Third-party cookies are drawing to a close in 2023…

But while other enterprise eCommerce marketers scramble for the panic button, a savvy few are quietly crafting customer acquisition strategies that will safeguard their future success in a cookie-less climate.

What can you do today to create a long-term customer acquisition framework that allows you to lead campaigns with creativity and experience, instead of third-party data?

Join us in March to start building your framework for cookie-less customer acquisition.

The Speakers:

Andreas Dzumla

LUX co-founder and CEO

Andreas is an ex-Googler and Dentsu Aegis Network agency GM who’s seen search marketing from all sides.

Rob Wall
LUX Commercial Director 

Rob’s responsible for cultivating and leading client relationships worldwide.

FAQ

Most frequent questions and answers

Firstly, you need to know that first and last-click attribution still works because these conversions are measured on your own website, and web analytics like Google Analytics, Adobe Analytics etc. work with first-party cookies..  

However, view-through attribution does currently rely mainly on third-party cookies.  

So, it will be affected. 

One solution to look at is server-to-server conversion tracking (e.g. Google tag manager).  This allows data to pass to secure servers without relying on the user’s browser. 

 

The unique IDs allow advertisers to bypass the need for cookies by storing a unique ID “server-side” when a user views or clicks on an ad.

 

Several “replacement” systems like Google’s Topics API, The Trade Desk’s Unified ID 2.0 or LiveRamps RampID allow view-through tracking across websites that participate too. Some of these are restricted to users with a relevant account, though.

 

Check out this article for more info: https://www.seerinteractive.com/blog/conversion-tracking-in-a-world-without-cookies/)

Using any of the above methods, you will track performance just as you did before – no need for vanity metrics.

Apart from the solutions mentioned in question one, we suggest re-watching the webinar recording from around 18 mins 40 secs.  

We talk about these four solutions:

  1. First-party data
  2. Contextual data
  3. Replacements
  4. Capture category demand

You can find the recording here: <link>

We also talk a little more about these solutions in question 10 below.

Here’s what Hotjar says on their website: “In order to process data about your visit to a website, Hotjar stores first-party cookies on your browser.” 

So as far as Hotjar users are concerned… no need to stress.

As we don’t know the ins and outs of every other behaviour tracking platform, we suggest reading through the onsite information for whichever one you use or are considering.

If the information isn’t clear, email them.

Firstly, conversions will always be observable since they happen on your website. i.e. purchase, lead conversion etc., tracked with first-party cookies.

However, what will change, is the availability of data for the conversion path outside of your website.  i.e. view-through tracking. 

But, as we already mentioned, server-to-server conversion tracking will still allow view-through tracking.

Finally, modelling is much more important – even now. 

Machine learning already plays a significant role when weighing the influence of different channels and steps in the path (click & impression).

We can only expect modelled conversions to become more intuitive and play an even more prominent role in the future of online marketing.

Media Mix Modelling (MMM) has its pros and cons.

Advantage: it gives you an objective answer for which channels to invest in how much.

Disadvantages:

  • It can be slow, as there are too many variables to consider, and it takes a lot of time to reach statistically significant results. E.g. If you only have a few months of data, you’ve got nothing to work with – you’re likely to require a minimum of two years’ worth before it’s useful.
  • As data collection takes so long, findings are often irrelevant by the time you implement anything.

And once again, check out the webinar recording from 18 mins 40 secs for some great solutions. 

Media Mix Modelling (MMM) has its pros and cons.

Advantage: it gives you an objective answer for which channels to invest in how much.

Disadvantages:

  • It can be slow, as there are too many variables to consider, and it takes a lot of time to reach statistically significant results. E.g. If you only have a few months of data, you’ve got nothing to work with – you’re likely to require a minimum of two years’ worth before it’s useful.
  • As data collection takes so long, findings are often irrelevant by the time you implement anything.

And once again, check out the webinar recording from 18 mins 40 secs for some great solutions. 

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