Last year, we introduced Consent Mode, a beta feature to help advertisers operating in the European Economic Area and the United Kingdom take a privacy-first approach to digital marketing. When a user doesn’t consent to ads cookies or analytics cookies, Consent Mode automatically adjusts the relevant Google tags’ behavior to not read or write cookies for advertising or analytics purposes. This enables advertisers to respect user choice while helping them still capture some campaign insights.
Without cookies, advertisers experience a gap in their measurement and lose visibility into user paths on their site. They are no longer able to directly tie users’ ad interactions to conversions, whether the users are repeat visitors or whether those users have arrived from paid or organic traffic sources. To help close this gap, we’re introducing conversion modeling through Consent Mode. This will help marketers preserve online measurement capabilities, using a privacy-first approach.
Now, Consent Mode will enable conversion modeling to recover the attribution between ad-click events and conversions measured in Google Ads. Early results from Google Ads have shown that, on average, conversion modeling through Consent Mode recovers more than 70% of ad-click-to-conversion journeys lost due to user cookie consent choices. Results for each advertiser may vary widely, depending primarily on user cookie consent rates and the advertiser’s Consent Mode setup.
How modeling fills in measurement gaps
Conversion modeling can help fill in blanks in media measurement at times when it’s not possible to observe the path between ad interactions and conversions. Conversion modeling through Consent Mode specifically addresses gaps in observable data from regulations on cookie consent in various regions. Conversion modeling uses machine learning to analyze observable data and historical trends, in order to quantify the relationship between consented and unconsented users. Then, using observable user journeys where users have consented to cookie usage, our models will fill in missing attribution paths. This creates a more complete and accurate view of advertising spend and outcomes — all while respecting user consent choices. Conversion modeling also upholds privacy by not identifying individual users, unlike tactics like fingerprinting which Google has a strict policy against.
Using modeling to probabilistically recover linkages between ad interactions and conversions that would otherwise go unattributed means more conversion insights for optimizing campaign bidding and understanding what’s driving sales. It’s important for any modeling approach to account for the fact that people who consent to cookies are likely to convert at a different rate than those who don’t.
Holistic measurement for your Google Ads campaigns
It’s important for advertisers to have accurate reporting so they can make their marketing investments go further. Advertisers using Consent Mode will now see their reports in Google Ads updated: for Search, Shopping, Display, and Video campaigns, the “Conversions,” “All conversions” and “Conversion value” columns will now include modeled conversions for consent gaps. All other Google Ads campaign performance reports that use conversion data will also reflect the impact from adding in modeled conversions.
Modeled conversions through Consent Mode will be integrated directly in your Google Ads campaign reports with the same granularity as observed conversions. This data then makes its way into Google’s bidding tools so that you can be confident your campaigns will be optimized based on a full view of your results.
For advertisers who want to optimize their campaigns based on return on ad spend or cost-per-acquisition, they can use Target Return on Ad Spend (tROAS) orTarget Cost Per Acquisition (tCPA) Smart Bidding strategies with Consent Mode. If you had previously adjusted targets to account for cookie consent changes, you can now go back to setting targets in line with your ROI goals. Note that you’re likely to see gradual improvements in reported performance as we recover lost conversions through modeling.
For advertisers who want to maintain their campaign spend, conversion modeling through Consent Mode also works with the Maximize conversions or Maximize conversion value Smart Bidding strategies in Google Ads. We recommend you make sure that the budget you’ve decided on is well-aligned with your spend goals.
If you’re an advertiser operating in the European Economic Area or the United Kingdom, have implemented Consent Mode and are using Google Ads conversion tracking, conversion modeling from Consent Mode is available for you today.
And if you aren’t using Consent Mode yet, you have two options to get started. You can implement it yourself on your website by following our instructions. Or if you need some extra help, we’ve partnered closely with several Consent Management Platforms, a few of which already take care of critical implementation steps on behalf of advertisers.
We are continuously adding new privacy-forward techniques to help our machine learning solutions better understand the aggregate behavior of non-consenting users, and offer actionable insights in reporting for deeper clarity on your marketing spend. We’ll be bringing conversion modeling through Consent Mode to other Google advertising products, like Campaign Manager 360, Display & Video 360 and Search Ads 360 later this year.
Personalization features in Google Optimize help businesses customize sites so their customers can find exactly what they’re looking for, when they’re looking for it. For example, marketers can display a special promotion on their site for all visitors, or provide product recommendations based on customers’ previous purchase behavior.
Multi-page experiences in Optimize help you more easily deliver what your customers are looking for. Now, when you create a personalization or experiment, you’ll see an option to add additional pages so that you can extend its reach throughout your entire site—from the initial landing page to the final checkout page. Let’s take a look at two examples:
Coordinated customization across your entire site
Picture this: You’re planning for a sale next month and will be offering a 20 percent off discount code to all visitors. You want to see if displaying this code across your entire site will increase site conversions. Because each type of page on your site has a unique layout, you need to find a different spot to display your promotion on each page.
Now with Optimize, you can test this idea by creating a single experiment and adding multiple pages to it using the “+ Add page” button.
From there, you’ll have the option to edit those pages so that you can display the promotion wherever it looks best in each case—whether that’s at the top of your site on the homepage or next to the pricing on your product page.
When you are happy with the results of the multi-page experiment, you can turn it into a multi-page personalization with just one click.
The right experience to the right audience
If you’re using Optimize 360, you have the added ability to focus your experiment or personalization to your Google Analytics audiences.
Using the same sale example, let’s say you want to offer a 35 percent off discount to your most loyal customers. You can create a multi-page personalization in the same way as described above. You can place the 35 percent discount banner and copy in all the pages that your loyal customers visit. When this personalization is launched, your loyal customers will always see this discount as they move from the home page, through your site, to the checkout page.
Want to learn how you can use this feature? Visit this article on our Help Center.
Multi-page experiences are already available to all Optimize and Optimize 360 accounts. You’ll be able to ensure your customers see the right message at the right time—even as they explore multiple pages on your site. And by creating a more valuable online experience, they’ll keep visiting you again and again.
Sometimes you’ve just got to confirm an unannounced product to put the rumors to bed, I guess. That was Google’s strategy this afternoon, following earlier rumors from Android Central that a chip shortage had put the kibosh on the mid-budget phone.
In a comment to TechCrunch, a Google spokesperson noted, “Pixel 5a 5G is not cancelled. It will be available later this year in the U.S. and Japan and announced in line with when last year’s a-series phone was introduced.”
That time frame would put the device’s arrival around late-summer, meaning it won’t arrive in time for Google I/O in May, as some speculated. Interestingly, the company appears to be limiting the device’s availability to two countries — at least at launch. That could, perhaps, be due to earlier-reported component shortages.
As The Verge notes, the company hasn’t been particularly precious when it comes to product announcements. The company took a similar approach ahead of the release of the Pixel. Either way, this isn’t exactly the standard big company approach to rumor denial, which is to either not answer or otherwise deflect.
Google may well be on edge about its Pixel line these days. The phone line hasn’t exactly taken the mobile world be storm, resulting in longstanding rumors that the company is looking to shake things up. That, in part, has seemingly been confirmed by some fairly high-profile exits.
Still, even while there have been issues on the premium side, the company’s budget “a” line has helped buoy its overall numbers. No word yet on specific specs, but the handset is not expected to be a radical departure from its predecessor.
People expect to interact with businesses when and how they like, such as browsing a brand’s website to research a product and then purchasing it later using the brand’s app. Getting insight into these cross-platform journeys is critical for businesses to predict customer needs and provide great experiences—but it can be very challenging.
Currently, many businesses measure app engagement with Google Analytics for Firebase and website engagement with Google Analytics. While each of these products separately offer powerful insights, getting a more unified picture of engagement across your app and website can be a manual and painstaking process.
To make this simpler, we’re announcing a new way to measure apps and websites together for the first time in Google Analytics.
Unified app and web analytics
First, we’re introducing a new property type, App + Web, that allows you to combine app and web data for unified reporting and analysis.
Reports for this new property use a single set of consistent metrics and dimensions, making it possible to see integrated reporting across app and web like never before. Now you can answer questions like: Which marketing channel is responsible for acquiring the most new users across your different platforms? How many total unique users do you have, regardless of which platform they use? How many conversions have occurred on your app and website in the last week—and which platform is driving most of these conversions?
You can also go deeper to understand the effectiveness of your marketing campaigns across platforms. For example, you can see how many users started on your app then visited your website to make a purchase.
Flexible event measurement
Understanding how people engage with your app and website means that you need to measure a diverse range of user interactions like clicks, page views, app opens, and more. We’re making it easier to measure those actions on all of your platforms in a consistent way. The new property type utilizes a more flexible event-based model for collecting the unique interactions that users have with your content, allowing you to measure any custom event that you set up.
This event-based model also allows you to automate the manual work of tagging some of the events on your site with no additional coding required. In addition to page views, enhanced measurement allows you to measure many common web events like scrolls, downloads, video views and more with the flip of a toggle in the admin settings for your property.
Given the many different ways people interact with your brand between app and web, you need flexible tools to make sense of your data and discover insights unique to your business. The new Analysis module enables you to examine your data in ways that are not limited by pre-defined reports.
There are a number of techniques you can use including:
Exploration: Conduct ad-hoc analysis by dragging and dropping multiple variables—the different segments, dimensions, and metrics you use to measure your business—onto a canvas to see instant visualizations of yourdata.
Funnels: Identify important steps to conversion and understand how users navigate among them—where they enter the funnel, as well as where they drop off—with both open and closed funnel options.
Path analysis: Understand the actions users take between the steps within a funnel to help explain why users did or did not convert.
Once you’ve surfaced insights from your analysis, you can use the results to create audiences and use those audiences to deliver more relevant marketing experiences to your customers.
Start measuring across platforms
The first version of this new app and web experience—including the new event model and new analysis capabilities—will be available to all Analytics and Analytics 360 accounts in beta in the coming weeks. If you use Google Tag Manager or the global site tag for Google Analytics today, there’s no re-tagging required for your website. To include your app data, you’ll need the Firebase SDK implemented in your app. See how to get started in Google Analytics, or if you’re an existing Firebase customer, here’s how to upgrade.
If your business has both an app and website, and is looking for a more complete view of how your customers engage across both, we encourage you to participate in this beta and share your feedback. We are working to make Google Analytics the best possible solution for helping you understand the customer journey and create great customer experiences across platforms. Your partnership is essential to help us get there.
SA 360 has features that aren’t available in standard Google Ads accounts or are more sophisticated. Bid strategies are one feature that makes SA 360 so powerful.
Read more at PPCHero.com
Consumers expect connected shopping experiences from research to purchase. But their journeys aren’t linear; they move around, visiting—and revisiting—multiple sites and apps, multiple times a day.
This makes it challenging for businesses to deliver a coordinated site experience, especially if they are running an experiment or personalization on their site. How do they make sure that the version of their site someone saw in the morning is the same version they see in the afternoon?
Google Optimize can now understand when a customer has returned to a site they visited before and deliver a consistent site experience. Let’s see how this works.
Imagine you’re a hotel business running a marketing campaign that promotes a 20 percent discount for the upcoming holiday season. When people visit your site in response to the campaign, you want to make sure you offer this discount to them throughout their entire booking experience, even if they come back multiple times before they make a reservation.
One part of your marketing campaign is paid media you buy through Google Ads. In this case, you would use Optimize to create a custom web page featuring the discount and then add the Google Ads rule to ensure this page is shown to people who first arrive to your site from your Google Ads campaign. There are likely many people who click on an ad, explore your site, then come back later to complete the reservation. Now, no matter how many other pages on your site people visit, or how many times they return over 24 hours, Optimize will automatically display that custom page to them each time.
Another way you promote this sale is through email. For this part of your campaign, once you create a custom web page with the discount offer, add a utm_campaign parameter named “holiday-sale” to the URL in the email. Then in Optimize, add a UTM parameter rule for “holiday-sale.” Optimize can now use that parameter to display the correct experience every time people who received the promo email visit your site. In addition to email, you can also use the UTM parameter rule in advertising campaigns managed with Display & Video 360 and Search Ads 360, or any other campaigns you are running that support UTM parameters.
Royal Bank of Canada is an Optimize 360 customer that has already begun using UTM parameter rules.
Together with their Google Marketing Platform Partner, Bounteous, they often use Optimize 360 to run personalizations across their entire website. Because most of these personalizations are focused on delivering the right content to the right user from their marketing campaigns, they were excited to start using the UTM parameter rule.
“The customer journey at the Royal Bank of Canada is rarely linear. We need experiments that can react as customers frequently engage and navigate our website. The UTM parameter rule gives us that flexibility, and it is changing the way we approach our campaigns.”
– Arnab Tagore, Senior Manager of Digital Analytics, Royal Bank of Canada
Both the Google Ads rule and UTM parameter rule are already available to use in Optimize and Optimize 360. We encourage you to go into your account and check them out and we look forward to sharing more new features that help you better meet your customers’ expectations and get the most out of your website.
With first-party data becoming more relevant and third-party cookies becoming a thing of the past, this leaves marketers questioning, how can I best prepare?
Read more at PPCHero.com
- Very few SMBs use multiple channels for their online advertising
- Facebook is the most effective channel based on the cost for CPM and CPC
- It’s important to remember that every business is unique when it comes to deciding on budget allocation
For any business in the software as a service (SaaS) space, data analysis and science are crucial to ensure they keep pushing ahead to reveal those insights that can really make a difference. With this in mind, the Cambridge MBA team looked to leverage Adzooma’s extensive data to identify new ways for SMBs to maximize their ad spend with cross-channel marketing.
For the team at Cambridge University, this was an exciting opportunity to produce some truly unique insights, given that even the big players such as Google and Microsoft only have data that pertains to their individual channels. The project promised to provide a much broader view and deliver some new insights thanks to the access to anonymized data from thousands of accounts across the three big platforms via Adzooma.
A cross-channel approach
The findings immediately identified that very few SMB customers use multiple channels (Facebook, Google, and Microsoft).
Although this wasn’t part of the main project, it was a really interesting piece of analysis and it’s something we’ve stressed the importance of a lot. Most people just stick to Google, for example, as that’s where they think they should be but that’s not always the best case for everyone’s business, and being seen across multiple touchpoints – or at least trying out multiple channels – can be crucial to digital marketing success.
Our analysis found Facebook to be the most conducive channel for SMBs based on cost (CPM, CPC) as well as return (impressions, clicks), however, it was Microsoft that came out on top for reaching a more professional and affluent audience.
The research highlighted the importance of pre-determining your specific target audience. Hence, when it comes to choosing the channel – or channels – for your business it’s really worth thinking about what you are trying to achieve with your ad spend and who you’re truly trying to reach.
What are you really trying to achieve?
Right at the offset, it’s important to think about your end goal and ask yourself who are the customers you are looking to target and what is the most efficient way to get to them.
Existing research told us that for SMBs acquiring new customers was the most chased goal on the customer journey followed by ‘generating awareness’, ‘generating leads’, and ‘retaining customers’.
Overlapping resolution methodology then allowed the team to determine the impact of cost on different marketing channels. This way, SMBs would be able to effectively determine which platform is best to use when similarities occur.
We found through the research that it was the choice of the channel itself that had the most significant impact on both CPM and CPC. Having determined a connection between channel and cost KPIs, further research was conducted to find out the average CPM and CPC across Google, Facebook, and Microsoft Ads.
While it was Facebook that was the most cost-effective channel on average for SMBs overall, the recommendations were that businesses should still look at the click-through rates of other channels to determine whether other factors such as industry or geography could make a significant difference.
If you’re choosing between Google and Microsoft, the results suggest using Google due to its high reach and low cost, however, Microsoft could also be useful, particularly as it offers high-level targeting and demographics that can be suitable for specific business types.
What is your ad saying?
Another factor that perhaps many businesses don’t consider when deciding on a platform is the sentiment of their messaging.
When analyzing the data this was another area where the research team saw differentiation depending on the channel where the advert appeared.
Microsoft proved to be the most popular platform when it came to a positive sentiment with a CTR of 4.2 percent, compared to 3.6 percent for neutral and 3.3 percent for negative sentiment.
Interestingly, the opposite was true for Google ads where negative sentiment proved most popular with users, gaining a CTR rate of 6.5 percent compared to 5.7 percent for neutral and negative messaging.
Again, it highlights how important it is to take that time to tweak your ads for testing purposes and learn what works best for your target customers so you can capitalize on your spends.
Every business is unique
It’s no secret that the one size fits all approach doesn’t necessarily work. All businesses are different and therefore their ad spend and utilization will of course differ.
Some people, as we all do, want to go with the stats and what has proven to have worked historically for businesses, and whilst that can be taken into account, that’s not to stay that it will work for every business. Therefore, it’s always important to remember to take the time to consider where you are spending and who you are trying to reach.
Plus, it is worth remembering that although Google, Facebook, and Microsoft Ads are the most popular online advertising platforms, there are alternative (and less expensive) places to list your ads including Reddit, Amazon, and industry-specific sites such as Capterra. Despite having fewer users, these are still effective as it’s often easier to reach your exact target audience and could work as an addition to your primary platform.
We hope that through this research we’ve provoked SMBs to think carefully about their target audience and specific objectives prior to ad spend allocation. What we’ve showcased here is that the advertising platforms explored within this study work effectively in their own right depending on the end goal and we hope these insights will enable SMBs to achieve greater overall results.
These learnings help determine how cross-channel partnerships can be best leveraged for SMB customers. As Facebook seems to be the most used channel by 70 percent of SMBs, and data analysis suggests it is optimal in terms of cost and return, the data will be used to scale Facebook features and opportunities. A lot of the learnings we unearthed from this study will also go directly into the core technology of the Adzooma product.
Rob Wass is Co-founder and CEO of Adzooma.
Akanshaa Khare is currently pursuing an MBA at Cambridge University and has five years of Product Management experience and three years of Consulting experience, helping consulting firms such as BCG and ZS Associates.
The post Cross-channel marketing: why you shouldn’t put all your eggs in the Google basket appeared first on Search Engine Watch.
In the United States, almost half of our food supply is wasted. That’s enough to feed everyone who experiences food insecurity four times over. “In a lot of ways hunger is not a supply problem, it’s a distribution problem,” says Leah Lizarondo, cofounder and CEO of 412 Food Rescue, a Pittsburgh-based nonprofit organization seeking to close the gap between food surplus and food scarcity.
In order to successfully achieve their mission to reduce hunger by redirecting surplus food to people experiencing food insecurity, Leah and her team need to recruit volunteers to download the Food Rescue Hero app and complete a local food pickup and delivery, becoming what they call “Food Rescue Heroes.” As a growing nonprofit organization, 412 Food Rescue has limited resources, though, and relies on technology to save time and invest in the right places.
A cross-platform understanding of volunteers
Historically, measurement across 412 Food Rescue’s digital touchpoints had been a challenge for the nonprofit. Key data was siloed between their website and app, making it time intensive to get a complete understanding of how people were engaging with the organization online. With help from their digital analytics partner Bounteous, 412 Food Rescue turned to the new Google Analytics.
The new Google Analytics allows us to look at our data across platforms — web and app — to understand the full journey of our users. We’ve been able to cut our reporting time by 50%.Sara Swaney
Director of Advancement, 412 Food Rescue
With that time savings, the team at 412 Food Rescue has been able to improve their marketing and focus on engaging more volunteers in the community.
“In order to recruit more volunteers, we needed to know where people were learning about 412 Food Rescue,” Swaney says. With a view of user engagement across platforms and devices, 412 Food Rescue was able to easily discern where the majority of its volunteers discover the organization, and what their typical journey is to get started. The team was able to see that new users are most likely to accept a Food Rescue and become volunteers within 48 hours of downloading the app. As a result, they adjusted their social media campaigns to drive app downloads on Mondays and Tuesdays, when most Food Rescues are typically posted in the app. By facilitating Food Rescues that users can immediately act on upon downloading the app, 412 Food Rescue was able to improve the user journey and convert more users to volunteers.
Automated insights introduce a new set of learnings
With automated insights generated through machine learning, 412 Food Rescue has been able to save time analyzing data and spend more time taking action. They learned, for example, that there was a dip in volunteer engagement on weekends, an insight that had gone unnoticed. Because they had been proactively alerted to the change in Analytics, they were able to quickly respond by increasing their marketing efforts on weekends to boost engagement and address the demand for local deliveries on those days.
Greater impact despite limited resources
Even without a dedicated analytics team, 412 Food Rescue is able to easily get a deep understanding of their data and use it to shift their marketing strategy, grow their network of Food Rescue Heroes, and secure further investment to ultimately expand to more cities and achieve their mission to end food waste and hunger.
Get started with the new Google Analytics today.
Google Analytics helps you measure the actions people take across your app and website. By applying Google’s machine learning models, Analytics can analyze your data and predict future actions people may take. Today we are introducing two new predictive metrics to App + Web properties. The first is Purchase Probability, which predicts the likelihood that users who have visited your app or site will purchase in the next seven days. And the second, Churn Probability, predicts how likely it is that recently active users will not visit your app or site in the next seven days. You can use these metrics to help drive growth for your business by reaching the people most likely to purchase and retaining the people who might not return to your app or site via Google Ads.
Reach predictive audiences in Google Ads
Analytics will now suggest new predictive audiences that you can create in the Audience Builder. For example, using Purchase Probability, we will suggest the audience “Likely 7-day purchasers” which includes users who are most likely to purchase in the next seven days. Or using Churn Probability, we will suggest the audience “Likely 7-day churning users” which includes active users who are not likely to visit your site or app in the next seven days.
In the past, if you wanted to reach people most likely to purchase, you’d probably build an audience of people who had added products to their shopping carts but didn’t purchase. However, with this approach you might miss reaching people who never selected an item but are likely to purchase in the future. Predictive audiences automatically determine which customer actions on your app or site might lead to a purchase—helping you find more people who are likely to convert at scale.
Imagine you run a home improvement store and are trying to drive more digital sales this month. Analytics will now suggest an audience that includes everyone who is likely to purchase in the next seven days—on either your app or your site—and then you can reach them with a personalized message using Google Ads.
Or let’s say you’re an online publisher and want to maintain your average number of daily users. You can build an audience of users who are likely to not visit your app or site in the next seven days and then create a Google Ads campaign to encourage them to read one of your popular articles.
Analyze customer activity with predictive metrics
In addition to building audiences, you can also use predictive metrics to analyze your data with the Analysis module. For example, you can use the User Lifetime technique to identify which marketing campaign helped you acquire users with the highest Purchase Probability. With that information you may decide to reallocate more of your marketing budget towards that high potential campaign.
You will soon be able to use predictive metrics in the App + Web properties beta to build audiences and help you determine how to optimize your marketing budget. In the coming weeks these metrics will become available in properties that have purchase events implemented or are automatically measuring in-app purchases once certain thresholds are met.
If you haven’t yet created an App + Web property, you can get started here. We recommend continuing to use your existing Analytics properties alongside an App + Web property.
- Once VMware is free from Dell, who might fancy buying it?
- Facebook faces ‘mass action’ lawsuit in Europe over 2019 breach
- Chinese hardware makers turn to crowdfunding as they look to go global
- Core Web Vitals & Preparing for Google’s Page Experience Update
- Conversion modeling through Consent Mode in Google Ads