Tbh, this is an unsponsored appreciation post for Mixpanel. Insights that stitch marketing with product data were historically impossible to do so for us, and I now have data that I only dreamt of having.
Goal: Un-silo marketing and product analytics
Traditional growth marketing courses recommend using tools like GA and GTM. However, these tools keep data siloed. They lack detailed insights into user engagement, activation, monetization, and retention.
The solution is to unify marketing and product data in one place. It'll save time and resources from spending money on the wrong channels. However, historically, this was challenging for small teams to accomplish.
The starting point: where we were
When I first joined Streamline, our analytics were almost non-existent, resembling those of any new SaaS company. We tried multiple software tools, including Google Analytics, Matomo, and Plausible. Despite spending a significant amount on consultants, the data we got was inadequate.
Our developers were particularly hesitant about diving into Google Tag Manager and marketing tracking, a common sentiment because of their unintuitive product.
The most perplexing issue was that most conversions were showing up as 'direct' traffic. The complexities of setting up Google Analytics were far greater than we had anticipated. We couldn't even track across our entire stack. At one point, we even considered relying solely on intuition and post-purchase surveys for insights because analytics tools were failing us.
The turnaround: embracing Mixpanel
The game-changer came when we started using Mixpanel for product analytics as well as marketing analytics. With the help of our engineering team and a consultant, we implemented simple product analytics. We used server-side tracking to bypass adblockers, a crucial step since 30-45% of our tech-savvy audience uses them. Unlike GA, Mixpanel could track these users.
The depth of our exploration
The more we used Mixpanel, the more we realized its capabilities. We were able to achieve full-funnel analytics across all our tech stack , and answer critical questions like:
User Behavior and Engagement
Where are users coming from? Track UTMs and referral domains.
What is their behavior in the product?
How many activate and take core product actions?
How many people drop off at each stage in the funnel (e.g., from a pageview on the marketing site to copy/download to search asset)?
Product Features and Metrics
Which features are popular with different segments of the audience? Should we pivot to a different ICP?
What happened to core metrics after we launched the redesign?
Where exactly are people getting stuck in the product?
Marketing and Outreach
We could send cohort data to lifecycle marketing tools (e.g., send email to users that start a trial but never take core action event)
Which partner is sending how many users? How many users sign up / start a trial / convert? To which plans? And when?
If we run an ad on Twitter, how many users copy/download at least a single asset? How many convert after a period of X days? For people that use the free product, do they retain?
Are we getting most of our new traffic from extensions and plugins?
SEO and Content Performance
Which SEO blogs perform best in terms of activation, engagement, and conversion?
Which user converted from which blog? (Something GA won't tell you). How long does a user retain from each blog?
Data Analytics and Modeling
Map attribution models based on last touch and first touch data.
Backport product signals to performance marketing and optimize for other goals besides conversions.
What's the word of mouth coefficient for the app?
Implementation team: who did what
The setup was achieved by just a full-stack engineer and myself. For the initial product analytics setup, I brought on a consultant. This took a few weeks of work + additional few weeks of testing data. The work with the consultant wasn't very successful so we eventually took it on internally.
For the marketing analytics this also took a month long implementation process. Our tech stack includes a react app, framer websites, a blog hosted on Ghost, a shopify store, and multiple plugins and extensions. We use a mix of server-side and client-side tracking to get the most amount of data.
We ran through the analytics setup ourselves and made some mistakes. That's ok and was expected!
For UTMs, we used Mixpanel's out of the box setup.
To stitch prelogin and post login data, we used a combination of mixpanel.alias (backend) and mixpanel.indentify (frontend). If you are using only frontend library, then mixpanel.identify will do all the work for you.
The UTM params are stored in the cookie but are only being sent on certain events and not all of them. I use the Initial UTM params on the profiles most of the time so it's not that big of a deal. But it can be a problem and you could use formulas to bypass this.
Google Analytics vs Mixpanel
We still have code lying around for GA4 but I honestly cannot stand their UX anymore. I don't recommend using it. Plus, Mixpanel integrates with Hotjar so you can see a lot more visual data alongside metrics.
The insights we get out of Mixpanel was historically impossible to do so and we couldn't be happier with the time invested. What we ended up with was a robust analytics system that I once only dreamt of having. This allows me to focus on channels that work and remove ones that don't. It also helps identify friction points in the product.
Thanks for reading!
I'm trying to better understand who my audience is. If you've got ten minutes, I would love to do a user research call: toption.org/10-minute
- Khushi Lunkad