Summary
Breaks down the calculation of the Accoil Analytics score based on event occurrence and weights, for understanding engagement scoring.
How this helps
Provides insights into the engagement scoring process, allowing for refined event weighting and more accurate scoring.
What goes into the Score
Your Accoil Analytics score is based on two things:
Events - the actions users take in your product.
Event weights - The importance assigned to each event.
That’s the foundation. Everything else builds from here.
How the Score is Calculated
Imagine you're tracking engagement in a CRM app. You might define your event weights like this:
Event | Weight |
Create New Lead | 9 |
Schedule Meeting | 7 |
Log Call | 5 |
Send Email | 3 |
Update Contact Info | 1 |
Now let's say a user performed these events over a specified period:
Event | Count | Weight | Score |
Create New Lead | 3 | 9 | 27 |
Schedule Meeting | 5 | 7 | 35 |
Log Call | 10 | 5 | 50 |
Send Email | 20 | 3 | 60 |
Update Contact Info | 15 | 1 | 15 |
Total Raw Score |
|
| 187 |
This gives us a Raw Score of 187. But raw scores alone don’t tell the full story — they need to be scaled to mean something across the board.
In order to give you a more “usable” and easily digested, we normalize everyone’s scores to a number between 1-100.
Normalization of Scores
To make engagement scores more meaningful, we scale them to a range of 1 to 100 using an exponential formula. This takes the full range of activity into account — especially at the higher end.
Here's how it works:
Calculate all raw scores based on the score configuration
Find the 90th percentile (this becomes the benchmark)
Apply an exponential transformation that normalizes scores relative to that point
This ensures:
The highest engagement scores represent true power users.
Scores remain dynamic as user activity trends shift.
A fair benchmark for comparing engagement across different accounts.
Example: Normalization in action
Let’s say these are raw scores across a group of users:
[475, 89, 101, 7, 3, 21, 2, 149, 223, 1, 13, 9, 37]
The 90th percentile here is 208. Based on that, here’s what the normalized scores look like:
Raw Score | Normalized Score |
475 | 90 |
223 | 66 |
149 | 51 |
101 | 38 |
89 | 35 |
37 | 16 |
21 | 10 |
13 | 6 |
9 | 4 |
7 | 3 |
3 | 1 |
2 | 1 |
1 | 0 |
Key features of this normalization:
Unlike linear scaling, it provides better differentiation between lower scores
Higher raw scores show continued improvement but with diminishing returns
The transformation naturally handles outliers without artificial caps
Scores remain proportional to actual engagement levels
Account Scoring
We use the same process to score accounts — just at a broader scale.
Add up activity across all users in an account
Normalize that score using the same 90th percentile method
The outcome?
Accounts with more engaged users will generally have higher scores.
Accounts with fewer active users will score lower.
Scores evolve as activity levels shift over time
This approach ensures that Accoil Analytics provides a comprehensive and fair assessment of user and account engagement, enabling you to make informed decisions based on accurate data.
Understanding Relative Scores
It's important to note that scores are relative to the overall engagement across all accounts. This means that maintaining the same level of raw activity doesn't guarantee the same score over time. Here's an example:
Day 1:
Account A Raw Score: 100
90th percentile threshold across all accounts: 200
Account A Normalized Score: 39.3
Day 30:
Account A Raw Score: 100 (unchanged)
90th percentile threshold across all accounts: 400 (increased due to higher overall engagement)
Account A Normalized Score: 22.1
This decrease in score doesn't mean Account A is doing worse – they're maintaining the same level of activity. Instead, it indicates that other accounts have increased their engagement levels, raising the overall benchmark.
This relative scoring approach:
Reflects real-world engagement patterns where "good" engagement levels evolve over time
Encourages continuous improvement rather than maintaining static activity levels
Provides context for how an account's engagement compares to the current user base
Helps identify accounts that may need attention even if their raw activity hasn't decreased
When you combine raw activity with score movement over time, you get a much clearer picture of how your users or accounts are really doing.