If you are a B2B SaaS business, you’ve probably felt the great frustration of trying to analyze the behavior of your customers, particularly when a customer is represented by a group of users.
We have a solution for your frustration: Account-Based Analytics.
Account-based analytics is the natural evolution of account-based marketing, which, if you are a B2B company, your sales and marketing teams most likely already use. They already have the tools, from CRMs to Marketing Automation services, that are designed to group multiple users as being a single account who will eventually become a customer.
But growth is inspired by so much more than just marketing and sales; it’s also found in customer behavior within a product, especially in companies that focus on product-led growth.
That’s why you need account-based analytics; to have an even deeper understanding of your customer behavior.
Account-Based Analytics allows you to track and analyze the data from all of the users that are a part of the same organization as the data of one account.
The best part of account-based analytics is that it behaves exactly like user-based analytics when a single user makes up an account (eg. B2C companies), but behaves very differently and in a more accurate manner when multiple users make up an account (eg.most B2B companies).
That’s why we decided to have InnerTrends utilize account-based analytics by default.
Let’s look at analyzing some of the most basic metrics from the perspective of user-based analytics versus account-based analytics.
Active Users vs Active Accounts
Let’s assume you have 2 customers (accounts), and each customer has 5 employees (users) that regularly use your product.
- User-based analytics will report 10 active users
- Account-based analytics will report 2 active accounts
Now, if 3 of those employees go on a holiday and their part of the work is taken over by their colleagues, you will have the following numbers:
- User-based analytics: 7 active users
- Account-based analytics: 2 active accounts
According to user-based analytics, the number of active accounts has dropped, and you may feel inclined to do something about it.
In terms of the usage of your platform, you still have 2 active accounts, and that’s what matters the most. The lack of activity as a result of employees on leave will be reflected in the activity analyses. If their tasks are not completed by their colleagues, then the engagement of this account will decrease.
Engaged Users vs Engaged Accounts
When we analyze engagement, we look at the actions that are completed inside of your product and are linked to value that is delivered to the customer.
With user-based analytics, every time a user completes such an action, they receive engagement points, and a score is calculated.
With account-based analytics, that score is calculated from the cumulated actions of all of the users that are a part of that account. That means that accounts with more engaged users will have a higher score.
Lack of engagement correlates highly with churn, but you should be interested in the accounts that are likely to churn, not individual users who might simply leave companies, go on holidays, or be replaced by others.
User-based analytics glorifies the engagement of lone users, while account-based analytics gives credit where collaboration happens.
In B2B SaaS businesses, it is very common to have one user sign up and start the onboarding process, and a different user finish the onboarding process. (When we talk about the onboarding process, we are referring to the process the account follows from the moment it is created to the moment of delivering the promise you’ve made to that account for the first time).
Particularly in B2B products where collaboration happens, the promise is delivered when two users collaborate for the first time.
User-based analytics isn’t able to capture this, as data is attributed to each individual user.
Account-based analytics, by design, captures when two users collaborate on the same account and, therefore, when the value is delivered.
In fact, for collaboration-based products, analyzing data at an account level is the only way to get an accurate image of how the product is used and how the value is delivered.
When a new company joins with 10 different users and they decide to upgrade, you won’t want to report a conversion rate of 10% (1 in 10 users paid), as user-based analytics would report by default.
You will want to report a conversion rate of 100% (1 out of 1 accounts paid), as account-based analytics would report by default.
While it’s true that you can have workarounds for this in user-based analytics (eg. saying that all 10 users paid and reporting a 100% conversion rate), workarounds are prone to errors and generating other misunderstandings in the data (you don’t have 10 customers, you only have 1).
Employees leave companies, and others take their role. When analyzing individual users (as you would do in user-based analytics), you will report the users that left as churned, and report the users that took over for them as new sign-ups.
The only thing that matters, however, is if the account churned, or if it is still using your product, which, no matter how many employee movements will happen within an account, account-based analytics will offer an accurate image of your account retention metrics.
Achieve Greater Accuracy With Account-Based Analytics
The most common theme amongst all of the metrics that we played out above is that when it comes down to user-based analytics versus account-based analytics, account-based analytics will give you stronger, more accurate data that is reflective of your customers’ behavior, and will enable you to improve your product with greater understanding of and attention to detail.