Reddit – Dive into anything

Here’s the hypothetical from the interviewer:

FB launched a Zoom-like feature. It was generally well-accepted and its usage is growing.

You work at Instagram. How would you evaluate if IG should add that Zoom-like feature?

(in other words, a synchronous communication app within a heterogeneous network (FB) is being evaluted for launch in an otherwise asynchronous homogenous network (IG).

My response:

Clarifying questions:

– Can some people use the FB Zoom feature with a higher / different access level than others?

– What requirements, minimums, or thresholds must be achieved in order to obtain higher access (such as:

o a Facebook business page,

o a Facebook business page with >1,000 followers

o a Facebook user with >500 edges (relationships since Facebook can process one trillion edge graphs) which the people (nodes) desiring higher access must have people

– Higher access might include: the ability to invite more than 20 people (20 being the number the hypothetical provided); the ability to place other companies’ advertisements in the Zoom invitation, the Zoom meeting, and/or the Zoom follow up notification; the ability to place advertisements of the host’s company in the Zoom invitation, the Zoom meeting, and/or the Zoom follow up notification; a longer Zoom meeting time (>60 minutes for example)

(clarifying questions partially answered but mostly deferred)

I would review the data from the A/B experiment on this feature’s usage and adoption at FB. I imagine this A/B test would have a dependent variable / control group / training set in machine learning (ML) (user behavior before Zoom feature) and an independent variable / test group / test set in ML (user behavior after Zoom feature). user behavior here would include the following variables which would be available in SQL tables for further analysis: frequency (did it increase or decrease after using this feature; in what ways did it increase or decrease (likes, comments, shares, marketplace (buying or selling), direct chat w/ other nodes (users), creation or removal of certain edges (relationships).

I’d also analyze datasets which tracked:

– how many invitees became hosts and set up their own FB Zoom meeting within 1,2,3,4 and +4 weeks;

– How many invitees purchased one of the host’s products that were advertised in the Zoom meeting?

– How many invitees purchased one of the non-host products that were advertised in the Zoom meeting?

The A/B experiment I’d design at IG:

Dependent variable / control group / training set: IG user behavior before Zoom feature

Independent variable / test group / test set: IG user behavior after Zoom feature

user behavior here would include the following variables which would be available in SQL tables for further analysis: frequency (did it increase or decrease after using this feature; in what ways did it increase or decrease (likes, comments, shares, marketplace (buying or selling), direct chat w/ other nodes (users), creation or removal of certain edges (relationships).

Last, I’d run an SQL inner join of the networks of people who used FB Zoom and the networks of those same people on IG. I’d use clustering ML (entropy weight k-means) to then look for trends or patterns which might explain why the Zoom feature was used more (or less) on FB than IG; which gender uses the Zoom feature more; which network (IG or FB) leads to more Zoom invites being sent out; do IG or FB Zoom meetings lead to more purchases? What type of purchases (category)? What price range? How does price, category and frequency of purchase vary based on GIS or location data of Zoom host and location of invitees? Do the data suggest this might be growing into something like the Influencers feature?