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How to be the go-to engineer for product analytics

Mixpanel

As data-driven product development continues to balloon in popularity, so does the need for accurate and sophisticated implementation of analytics tracking in software products. All over the code, you have ugly calls to complex analytics APIs littered amongst your views and business logic. You’d be celebrated.

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How to Deal With Unknown Unknowns in Project Planning

Amplitude

This is a guest post from Dillon Forest, cofounder, CTO & product manager at RankScience. When your team is trying to build something that doesn’t currently exist, the development process is full of moments of uncertainty. Do some user research. The uncertainty of technical products.

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Why it’s never too early to add product analytics to your app

Mixpanel

So it’s tempting to think the additional add-on of measuring user events in your code (i.e., implementing product analytics) is something that can wait until your team is bigger, until you have more users, or until you have more money. You know where users clicked and didn’t click, when they clicked, and in what order.

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The Disproportionate Impact of Coaching on Startup Survival

Bain Public

MAIN: Developing Entrepreneurship in Quebec. MAIN , also known as Mouvement des accélérateurs d’innovation du Québec, states that their mission is "to improve the cohesion and efficiency of the business incubator and accelerator ecosystem and to increase its impact on the development of entrepreneurship and innovation in Quebec.".

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Always implement analytics as part of feature development. Here’s why.

Mixpanel

Here are eight reasons you should always implement your analytics in the same sprint as your feature development and not at a separate time after development is complete. When engineers implement features, they write code. Analytics should be tested alongside features. Implementation is better with less context-switching.

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Data thinking vs. product thinking

Mixpanel

Data has fundamentally changed how we design and develop products, but there’s a difference between simply reacting to data points and applying the level of analytic rigor needed to yield meaningful results. There’s an enormous amount of ambiguity when it comes to developing products. Here’s how. But you’d be wrong.

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The Podcast Ecosystem in 2019 – a16z’s 68 page analysis (Guest essay by Li Jin)

Andrew Chen

No wonder the consumer investment team ended up digging into this trend by doing a market map report — the analysis led by Li Jin and including work from myself, Connie Chan, and others. Without further adieu, here’s the a16z consumer team’s definitive analysis of the podcast ecosystem in 2019. Thanks, Andrew.