Many may naturally think that taking a privacy-first approach to marketing is going to cause loss of data, less effective targeting and reduced leads. Actually, it’s an opportunity for growth in 2022 if you apply the right methodology and use the right tools that have been built with user consent in mind.
There are three broad phases for the life cycle of customer data that we can acquire online and offline. In this article, we’ll focus on the methodology and tools that can be used to improve growth – not impede it.
Of course, we have to start by actually gathering data. There are two main things we need to be thinking about here.
I’m sure most – if not all – of you are already using Google Analytics or other tools to capture data about your users’ behaviours on your website or to ask customers key questions at point of contact. This is a great starting point, but there’s one proceeding step that many brands miss: having a data strategy.
My colleague, Ben, has already provided some information on how to create a great 1PD strategy already and it’s important enough to repeat. In an age where we have lots of opportunities to collect data and great responsibility over what we do with it, we need to take the time to think about why we want it – and what we’re actually using it for. Not doing so could lead to eroded trust in users; according to research by Statista, under privacy-first ethics and regulations such as GDPR, we should only be collecting the minimum amount of data and that data should have a purpose.
In my days as a Business Intelligence Developer, a colleague summarised the importance of data perfectly: data is used to make reports and reports are used to make intelligent decisions.
Data in and of itself is often meaningless: having looked at millions of rows in databases over the years, I completely agree! But data is used to measure certain important metrics that help you make better decisions.
There’s no point reinventing the wheel here – Google Analytics is an excellent measurement tool for web activity. However, with tools such as Google Analytics 4 and Enhanced Conversions, not only are you using great tools for measuring, you’re also using smart tools that have a privacy-first approach by design.
Google Analytics 4 isn’t just about using the latest tool: the sooner you can start using it, the sooner you can start preparing for the cookieless future. These types of tools recognise that we are no longer in the Age of Precision, where we use exact targeting based on widespread third-party data collection, but have now moved into the Age of Projection, where we recognise that not all users consent to sharing data. We have to make educated guesses on targeting and behaviour.
By acting and adopting now, this gives us a head start with collecting data that can be used to project the gaps when users aren’t willing to share their data once third party cookies are no longer usable.
Enhanced Conversions take this a step further and can be used to match ad activity to Google’s own user data, helping to fill in some of those blanks where users have given consent to Google. Given that Gmail alone has a 27.8% market share and is only one of the suite of products collecting data for Google, that’s a huge number of users that are willing to consent to help us understand them and show them the content they want.
Now comes the best part: actually using the data to make intelligent decisions.
We must always avoid the trap of assuming we know what our customers need, and instead prove it with research and data.
A simple methodology you can follow and should be following is Test & Learn, which is very common in disciplines like paid media advertising and CRO.
It’s a relatively simple process but hard to master, built on 3 key steps:
- Use the data you are now measuring to create a theory on how customers may respond to activity. This could be as complex as testing a totally new user journey or modifying a layout to improve conversions. You should identify key metrics that can be used to understand the effectiveness, such as conversion rate.
- Test the theory by launching it, using split testing to create a benchmark data point against your current activity. The test should run for just long enough to get a sample large enough to review its effectiveness.
- After the test is complete, you can now review the data. Benchmark the data against your existing activity and understand if it has delivered the results you expected in the theory identified in Step 1.
You can – and should – continue to repeat these steps with each new test, all focused on growth in a privacy-first future.
If you’re running paid media activity (as almost everyone should be), a great example of this test and learn approach would be to set up a broad match campaign. Broad match takes a wider approach to targeting by moving away from very specific groups of keywords to taking a wider range and matching some of the keywords, targeting customers that may not usually see your ads.
There’s a lot more that can be done too, especially if you’re already using automation tools like Smart Bidding in Google Ads. Customer match is a great way to improve targeting based on a customer’s behaviour, and audience expansion can help widen the net to include other types of customers based on your existing targeting.
The tools are great and crucial for growth, but they’re only useful if you benchmark with comparative data to measure effectiveness.
This whole article can be summarised in three simple bullets.
We need to:
- focus on building trust in our brand by taking a privacy-first approach to our customers’ data…
- …whilst concurrently investing in technology which is prepared for a cookieless future and…
- …testing and learning across all marketing activity
Taking this approach will help you turn the threat to traditional digital marketing targeting and segmentation into an opportunity for growth.
The death of cookies doesn’t have to mean the death of marketing as we know it. Not sure where to start? Get in touch with us.