Product Analytics Newsletter
Edition #13
July 1, 2019

(Image Source: MarketWatch)

Good morning product owners!

Summer is here, sun is shinning, birds are singing and heat is heating. And you know what? This newsletter is not taking any time off

We’ll fight through the appeal of sipping drinks around a pool, going on long bike rides, complaining about heat waves, etc. Just pure dedication to bringing you world-class (nothing less) product analytics goodies to your mailbox each week.

Ok, that’s not true… I’m taking part of August off… 🤷‍♂️

Enough with the delirium provoked by heat stroke and let’s just move on with the 13th edition of the Product Analytics newsletter.


Slack’s Non-IPO
Wouhou, another product we love is going public! That means we can speculate on their initial public offering’s starting price, predict by how much they will stumble and post blogs on how that was all predictable.

This time around, it’s Slack We all just love Slack 🤩 But it’s not really an IPO…

Full story on what a non-IPO is below (I fell asleep halfway).

BUT, I also added a story someone shared… on a Slack group (🤯), about the key metrics that helped propel Slack to where it is today, as well as how those metrics shows some of the challenges it’s facing. And this story got me fully energized again!

Net Dollar Retention Rate (NDRR)
What the hell is NDRR? Well, this is directly related to that Slack’s metrics article I shared above. If you read it (you did, right?), you probably stumbled on that NDRR metric and kinda stared at it for a few minutes before giving your brain a rest. That’s unless you were already aware of what NDRR was (🤓).

In any case, I wanted to go deeper into that metric cause, to be honest, I just didn’t get it

I opened my favourite search engine (Webcrawler) and stumbled on that article referenced below. It explains why committed monthly recurring revenue (CMRR) is an important metric but a misleading one, and how NDRR should be the golden standard when evaluating the financial health of a product.

Mandatory reading imo.

Why the Most Important Success Metric in SaaS is Misleading“, by Blake Bartlett

Influencing Your Newsletter Metrics
In last week’s edition we talked about some novel newsletter metrics, such as the CPEEA (Cost per engaged email acquired). In the end, we tend to focus on just a few (our focus is on Clickthrough Rate) and those are the ones we want to influence.

So you have the data, the metrics and the will to influence them. How should you get started?

You’ve probably heard of the Jobs To Be Done framework, but how can you use it in a newsletter context? Luckily the framework’s name says it all – you first need to answer what job your newsletter is hired to do.

Once that is figured out, you can evaluate how your newsletter is performing, zoom in on what could be improved, set up experiments and analyze how treatment influenced your targeted metric.

How we used a Jobs To Be Done framework to iterate on our newsletters“, by Anika Anand

Current State Of Our Data Warehouse
I just wanted to set things up here for the next couple of editions where I will go deeper into my own newsletter’s analytics. Here’s the state of our ETL right now.

Here’s what’s happening in here.

First off, my goal for now is just to have 2 clean entity tables: dim_user and fct_event. That means that whenever you fine folk open this newsletter, click on a link and visit my website to view a page or trigger some kind of other event, this data is being mined, transformed and populates those 2 final entity tables.

A word on my sources so far:

  • Asana experiments: this is my “less than ideal” way of documenting and keeping track of all experiments I run for the newsletter, but also my website, Intercom, etc. Unfortunately, it’s a manual process to move data from Asana to the staging area of my data warehouse (I need to improve this out one day).
  • Mailchimp data: have I told you that I have a professional crush on Stitch Data? Well I do. Their Mailchimp integrations allows me to automatically get some very granular data about my newsletter’s audience and their activity. 🥰
  • Segment: Segment allows me to grab data from multiple sources (in this case my website and Intercom, and Hubspot to come up soon) and pipe that data to a bunch of destinations, such as my data warehouse. This is super rich data (well as rich as you make it to be more precise). 
Alright, so all that data taken individually would be chaos to make sense of. This is where dbt does its magic 🧙‍♂️, by facilitating the whole ETL (and much much more) process and get to a state where that data is consumable. If you’re not familiar with dbt, I think this post I published a while back will help you get started.

My journey introducing the data build tool (dbt) in project’s analytical stacks“, by yours truly

Next week, bring your swimsuit, cause we’re diving into the data. ‍♂️

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