Finding Metrics That Matter

All metrics are shortcuts. When we’re faced with uncertainty, we use metrics to break our problem down into simpler, tangible pieces that we can understand.

Metrics are simple proxies that allow us to transform difficult questions into empirical, demonstrable ones.

When we’re faced with a difficult question, we often answer an easier one instead, usually without even noticing this subtle substitution. This is what economists call an availability heuristic — a mental shortcut where we use what we already know, rather than complete information, when making a decision.

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The Next Generation of SaaS Won’t Optimize for User Engagement

A few weeks ago Hiten Shah explained in a new interesting post why the most successful SaaS companies of the future will focus on usage, just like Facebook. In the write-up, he goes very deep into his explanation bringing examples of world-class SaaS companies like Trello, Slack, and Dropbox that are all building their strategies around this consumer-oriented product approach.

He predicts that this is how the next generation of SaaS will look like.

While I was reading Hiten’s post, I immediately recalled a frugal email conversation I had last month with Patrick Campbell, CEO at Price Intelligently.

Patrick briefly introduced me to the definition of what he calls “anti-active usage” products.

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Data Dictionary: How to Build one for your SaaS business

At the beginning of every startup, the most helpful data usually isn’t quantitative; it’s qualitative. You can’t measure very much in the early days for the simple reason you just don’t have enough data.

Analytics products like Google Analytics, Hotjar or Mixpanel won’t help you understand what problems your users really need to solve or what features you should prioritize after launch. At this stage, the only way for you to have a reasonable perception of the needs and the trends is to go out of the office and talk to people.

But when companies start to grow, they rapidly orient from a qualitative to a quantitive data collection approach. Which means they quickly start paying more attention to statistically significant numerical data than descriptive data, usually harder to analyze.

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AI implications on Marketing and Analytics

In his annual letter to shareholders, Jeff Bezos wrote:

Big trends are not that hard to spot (they get talked and written about a lot), but they can be strangely hard for large organizations to embrace. We’re in the middle of an obvious one right now: machine learning and artificial intelligence.

We’re probably not yet in the middle. We’re just getting started in shaping up the next industrial revolution with AI. This post is a deep dive into the why and the how Analytics and Marketing SaaS products will use Artificial Intelligence (AI).

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