Envoy's Analytics Evolution

How I helped Envoy take control of their digital analytics stack

The Challenge

Great teams iterate quickly - sometimes that comes with challenges.

Non-compliant Tools

There's a natural tension between data collection and the need to protect users' privacy - the tricky part is getting that balance right.

Privacy frameworks like CCPA and GDPR have different requirements for how visitors' data should be managed - these rules vary not only by region, but also govern different classifications for how data can be used, including analytics and advertising.

Envoy's marketing team needed the ability to run effective and compliant media.

Ambiguous Metrics

For an organization to succeed, teams need to be pointed in the right direction. If that direction isn't clear, teams naturally interpret their own objectives - that leads to confusion when those definitions don't match.

The lack of a unified measurement plan meant that internal success metrics did not always align to partners' performance reports.

Lack of Trust

Digital analytics is an inexact science - there will always be gaps in data collection, and marketing teams are used to operating with that ambiguity.

But when those tools aren't collecting data in a way that's relevant to the business, the folks making decisions are flying blind.

Envoy's marketing team is elite - the Google Analytics 4 deployment wasn't measuring up.

The Approach

What value are we creating? Who is the audience? How are we going to get there?

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Discover

The first step in any project is understanding the lay of the land. I conducted in-depth stakeholder interviews and system reviews to paint a full picture of our current and future state.

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Design

With a clear measurement plan in place, the changes we needed to make became self evident - it was just a matter of designing the schema and data layer code necessary to gather consent and corresponding measurement signals.

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Deploy

Once we had our goals identified and a plan agreed to, it was time to make it happen. Deployment here was a mix of tag management, data layer code development, and platform configuration.

What I Built

Measurement Plan

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The measurement plan is a living document that captures key areas:

  • What actions do we want our users to take on our site/app?
  • How are we going to translate those actions into measurable metrics?
  • How are we going to categorize and segment our users?

By sitting down with key stakeholders across the marketing organization, we were able to identify the specific interactions that would be most valuable for our business, and map those to measurement requirements.

Just what the heck is a lead anyway? And how do we measure it?

Data Layer

example dataLayer push code

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Event Capture

Once we've identified which events we care about, it's time to actually capture those actions so that we can analyze them.

The best way to get this done is via the data layer - a global object that functions as a sort of go-between, capturing user interaction for platforms to then make use of.

While the data layer is a general concept, in this case we're talking specifically about the Google Tag Manager dataLayer - a kind of data layer API created by Google that interacts natively with various advertising and analytics platforms.

Example

Curious to see how it works? Try out the form below! The output will be available in your browser console, just type 'dataLayer' and expand the list to see it.

Example Event Generator

You should see something like this:

an example dataLayer output
We've got the requirements - now how do we actually capture those interactions as data?

Analytics & Optimization

example analytics report

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Insights & Opportunities

Dashboards can help visualize performance, but the real value lies in identifying areas of friction. Analysing user behavior allows us to identify areas for A/b tests to improve our digital properties.

Audience Activation

By segmenting our users based on key attributes like how they found us (a LinkedIn post, a Google search...) and what content they engaged with, we're able to better understand which areas of our sites and apps are under / overperforming.

Critically: we can use those same user segments as audiences in our digital marketing campaigns. By integrating analytics and advertising platforms, we're able to unlock targeting opportunities for data driven optimization.

an example dataLayer output
We've got the data - how do we actually use it?

Data Privacy

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How are users allowing us to use their data? Are we allowed to capture interactions on our own site? Are we allowed to use that data for advertising purposes? Ultimately, privacy compliance means respecting user choice - and requires a few key elements:

  • A data protection officer (DPO) - the individual (or group) responsible for outlining how a business operates with respect to data responsibility. Working directly with Envoy's DPO allowed for a clear stream of communication to identify requirements.
  • A consent management platform (CMP) that can identify visitors' point of origin and respond with the appropriate privacy options. The platform should gather and store user privacy choices, and interact with relevant advertising and analytics tools to allow or forbid data sharing as appropriate. In our case, the platform was Cookiebot.
  • A marketing team comfortable with operating in ambiguity. Users that opt out of data sharing are a blind spot for the business - the solution lies in the team's ability to see and predict general trends, as well as model performance based on observed results. Naturally Envoy has this one on lock!

Respecting user privacy is a key value and tenet of the business, and this principle guides system implementation and marketing operations throughout the customer journey.

How do we get permission to do all the cool stuff we have in mind?
Questions?

email: max (at) workingwithmax dot com


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