Would not make a difference, you need to look at the excel file for better understanding anyways

these ‘sell me this pen’ ass non-data set questions

god hiring managers are scum

then why screenshot it at all by that logic

Pls update me if you post this on reddit so I can peep the responses

Which subreddit?

reddit.com/r/marketing

I peep this one usually. Its the biggest one I think

But this is more of a data a***ytical task not sure if they're going to be able to answer it

Yeah we need those column names, OP. That's the only way we're gonna be able to identify the metrics

yeah hard to work with just the screen shots. these type of questions definitely require the raw data and being able to physically look at / manipulate it.

definitely not a usual test for an entry level job, at least imo. seems a bit more mid/senior

I've had few other Excel assessments for another a***ytical jobs and it wasn't this complicated and was able to complete it without any help but this got me confused

I even talked to someone that I know who's senior a***yst and he had trouble with this lol

U got a bigger chance there than ktt2 lmao

Updated

For the first question, the client should optimize toward "Purchases" (Column O) but that feels too obvious

@op can you share the file here? just based on the screenshot it looks like an impression file dataset. i used to work with these daily when i worked in ad effectiveness

also, the fact that they’re making you jump through so many hoops for this associate role is insane.

feels like recruiters and hiring managers are just doing the most these days.

possibly, unless the amount of purchases just means customers purchased more in quantity but less in dollar amount, but customers who watched the x second ad may have purchased less, but spent more

the first question is almost certainly “conversions” btw. unless there’s something i’m missing — some of the column names aren’t fully visible which is why the actual file would be useful

feel free to edit out any incriminating info before uploading OP

How do I share the files here?

upload it to mediafire
like i said in another post, make sure you edit out incriminating info because there are some weirdos on this forum

i’m gonna clean my bathroom and shoot up some H. will be back here in like 45 minutes and hopefully i’ll be able to answer a few of the questions

Done

@op

1. Purchases - View Based Post Exposure to the Ad

One of the key assumptions I made is that since there is no column for "revenue", all purchases are of the same dollar value. So the goal is to maximize the number of purchases which will lead to the highest revenue possible.

2. You will want to make a pivot table with the dataset, the key columns being E and O. Your final table should look like this, but with the real numbers:

From there you'll be able to determine if there's anything notable from the results.

3. You will want to make separate pivot tables for each combination and performance metrics (impressions, clicks, purchases).

These combinations will come from the ad name (Column C). Pay super close attention because the descriptors can trip you up. Check out this example below:

I filtered the ad name column (C) by the word "Merch" as I found that it directly correlates with "Ubiquity" as the value proposition. You will want to find similar correlations between terms. Here are some that I quickly found:

SSI = Speed
LFF = Simplicity
Merch = Ubiquity
Uni = All 3: Speed, Simplicity, Ubiquity

If you want to be extra, you can also factor in the amount spent (Column L) to determine what were the best/worst performing combinations based on investment. Will show you went the extra mile.

4. The three variables are "speed, simplicity, ubiquity". You'll want to find which variables are best across performance metrics (impressions, clicks, purchases).

5. I would use a correlation matrix. Assuming you can find one online.

6. Use pivot tables to figure out which test cell (from column D) has the worst results across the performance metrics.

Figure out the worst two, then do the rest.

7. Use pivot tables to figure out how well the A55-64 group (from column I) is performing across those metrics.

If metrics are good = sure, allocate more budget towards A55-64
If metrics are bad = the goal is maximizing revenue, it would be an inefficient use of budget

8. Leverage the previous pivot tables you've made for the earlier questions to figure out which audiences and creatives are most efficient

9. Dependent on #8

basically i've done all the thinking for you. all you gotta do is the work -- if you can't do that...

op crowdsourcing his job application homework to ktt2 im crine

that company is f***ed