How to Make Your Social Media Feed More Appetizing to Consumers
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The Top Three QSR Insights Marketing Managers Must Know
It’s no surprise that brands in the QSR category face a constant threat of falling into the “Sea of Sameness” when it comes to social content and digital media activations. For example, what restaurant did the below chicken sandwich come from? Popeyes, Chick-fil-A, or Raising Canes?
The answer is none of them. This image of a KFC fried chicken sandwich doesn’t tell us anything about their brand. It just confirms the obvious that they sell chicken sandwiches.
Another key similarity among QSR brands is the claim of taste as their differentiator. Considering you can’t taste a sandwich on your screen, it’s critical for those in the category to find more unique ways to stand out and engage with consumers.
Where Brand Content Meets Culture
CultureTap is Empower’s trend identification platform that helps brands participate in moments that matter. Unlike social listening tools, CultureTap isolates relevant cultural, category, brand, or competitive trends so smart marketers can act before it is too late.
Aligning brand content with culture is a solid way to ensure relevancy and uniqueness among competitors. Through CultureTap, we have compiled three ways that your QSR brand can stay relevant in 2022.
1. Pop Culture Competency
Posting memes or pop culture-related content earns an engagement rate that is seven times higher compared to those brands that ignored on-the-rise content. In fact, in the QSR category, 100 percent of the top 20 posts during the last 180 days are memes. For a bit of inspiration, here are a few favorites:
If memes are not a good fit for your brand, that doesn’t mean you have to give up on connecting to culture. QSRs are quickly realizing the star power behind celebrity and name/image/likeness (NIL) partnerships. From drag queens to NBA athletes, celebrity-themed meal mentions have steadily increased over the past two years, leveraging those campaigns to connect with different audiences. Collegiate teams and athletes are another in-demand partnership thanks to the NCAA’s new rules on NIL. Cane’s, Outback Steakhouse, and Arby’s are a few brands already touting their NIL partnerships–and they are not shy about it. Said brands almost immediately started sharing these new ambassadors on their social media platforms.
2. Menu Hacks
Search queries for “Menu Hack” are skyrocketing, recording a 3,200 percent increase in February 2022. Menu hacks serve as a secret menu and provide an audience with a feeling of exclusivity, as if the brand is sharing something top secret. It is an easy tactic to encourage the viewer to try the menu hack. An added bonus is when consumers order the hack, then post about and review their experience. Not only is it free promotion, but it is also content that may reach people who are not in the target audience or may not follow the brand’s social media accounts. McDonald’s encouraged this with its own sponsored hashtag #McdonaldsHacks (earning 7.9+B views), while KFC posted a “Secret Menu” on its app and partnered with influencers to showcase #KFCSecretMenuHacks.
3. Marketing More Than Just the Menu
Limited-edition merchandise is a strong strategy, as merch mentions within the food service category noticed a 56 percent increase from 2020 to 2021. Y2D data indicates merch mentions are on track for another increase, adding to the value of these collaborations.
Partnering with apparel and accessory brands generates strong engagement with merch content, earning between 1,200 percent to 16,900 percent more engagements on average. Collaborations are valuable and, when done correctly, can easily trigger traction on content feeds.
While trends are invaluable to a social media strategy, brands cannot count on one style of content. It is important to find a groove with content to fit your brand’s personality. Viewers need unique consistency with the added ingredients of realness and humor.
Worried you are going to leave your consumers hungry? Reach out to us to learn how CultureTap can help your brand become more appetizing to consumers.
The Future of Search Part 5: The True Future of Search and How it Ties to Retail
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This is the last (congratulations, you survived) section of a five-part series detailing the near-term evolution and potential long-term future state of search. The first sections dealt with Google reacting to and advancing beyond the cookie-less dilemma while also zeroing in on potential new retail-focused profit centers. This final section details the future state of search, and all the various ways its connected to retail.
The True Future of Search
When envisioning the future of search, a lot of buzz-worthy topics come to mind. Topics like visual, voice, wearables, augmented reality, and virtual reality, to name a few. When thinking of how the future of search is connected to retail, these same topics tend to bubble up.
What to Wear
The future of search will be a constantly shifting nebulous of data, imagery, stimuli, and environmental cues tied closely to each individual consumer and the world around them. The key phrase there being the world around them.
To achieve its future state, Google will have to buck the proverbial shackles that have limited its potential. It’s a prisoner of its own devices (no pun intended), currently existing solely within a screen-driven environment. It will need to move beyond screen-only interactions. Break the fourth wall. Escape from its device-based chains to become a passive observer instead of just something accessed via a portal.
Once this happens, Google can move beyond its other limiting factor – the need for an input. As of now, Google only acts upon something when it’s engaged. Be it a text-based query, a voice-based question, or some other form of input. It needs to be interacted with before it can, in turn, interact.
The shift from domain to a physical (either wearable or implantable) device is what will allow Google to extend beyond these limitations. This will also require a fundamental shift in how people perceive Google (and search in general) as it evolves from a digital entity to a more physically manifested device – one that becomes part of the users’ daily endeavors.
Learning from Past Mistakes
It’s not as if it’s something they haven’t already attempted. Everyone knows Google Glass was an epic failure due to multiple reasons that ranged from fashion to adoption to privacy. However, this type of technology has to be viewed as more of a stepping-stone than a hard stop. Other pioneers across social, research, etc. are looking into wearables and their viability because they understand the value.
Specific to search, instead of waiting for someone to enter a query, provide input, give feedback, etc., a wearable device can constantly absorb environmental cues from its immediate (and not-so-immediate) surroundings. Going the wearable route means Google is no longer relegated to a phone in your pocket or a laptop in your office.
This ties together neatly with retail because both Google and retail’s futures involve an evolution beyond their current physical limitations.
Retail and Search’s Futures Intertwined
Retail specifically has seen a few fundamental shifts specific to online. Retail browsing was historically something conducted in-store or in-print. The advent of the internet changed this, bringing first the browsing capability to an online format and then the transactional capability. Once consumers became accustomed to shopping online, mobile format took hold. The idea of shopping via a device became mainstream.
But like search, retail – until a few years ago – was trapped, waiting for an input to provide recommendations (outside of using invasive cookie-based ad tech). Because of this, one could say Google and retail are inextricably connected in their need to gain access to the consumers’ environment.
Instead of waiting for a consumer to take out their phone to text or image search an item, the device could anticipate such needs in-store, at home, or the world at large. And it’s Google’s ability to capture product information and house it in one consolidated location that provides the foundation that this next leap for retail can be built upon.
A quick and easy example of the puzzle pieces assembled would be a reaction to a simple visual cue – like someone waiting in line and taking notice of a jacket the person in front of them is wearing. Without a prompt, the device would be able to, based on environmental signals, do the following:
Recognize what the product is (who makes it, color options, sizing comparisons)
Gauge the viewer’s level interest (based on length / intensity of glance, heart rate)
Access their browsing / purchase history (understand stage in funnel, conversion propensity)
Present visual, audio and text-based representations of the product (enhanced PDP)
Assist their path to purchase (find retailers, compare prices, shipping options)
As noted, this torrent of outputs would be based on a single, unsolicited input from the consumer’s field of vision. It would obviously need to be AI-driven and user-specific to ensure it engages at the right moments and mitigates inundation. When executed effectively, this type of interaction embodies anticipatory search. This is the idea of a unique, intelligent device using every cue and stimuli at its disposal to anticipate queries instead of waiting for inputs.
All previously detailed Google-specific retail advancements would aid this process, including Shopping Graph, Image Search, Recommendations, Buy on Google, etc. They’d all play a role in this new environment that requires massive amounts of data tied to AI-led recognition and predictive decision-making.
Again, this is future-focused stuff. It doesn’t exist today in a consumer format, and one can only imagine the privacy concerns associated with such an approach. However, all signs point to this – or some variation – being the next iteration.
Where to Next?
Retailers have been experimenting with similar AR-driven efforts to bring their static, online product catalogs to life in the real world. The applications are endless – from digitally inserting a coffee table into a living room to trying on shades of eye shadow via an app enhanced with facial recognition.
The metaverse could play a key role in all of this as well, potentially helping vault over a few of the technological and adoption-specific gaps. Everything in the metaverse can be tagged and documented. Everyone in the metaverse is fair game. There likely wouldn’t be the same privacy concerns that would exist in the real world with someone wearing an AI-fortified camera capturing their field of vision.
Therefore, the Metaverse can serve as a proving ground or sandbox in many ways for this next step in search. It can be tested in a less invasive format before bringing the more nuanced, next generation version to life in the real world.
The options are truly endless. However, making the most of this future iteration requires a lot of things to happen – and a lot of behavior to adjust. This isn’t a scenario where a great product is introduced and immediately changes the world. Consumers must gain something from these advancements in order to fold them into their everyday lives.
The concept of search engines changed the way we look at the world. It changed how we access information, consume media, interact, and beyond. For this next evolution of the search engine to thrive, it will need to change the way we look at the world once more – and it wouldn’t hurt to bring retail along for the ride as well.
The Future of Search Part 4: The Retail-Focused Evolution of Google’s SERP
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This is part four of a five-part series detailing the near-term evolution and potential long-term future state of search. The first few sections dealt with Google reacting to and advancing beyond the cookie-less dilemma while also zeroing in on potential new profit centers. This fourth section details the in-progress reshaping of Google’s SERP in an attempt to make it more retail-focused.
Moving Retail Search Up a Level
As mentioned previously, Google has seen themselves become a conduit for other retailers. At best, they’re a place consumers visit prior to a transaction. But more often than not, they’re getting skipped over entirely, seeing as upwards of 74 percent of U.S. consumers begin their product searches on the Amazon.com site.
Google understands the inherent value in being a larger part of the retail ecosystem. Instead of a site consumers use to connect to retailers, they are a one-stop shop for everything retail. Recent changes to their Search Engine Results Pages (SERPs) speak to bringing the retail experience up a level, eventually enabling consumers to perform their entire purchase without leaving Google.
The Evolution of Google’s SERP
Google’s SERP has evolved over recent years. There has been a shift in prioritization – focusing on pages that sell over pages that inform.
Not too long ago, a search for a product would produce an SERP composed of mainly pages from that brand’s site. But with the proliferation of retail and the evolution of product detail pages (from static, one-dimensional pages to product-specific content hubs), Google’s algorithm has shifted towards highlighting product detail pages (PDPs) and the information stored within. This transition from educational to transaction-focused content has led to a complete renovation of Google’s search results.
When searching for a product, the SERP might contain one brand.com listing while the rest is retail.com focused, including the different promoted iterations (text ads, shopping ads, etc.). This shift is a direct result of PDP content representing the best answer for the query. This gives Google’s algorithm no choice but to highlight them.
Knowing this, Google has decided to pull that content up, in a way, by extracting it from the PDPs themselves. The goal being to house it all in one, central location for consumers – Google’s SERP – as opposed to across multiple, disparate brand.com and retail.com sites.
Google’s recent partnership with Shopify, the introduction of Shopping Graph, their shift towards more visual elements, and the introduction of Buy on Google are just a few examples of this.
Google and Shopify’s Partnership
Last year, Google announced a partnership with Shopify, making it easier for Shopify users to create ads on Google.
It’s similar to Shopify’s partnership with TikTok, which made the process of creating a storefront on TikTok easier for Shopify users. Shopify merchants with TikTok for Business accounts can add a shopping tab to their TikTok profiles and then sync their product catalogs to those profiles.
Similarly, the Google partnership requires Shopify users to push their product catalogs into Google’s Merchant Center tool – a pre-req for any advertiser using Shopping Ads. The linking of the product catalogs ensures Shopify users can easily advertise their products on Google while also ensuring Google has access to the product data of these very same users.
Another interesting angle at play here is the casual introduction of Google Shopping Ads to lower revenue merchants. Shopify has millions of users – a large chunk of which consists of smaller DIY shops. They’re the preferred ecommerce platform for such entities. Google is likely betting on these smaller Shopify clients applying the same roll-up-your-sleeves approach to digital marketing that they’ve used with their commerce efforts.
In this case, the automation built into Google Shopping is a great fit. Set it, forget it, and let the algorithm take hold. It’s the perfect storm Google has been hoping for based on the recent evolution of their advertising products. They’ve introduced so much automation in the last few years it’s almost hard to keep tabs. Within Google Ads in particular, they’ve spent the last few years developing features designed to push advertisers towards a more hands-off approach.
Oddly, there used to be a distrust of their algorithm. This distrust was rooted in the idea of the entity owning the product while also being in control of how the money funneling through that product is spent. But at this point, it’s almost foolish to fight since we know all of the user-based data signals they incorporate into each auction-specific decision. Algorithmic features like Smart Bidding, Dynamic Search Ads, Responsive Search Ads, etc. go far beyond what any manual attempt at optimizations could ever muster.
Google has been pointing towards mass consumption for a while. The Shopify integration is them officially putting a no experience required sign above their platforms. Just pump in your product data and they’ll do the rest. Whether it’s a mutually beneficial relationship is yet to be seen.
The Introduction of Shopping Graph
Google introduced the idea of Shopping Graph at the same time it revealed its Shopify integration. Shopping Graph can best be described as a commerce-focused variation on their Knowledge Graph. The goal of both features is the same ¬– keep users from leaving Google.
Shopping Graph pulls together all form of product information into a single location – Google’s SERP. It’s designed to give consumers all the information needed to make a purchase decision. Product details will be pulled from brand.com, retail.com, reviews, videos, and, as noted before, the product information brands themselves provide to Google via both Merchant and Manufacturer Center.
One of the main differences between Knowledge Graph and Shopping Graph is how they accumulate data. Knowledge Graph’s process is seen more as collecting and then disseminating. Pulling information in and then regurgitating the line of best fit. Shopping Graph’s process is more dependent on merchants willingly providing information via their product catalogs.
Eventually, brands will need to provide this information to be successful on Google.com. It’ll be to every brands’ advantage to submit their product data, and it will be to Google’s advantage to absorb it.
But again, it’s Google wedging themselves into the retail conversation. If all goes right, the recent trend of Amazon being where most product-based searches begin could become a thing of the past.
As more and more retailers supercharge their selection, expand their omni-channel capabilities, and strengthen their logistical efforts, Amazon might soon find itself on less steady ground. If volume, logistics, preference, etc. begin to teeter across the online retail ecosystem, Amazon may soon be vying for supremacy instead of owning it. They could easily fall back to being a stop instead of the stop along the commerce journey.
If this happens, Shopping Graph puts Google in position to recapture the title of go-to product search engine and claim the title as the one-stop shop for any consumer product needs. This keeps users on Google’s site while providing more opportunities to capture consumer data and engage via advertising.
Now all Google needs is to find a way to get consumers to transact on their site as well.
Buy on Google … with Buy on Google
Buy on Google – which officially rolled out last year to all merchants – hasn’t quite taken off. Despite this, it’s easily their most ambitious attempt at injecting themselves into the traditional retail.com experience.
Despite being a no-brainer for Google, Buy on Google represents an obvious mixed bag for sellers. The plus for Google is the minus for sellers in that it keeps consumers within Google’s domain for the entirety of the purchase process. Google’s offerings had previously linked out to seller’s sites to complete the sale which, in turn, allowed sellers to collect data, engage with consumers, etc. Buy on Google’s model puts a hard stop in place, preventing traditional, on-site DTC or retail.com transactions from occurring.
It also sits within an odd location. It’s a feature that isn’t always the easiest for consumers to reach, seeing as it currently requires a click on a paid ad to access. Despite Google’s recent introduction of free Shopping Ads and previously mentioned attempts to make their ads ecosystem more accessible, it’s still difficult for sellers to act on Buy on Google without … buying ads on Google.
The most recent numbers point to sellers feeling this pain, with just shy of 7,500 sellers adopting Buy on Google as a transactional option at the end of 2020. This represents a little under 1/10th the number of sellers within Walmart’s marketplace. It is also nowhere near Amazon’s vendors/sellers that soar over two million. The data also pointed to the platform often accounting for negligible sales, which is not great.
The inconsistencies and gaps noted point to Google still needing to figure this piece out. It’s hard to imagine them becoming a legitimate part of the retail conversation without offering some level of marketplace for consumers to transact on, especially since the assumption is that they’ll figure a way to make themselves more relevant within this arena.
Activating on Visual Elements
The underlying connective tissue across these retail-based enhancements is the visual component. AKA, making shopping ads more easily accessible. The Shopping Graph pulling PDP content upward, giving consumers the ability to Buy on Google. They all require a shift to a more visual SERP.
Amazon is incredibly particular regarding its image requirements. Certain sizes, pixel counts, backgrounds, text limitations, and static vs. lifestyle are just a few that come to mind. Every image needs to fall within a set of guidelines. Why? Because imagery can make or break an online interaction. And the absence of imagery? Well, in retail it’s a death knell. Google knows this – thus the shift from their previously text-heavy ecosystem.
One of their more recent advancements is multi-search, which gives users the ability to search via an image in concert with a text-based query. Google then cross-references both queries and provides an output. This focus on visual inputs allows consumers to streamline the search interaction – quickly finding results instead of fumbling for ways to describe an item via form fill.
Imagine someone finding the exact dress they’ve been looking for within an image online, but a mention of the designer is nowhere to be found. Instead of multiple, exhaustive, text-based queries, they can use Google Lens to capture the image and then layer the color variation they’re looking for in the form of text. The interaction with Google goes from:
A text-heavy, open-ended query capable of producing far-reaching, irrelevant results:
To an image and color specific, multi-modal search designed to produce tailored results (pun intended):
This is all by design, and a major component of Google’s most recent search algorithm updates.
The first such update was titled Bidirectional Encoder Representations from Transformers, or BERT for short. Then came the most recent iteration known as Multi-task Unified Model, or MUM. Both were designed to accomplish two things:
To make normal people feel like absolute idiots when trying to recite their names
To push Google’s Natural Language Processing (NLP) capabilities forward
The second goal was centered around helping Google evolve by grasping not just the content of a query, but also the context. This goal goes so far as to understand the context of specific words in search queries.
Speaking to Alexa is a great example of this. Ask Alexa a canned question it’s designed to answer (what’s the weather today?) and it will more than likely provide an adequate response. Try to attempt to engage in dialogue or some semblance of conversation, however, and its limitations arise rather quickly.
This is where NLP comes into play. A stronger NLP capability would enable Alexa to take in vocal commands and questions, process them, and return a relevant response. It’s the ability to understand complex questions and pathways that separates what we currently understand voice search to be from what it should be. Google – through this newest search algorithm – is striving for the latter.
BERT – with its focus on NLP – signified an initial shift towards more complex queries and, in turn, more exact results. For example, the tech behind BERT gave birth to featured snippets. Although they weren’t perfect, they did represent a new output for certain queries. At a base level, this achieved Google’s goal of providing answers and not just results. If Google can provide answers, then why can’t it anticipate them as well?
The idea of anticipatory search – which is more future-focused version of the current iteration – incorporates historical, behavioral, environmental, and predictive signals. Even though technology isn’t there to accomplish this type of interaction at the moment, the willingness on Google’s end is. They’re investing time and talent to get there.
The sphere of retail intersects the future state of search along multiple touchpoints. However, this is where they most violently collide. The idea of predicting or anticipating a need before it’s fully realized, ushering someone down a theoretical funnel in one fell swoop can flatten it in the process – anticipating a purchase before the consumer can themselves.
It’s guiding a lot of the products Google is developing (as mentioned) while also helping usher in the future iteration of their best-known product: Google Search.
Interested in learning about the future state of search, and all the various ways its connected to retail? Read Part 5.
Display for retail media has traditionally involved managed service buys, or media bought by the vendors via an agreement made between the vendor and the client specific to audiences, placements, cost-per-impression, etc. To buy display placements on a retailer site, an advertiser was required to go through this route.
Historically, retail media digital direct buys have been categorized as nothing short of cumbersome. The lengthy lead times, high budget minimums ($500k in certain cases), delayed, and less-than-timely reporting have all left a bad taste in advertisers’ mouths. It’s no longer considered a “win” to simply run digital via a retailer. The novelty has worn off.
A Required Investment
Often, these digital engagements are run – at least in part – to fulfill a contractual obligation with the retailer. To remain in good standing overall, the advertiser/seller is required to drive a certain minimum dollar amount through the retailer’s media platforms (either via managed or self-service). Advertisers to date have been clamoring for more than just access to first party, transactional data. If they’re obligated to spend, they want more control. As a result, the platforms are doing their best to accommodate.
Retailers know they’re at a tipping point. They want sellers/advertisers that view themselves as partners, not prisoners. If sellers/advertisers simply hit that minimum required threshold for advertising spend then move on, it’s not a tenable, long-term relationship.
Retailers need to provide retail media networks that a) make doing business easier while b) proving out true return from the investment. Walmart Connect’s understanding of this need for profitable, self-service offerings has been reflected in their most recent quarterly earnings.
Display Self-Service
Digital direct buying has historically been a managed service task – the buyer plans, the vendor executes. The mere notion of “Self-Service Digital” is almost an oxymoron. However, now everyone is looking for ways to do things more efficiently. Waiting several weeks for creative to be produced doesn’t lend itself to the idea of efficiency.
Enter Walmart Connect’s Display Self-Service (DSS) platform. A do-it-yourself interface designed to help digital marketers get on-site Walmart Connect display buys up and running at warp speed. The platform is accessible via Walmart Ad Center – the same path used for self-service sponsored products (search).
It enables manual setup for campaigns, line items, audiences, creative, tracking, along with the following advantages:
72-hour creative verification process, as opposed to 6-8 weeks
10 days for brand audience (behavioral) audience approval
No campaign spending minimum – only a 1k impression per line-item minimum
It has all the pieces necessary to launch a campaign of any size in short order, without relying on a vendor-side team.
A Shift Towards Self-Service
This type of platform represents a sea change for digital buyers who are very used to planning and then handing the results off. They deal with a plethora of setup-related tasks that require a check off prior to a campaign launching. Once it launches, they rely on vendor reps for updates, optimizations, reporting, etc.
This new landscape incorporates more of a programmatic approach to buying – one that brings the self-service angle to an on-site, digital buy. Programmatic buyers – search, social, display, audio, streaming – are used to building from the ground up. They were raised in interfaces, molding campaigns from inception to conclusion. Many digital buyers will have to acclimate themselves to this type of approach – specifically, the task of building campaigns manually.
Once the technology and features available in platforms like Walmart Connect’s DSS tool are on-par with a managed service offering, self-service will become the mode of choice. Who owns that buying path will vary by advertiser. The likely owner will be a hybrid programmatic/digital buyer with experience in a) building via self-service and b) planning managed buys through a vendor.
The shift towards self-service represents an exciting time. One that can remove the limitations normally associated with managed service buys. It can also provide those willing to take on such platforms a chance to become less reliant on outside planners and buyers, making their in-house offering even stronger.
The Future of Search Part 3: How Google Can Follow in Amazon’s Footsteps
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This is part three of a five-part series detailing the near-term evolution and potential long-term future state of search. The first two sections detailed Google’s path towards compliance and need to drive revenue in a post-cookie world. This third section focuses on a potential about-face as it relates to their profit centers.
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
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.
Recommendations AI
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.
The Future of Search Part 2: Google’s Path Through the Post-Cookie World
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This is part two of a five-part series detailing the near-term evolution and potential long-term future state of search. The first section detailed how Google will attempt to comply with the loss of third-party cookies. This second section focuses on Google’s potential efforts to mitigate damage related to compliance, and how lingering effects could reshape how they view and deploy their capabilities.
Google in the Post-Cookie World
For digital advertising, the deprecation of cookies is understood as an eventual path towards a modernized, less invasive, more personal way of targeting consumers.
For Google, the ends don’t justify the means. For them, it brings a near-term loss in targeting prowess along with a reduction in outcome-driven visibility. The extent of their reliance on third party identifiers and tracking is evident in their continued hesitation to move on. They know their platform, as is, will experience growing pains once they flip the switch.
As noted on the targeting side, they hope to fill that gap by retaining a sense of audience-driven focus with Topics. On the reporting side, what they’ll lose in tracking they’ll attempt to make up in modeling.
But in both instances, they’ll be hamstrung early on.
Take reporting as an example. Google has historically calculated one of their in-store metrics – store sales direct (SSD) revenue – in this same modeling-driven manner.
SSD is a feature that companies with in-store loyalty card transactions can deploy by providing Google access to loyalty card transactional data. Google can then match transactions to any Google Advertising interactions to attribute value. They then fill in the remainder of their attributable in-store sales (i.e., non-loyalty transactions influenced by Google Advertising) via modeling. They take a known fraction and use it to help extrapolate an unknown total.
The problem is most Google-funded modeling efforts need to be taken with a grain of salt. In the case of the previously noted SSD data, only a fraction of what Google’s models pump out can be trusted – i.e., their models often exaggerate. This leaves advertisers needing to adjust – or downplay – the numbers provided. This begs the question: Will their modeling efforts in relation to cookie deprecation be met with the same level of skepticism?
If so, marketers will see a reduction in their revenue-based output from platforms like Google Ads simply due to in-house metric adjustments. And when the perceived revenue for one tactic goes down, marketers are often forced to push dollars towards other, more reliable tactics. This could easily happen to Google, especially if the expected performance dips associated with cookie deprecation infiltrate tried and true tactics like paid search.
If Google starts to see a downward trend in revenue, how will they respond? Their most recent quarterly reporting demonstrates just how reliant their revenue totals are on their advertising platforms. How – beyond a shift towards a more contextual focus – will Google look to right the ship that might extend beyond its advertising platforms, potentially affecting Alphabet as a whole?
Driving Profit in the Future?
Google is constantly investigating additional revenue streams. They know they can’t forever lean on their ad platforms. They’ll eventually need to capitalize on profit centers beyond media ventures.
One great example is their cloud offering, which they’ve done a tremendous job driving revenue through to date. However, this offering only comprised 7.5 percent of total revenue in 2021. The lion’s share, as expected, went to their advertising products (such as Ads, GDN, YouTube, etc.). These platforms constituted a whopping 81.3 percent of total revenue this past year. Long story short, if revenue via ad platforms is going to be diluted by the upcoming cookie deprecation, other revenue streams need to become a focal point.
Amazon, for example, just had its first quarter wherein revenue derived from services outweighed revenue driven by sales. Think about that for a second. The world’s largest online retailer no longer specializes in retail. Amazon is now just as much a services company as it is a purveyor of goods.
Google has watched this growth firsthand. Fewer and fewer product searches begin on Google. And even when they do, Google has become no more than a conduit for consumers to reach – and transact via – Amazon. Don’t think that’s been lost on them. That they’re not going to do everything within their power to pull off the same level of flawless, 180° about-face Amazon so effortlessly executed.
Interested in learning about a potential about-face as it relates to Google’s profit center? Read Part 3.
