Wave farewell to third-party cookie data, because Google has jumped on the privacy bandwagon, too. Marketers can no longer get away with intrusive data collection or behavior tracking. Those that haven’t already need to rethink their strategy in ways that are built around respecting individual privacy concerns.
In a recent McKinsey & Company article, “The demise of third-party cookies and identifiers,” the authors noted “the $152 billion US digital advertising industry will lose access to most third-party data, which has powered programmatic advertising (advertising purchased and sold using software).”
This may sound catastrophic, but it doesn’t have to be. Many businesses are exploring exciting new ways to understand, reach and engage with their customers in a more meaningful way, while respecting their privacy. They’re finding more nuanced, effective ways to use data, both internal and external, to reach better decisions and more accurate predictions. They’re using AI and ML to identify customer cohorts and develop super-relevant, carefully targeted messaging, products and offers. Let’s take a closer look at how that works.
What’s Changed, Exactly?
Google has joined Firefox and Safari in phasing out third-party tracking cookies [editor’s note: after this article was written, Google pushed off the implementation of this move until late 2023]. Back in October, Apple did something similar — it announced that, as of the iOS 14 platform update, its operating systems will require user consent for IDFA (Apple’s ID for Advertisers) to work. This essentially boils down to the same thing: no third-party cookies for sites and advertisers.
None of this is new. We’ve known this was coming for a while now. The changes will force brands to rely less on Google and find new ways to acquire and utilize first-party data. Or is there another way?
Related Article: That’s the Way the Cookie Crumbles
What Does This Mean for Companies Reliant on Cookie Data?
The McKinsey article above notes, “Advertisers and publishers will now need to depend primarily on their own first-party data, or on data from walled gardens, contextual targeting, and greater support from data platforms.”
The good news is, frankly, you’re better off without tracker cookies.
First of all, people really hate the feeling they’re being spied on — and that’s exactly the impression created by cookies-based advertisements. The whole reason all these companies and browsers are dropping cookies is because so many people have raised privacy concerns or made the decision to block cookies themselves. It’s a bad idea to force an approach on your customers that they really don’t like.
Secondly, you never got the true picture anyway. You aren’t really tracking a customer’s journey. Depending on the device and who uses it, you’re either getting disjointed snapshots of one person’s journey, or a whole lot of journeys all jumbled together. That’s hardly a great starting point for an effective marketing strategy.
Thirdly, third-party cookies are a blunt force tool. All they do is tell you what a person looked at. They don’t give you any context or nuance to help you understand why. They don’t help you to get a complete picture of this person — or whether they’re a potentially high-value customer you should prioritize in your marketing.
Seen from this perspective, the death of cookies is probably a blessing in disguise. Instead of relying on this flawed approach, it might just be the nudge you need to restructure your marketing data acquisition and analytics strategy around data that actually delivers business-critical insights.
Related Article: The Demise of the Cookie and the Rise of First-Party Data
Why External Data Matters More Than Ever
The key to rising to this challenge is to start taking data variety, quality and accuracy seriously — and to focus on getting a complete picture that fills in all the gaps.
You may be thinking that’s easier said than done. And sure, if you’re trying to collect all that data yourself, it’s a mammoth task. But in today’s data landscape, most companies, no matter their size, recognize they can’t amass all the data they might ever need by themselves, in-house.
Instead, forward-thinking businesses look to external data and alternative data sources to supplement their internal datasets. A recent survey by my firm, Explorium found four in five respondents agreed they saw external data as “very valuable” and almost as many (79%) said they were beefing up their data acquisition efforts in 2021.
External and alternative data sources range from geolocation data to footfall to sales patterns to weather information. Basically, anything relating to questions about the market or your customer base that you can’t answer internally.
This kind of data enriches your existing datasets and helps you build powerful predictive models of ever-increasing accuracy. If you use a data science platform that pre-vets and automates your connections to external data sources, this becomes a swift, seamless process too, allowing you to combine and augment datasets with just the details you need. Ultimately, this means better targeting, smarter allocations of marketing budgets and vastly improved ROI.
6 Steps to Prepare for the Cookie-Less Future
Here’s how to prepare for the demise of tracking cookies (and get your new strategy off to a flying start).
1. Get to Grips With Your Customer Journey
Guiding a customer from first contact to making a sale is a delicate art. You need to deliver them the right messages at the right moment, appropriate to where they are on their journey. You need to make multiple small conversions along the way. To do that, you’ll need to make certain predictions about how different types of customers will behave. And for that, you’ll need data.
2. Evaluate Your Internal Data
Run an audit of your existing data resources. What are the strengths? What can it tell you about your customers, your business or the wider market that can help you make better predictions and decisions, matched to the stages of the customer journey you’ve identified? What new questions does it raise? How does it help you identify and engage with the right customers for your business?
3. Note the Gaps
Now that you’ve established what you have, concentrate on what you lack. What questions can’t be answered with this data? What specific gaps are in the dataset? Utilize data platforms to understand the impact of alternative data sources on your predictive power and business objectives.
4. Identify the External Data You Need
Next, ask yourself who would have access to this missing data. Where might it come from? Who would collect it? Where would you find this? Do you need to go direct to source to find it, or can you access it via a data platform that automates your connections? There’s a lot of data out there and it’s easy to get distracted – don’t lose sight of the fact that you’re seeking external data that helps your predictive models perform better.
5. Ensure it Complies With Privacy Regulations!
If you’re buying datasets directly from the source, rather than accessing through a platform or system that vets them for you, make sure it’s compliant with all relevant privacy laws, wherever you plan to use it. The last thing you want is to base your predictive models on data that’s then whisked from under your nose once you’ve come to rely on it.
6. Quality Check Before You Commit
Buying access to multiple datasets quickly gets expensive if those datasets turn out to contain little value. Before you sign on the dotted line, ensure it’s current and up-to-date, relevant to your question and is positioned to deliver the ROI you want.
Related Article: How Marketers Can Break Their Third-Party Data Habit
Final Thoughts: Privacy-Friendly Data Acquisition
The bottom line is this: if you still rely on cookies for your advertising, you will need to adjust and adapt. Thankfully, with the availability of external data, that transition may not be the roadblock it appears, and will give a strong foundation for your advertising programs in future.
Ajay Khanna is the CMO at Explorium, the automated external data platform for advanced analytics and machine learning. Previously, he was the Vice President of Marketing at Reltio.