You are reading part 3 of a 3-part privacy playbook. Jump to section 1. Build direct relationships with your customers or 2. Ensure your measurement remains accurate and actionable.
Rely on first-party data to engage audiences
When you pull insights from your first-party data, you can improve the relevance of your communications with your audience and deliver more meaningful experiences in a privacy-safe way.
For instance, you can use first-party data to engage with your best customers. When people share their contact information with your business, you can use Customer Match to reach those same users again and again as they move across Google properties, including Search, the Shopping tab, Discovery campaigns, Gmail, and YouTube.
Approach
Norwegian telco Telia was looking for ways to convince existing customers to upgrade their contracts. First, it found eligible customers by scouring its own CRM data. Then, it worked with its media agency Carat to reach those customers with more relevant ads. Using a hashing algorithm, email addresses were uploaded into Google Ads through Customer Match so that Telia could launch highly relevant campaigns that matched the mobile usage and existing contract status of recipient customers.
Results
Telia’s Customer Match campaigns accounted for 69% of upgraded mobile plans at a conversion rate 22% higher than the average of the original ads from the original campaigns. As a result, cost per acquisition fell by 23% compared to the average CPAs, and both Telia Smart and Telia UNG saw overall revenue growth of 15%.
Learn more about how Telia was able to add a personal touch to its marketing
Once you’ve established a privacy-safe measurement foundation, use Smart Bidding to take action on this data. Smart Bidding strategies use machine learning to optimize for conversions or conversion value in each and every auction.
It’s worth noting that because some conversion types are typically worth more than others, one size does not fit all. Using strategies like Maximize conversion value with an optional Target return on ad spend (ROAS) can help you optimize for total value — rather than volume — by automatically adjusting bids to reach the highest-value customers.
If you are already bidding toward value, consider using your first-party data on more advanced strategies, such as bidding toward profit or expected lifetime value. By adjusting Google’s machine learning inputs you can get closer to your business’ targeted outcomes.
Source: Google Internal Data, 2021.
On average, advertisers who switch their bid strategy from a cost per action (CPA) target to a return on ad spend (ROAS) target see 14% more conversion value at a similar return on ad spend.6
Learn more in our technical paper, Setting Smarter Search Bids.
Source: BCG, Responsible Marketing With First-Party Data, May 18, 2020.
Optimize for advanced bidding solutions on Android like Target CPA or Target ROAS* to acquire high-quality customers likely to complete your predetermined in-app actions.
Consolidate iOS App campaigns and use only target cost per install or Target CPA bidding with eight or fewer app install campaigns for each of your iOS apps to maintain optimal performance due to SKAdNetwork’s campaign limitations.
*Target ROAS is now known as Maximize conversion value with an optional Target ROAS.
Approach
Stand Up Stations, a custom-branded sanitization installation company, was facing increasing competition at the onset of the COVID-19 pandemic and needed to maintain its first-mover advantage. The company used Google’s Smart Bidding strategies to automatically optimize bids based on contextual signals, like buying propensity and browser activity, to get in front of customers when they were searching for personal protective equipment.
Results
By using a Smart Bidding strategy to optimize for conversion value, the company saw a 20% reduction in CPA and a 4X boost in ROAS, allowing Stand Up Stations to scale its investment and increase sales by 440%.
Use automation to help you discover new audiences
One of the most exciting benefits of machine learning is its potential to help marketers reach qualified audiences — even when some signals are limited.
Google audience targeting can factor a wide range of signals with the help of machine learning to reach interested users and optimize which ad to show them.
Signals are user attributes, including:
- Who they are (their demographics)
- What their interests might be based on websites and apps they use
- What context they’re in at the time of the auction, such as the content of the web page they’re browsing
Here’s how it works
Audience solutions in Google Ads will rely on as many of these signals as are available at the time of an auction to help advertisers deliver the most appropriate messages.
For instance, even when cookies are available, audience solutions will combine those user signals with contextual ones to determine someone’s interests and preferences. And, in cases when cookies are limited — either because of browser restrictions or consent choices — audience solutions automatically turn to alternate signals (such as the context of the ad placement) to determine relevance.
Automation does more than increase relevance. It can also find new and receptive customers, either with optimized targeting in Google Ads or targeting expansion in Display & Video 360.
Learn more about how automation can help you reach new audiences
Advanced: Supercharge your marketing with cloud technology
Increasingly, cloud-based solutions are being used by marketers to manage data while protecting user privacy. That’s because cloud technology offers inherent privacy and security advantages when it comes to storing and organizing large data sets, such as encrypting all data by default and setting parameters for who has access to that data.
Here’s how it works
By consolidating first-party data into a cloud-based data warehouse like BigQuery, a data scientist or analyst can help you do more advanced analysis. When you uncover new and more powerful insights, you can act on them by integrating them with your marketing tools.
For example, using historical customer information, data scientists can train machine learning models to predict or anticipate the outcomes of future interactions with your customers, and those like them.
Approach
When the COVID-19 pandemic brought travel to a halt, Alaska Airlines had the foresight to start preparing for travel’s return. They partnered with their Google Marketing Platform partner Adswerve to build a marketing data warehouse using Google Cloud, which tied together first-party data across their CRM systems, media campaigns, and site analytics.
Once the data warehouse was up and running, Adswerve helped Alaska Airlines use Google Cloud’s advanced machine learning capabilities to uncover new audience insights and growth opportunities. For instance, Adswerve’s data scientists were able to build models from the consolidated data that could predict a customer’s lifetime value based on information like origin and destination airports, preferred travel dates, and loyalty program activity.
When the predicted values were fed into Search Ads 360, the marketing team at the airline was able to adjust bids accordingly and increase the return on investment from their Search campaigns.
Results
Alaska Airlines improved its ROAS from paid search by 30%.
Discover what the future looks like
The Privacy Sandbox aims to develop new technologies that can offer sustainable solutions for delivering interest-based and remarketing ads.
Here’s the proposed solution for interest-based ads
A person’s browser could help match relevant ads to people based on their most frequent and recent topics of interest without tracking the specific sites they visited or even having the ability to identify them.
Here’s the proposed solution for remarketing
When people visit a company’s website, and they take an action that’s valuable to the company (such as viewing a product), their interest will be recorded on their device, limiting the amount of data shared externally. Then, when they visit other websites with ad space, the browser helps inform what ads may be most relevant without exposing people’s browsing activities in the process.
Privacy Sandbox technologies will work along with capabilities like first-party data and machine learning to power Google’s audience solutions. For example, Google Ads and Display & Video 360 will combine the Privacy Sandbox technologies with a wide range of other available signals to match audiences with your interest-based ads, so that you can continue to reach the right audience without the need for third-party cookies.
Preparing for the future
Growing concerns around user privacy have impacted every corner of the digital advertising industry. And while the digital advertising ecosystem continues to improve in response to privacy concerns, here are some additional steps that organizations can take today to stay ahead.
- Create a center of excellence. Some companies have established a dedicated team of legal, data science, and marketing experts whose focus is to stay on top of privacy trends and come up with contingency plans for future scenarios. If your company doesn’t have the resources necessary to support a privacy team, marketers can still add value by staying up to date on the latest privacy best practices by subscribing to Think with Google.
- Learn about new privacy-preserving technologies. The Privacy Sandbox is an open-source initiative, developing new technologies centered on advances in anonymization, on-device processing, and other privacy techniques. In fact, anyone can submit proposals and run experiments. So work through your industry associations or lean on your ad tech vendors to make sure your business needs are being considered as these technologies are conceived and built.
As we’ve seen from the companies featured here, respecting user privacy doesn’t have to come at the cost of business results. Quite the opposite: The tools and resources available to digital marketers today can, in fact, create new opportunities to connect with customers — all with privacy in mind.
Explore the series
Chapter 1. Build direct relationships with your customers
Chapter 2. Ensure your measurement remains accurate and actionable
Chapter 3. Drive performance by keeping your ads relevant
