How to build out a Google Analytics data model to achieve clarity on company acquisition ROI
Attribution is a murky issue for many SaaS companies – how to track one user’s interactions with your service through multiple browsers, devices, apps, channels and other touch points. For B2B SaaS companies there is the added complication that multiple individuals within one company may be involved in the decision making process.
However, as a B2B service it is precisely this company view that you need to get a true picture of your ROI. How much cost have you incurred for the conversion event/revenue received from that one company/purchasing unit?
Luckily Google Analytics can help. Using the features User ID and Custom Dimensions, you can create unique IDs that consolidate all the information you have about an individual and company, and track that back to their interactions with your campaigns. You can then associate the costs of those campaigns with your conversion from that company.
Standard tracking of B2B marketing gives a confusing picture
If a customer journey spans different devices, a standard google analytics setup will track them as different users, even where the user is signed into the same account: “For example, a search on a phone one day, purchase on a laptop three days later, and request for customer service on a tablet a month after that are counted as three unique users in a standard Analytics implementation, even if all those actions took place while a user was signed in to an account.”- Analytics Help
Such over-counting gives a distorted picture of your interactions with individual users. Multiply that by the many individuals that may be involved in one company’s journey to purchase and you have a very unreliable picture of your real ROI for a converted company.
The Solution: Track individuals not clicks
As ever, your ability to get a complete picture of where your business is coming from is only as reliable as the tracking you have set up and the information available to you. However, Google Analytics does allow you to create IDs that consolidate multiple touches from different sources where you can assign values that identify it as the same person. If set up well, the resulting view gives a much more comprehensive idea of what you have spent and received from one user and from one company.
Step 1. Implement User ID in Google Analytics
The User ID feature in GA allows you to associate multiple devices with one user and therefore get a more accurate user count in your reporting. It is the first step on the journey to rationalising your session data into individual users and companies.
To implement the User ID feature you must be able to generate your own unique IDs through your own sign in/authentication system. Then you need to include these IDs whenever you send data to Analytics. Analytics can track the activities of these IDs through different sessions and touchpoints and through data in some other tools in the Google suite (integrations possible with Google Ads and Search Ads 360).
GA can even track back the activities of User IDs to periods before implementation using a feature called, session unification.
Step 2. Assigning User IDs to Companies
Once you have User IDs successfully implemented the next part is relatively simple. Using the Custom Dimensions feature you can assign individual User IDs to companies and get a company level view of the activity you are tracking in GA.
So add your newly created company Custom Dimension to your report in GA and track all the sessions that came from that company in one line.
Using Custom Dimensions to get more information
Once you have a common User ID across your systems and Google Analytics, you can take the process further and pull in additional information about users into GA. For example, pulling in information from your CRM system about a user’s age and gender or tracking a sign up event on your system in GA.
This is all possible with the Custom Dimensions feature that allows you to specify your own dimensions. There are up to 20 indices available for Custom Dimensions with a free account and up to 200 in 360.
Step 3. Associating cost data with companies
The next step is to bring in cost data to get to what you are really after: a company level view of your ROI. This part is complicated by the fact that you cannot attribute costs to specific sessions (and therefore directly to User IDs or companies) in GA.
What we can track, once we have User IDs set up, is the interactions of users from a company with our campaigns. Then, if we look at the day/days prior to a company sign up event, we can assign the average cost per sign up of the campaigns the company interacted with during that period. So….
You know the day of a user sign up. In our example each row represents the signup of a different user (User ID 1,2,3) from the same company (Company ID CA-553 387). We know exactly from which campaigns/channels these sign ups came in. (thanks to UTM tracking parameters)
We also know the exact cost of those campaigns/channels for each day they are running and we know the number of sign ups. So we can calculate cost per sign up for each day for each campaign/channel.
For example: on 11.4. we spent 250€ on Facebook and generated 20 sign ups with the average CPS 12,5€ (Cost per sign up). Only 1 sign up is connected to mentioned company ID. Other 19 sign ups are from different users and companies.
You can add together the cost per sign up from each of the campaigns that users from that company interacted with over the monitoring period (as you have defined it) and deliver a total cost per sign up for that company.
Step 4: Enjoy a company level view on your ROI and optimise!
With all these GA features set up you can start enjoying the view you really need to assess the success of your B2B marketing activities: company level ROI that compares the cost of all the touchpoints you can effectively monitor back to the final conversion event or spend by that company. From here the potential for optimisation opens right out. Which channels are reaching your best performing companies? Which users are making these decisions happen? Where can you cut back and still get results?
Your view is broadened from just looking at the channels and campaigns you are running, towards a rounded analysis on how you are interacting with your target companies.
