The privacy changes facing digital analytics

This week’s “week in review” is a two parter. For Part One, enjoy my recap of “The privacy changes facing digital analytics”. Next week we talk solutions. No chapter this week as I’m working on consolidating the chapters to date in a fun recap on the blog. Stay tuned. 

 

Working in technology, you get pretty used to click-baity headlines that suggest “This [insert upcoming privacy change] is coming soon, beware!”

 

It’s a byproduct of the algorithms we’ve lazily come to rely on to serve us up content we’ll actually click on (which usually all have click-baity headlines because that’s what we click, the toxic cycle continues…). 

 

Of course, anyone who is close enough to the jobs to be done knows that it’s truly never as simple as something being dead, nor something being the best thing since sliced bread. 

 

There really is no such thing, and I’m sorry, but I now have to say the one thing everyone hates to hear…. 

 

“The truth is it depends”

 

But it does.

Here’s what it depends on. 

 

It depends on your use case; the specific question you are asking and the decision to be made.

 

It depends on your people, their skills, their appetite to learn, to adapt to change, the agendas they are all uniquely pursuing that may conflict with one another.

 

It depends on your processes and how well people can adhere to the process that was defined and where consensus was reached (how many people in your business do you KNOW adhere to their UTM tracking links in the way we all agreed to?) 

 

It depends on your technology and its capability to deliver on your needs. 

 

Finally, of course, it depends on your data. It’s configuration, quality, completeness and accuracy. 

 

So with that disclaimer now announced, let me share that this week, after receiving the recent news that Apple’s will soon be stripping identity parameters in Safari – my anxiety peaked yet again. Call me triggered. In short, this impacts some of the granularity we can otherwise generate to conduct customer journey analytics at an ad level (linked to a customer profile) and to collect browsing behaviours to a user ID. As one of my colleagues put it, it’s not the end of the world but it is still yet another reminder of the path we are all still on to seeing larger and larger gaps forming within our previously easy-to-track digital world. 

 

Now, as we’ve covered above, shouting “beware” is often a gross exaggeration. Our digital analytics will still be useful though as changes that are made to protect privacy continue to progress. We do however need to consider the limitations of the data we are able to access in our sample to ensure they are representative enough to offer a fair interpretation of channel and omnichannel interactions. This sample must be representative enough to afford us the ability to meaningfully optimise based on within-channel granularity too (that is, spend between campaigns, ad types, audiences targeted, placements, creative impact and more). 

 

When announcements like this one (no matter how small) continue to add to the privacy protection timeline marketers can take another little hit on that underlying sample and the granularity – and we still know that there will be more to come and the ask of analysts will be to keep abreast of the changes and understand their impacts to their reports so we can be better informed. 

 

So the question I pose today is, are you informed? 

 

Today’s newsletter offers a quick history lesson and a reminder on some of the major events in tracking history. Then we can pull apart how these moments have impacted the world of analytics as we know it. 

 

 

 

 

 

 

 

 

 

 

 

 

 

As of today, Similarweb provide the following breakdown of browser share of market. 

 

So what do all these privacy changes mean for the analytics world? If you’ve ever heard me bang on about this in some sort of speaking opportunity, I’m always reminding everyone as the ultimate level-set that all data is sampled. It might seem obvious but I do find that most people do forget that. 

 

This notion that we could ever get close to tracking every interaction and stitching them into one, consolidated journey is a lovely utopia to aim for though of course we know it’s not ever exhaustively likely. 

 

What about offline channels? 

What about word of mouth? 

What about the fact that the brand is so powerful, they were going to buy from you anyway before we intercepted them with paid activities that were not even really needed? 

 

Beyond the quant data collection alone, there are then the assumptions and business rules we apply to draw conclusions about these touchpoints.

 

This includes things like “On average it takes approx 7 days for people to make a purchase, let’s set that as our attribution window” to “Based on our strategy I’ll split my channel groupings by paid and organic social even though we’re boosting the same organic content to gain additional reach” to name only a couple.

 

So just like we referenced a few weeks back when discussing MMM,  “All models are wrong but some are useful”.

 

For this reason, touch attribution models with the sample data we can acquire have a track record of being immensely useful. One case study I found speaks to an e-learning company adopting a multi-touch attribution model to yield a saving of a quarter million dollars while managing to drive up their ROI by 30% (way to cut out the dead weight in your Google ads)! 

 

This alone speaks to how powerful digital analytics tools can remain for channels where there is significant spend. Why? Because we can get granular. The success in this case study and the successes I’ve achieved through adopting MTA outputs have been more powerful to optimise within-channel and through leveraging the power of digital tracking to provide granularity of insight at a campaign, keyword, placement, format, device, creative and landing page level. 

 

Ahh I remember the days sitting in my Google Search Console and Meta Ads Manager customising my UTM parameters and comparing the data within and outside of the platforms to optimise them…. (let’s tackle data discrepancies between walled-gardens and analytics platforms another day). 

 

[Actual evidence of me geeking out with Facebook nail art circa 2012. Cool or loser? You decide]

• You rely on cross-device interactions or know customers are likely to browse and buy across a longer path to purchase (note, if you’re on Google stack leveraging Google Signals, Google promises a bit of mitigation to this in GA4)
• You happen to know a disproportionate number of your users use particular browsers or devices (e.g. you have an intermediated sales force with an app that only works on iOS but want to track your B2B paid and owned marketing)
• You have a particular cohort of customers (with similar behavioural, attitudinal demographic, psychographic, geographic, and firmographic traits) who are opting out or not providing consent to track
• You have a devastatingly low authenticated user count and the capability to capture customer data, uniting behavioural activity to a stored user profile (for those who consent to it!)

 

So what are the impacts at a glance? Why should we care anyway? Here’s what these privacy impacts all boil down to at a high level.

 

 

Seeing more (not set) in your analytics platforms across various dimensions? “Direct traffic” appear to be increasing? Seeing weird changes in your device and browser ratios?

 

To get a sense of the impact to your business, consider mapping the timeline above to better interpret the output of your analytics systems and temper your outputs to updated ratios based on what you know you might be missing. Then, always combine the output of with in-platform reports (e.g. in your buying platforms like Facebook and Google directly) and Marketing Mix Modelling (MMM) for the most comprehensive outlook. 

 

 

When we cannot know, we imply. Or at least the algorithms do. When algorithms cannot tie hit and session data to the user level, we leave behind “deterministic” inferences towards implied “probabilistic” models. This is the difference between Google Analytics being able to go “I KNOW you were Kate Cook who clicked my Instagram ad on mobile and later purchased via your desktop computer”. 

 

For Google specifically (which we’ll use to follow the example) it will still struggle with this use case. 

 

It will however be able to bridge the gaps on our behalf for Google owned channels (e.g. you saw an ad on YouTube, owned by Google and later purchased on desktop where you are logged in on Google Chrome). 

 

 

Without consistent and universal tracking, examples like the above leave it up to the analytics platform’s algorithmic approaches to link everything it can while leaving all that it cannot link in its black box. 

 

The gaps are widening in our attribution data and we lack the visibility to know exactly what. That is, unless you are building your own solutions (using tools like Snowplow and your own MTA solutions), there is a more compelling reason to, if only for the transparency and its ability to better inform the interpretation.