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.
Your Accoil Analytics score is based on two things:
Events;
Event weights (the weights you assign them)
Events represent the main actions users can take in your product.
How the Score is Calculated
Let's consider a hypothetical CRM application with the following events and weights:
Event | Weight |
Create New Lead | 9 |
Schedule Meeting | 7 |
Log Call | 5 |
Send Email | 3 |
Update Contact Info | 1 |
Suppose 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 |
The total of these event scores is the Raw Score, which in this example is 187.
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 normalize the scores between 1-100, we apply Winsorizing at the 90th percentile threshold. This process involves the following steps:
Calculate all raw scores based on the score configuration.
Identify the 90th percentile raw score value.
Set that 90th percentile score at a normalized value of 100.
Scale all other raw scores against that value.
Example Normalization
Consider a set of raw scores:
[475, 89, 101, 7, 3, 21, 2, 149, 223, 1, 13, 9, 37]
The 90th percentile for this set of scores is 223. Any score above 223 is given a value of 100, and all other scores are normalized using the formula (raw score/223)×100.
Raw Score | Normalized Score |
475 | 100 |
223 | 100 |
149 | 66.82 |
101 | 45.29 |
89 | 39.91 |
37 | 16.59 |
21 | 9.42 |
13 | 5.83 |
9 | 4.04 |
7 | 3.14 |
3 | 1.35 |
2 | 0.90 |
1 | 0.45 |
Finally, an exponential function is applied to these normalized scores to better represent differences in the raw scores, resulting in a final engagement score on a scale from 1 to 100.
Account Scoring
When scoring accounts, Accoil Analytics aggregates all activities within an account, regardless of the number of users, and normalizes this against the activity of all other accounts.
Therefore, an account with 20 engaged users will typically score higher than an account with 5 engaged users, and an account with 5 engaged users will likely score higher than an account with 1 engaged user and 19 less engaged users.
This detailed process 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.