature of Google itself. They needed to bend and flex a bit to insert themselves into the retail conversation. This is them attempting that.
Cloud Retail Search
With Cloud Retail Search, Google is looking to bring their search expertise to individual retail environments. It’s a fully managed service route designed to help solve a problem Google has dubbed search abandonment. When a consumer doesn’t immediately find what they’re looking for via on-site search functionality, they’re likely to give up – or abandon – their search.
Google has estimated ¬– according to research conducted jointly with The Harris Poll – this phenomenon costs retailers roughly $300 billion a year. This same research noted that 94 percent of all consumers had participated in this search abandonment epidemic. We’re all guilty – blood on our hands!
Google has noted the service will use intent, context, etc. to produce “high-quality product search functionality” embeddable into the retailers’ platform of choice. This takes the power of Google search and drops it into unique retail.com domains for a potent proposition that’s also a simple reimagining of how their search product can be deployed.
Vision API brings an image search-driven, visual component to these very same retail.com domains. It provides the technology required to make “search via picture” a reality.
When consumers discover a product within an image, they want to quickly understand who makes it, what it costs, what colors it comes in, how they can buy it, etc. This process can be even more frustrating when trying to find something seen elsewhere by searching on a retailer’s site. Now, instead of endlessly sifting through pages of items, the consumer can simply upload the image that’s sparked their interest and let Google’s image-driven tech make the connection from there.
One would assume ¬that the deployment of both Vision API and Cloud Search could open the site up to bonus functionality (i.e., multi-modal searches incorporating both imagery and text), but that’s yet to be determined. Either way, this is exactly the type of tech individual retailers can pilfer in a hands-off, less dev-intensive fashion.
Lastly, a product that isn’t new but has become more prescient with the recent growth of all things online: Recommendations AI. Google describes it as being “able to piece together the history of a customer’s shopping journey and serve them with customized product recommendations.” This is an incredibly useful tool as it relates to basket building, revenue per transaction, etc.
It’s the same tech – and the same level of signals – driving the recommendation functionality behind Search, Display, and YouTube. Although, this variation is much more innocuous and less agenda-driven. Instead of recommending videos around hate speech, political extremism, etc., this version will focus on slightly less divisive recommendations, such as scarves and eyeliner.
The value here is really buried within the data. Your typical retailer’s understanding of a consumer ends where their site does. Google’s understanding of that very same consumer extends well beyond those walls. This bigger picture approach allows Google to deploy a deeper understanding of each user.
For example, the typical consumer might buy this hat with these shoes, but this particular consumer is highly inclined to buy belts with their shoes. Knowing this, Recommendations AI will show them matching belts instead. It’s a nice little sleight of hand, accomplishing two tasks:
- Increases Revenue Generated: average basket size, revenue per transaction – all that good stuff
- Bolsters Brand Favorability: provides the “this brand gets me” aura every consumer desires
Retailers investing in this type of tech have clear, trackable outcomes, which makes return on investment easily decipherable. It’s also priced out based on predictions, with the first 20 million costing $0.27 per thousand and a sliding scale from there.
These offerings have enabled Google to implant their tech into retail.com environments. Allowing them to dig their claws deeper into the overall retail process while also driving additional revenue.
Interested in learning about the in-progress reshaping of Google’s SERP in an attempt to make it more retail-focused? Read Part 4.