Cloud Quest #48: The Analytics of Film Production

[AYXAcademy]

 

Hi Community,

We posted the solution JSON file to Cloud Quest #47. Check it out and let us know what you think! Send suggestions to academy@alteryx.com or leave a comment below!

Let’s dive into this week's quest!

1. Download the provided JSON file containing your starting data and workflow files.
2. Upload Start Cloud Quest 48.json file to your Alteryx One library.
3. All necessary datasets are contained within Text Input tools in the workflow.

For more detailed instructions on how to import and export Designer Cloud workflow files, check out the pinned article Cloud Quest Submission Process.

 

Scenario:

Your film studio is starting production on a new project. As the film’s producer, you need to recommend the top five actors who offer the strongest mix of popularity, past financial performance and dependable on set behavior. Then recommend the top three shooting locations that will give you the strongest financial performance while keeping location costs low. The dataset contains a list of actors, their popularity and diva scores, and information about the performance of their previous films.

Tasks:

• Rank the top five actors by their average Value Score across all previous films.
• Rank the top three locations by location score: average revenue divided by average cost ratio.

Notes: 

• Popularity Score (1 to 20): higher values indicate stronger marketability.
• Diva Score (1 to 100): lower values indicate better on set behavior.
• Normalized previous film revenue: the actor’s contribution to box office results.
• Value Score: the average of the scaled popularity score, the scaled and inverted diva score, and the normalized revenue.

Hint: Normalization means you place different numeric fields on the same scale. You take each value, subtract the minimum of that field, and divide by the full range. This produces a number between 0 and 1. It lets you compare popularity, revenue and diva score without one field dominating the others because of its original size.

Image: Generated by Google Gemini, Dec 03, 2025, https://gemini.google.com.

 

Earn Cloud Quest badges:

After completing your quest, head back to your Analytics Cloud library:

• Download your workflow solution file.
• In your reply, attach both your JSON solution file and a screenshot of your workflow.
• Keep submitting—every solution gets you closer to earning more Cloud Quest badges!

 

Here’s to a successful quest!

- The Academy Team

Download Start File | Download Solution File

[RWvanLeeuwen]

Hi @AYXAcademy ,

The workflow you shared seems to be the incorrect file.

Spoiler

I just opened the file

[bradshaw]

Had to remove the provided solution, and then managed to solve it

Spoiler

[Qiu]

So it is not just me that was suprised the provided start file has already a solution flow there. 😁

[Qiu]

I had to looked up what is formula of Scaled or nomalized.
Since my answer is different with the provided answer so checked the solution flow and really doubled the way of "normalization" for revenue is corrent or not.

In my opinion, we should take one Max Revenue from all data then dived revenue of each actor by max revenue.

Take difference of min and max for each actor is not fair I believe.

Not like this
if [Revenue Max]-[Revenue Min]=0 
then 0
else ([Revenue ($M)]-[Revenue Min])/([Revenue Max]-[Revenue Min])
ENDIF

should be
([Revenue ($M)])/([Revenue Max])

Spoiler

[RWvanLeeuwen]

I tried the challenge again, and even though the file is still the same, I just deleted the Tools from the canvas and built out my own approach

Spoiler

As Adam Savage would say "I reject your reality and substitute my own"

[RolandSchubert]

Spoiler

[alexnajm]

A bit sad because the answer was already provided, but wouldn't have done much different - I merged a couple tools together and had a slightly different order of tools

[AYXAcademy]

Apologies everyone for the mislabeled start file! It has been updated with the solution removed. 

[AkimasaKajitani]

My solution!

Spoiler