Before marketers can draw actionable insights about their channels, executives can assess progress against key business objectives, or analysts can report on the health of the business, two things need to happen:
That’s where a measurement plan comes into play: it’s an underutilized map that lays out crucial data needed to measure performance across key objectives and initiatives (and just about anything really).
It can be used to create alignment around which metrics define success, help prioritize them at virtually any level, and shape a foundational data set that supports all stakeholder needs. But it can also provide helpful documentation for analysts, data engineers, and developers who build and configure reporting on the backend.
In this article, we’ll break down:
Plus, we’ll send you off with a template to help you get started on the right foot.
A digital marketing measurement plan can take many forms, but at heart, it’s documentation of the foundational data inputs that will support insightful and accurate reporting.
This type of high-level measurement strategy is defined by business goals and objectives. It’s built with the fundamental components needed to gauge the success of marketing efforts in line with those objectives. This includes both the first-party data measurements that are necessary and the technical considerations for making them available.
A measurement plan DOES define what is needed to measure the results of a marketing plan.
It DOES NOT define how to measure the success of a marketing plan using those results (i.e. it doesn’t map out actual numerical goals or benchmarks that define success).
It’s an exercise in strategically stocking the restaurant pantry, so to speak. It doesn’t tell the chefs what to cook or how to cook it. But it does use their input to plan the ingredients that should be on hand - and how to get them - to ensure they can make dishes on the menu.
A measurement plan DOES define what is needed to measure the results of a marketing plan.
It DOES NOT define how to measure the success of a marketing plan using those results.
A plan to measure marketing performance can support the successful implementation of data pipelines, create alignment around reporting needs, and foster a more collaborative understanding of how different elements of the marketing plan connect to support broader objectives.
One of the most important pieces of implementing the right reporting is the ask. A measurement plan maps the ask out clearly for data and analytics stakeholders, so they know exactly what’s needed to support reporting for the team. They’re data experts, so consulting them in the creation of this type of digital marketing measurement model will help bridge ideation to implementation.
Since an effective and comprehensive measurement plan requires input from cross-functional stakeholders, it’s a great opportunity to get on the same page from the get-go. This ensures everyone is speaking the same language and working toward optimization with the same understanding of what really matters.
It can also serve as a useful tool to create cross-functional alignment around shared objectives and initiatives for synergistic decision-making. For example, marketing campaigns that span channels will look very different in each of those channels depending on whether the primary KPI measures traffic, clickthrough, or conversion.
Of course, different stakeholders will be more focused on different key performance indicators (KPIs) based on how they support the larger marketing funnel. Together, you can map out what’s needed to gauge the impact of each stakeholder’s focus area, while creating a shared understanding of how they ladder up to support a business, initiative, or campaign measurement framework in aggregate.
To create a measurement plan, it’s crucial to think through each component diligently and keep the primary objectives top of mind. Generally, the marketing measurement framework for a plan includes:
Building for first-party data is not only a way to gain deep user insights but also to ensure you can continue to do so as external data becomes increasingly limited.
You have to build plumbing before water can flow. In the same way, the flow of accurate and useful data depends on building the right pipeline. It requires teams to map key data sources, where those sources connect, and where they flow out and collect. Much like that might look different between two houses, it will look different from business to business.
The dev team is often your best partner in determining and confirming this info. Primarily, this piece of the measurement plan is focused on three questions:
Marketing evolves with user expectations, and 71% of marketers are failing to meet those expectations — including expectations around privacy. As the topic grows in importance for users, the landscape will continue to evolve and further limit data that is available via third parties. (Cambridge Analytica, anyone?)
Building for first-party data is not only a way to gain deep user insights but also to ensure you can continue to do so as external data becomes increasingly limited.
Include in your plan:
The correct implementation of tracking pixels, connection of data engines like Looker, and setup of an analytics platform can vary depending on the front end. Additionally, when multiple platforms are used to serve content, it can require work on each platform to get data in the same pool.
To identify platforms, you can use the Chrome extension Wappalyzer to see a site’s tech stack. However, the data is specific to the page in the browser, so if different pages are built by different platforms (it happens more than you think!), this isn’t the best solution. For example, a lot of sites may use a tool like Unbounce to create landing pages or a CMS like WordPress to host a blog, while the core product offering is served via a different platform.
Instead, use this as an opportunity to make friends with the dev team, pose the question, and explain the why behind it. Include the following in your measurement plan:
A multi-pronged marketing strategy can include any number of traffic sources, and the traffic from each of these sources behaves differently. To understand how each segment behaves and adjust efforts in channels accordingly, a marketing measurement strategy should account for the many paths that bring users into (or back into) the customer funnel.
Include any of the following common attribution sources in your measurement plan:
You can take things further by defining key referrers within each of the sources. For example, not every social media platform is a focus area for every business. An eCommerce company may focus heavily on Instagram and TikTok, while a B2B SaaS company is probably more focused on LinkedIn and Twitter.
Within the marketing funnel, different user behaviors are aligned with different stages of the funnel. Tracking these creates a trail of breadcrumbs that’s the foundation of understanding where users are in the customer journey.
Generally speaking, different types of events, actions, or conversions will align more closely with different sources of traffic, landing pages, user segments, etc. For instance, sessions generated from organic social may not drive purchases at a high rate, but those users could be more likely than others to download a resource in exchange for their email address.
Within the marketing funnel, different user behaviors are aligned with different stages of the funnel. Tracking these creates a trail of breadcrumbs that’s the foundation of understanding where users are in the customer journey.
This type of information would help better understand where organic social users are in the customer journey, as well as how to optimize the channel and content to path users down-funnel. It’s the secret sauce that helps marketers create conversion-optimized funnels that align digital experiences with user expectations at every stage.
Collaboration across the marketing team can help outline these needs, and UX/product stakeholders can also bring an interesting and important perspective. (After all, they’re invested in the funnel too!) Primarily, this section of the measurement plan should answer the following questions:
Whether a site is an eCommerce company trying to drive purchases or a SaaS website looking to win qualified sales leads, there’s a primary action the business wants the user to take — aka a conversion. This should represent the lowest point in the site’s digital marketing funnel.
A conversion in a marketing plan can take the form of events like the following:
In an ideal world, we would love it if every user who came to the site converted, but no one has a 100% conversion rate. A user’s journey through the marketing funnel will often have many touchpoints, including many visits to the site.
Along the way, there may be smaller actions that they take - or micro-conversions - which can help signal that they are engaged and show where they are in the funnel. For example, if new users visit the site and sign up for email, it’s a strong signal they’ve made the leap from awareness to interest. In turn, they’re more likely to take the next step on the road to becoming new customers.
Some examples of micro-conversions in a measurement plan might include:
A bounce isn’t the most telling measure of whether a user is engaged. So while GA4 does have an engagement rate measurement, defining custom measures of engagement can provide more insight. In part because the rise in the use of cookie banners has made the generic engagement rate less telling.
Maybe users are more likely to convert when they interact with embedded media like video. If video views aren’t tracked, that’s an insight that might never come to the forefront.
While these types of custom engagements can run the gamut, here are some common inclusions in a measurement plan:
As we’ve talked about, different types of users are more likely to take different actions. Those actions represent common threads that group users in a measurement plan and point to the probability of a conversion outcome.
Through this type of segmentation, marketers can better align their efforts with specific audiences to produce more effective experiences.
Analytics platforms like GA4 generally have default groupings available for foundational segments such as traffic-acquisition sources. However, creating audiences allows for much richer segmentation that groups users based on any number of conditions working together.
A measurement plan might propose creating audiences such as:
Through this type of segmentation, marketers can better align their efforts with specific audiences to produce more effective experiences.
The final element in a digital marketing measurement plan outlines the considerations for implementing data needs at a technical level. It includes everything from the analytics solution to the specific event parameters that will allow event tracking within it.
This is an area that the Data/Analytics team (if there is one!) should own, sourcing any requirements from Marketing. Implementation planning should consider:
An analytics solution like GA4, Heap, or Matomo is necessary to manipulate and manage data. The primary users are members of the analytics and marketing team who conduct investigations and create reporting.
In addition, there are several complementary tools that a brand might adopt to add another layer of data and user understanding. Think: tools that provide functionality such as A/B testing or heat mapping to support site optimization (e.g. Crazy Egg).
It’s worth noting that many of these tools depend on the API of the primary analytics platform to populate reports and metrics. Here’s documentation on how to add a GA4 property if you already use Universal Analytics.
Not every member of the team is equipped to run their own reports in an analytics tool. That’s why a dashboarding tool like Looker Studio is an important part of implementation planning. Here analysts and channel owners can create shared, self-updating dashboards serving the most relevant reports from the analytics solution.
The codes and related fragments that enable measurement are known as tags. A tag manager is optional, but it is highly recommended for the safe and simplified deployment of analytics configurations on a site.
The tag manager allows analysts and data-savvy marketers to create tags and establish the trigger events that make them fire. The collection of tags, triggers, and variables usually makes up a container, which you may have to work with a dev team to implement. The most commonly used TMS is Google Tag Manager.
For the custom events mapped out in the Definitions and Events section of the measurement plan, what are the parameters that will enable them to show up in the analytics solution?
Analytics platforms may or may not have default events available. For instance, Heap and Matomo require all events to be defined while GA4 has several default events available out of the box. Either way, some of the events that are uniquely important to a business will require the implementation of custom parameters.
These will be set up in the tag manager and some may require the configuration of custom dimensions and metrics to enable the correct data output. Based on the platform and API a site is using, creating and tracking custom events will look a little different. We’ve included relevant documentation for popular platforms below:
Now that you know what goes into a measurement plan, why it’s included, and who you’ll need to work with, it’s time to start tailoring a plan for your business.
We’ve created a measurement plan template to show you what one looks like out in the wild, so you have a jumping-off point for your own. And as always, we’re here to help connect any dots.
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