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Great question. We are working on a toolkit for vision-driven product development. Rather than answering every feature and design question through A/B testing (which also of course has it's proper time and place in the process), the toolkit starts by defining a clear vision, a step-by-step guide to translating the vision into product strategy, and finally a filter for evaluating product strategies (which helps answer your about above).
This filter is a 2x2 rubric that helps you evaluate features as good vs. bad vision fit and high vs. low sustainability, i.e. how expensive are they to build or how much revenue do you expect them to bring in. Something that is in the high vision fit and high sustainability quadrant is an obvious "Do it!". What's more tricky is something that's a high vision fit and low sustainability, which we call "Investing in the vision" quadrant. You'd take on these product strategy elements if you can afford it. The counter to this quadrant is the low vision fit and high sustainability, i.e. Vision Debt column. These are elements of your product that you add very carefully because similar to the concept of Technical Debt, if you're taking on Vision Debt, you'll need to plan on servicing that debt over time.
Here's a link to a blog post that talks about this filter. https://medium.com/radical-product/three-diseases-your-product-can-catch-and-how-you-can-prevent-them-77a9500d5f07
And here's a link to the Radical Product talk and free toolkit: https://www.radicalproduct.com/
In all honesty, when I did statistics in school and even for my master's thesis research paper I never thought I'd EVER use this stuff again. Turns out I was pretty wrong...
Thx for the heads up, Original link had changed. Fixed now.
This post takes to "Page not found" :/
Link to the full report is here: https://www.brandwatch.com/reports/brand-visibility-report
Let me know what you think in the comments!
That's a complex concept.
First, you need a Growth Model to justify how many users you are bringing in and the conversion level in each cohort for each channel.
Then you need a Financial Model to figure out the level of investment input for each channel.
I think once you complete these 2, you have an idea on how to define this "growth equation" for the company.
If you are presenting this, I would macro the equation to the Growth Model
Our go-to data enrichment provider for this kind of thing is Clearbit. Good mid-market data for North American markets.
But, the challenge isn't so much just getting the data. That's a raw material. It's how you process and transform that data into something actionable (lead qualification, email segmentation etc.). This is where you need customer data integrations, custom scripts or a customer data platform like Hull: https://www.hull.io/blog/clearbit-better-with-hull/
I did something similar in my last role. Really, you need some level of volume to start breaking this kind of stuff up and draw statistical significance.
Signsups, paid conversions etc. are all easy to measure, but hard to find causation. The "aha!" moment is a key part of your growth equation to identify some of those leading triggers and drivers of growth.
At Hull, we've researched how different companies like Typeform, Appcues, DigitalOcean, Moz and others of different sizes and stages go about finding these signals - we found three methods:
1. Raw product usage (quick start, easy. Less than 15 minutes). Guess at a trigger.
2. Quick regression analysis (identify more and better opportunities. An hour or less). Remember, specificity allows you to hone in your content on what matters and convert more. Find a handful of triggers.
3. Data science and artificial intelligence. Find many triggers, and deliver precise content (ongoing, five-figure spend)
Since these increase in complexity and potential return, start with the simplest. The basis of the more advanced techniques comes from the simpler techniques anyway. Start with a guess.
You can read more about that here: https://get.hull.io/complete-guide-pqls/chapter5/
At my last role at inbound.org, the key trigger to becoming a contributor (which drives all the content and engagement across the community) was completing a profile. 30% of users would fully complete their profiles, and then 25% of these would become contributors. I found this with simple regression analysis in Google Sheets using data pulled from our backend database.
Once you've identified these causal moments, you can start to build out these growth models that join the dots between your key SaaS metrics like CAC, LTV, number of signups etc. and inform your growth experiments.
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