'Purchases on Google' Shopping ads test is running on iOS devices

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Google appears to be testing Purchases on Google ads on iOS devices.

Purchases on Google ads enable consumers to buy products shown in Google Shopping ads right from Google-hosted landing pages when users have payments set up through their Google accounts. The product launched in pilot on Android devices in 2015 and opened up in beta to US advertisers this spring.

Below are a couple of examples of the Purchases on Google ads we spotted this morning on iOS. Each is slugged with “Easy checkout.”

It’s not clear how long these ads have been available on iOS. With the initial pilot launch in 2015, Google said Purchases on Google would come to iOS in the “coming months,” but it appears to have taken much longer than that, perhaps closer to the beta opening up. We’ve asked Google for comment and will update here if and when we get a response. Update: We received confirmation that these ads have been available on iOS for several months. They’ve clearly been flying under the radar, though.

The “Easy checkout” messaging and icon is a change from the previous iteration that showed a blue “Buy on Google” at the top of the ad. We’ll certainly continue to see messaging tests here.

The impression volume for these ads continues to be quite limited on all devices. Additionally, with the advent of so many variations of Shopping ad formats now available — Showcase ads and ads in knowledge panels, for example — it’s not easy to find Purchases on Google ads.

The product is can be seen as part of Google’s broader mission to improve mobile web experiences and conversion rates, including a current test to send mobile search ads to AMP-enabled landing pages.

Quick view

The “Quick view” links at the bottom of the ads shown above is part of a mobile shopping update that Google announced ahead of Black Friday this year. Clicking “Quick view” on any of the product ads brings up a preview showing a bigger image, product description, reviews and seller ratings. Here’s an example from Google showing how it works:

Google introduced “Quick view” previews in Google Shopping ads in November.

The “Quick view” links also seem to be fairly limited and are not showing with most product listing ad results we’re seeing.

The lowdown on driving app downloads with Universal App campaigns

Universal App campaigns (UAC) help you find new app users across Google’s largest properties: Google Play, Search, YouTube and Gmail, as well as millions of websites and apps across the Google Display Network. Back in August, Google (my employer) announced that all app install campaigns in AdWords are becoming UACs.

Whether you’re starting UACs for the first time or are looking to get the most out of existing UACs, here are some best practices that I’ve discovered from talking with a bunch of other Googlers.

Getting up and running with UAC

The first key step is defining your goal. You’ll need to set a target based on one of these key performance indicators:

If you care about different metrics in different situations, create separate campaigns for each desired outcome.

From there, you’ll need to set up a few more items:

A daily budget. When you’re driving installs, this should be your target CPI multiplied by the number of daily installs you want (shoot for at least 50 to get enough data). When you’re driving in-app actions, it should be your target CPA multiplied by desired daily actions, shooting for at least 10.Your desired user action, which includes stuff like the first install or first open. This could also be your desired in-app action, like making a purchase or completing a game level.Creative assets, which is where you have some real flexibility. If you’re on a smaller budget, AdWords creates those ad assets on your behalf. Bigger advertisers can add a bunch of images and advanced creative assets (we’ll talk about those a bit later).And one final, crucial component: measurement. Do what you need to do to ensure that you’re measuring all of those actions.

How AdWords knows where to serve ads

So, how does AdWords know where to reach those potential new users without keywords, data feeds or any other targeting? Starting with the info about your app itself (its App Store or Play Store description), it examines signals like search queries on Google.com and Google Play, web crawl data and more. This data is mapped across all of the channels where we place ads and updated multiple times per day. That’s how AdWords can quickly pick up on new trending keywords like a sports event or an upcoming holiday and make sure it serves your app in the relevant context, across different properties.

Looking at users who’ve completed your selected action along with those who haven’t, AdWords evaluates a user’s auction signals. This is stuff like device type, the network they’re currently on, which apps they already have, and plenty of other insightful info. From there, patterns from converting users are identified. These patterns are then used to predict future auctions, where and how to bid, and what creatives to serve to other users who fit similar characteristics.

So it’s like DSA + Smart Bidding + similar audiences + a bunch of other stuff, all at the same time, across networks. Plus, it gets better the more it does it.

How you should manage UACs

Although UACs are more automated than other AdWords campaign types, you still have important levers at your disposal.

Update your bids

The target CPI/CPA/ROAS bids you set and modify have a strong influence on how your campaign performs. I definitely recommend staying on top of those targets. As you make any changes, it’s a good idea to adjust targets or budgets up or down 20 percent at most to avoid any drastic changes in performance. Once you’ve made a change, try to wait for at least 100 conversions before making another update. It takes time for automation to respond to new inputs, so be patient. If you’re curious about what impact a bid change might have for you, check out the bid simulator tool.

Provide great ad components

AdWords optimizes what content will show in your ads across channels. It’s best at doing that when it has a bunch of stuff to choose from in your Universal App campaigns.

When it comes to ad text, include a clear call to action. Write standalone sentences. AdWords automatically combines them to create the best text ad. And keep these short, sweet and focused on one unique selling point.

And when it comes to videos and images, don’t be shy. Add what you’ve got. You can (and should) upload 20 images and 20 videos to your campaigns. Plan to add multiple landscape images so AdWords can mix and match different backdrops across different types of users.

I mean what I said about videos, too. Adding videos gives you a lot more opportunity for your app to get noticed. Focus on different video assets in different ratios, like landscape, portrait and square, so AdWords can maximize reach across all properties, including rewarded, YouTube and native ads. After your creatives have time to run, check out the Creative Asset Report in your account to see how each of your creatives is performing.

Steer your automated campaign

Along with bidding and creative options, there are some considerations that might pop up as you get used to managing these campaigns.

Don’t worry about account structure

While countless articles on SEL have been written about how you should structure ad groups and keywords within your campaigns (including by yours truly), don’t worry about that for UAC. Query-level data is leveraged across campaigns and ad groups for search, and impression-level data is leveraged across GDN (Google Display Network) and YouTube.

Protect your brand

I love that Universal App campaigns are about driving conversions. And brand sensitivity is an important consideration as well, which I also love. By default, there are four brand safety filters enabled: not yet labeled (video and content), mature audience (video and content), tragedy and conflict (video) and sensitive social issues (video and content).

On top of those defaults, you can exclude mobile app categories, topics and autodirector videos. And, of course, you can use negative keywords. Negative keywords in UAC apply to all properties, from Google search to YouTube and everything in between. They’re a great way to protect your brand, but they could also blot out some of your traffic. Use negatives with care.

Don’t worry about cannibalization

While your standard search, GDN or YouTube campaigns and UAC will at times be eligible for the same auctions, only one campaign per account (or linked accounts) enters the auction. You aren’t going to bid yourself up with overlap (a common myth in search that I’ve been trying to quash for years).

AdWords chooses which ad to enter into a particular auction based on your active bids and past campaign performance. What’s in your best interest, auction-wise, should be chosen to show. One consideration: If you’re finding that your campaign isn’t getting the traffic you want it to, you might need to raise your bids to make it more competitive in those auctions.


It’s important to understand how to set up Universal App campaigns for success. It’s also important to know what you should be doing to ensure that these campaigns reach their full potential.

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

Apple Search Ads expanding to Canada, Mexico & Switzerland

The Apple App Store has announced it is expanding support for Search Ads into Canada, Mexico and Switzerland.

Apple released Search Ads for the App Store a little over a year ago, with previous support limited to the US, the UK, New Zealand and Australia.

Apple’s Search Ad product is the iOS version of what Google Play has offered in its store for Android devices since 2015. Both are aimed at driving app discovery by users.

Search Ads are generated automatically from app metadata, with advertisers setting a daily or total campaign budget. Ads appear based on keyword searches specified by advertisers, along with demographic segments such as gender, age and location. Advertisers can also separate bids by device: one bid for iPhone users, another for iPad users.

A hands-on review of Apple’s Search Ads upon its release in the UK outlines the pros and cons of the platform, along with some items to look out for.

Apple is still offering a $100 USD credit for first-time advertisers. The newly-added countries will be available on the platform starting October 17.

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

AdWords app-install campaigns to sunset as Universal App Campaigns take over

Google launched Universal App Campaigns (UAC) roughly two years ago to help developers drive app downloads. UAC has co-existed with AdWords app-install campaigns since that time.

Now the company is moving all app-install ads under the umbrella of UAC. Google said that as of October 16, all app-install campaigns will run as UAC ads. All current app-install campaigns will stop running on November 15; so developers and publishers need to convert their campaigns accordingly. (Google’s blog post has instructions on how to do this.)

The two types of mobile-app campaigns offered different features and capabilities, with some distribution overlap. AdWords app-install ads offered more direct control over placements (single channel, multichannel) and bidding (CPC, CPI and so on) but were also more complex to create and manage.

UAC ads are automatically distributed across multiple Google channels (search, GDN, YouTube, AdMob and Google Play) and use a CPA model. UAC radically simplifies ad creation and optimization with automation and machine learning. From Google’s discussion of how UAC works:

[Y]ou don’t design individual ads for universal app campaigns. Instead, we’ll use your ad text ideas and assets from your app’s store listing to design a variety of ads across several formats and networks. All you need to do is provide some text, a starting bid and budget, and let us know the languages and locations for your ads. Our systems will test different combinations and show ads that are performing the best more often, with no extra work needed from you.

Google said that 50 percent of all app downloads across its network are now being driven by UAC. One factor behind UAC’s performance is the ability to bid and optimize against a range of goals: CPI, CPA or ROAS and set up automated Smart Bidding based on those goals.

Google is also seeing a shift from pure install-driven campaigns (download) to those that focus on engagement or specific in-app actions (hotel booking, first ride and so on). The company said that marketers who “optimize for in-app actions with UAC, on average, drive 140 percent more conversions per dollar than other Google app promotion products.” This is at least in part because ad creatives and CTAs are likely more compelling than plain-vanilla download campaigns.

Google indicated that one in four mobile-app-related ad dollars is now focused on promoting in-app events/conversions. The company has 2 billion active Android users globally, and Google Play is live in 190 countries.

Beyond the migration itself, what’s also significant about the shift to UAC is what it represents about the future of Google’s ad business. It shows that the company plans to infuse more automation, goal-based bidding, machine learning and auto-optimization across its various channels to simplify ad creation and improve performance — for advertisers and itself.


Google offered Zynga as a case study of success with UAC ads. The latter delivered the following from Kimberly Corbett, VP of User Acquisition. I’ve slightly edited her longer statement, provided in email:

UAC campaigns have allowed us to trim down time spent optimizing while increasing our time advancing our media buying strategies by focusing on larger growth opportunities. We have titles where we’ve run large numbers of stand-alone campaigns without notable traction for scale and performance, but by transitioning to UAC event optimization, we’ve been able to increase performance with event optimization by 97% in one month.

Initially, we ran a UAC Target ROAS alpha campaign with Google, where we gave the campaign a revenue goal to hit. After running the campaign for about a month, we noticed that not only were goals being hit, but some were exceeded by as much as 54%. As a result, we expanded UAC Target ROAS to more games in our portfolio to achieve increased scale and performance.

Google home service ads come to the East Coast & open up to more service categories

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Google continues to expand its ad product for local service providers. This week, the company announced home service ads are available to more businesses in more cities.

Painters, electricians and towing companies can now run ads through AdWords Express, joining locksmiths and plumbers, house cleaners and handymen.

Google launched home service ads in 2015 with a pilot in San Francisco. The ad product is also available in Stockton, San Diego, Los Angeles and Philadelphia — marking its first availability on the East Coast — in addition to the San Francisco Bay Area. As is its practice, Google has regularly tested new formats for these ads over the past couple of years. 

The product takes aim at fulfilling consumers’ heightened expectations for sourcing and booking providers on demand online. It’s a sector that offers big market potential for Google. Home services is predicted to be a $435 billion market globally by 2021. It’s not alone in this area, with Yelp, Angie’s List and Amazon Home Services among those vying for market share. In May, IAC acquired Angie’s List in a deal worth more than $500 million, merging it with competitor HomeAdvisor.

After going through an application and background check process, providers can set up their Google Home Service ad listings in AdWords Express. The ad blocks typically appear at the top of the results page, with the organic pack of listings displaying farther down the page.

Branch offers first deep-linking from ads on AMP pages to app content

Google’s AMP pages are known for their speed in loading from mobile search results. But, using a stripped-down version of HTML, AMP pages still lack much of the functionality that helps publishers make money.

To help fill that gap, deep-linking provider Branch.io has released a version of its Journeys, called AMP Journeys. The Palo Alto, California-based company said this is the first time deep-linking has been offered from ads on AMP pages to apps.

The original Journeys provided deep links from mobile web content directly into specific content inside an installed app. AMP Journeys delivers banner ads (which sit at the bottom of an AMP page) or interstitial ads (which reside between AMP pages), and then deep-links inside those ads directly to specific content inside an installed ad.

[Read the full article on MarTech Today.]

Bing Ads rolls out expanded device targeting globally, supports mobile-only campaigns

Last fall, Bing Ads began piloting new bid adjustments for devices that included the ability to set bid modifiers for desktop and broader ranges for tablet and phone. Today, those changes are rolling out globally. And new today is the ability to set negative bid modifiers for desktop.

The top bid adjustment range for all devices is +900 percent, and now all devices can be bid down by as much as 100 percent. The 100-percent range means it’s now possible to run mobile-only campaigns.

Advertisers will start seeing the new desktop bid adjustment starting today, and everyone will have it within the next few weeks in the UI, the API and Editor.

Advertisers running AdWords campaigns can also now import their device bid adjustments into Bing Ads.

IAB: Paid search was 48 percent of total digital spend in 2016

Paid search advertising accounted for roughly $35 billion in 2016, split almost evenly between the desktop and mobile. That’s according to the IAB’s 2016 revenue report, released yesterday.

On a percentage basis, desktop paid search declined by 10 points and was down by a little under $3 billion in real dollars. However, overall paid search revenues were up vs. a year ago by almost $6 billion.

Mobile ad spending surpassed spending on the desktop for the first time, and represented 51 percent of digital ad spending in the US; in Q4 2016 it was 53 percent.

Total digital ad spending was $72.5 billion in 2016, up 22 percent from 2015. Mobile was responsible for growth across all digital formats: search, display, video, social and so on. Overall, mobile spending (across formats) came in just under $37 billion in 2016.

Growth of mobile ad spending in the US

Read a more complete breakdown of the IAB report on Marketing Land.

Apple expanding successful Search Ads to three new English-speaking markets

Apple launched app-store Search Ads in October 2016. Since that time they’ve received praise from developers as a high-converting, high-value app discovery tool.

Today the company is announcing that Search Ads will become available in the UK, Australia and New Zealand. The booking UI opens today, and ad serving begins on April 25. Developers running campaigns in the US will be able to clone their ads for the new markets.

AppsFlyer issued the following assessment about Search Ads’ performance in a report issued earlier this year:

[Apple had] the best retention in iOS North America, while proving their ability to scale with the third highest number of installs of non-gaming apps. With the strongest debut index performance we have seen to date, Apple came in #3 in the Power Ranking in question.

Beyond strong retention metrics, Apple said that, on average, its Search Ads are seeing conversion rates of greater than 50 percent, meaning that half the time a user clicks an ad, there’s an install. The company also said that average user acquisition costs are $1 or less. This compares very favorably with competitors’ metrics, although costs-per-install vary widely by geography and app category.

According to some third-party estimates, the average CPI on Facebook is in excess of $3.

One of the reasons for the success of Search Ads, the company says, is the focus on ad relevance, which is given priority over bid price. Effectively, there’s no way for a less relevant app-install ad to beat a more relevant one with higher bidding.

In this way the company hopes to keep the playing field more level for smaller developers. Because Apple doesn’t rely on advertising to generate significant revenue, it can afford to heavily prioritize relevance over spend.

The app-install market, which represents a substantial component of mobile advertising revenue today, is north of 5 billion dollars.

Patent 2 of 2: How Google learns to guide purchasing decisions

In my last article, I explored a patent which focused on how Google learns to influence and control users. This patent described a system to adjust and correct users when their system determines that said user is making a mistake. In this system, we saw Google influence users and suggest alternatives when it deemed a user was acting in a way that would prevent then from performing a desired action in the future.

In this article, we’re going to build on this with an analysis of a second patent, “Guided Purchasing Via Smartphone,” which was granted on March 16, 2017. From there, I’ll discuss how these two patents work together to predict significant changes ahead — and some amazing opportunities for paid search marketers.

As always, I want to remind readers that filing or being granted a patent does not necessarily mean that Google will be implementing all or even any of the techniques and technologies discussed. However, as I mentioned in the last article, we can already see elements of these two patents in use, and that’s a good indication that Google is moving in this direction.

So, let’s begin with our analysis of the patent, “Guided Purchasing via Smartphone.”


In the abstract, we find the core idea being patented: a system that is built to understand a user’s intent to purchase a product based on that user’s smartphone behavior. The idea is that when purchasing a given product, there is an expected sequence of tasks that must be completed, and the system can figure out where a user is in that sequence, based on their behavior. The patent further describes notifying a user and guiding them to the next step in the sequence towards the completion of the purchase.

Technical Field

Technical Field (Section 1) notes that the smaller user interface (presumably compared to a PC) and the more fragmented engagements we have on smartphones are the driving forces of the patent. Essentially, these two issues are what need to be addressed to improve purchasing on smartphones.


The challenge outlined in the patent is caused by a shift from the predictable online sessions of the desktop environment being “supplemented, if not replaced, by fragmented interactions using smaller interfaces existing on smartphones and smaller tablet computing devices.”

In other words, widespread smartphone adoption has changed online behavior, as users have shifted from single long sessions into what Google refers to as “micro moments.” From checking the time to chatting with friends, use of a smartphone — and even a smartphone transaction — may take place in small fragments of time over multiple sessions.


The summary has multiple key sections that I’ll be looking at one by one:

Section 5

Section 5 again describes the concept of predicting a smartphone user’s intent to purchase a product, based on an expected sequence of events that tends to lead to its purchase. The system could learn to determine the user’s current position in this sequence and notify him or her of the next step. We’ll get further into how this works (and exactly what it means) as well as why PPC managers can get ready to start salivating.

Section 7

In Section 7, we read about advertising bids based on where the user is in their journey towards a purchase — that is, how far along the expected purchasing path are they, where are they specifically and what is the next expected step? This idea is exciting unto itself (basing ad targeting on where a user is in a sequence of tasks expected to end with a conversion), but I’m going to ask you to stick with me here — while this is an interesting idea, we’re just getting warmed up!

Detailed description of the example embodiments

Section 19

While Section 19 is mainly a repeating of what we read in the technical field section, there’s one thing that’s very important to catch. The patent is about guided purchasing via smartphone; however, there is zero mention of organic. Section 19 makes it clear that the purpose of the guiding is to drive ad opportunities, not organic search clicks.

Section 31

Previously, we’ve read that the user’s intent to purchase a product was determined from a series of actions they had taken. In Section 31, we see that these actions aren’t simply related to queries in search but could also include their interests on social media pages:

In some embodiments of the present technology, a user’s intent can be determined in other fashions, for example … by the guided purchasing server … querying the user, via the user’s smartphone …, after observing the user’s interest in social media pages featuring a product[.]

Presumably, this could be applied to additional smartphone actions as well, such as text messages; however, text isn’t mentioned in Section 31.

Section 32

This section mentions the use of machine learning and provides a better idea of how this system works. A system is developed by Google to analyze the sequence of tasks taken prior to making a purchase of a specific item across the population as a whole. This data is then used to develop an optimal order that these tasks should be completed in, and the user is then guided down this path.

We’ll read about how exactly that happens shortly. What’s important here is that the data is collected, and an optimal path is determined for the user to make a purchase. The path is optimized and may potentially aid the user in making better decisions for their needs.

Section 36

One of the tasks in the sequence outlined by Google may be a user establishing a budget for the product they intend to purchase. In that event, the other tasks associated with the purchasing process may be adjusted:

The purchase budget can then be used to determine one task sequence, from among a plurality of task sequences based on the user’s response to the budget query.

Presumably, this would be to avoid asking the user questions when one or more answers may result only in products that exceed the user’s budget.

There are a couple of interesting things here. The first is that Google is definitely taking the “bird in hand” approach. If the user doesn’t have a budget of $500 for a camera, let’s not show it to her because then she might decide to save up and not click on a paid ad at all (I’m guessing at this, but it seems likely to me).

As we saw with the previous patent we looked at, this may also be to reduce stress on the user. Asking questions or showing people options they can’t have is stressful and can be disheartening. Establishing the budget to avoid not just displaying products that aren’t applicable but crafting the experience so the user never encounters something they can’t have would make the process more enjoyable.

An additional angle I have to consider here as well, also related to the previous patent we looked at, is the notion of making recommendations and adjustments without the user’s knowledge. I would not find it unlikely that the technique would be advanced to monitor past purchase behavior to determine a likely budget shopping pattern and go from there.

Section 37

This section adds to the system the ability to recognize when there is a deadline for the intended purchase, determining which tasks need to be completed in what timelines for the deadline to be met, and notifying the user at these various stages.

One can think of applications for this (such as a sale), but what I find interesting here, too, is the combining of the deadline idea in this patent with the “correcting potential errors” discussed in the last article.

For example, consider the scenario of needing to purchase some new shoes for a wedding. A user may start looking months in advance, but if he doesn’t take steps towards making a purchase as the day approaches, it is logical that the systems would combine to help him avoid error (i.e., failing to buy the shoes). This could take the form of smartphone notifications alerting the user that to receive the item in time, he’ll need to proceed down the conversion funnel.

Now it gets exciting

I promised earlier that PPC managers could start salivating. Well here’s where that promise plays out.

Section 44

This section uses a camera as an example of a product that user intends to buy. In this hypothetical scenario, the system “determines that the user has yet to complete the ‘choose camera features’ task, the ‘review camera accessories’ task, and the ‘choose a merchant’ tasks,” which are all part of the sequence of actions leading to a camera purchase.

The data that needs collecting then is the features the user wants, so the system will prompt the user to “choose camera features.” Rather than simply displaying all digital cameras, Google wants to act as a filter.

Section 45

In Section 45, we continue the hypothetical camera example and see that two cameras (Mod1 and Mod2) are selected as appropriate for the user based on the feature set (A) that he or she is looking for. On top of this, the system has also selected merchants (MerchX and MerchY) who carry the Mod1 and Mod2 cameras with feature set A.

IMPORTANT: It’s important to know, before moving forward, that the referenced “feature set A” may represent a single feature or a grouping of features. For example, it may represent the single feature “50 inch” for a product “white board” where the only filterable feature is size. On the other hand, in my own quest for a motherboard, a feature set may include a variety of factors such as socket type, CPU support, chipset, onboard video, max memory, memory support and more. A variety of feature sets would exist for a product like a motherboard on Google’s end, and this information would be used to determine the product displayed, the merchants selected and — here’s where it gets interesting — the bid amount charged. (More on this shortly, as this is probably the most exciting aspect of this patent.)

Section 46

This section outlines how the merchant’s sales portal will work in conjunction with Google’s own systems to help the user complete the purchase. Worth noting is that, in this series of events, the user does not leave Google — the e-commerce system simply works with (or perhaps through) Google to complete the transaction.

Section 48

The one or more computing devices can select one or more advertising bids based on the smartphone user’s intent to purchase the advertised product, the determined sequence of tasks for purchasing the advertised product, and the smartphone user’s task state …

It get kind of interesting here as we see discussion of the bids being based on the user’s perceived intent (as determined by their position in the sequence of tasks completed towards making a purchase). So to be clear, implied in this section is that bids will be determined based on what one is willing to pay at various stages in the purchasing process.

Before we go any further, I’m going to include a table right from the patent, which will apply to the next few sections:

The table above represents bids across three different advertisers (MerchX, MerchY and MerchW). For each new piece of data Google collects along a user’s path to purchase — desired product, product type, product features, model and so on — the user’s intent becomes better defined, and advertisers are able to bid different amounts at each new stage in the sequence.

Section 52

For example, let’s say the only data available to the system is simply that the user intends to purchase a camera (see the “Product Intent” column in Table 1 above). Similar to current bid strategies, the system selects the highest bids based on that information — therefore, the systems displays one ad from MerchW and one ad from MerchY, as they are the two highest bids for the only relevant piece of information we have (“Camera”) at $0.59 and $0.58, respectively.

The system itself then provides additional information to aid the user in understanding different camera types (see the “Type” column in Table 1), perhaps giving organic some of the non-commercial traffic at least. In this example, the camera types include Instant Film, DSLR and SLR. From there, the user refines their purchase intent by selecting a camera type.

Section 53

Having now selected a camera type of “DSLR,” the user is then prompted to select features (“Feature Set” in Table 1). Google will guide the user through a series of questions and provide information to enable the user to make their decisions on the features that are important to them.

During this stage of the process (i.e., while features are being selected), the user will now see ads from MerchX ($0.65) and MerchW ($0.68), as they are matching the current criteria (DSLR cameras) and have the highest bids for the combined Product Intent and Type.

By the end of the stage outlined in Section 53, the user will have selected the full scope of their required features, which brings us to…

Section 54

In the next step, the user is now selecting which model of camera he or she would like from those available that meet the criteria established (camera type, budget, features and so on). You can see in Table 1 that there are a variety of feature sets represented.  Each of these sets is meant to represent certain criteria in the camera (such as display size, weight and zoom capabilities).

In this case, the user’s desired features are represented by Feature Set D-A. As such, one ad each from MerchY ($0.77) and MerchX ($0.76) will be displayed, as they are the highest bidding merchants with cameras matching the desired Product Intent, Type and Feature Set.

Digging deeper, Google recognizes that there are two models of DSLR camera that match the desired feature set, Mod2 and Mod3, each of which have specific bids associated with them. In Section 54, the system is carrying further the clarification stage and providing the user with links to information on these two models of camera for the user to choose specifically which one they would like (Mod2 in this example).

Section 55

With the specific camera now selected, the user is given the choice of merchants to purchase from. Based on the previous decisions made by the user, the system now looks for the merchants who carry the Mod2 DSLR camera with feature set D-A and asks the user to select from those available. During this stage, the system only displays a single advertisement, which is that from MerchX, as they are the highest bidder on Mod2 DSLR cameras with feature set D-A.

However, the user is also presented with links containing information on different merchants that sell the correct product (MerchX and MerchY) so that he or she can select the merchant they prefer.

Section 57

Here, we see post-purchase advertising now being displayed based on previous purchase behavior. In this example, the advertisement being sent to the user is for a nearby photography school due to their purchase of the camera previously. This is interesting enough, but Section 58 really adds some punch.

Section 58

Section 58 starts off pretty dry, simply noting that advertisers can be charged based on user engagement with an ad and the frequency at which it is displayed. Fairly predictable, but what’s interesting is the part that reads as follows:

[A]dvertisers can bid to add tasks to a determined task sequence, and the guided purchasing server … can conduct an auction awarding added tasks to one or more advertisers.

What this means is that the task sequence towards a purchase will be made available to advertisers, and advertisers will have the opportunity to bid to add tasks to the sequence. Some application of this would be fairly straightforward, such as adding in a task related to the weight of the camera if that was not part of the initial sequence (useful if you sell a particularly light camera). But it could also be extended and used to disrupt the natural leaders in a space if a new product is available with unique features.

Let’s imagine for a second you have just manufactured the first camera to connect to a holodeck (yes — I’m that kind of nerd). Adding the question, “Would you like your camera to connect to a holodeck?” into the sequence would dramatically change the feature sets applicable and basically give you the sale. This is one of the more interesting aspects of this patent.

So, what does this tell us about paid search?

This patent is full of ideas that I would view as highly likely to be implemented, and while the patent was written to focus on smartphones, the growth in voice-first devices and the push to get Google Assistant into more phones can carry the idea even further into a world where the questions and answers guide the user down a purchase path without any visual display at all.

I mentioned above that we’ll be looking at how the two patents discussed in this series tie together, but before we do that, let’s take a moment to quickly summarize what the system outlined in this patent does for the paid search manager:

It provides an environment where a user is guided down a purchase path thorough a series of short, fragmented interactions (“micro-moments”) that occur while the standard smartphone user is on their device.It provides an environment where ads can be triggered based on social media engagement and other data associated with the device, such as location (Did the user enter a specific type of store?), interactions with apps or previous purchases.It provides an environment where machine learning and/or AI is used to create likely paths and task sequences associated with a conversion and assign a likelihood to a user’s behavior given where they are in this sequence.It provides PPC managers the ability to bid and advertise differently at different stages in the buying cycle. A different ad and bid can be used early in the buying cycle than is used further along when the consumer has gotten more specific in their needs and budget.It provides PPC managers with the ability to inject tasks into the conversion path sequence that may serve to offer better information/ads or provide a scenario where a unique feature may restrict some of the other advertisers.

In my opinion, the biggest features discussed involve the ability to customize ads and bids on specific features and position in the conversion path, and the ability to bid to add tasks to the sequence that may help adjust the conversion path in your favor.

How do the two patents relate?

At the beginning of the first article in this two-part series, I promised that the two patents would tie together. Some of the overlap has already been specified above. For example, we’ve discussed the deadlines referenced in the patent we’ve been covering today and how they may be used to help users avoid errors, as was covered in the last article.

But the ties run deeper. If you’ve read both articles, you’ve suffered through thousands of words  — so rather than give a technical analysis of how the systems tie together, I will instead illustrate it using a basic, hypothetical interaction between a user and their smartphone (indicating which patent is being utilized where). I will be giving a voice-based example, as it’s more colorful; however, one can easily imagine the same interaction taking place via notifications and manual input.

User:  OK Google, navigate to Billy’s Dry Cleaning.

Google: Will this be to have your suit cleaned for the wedding you’re flying to tomorrow afternoon? (Error correcting patent)

User: Yes.

Google: Billy’s Dry Cleaning is closed tomorrow, and you would not be able to pick it up. Would you like me to find a dry cleaner close by that is open tomorrow and has next-day pickup? (Error correcting patent)

User: Yes.

Google: Willy’s Dry Cleaning is one block away and is open tomorrow and has next-day pickup. Would you like me to set your navigation there? (Both patents: error correcting in offering suggestions to meet future event needs and the guided purchasing patent in providing an advertisement for product “dry cleaning” with the feature set “open Sunday” and “next-day service.”)

[The user drops off dry cleaning and gets back in car.]

User: OK Google, where can I get a laptop?

Google: Is the laptop a wedding gift? (Error correcting patent)

User: No, it’s for me.

Google: How much would you like to spend on your laptop? (Guided purchasing patent)

User: Under $1,000.

Google: Is this for work or for pleasure? (Guided purchasing patent)

User: Work.

Google: Do you need it to have Microsoft Office? (Guided purchasing patent)

User: Yes.

Google: Do you know the specification you would like it to have or would you prefer to answer some questions about your use? (Guided purchasing)

User: I’ll give the specifications.

Google: OK. Let’s begin with the processor. Would you prefer AMD or Intel? (Guided purchasing)

User: Intel.

[We’ll skip the rest of that series of questions and jump to the last in that set; however, each of these relates to the guided purchasing patent.]

Google: Do you need this by a specific date? (Guided purchasing patent)

User: I would like to get it today to have for my trip.

Google: If you would like to pick it up locally, there are two stores near you that carry the Acer XYZ Notebook, which meets or exceeds all your requirements. You can also order it from Acme Computers Online for next-day delivery to your hotel for $24 less, including the shipping to your hotel. Would you like to pick it up or order it to the hotel? (Guided purchasing with elements from the error correction patent)

User: I’ll pick it up.

Google: Both MerchX and MerchY have that item in stock. MerchX lists the Acer XYZ at $10 less but is 20 minutes farther away. Which merchant would you like to visit? (Guided purchasing with elements of the error correction patent)

User: MerchY.

Google: Setting navigation to MerchY. (Error correction)

As you can see, the patents could easily intertwine — and this just one simple example. We could also add suggestions to stop for gas on the way to MerchY to avoid having to do so on the way to the airport tomorrow, or include suggestions to set alerts and reminders to prevent the user from missing the flight.

The future is now

I personally view the error correction as occurring presently to certain degrees and on the inevitable list moving forward. It’s a natural evolution in technology, and I don’t believe there’s any debate as to whether the systems described in this patent will impact our daily lives. This brings us to the guided purchasing patent. As mentioned previously, while the patent itself is written for smartphones, I personally view the processes and systems as even more relevant in a voice-first search environment (which includes voice-based interactions with smartphones), and in this area it’s again an inevitable step in the evolution of search and personal assistants.

The only portions of the patents that aren’t inevitable but which I still consider to be highly likely are the sections of the guided purchasing patents that involve adding tasks into the Google-established sequence involved in a purchase. It’s an opportunity to make money for Google that (properly controlled) could provide a solid value for the consumer, so a win on both counts.

As advertisers, one of the areas we need to start thinking about and readying ourselves for is a time when we can bid and craft ads for specific points in a conversion path and when specific conditions are true. You won’t just need to bid on a term like “acer xyz” — you can bid on it only if it’s a recommended product at the end of an established purchase path and when the laptop features sought by a user match those of the device. You won’t be showing up for people just looking up specs or people whose need aren’t met by the laptop.

If these patents are any indication, it’s going to be a very interesting next couple of years, and if you’re involved in AdWords management, buckle up. It looks like it might start getting even more interesting in the very near future given the pace at which things are moving.

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.