Building a solid Analytics Measurement Plan [step-by-step + free template]
- Set your business objectives and KPIs
- Business Measurement Plan
- Check that our data is accurate
- Enrich your data with additional information
In this post I’m going to show you how to design an efficient analytics measurement and technical plan to help you:
- Have CLARITY on what is and what needs to be tracked & configured in your analytics account
- Correctly INTEGRATE additional analytics implementations (without breaking existing analytics setups)
- Save TIME spent in communication when working with new analytics hires.
In short, this plan is designed to be used by anyone who works on your company’s analytics account.
That includes your marketing team, analytics specialists and any outside help you might hire to work on your analytics.
To get more context on how each section of the template should be used, keep reading.
We’ll focus on two parts:
- The Measurement Plan: This is the “What” part, where we decide what needs to be measured.
- The Technical Plan: This is the “How” part, where we describe how we’ll track our relevant metrics.
Let’s dive in:
STEP #1: Set your business objectives & KPI’s
Let’s start by looking at our main business objectives and deriving our most important metrics from that.
Let’s say you’re a software company and your objective is to increase revenue and grow your customer base. From these two we can derive the measurable metrics:
Increase revenue (KPI)
1. Number of purchases (metric)
2. Average order value (metric)
3. Revenue (metric)
Increase customer base (KPI)
1. Number of free trials (metric)
2. Conversion rate from free trial to customer (metric)
By understanding all of these measurement levels, you’ll understand what and how you need to track to get the insights that are valuable for your business.
So outline your main objectives and let’s move on to the next phase:
STEP #2: Business measurement plan
Now I’m going to show you HOW you should be thinking about what metrics to measure, in order to squeeze the most $$$ out of your analytics account.
The example above was basic, just what you’d expect. Tracking sales, revenue, sign-ups, etc.
The insights that lead to real business growth tend to come from a more in-depth level of analysis.
Here’s an EXAMPLE.
This time you’re an e-commerce business that has a lot of traffic, but a very low conversion rate. And increasing your conversion rate is the main priority.
But how do you do that?
We can start by understanding the behavioral patterns of high-converting visitors VS low-converting visitors.
And an important micro behavior to look at if you’re an e-commerce would be visitors’ interaction with website filters:
- Are your top customers using filters more often?
- Which filter type brings in the highest conversion rate? What about average order value?
- Is there a certain preference towards filter types depending on the channels they are coming from or the category they are visiting?
These sort of insights are a gold mine.
But you need to be tracking the right metrics (like filter usage) in order to get the full picture.
There is no exhaustive list of actions you should track as it’s different from business to business.
Think about the actions that users take on your website because by tracking them, you can, later on, do more in-depth analysis that leads to revenue-boosting insights.
STEP #3: Check that our data is accurate
There’s no analytics tool that offers a “one size fits all” solution.
Unfortunately, that’s not going to change anytime soon.
This is why I highly recommend you take data accuracy seriously, so you can be confident that you’re making business decisions based on accurate data and not some skewed numbers.
When working with our analytics clients, we’ll often start by doing a health check of their analytics account(s), because without this crucial step we risk arriving at conclusions based on inaccurate data.
This is where the data accuracy action plan comes into place and it’s an important part of the “Analytics Strategic Plan”.
A few examples of configurations that help improve data accuracy:
- Cross-domain tracking
- Custom channels
- Self-referral exclusion
- UTM tagging
STEP #4: Enrich Your Data With Additional Information
There are a few things CMOs dream about and one of those is capturing data from multiple customer touch points and having it all organized in one place.
And this can be done to a certain extent with Google Analytics.
- Integrating offline data with Google Analytics to understand the full journey of your customer.
- Integrating CRM data with Google Analytics to create Google Ads Re-marketing Audiences.
- Or you can simply integrate Google Ads data with Google Analytics to understand beyond clicks and conversion rate.
There are endless options to enrich your data and easily connect the dots but I recommend you explore the possibilities that make sense for you.
STEP #5: Create A Technical Implementation Plan
Everything we covered so far will help you think more clearly about your analytics, as well as stay organized and on the same page with your team when it comes to what’s being tracked right now.
This final step goes into detail about HOW each metric is going to be tracked.
However, I want to give you a heads up: a technical plan doesn’t contain the coding lines or every step of the implementation process, but it does enclose the main guidelines.
Web analytics implementations deteriorate over time as the site gets updated. So before you make a change, take a look at the implementation plan and see what it might affect.
That being said, let’s explore what should be documented:
- GA Settings Variables
- Custom Metrics & Dimensions
Warning: We’re about to get a bit technical. 🙂
EXAMPLE #1: GA settings variable
Google Tag Manager (GTM) released a new feature last year called “GA Settings Variable” that adds a bit of scalability to building tags.
Which is great.
That’s why the GTM tracking implementation journey should always start with your GA Settings Variable, which will be a part of any tag you create and will set the tone for what Google Analytics will register.
Some examples of general settings that you might need to attach to every hit:
- settings for cross-domain tracking
- IP anonymization
- advertising features enabled etc.
EXAMPLE #2: Events
Top of mind questions when I see events within an analytics account are:
1. What does this event track, on what page and when is it triggered?
2. Is it implemented in GTM or is it hardcoded?
3. Does the event impact metrics like bounce rate or session duration?
But who wants to spend all that time figuring the answers to those questions?
This is why it’s helpful to have an overview of all Analytics events and where/how they’re configured.
Ladies and gentlemen….
I present to you the Analytics Technical Plan (events section):
Whenever an event category is not sufficiently self-explanatory or someone new doesn’t know what an event category stands for, or…
Whenever the website structure changes and your team has to simply adapt the analytics implementations without breaking your entire setup…
It is incredibly helpful to have this technical plan in place that brings light to the chaos.
EXAMPLE #3: Goals
Most of the time, events are interconnected with goals [events-based goals], but one precious remark about the latter is that you are limited in terms of how many goals you can have.
As some of you may know, a Google Analytics account only allows you to set up to 20 goals and once you saved one goal you can’t delete it but only overwrite it.
Even with annotations, this can still be confusing when looking at historical data across years.
That’s the reason why it’s important to use your available goals wisely, according to the plan.
Our recommendation is to divide them into 2 main goal types:
1. Macro Goals
2. Micro Goals
And then assign a few details so it’s clear as crystal what’s in there:
- Goal type: destination, event-based, duration, pages/screens per session
- Additional condition
- Funnel path (if required)
EXAMPLE #4: Custom Metrics & Dimensions
We went over the importance of asking deeper questions when looking at your analytics data.
And custom dimensions & metrics are a big part of that.
You can, for example, use the User ID dimension to collect additional information about your visitors, like their login status (are they logged in or not?).
You can collect more descriptive information as well.
Have your users filled out a form that asks about their profession?
You can collect this information as a user-level custom dimension, and then use it to look at how users’ behaviour patterns based on profession.
This article from Moz highlights some additional examples and goes into how to set up custom dimensions to measure performance.
Often times your analytics account can get out of hand, and you spend hours trying to figure out how things work and why it’s showing the data that it’s showing.
This leads to a lot of confusion, bad decision making and loss of valuable historical data.
By having a measurement plan in place, your team and anyone else with access to your analytics account will have clarity on what’s going on, and things will go much smoother.
To summarize the 5 steps:
1. Set business objectives & KPI’s
2. Analytics measurement plan
3. Check data accuracy
4. Enrich your data with additional information
5. Create a technical implementation plan
Another very important aspect I want to leave you with is understanding that this is not just a one-time thing.
Creating an initial “Analytics Measurement Plan” is as important as making sure that you are updating it every time someone makes a change, otherwise data quality degrades as the business evolves.