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Measuring product engagement is, in some ways, a nuanced game. Especially if you want to do it right — if you want to do it at a level that makes it as helpful and actionable as possible.
One of the nuances of the engagement scoring game is the fact that different groups of users (or accounts) engage with your product in different ways. In fact, in many cases, this is by design! Different groups of users are supposed to engage with your product in different ways.
Most likely NOT a reflection of your user base.
For example:
- New users do things during their onboarding/activation phase that are different than what “mature” users do during ongoing engagement;
- Trial users may use a product differently than paid users;
- Different user types (ie — teachers vs students; buyers vs sellers; players vs coaches; etc) may use completely different feature sets;
- Users with different access-levels (ie — Admin, Manager, Team Member, View-only) have very different usage profiles;
And many other examples. I’m sure you can think of several unique usage models for your product as you read this.
These different usage models are incredibly important in the context of truly understanding engagement with your product. In these cases, “engagement” needs to be defined differently for each unique usage model. Each usage model needs its own unique scoring profile.
Engagement for a new user should be defined differently than engagement for a mature user.
Engagement for teachers who are creating lesson plans in a product should be defined differently than for students who are using the plans.
Engagement for Admin users who manage account configuration tasks should be defined differently than for Team Members who should be using the product to manage daily activities.
And this is why, in Sherlock, you can target different groups with unique, individual engagement scoring profiles.
Creating multiple scoring profiles in Sherlock
In Sherlock, you create an engagement scoring profile by defining your most important product events and giving them a weight based on how important each event is to your product.
Creating an engagement score in Sherlock
But instead of creating only a single scoring profile that applies to all your users, you can create different scoring models for different sets of users.
These users can be defined by user traits (user type = “admin”) or account traits (account status = “trial”) or other parameters (signed up < 30 days ago, etc).
Define the unique group and target a filtered list of users
Then you can define a unique scoring profile for each of these groups. You can select different events that define engagement for that group, or weigh the same events differently, or both.
The point is that this functionality will allow you to truly hone in and understand engagement in a more nuanced way than you have been able to before.
Toggle between your various scoring profiles
Once you have multiple scoring profiles in Sherlock, you can toggle between them and get a complete picture of engagement across different slices of your user base.
You can see:
- the top users in each profile;
- the top accounts for each profile;
- which events are driving engagement in that profile;
- etc
But that’s not all…
Part of our goal at Sherlock has always been to make engagement data “actionable” — make it something you can use to improve your operations. And that extends to these multiple scoring profiles.
When you create multiple scoring models, you can select which profiles you want to send to your various Destinations (Intercom, Salesforce, Slack, etc).
There may be specific scores that you want in specific destinations and not others. For example, you will likely want to push only scores from trial accounts to your CRM, but all your scores to your support tool, etc.
The complete picture
You know as well as anyone that not all users engage — or are even meant to engage — with your product in the same way. And therefore, in order to truly assess your product’s engagement, you need to be able to create engagement scoring models specific to these unique usage patterns.
Only then can you get a complete picture of engagement with your product and drive the right actions to push that engagement forward.
