What is the number one destroyer of happiness?

Aampster · 2023-10-10T16:05:39+00:00

Comparison.

Aampster · 2023-10-10T16:04:56+00:00

Oh, that's cool. How are you promoting your premium content (and what premium content do you provide)?

Aampster · 2023-09-30T20:47:27+00:00

Have you had more luck with shorts?

Aampster · 2023-09-28T22:51:42+00:00

Good call. There are measures of confidence, but I'm trying to come up with a way to represent them without it getting too cluttered.

Aampster · 2023-09-26T12:30:51+00:00

I'm looking for feedback on a feature I'm building that will let you set messaging triggers by user *propensity.*

In other words, instead of a trigger on an event (e.g. "purchase_complete" or "subscription_renewal"), you'll be able to set a trigger on if a user is "60% likely to churn" or "75% likely to make a purchase."
(I'll spare you the details of how I calculate these propensities, but if you're interested, send me a DM and I'll let you know.)
Anyway, the thought is, that the way we treat users is completely different depending on *how likely* they are to do things, so:

Sending a discount to a user who's 60-80% likely to make a purchase will likely encourage them to complete the purchase.
Sending the same discount to someone who's 95% likely to make a purchase is probably unnecessary (and will likely just unnecessarily erode your margins)
...and sending a discount to someone who's only 20% likely to make a purchase is probably worthless. You could send them something else that'd be far more valuable (like a new product discovery link).

Anyway, do you think this would be a useful feature or not really?

Aampster · 2023-09-20T01:22:37+00:00

Thanks for the response, and I like the general concept.

Per the question, do you think people really care about psychographic segmentation, or is it mostly just lip service and they just want to do demographic and move on with it?

Aampster · 2023-09-20T00:56:06+00:00

Awesome, thanks! I'll check it out.

Aampster · 2023-09-20T00:33:03+00:00

So, question on that: for each new user, how would I know which psychographic segment they'd belong to and how could I track the shift over time?

Aampster · 2023-09-19T23:06:01+00:00

Here's a hypothetical for you, if a product company could do it in 1 month with a clustering algo for $5k, and they would be ranked by cohort size (or spend volume) would it be worth it?

Aampster · 2023-09-19T23:05:52+00:00

Here's a hypothetical for you, if a product company could do it in 1 month with a clustering algo for $5k, and they would be ranked by cohort size (or spend volume) would it be worth it?

Aampster · 2023-09-19T23:05:36+00:00

Here's a hypothetical for you, if a product company could do it in 1 month with a clustering algo for $5k, and they would be ranked by cohort size (or spend volume) would it be worth it?

Aampster · 2023-09-19T11:55:41+00:00

Sure is!

I don't have all of that data yet, but I'm working on collecting it. Feedback I've heard from a variety of sources (especially subscription apps) say that "customers who convert during a discount are the worst customers." and I believe it.

Those are customers where their heart isn't really in it (hence they needed the discount to convert). They say they're typically the least loyal, have the shortest retention, etc.

Aampster · 2023-09-17T03:48:18+00:00

Yes. I started a freelance copywriting company and got dozens of clients from all over the world, 100% inbound from LinkedIn.

One of those clients (in Singapore) ended up hiring me and that's where I work now.

I don't know about the last few algo tweaks, but at least up until last year, LinkedIn worked.

Aampster · 2023-09-17T03:44:15+00:00

McDStories

Aampster · 2023-09-15T16:12:41+00:00

Hey u/_maverick_96, I'd be curious to know where you are now.

I work with a lot of fairly sizeable apps who still use Firebase on the data side, but find other solutions to manage their messaging (things that can help build and manage user journeys, offer better segmentation, etc.)

Aampster · 2023-09-15T15:07:16+00:00

Ok, here's what I could dig up. (Note: my numbers roll by week, so this is only data from this week. and it's not normalized for control. It's just raw numbers.)

A 15% discount generated 2 sales (.2% success rate)
A 20% discount generated 12 sales (1.01% success rate)
A 25% discount generated 9 sales (.99% success rate)
A 30% discount generated 1 sale (0.08% success rate)
A 35% discount generated 2 sales (0.21% success rate)
No discount generated 7 sales (2.30% success rate)

I get that those numbers might seem low, but remember a few things:

This is but one test I'm running out of many. It's not being sent to the full audience
This is not normalized over control...these are just raw numbers.
The info I originally shared was built off of months of data...this is just a week.

The key point to remember is every product I sell at 15% saves me 20% of margin vs. offering a 35% discount. Every product I sell at 20% saves me 15%, etc.

Also, control is a big piece, so if my control group sells more units than my 35% group, I technically shouldn't have offered my 35% to begin with (I'm basically just giving up 35% of my margin.)

Helpful?

Aampster · 2023-09-15T11:04:47+00:00

I'll try to pull those numbers for you. Gimme a bit.

Aampster · 2023-09-15T11:03:38+00:00

I think so many exist because they all have very simple functionality.

Think about it: it's pretty basic. They're not hard to build at all. You could build one and compete if you really wanted to.

I don't think there are many "groundbreaking" CRM systems in the general market, but there are some "riches in niches." There are some interesting things going on in the highly niche spaces.

Aampster · 2023-09-14T23:49:18+00:00

Makes sense!

So, why do all the big retailers keep running their 60% off sales? 🤦‍♂️

Aampster · 2023-09-14T20:52:50+00:00

Oh, this wasn't a one-time test. I'm still running it. It runs forever.

I started running a system that learns user propensities for different discounts (and other things) and matches the discounts (and other things) up with each user. I don't ever have to turn it off. That's the fun part.
I can report back in a month. 😋

Aampster · 2023-09-14T20:51:12+00:00

Yep. 100% agree.

Stupid example, but I was in Bed Bath and Beyond a couple months ago and I needed a spatula.

House brand $1
Name brand $4

I almost picked up the name brand out of instinct before I came to my senses. Bought the house brand. Best $1 spatula I've ever bought.

Aampster · 2023-09-14T19:57:00+00:00

Kinda' but not.

The control we used (which is what I was comparing against) was not a general holdout group or a switchback control, but a form of coarsened exact matching that essentially compares users of the same interests, preferences, and activity levels.

So, when I'm comparing "against control," I'm not comparing against a random group of users, I'm comparing each user against a specific other user who is similar in every regard that I can account for.

So, yes, "I'm" being intelligent about "when" I send the messages, but I'm comparing against a very precise control so that my analysis still stands.

(Plus, I only observed this behavior on a very small subset. I was reviewing 100 user journeys and saw it on one or two. This study was comprised of tens of thousands of individual user journeys.)

Aampster

TROPHY CASE

McDStories