The ICE score of growth hacking or CRO prioritisation has its limitations.
While I used the ICE score for my growth campaigns for over 3 years, I later replaced it with the SCIPE score.
In this post, we will discuss both the ICE and SCIPE score, how to use them, and their differences.
The ICE score
The ICE stands for Impact (I), Confidence (C), and Ease (E). And these are 3 values you can assign to your growth hacking experiments. Based on a multiplication of these 3, you can decide the experiments you want to run first.
- Impact – Will the experiment have a positive impact on the metric?
- Confidence – How confident are you that this will work?
- Ease – How easy is it to implement this?
Here’s an example:
As you can see in the above ICE score calculation, Idea #2 becomes as winner as the I,C,E scores are highest and gets the highest priority.
Now, let’s look at the SCIPE score.
The SCIPE Score of Growth Hacking
The SPICE score stands for Scalability (S), Past data (P), Impact (I), Confidence (C), and Ease (E).
I coined the SPICE score in 2022 to improve the growth experiments for my clients, and it has helped our agency prioritise better.
- Scalability – How scalable is this experiment?
- Past Data – Do you have any past data to show that this works?
- Impact – Will the experiment have a positive impact on the metric?
- Confidence – How confident are you that this will work?
- Ease – How easy is it to implement this?
Let me explain taking the same example as above, but this time adding the Scalability and Past Data factors in the equation.
Now you can see Idea #3 getting the highest score as it’s the most scalable experiment.
You might eventually run out of people from the existing community (in Idea 1) and past customers (Idea 2) as these are fixed data sets.
However, on LinkedIn you can attract a huge data set.
This is how adding the S and P scores can change the experiment priorities.
Do note that SPICE score is more for advanced or professional growth hackers who have experience as past data will be available when you have run multiple growth experiments in the past.
And it’s important to leverage the past data for your future experiments. That’s what the SPICE score does extremely well!
