Category Archives: Growth & Analytics Strategies

Tinybop-cover

Meet the product team that’s using data to shake up apps for kids

Remember the days when online gaming consisted of terrible design and graphics with heavily pixelated characters? Thankfully, technology has improved and those days are behind us.

Launched in 2012, Tinybop is part of a growing market of educational apps for kids. In an age where tech rules, they’ve adapted to the changing landscape to accommodate how children learn and play. Their slogan says it all: Toys for Tomorrow. Continue reading

exponential growth

What Exponential Growth Really Looks Like (And How to Hit It)

Exponential growth is one of the most powerful forces in nature. Here are three quick stories to prove it:

  1. In 1859, an English farmer named Thomas Austin brought 24 rabbits with him to his new home in Australia. As it turns out, they took quite well to the environment down under. Six years later, there were 22 million rabbits all across the continent.
  2. In 1945, a group of physicists split an atom in the New Mexico desert. When they did, two new atoms split. After that, four atoms split—and eight, and sixteen, and thirty-two, and so on, eventually producing the largest explosion then recorded.
  3. In 2004, a social network invented at Harvard was so popular that everyone who joined invited several of their friends. Not wanting to be alone, they all invited their friends too—and so on. Now there’s more than a billion people using it.

Facebook, the Manhattan Project, and Australia’s rabbit infestation were all driven by this one force. Alternately cute (rabbits!) and terrifying (the nuclear bomb), exponential growth can start from what seems like nothing to create huge explosions and worldwide phenomena.

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product analytics + customer

How Product Analytics Will Tell You Which Customers You Should Get Feedback From

Product managers are always getting told to talk to customers more. That’s simple enough to do when you’re first getting started, but gets harder and harder over time. As your customer base gets bigger and the number of things you have to do grows exponentially, picking the right set of customers to talk to becomes a challenge.

“It was easy in the beginning, because we knew most of the people using the tool as we worked on the initial version,” says HubSpot’s Dan Wolchonok.

“As we got bigger, my feelings usually boiled down to these four words: talk to customers more. I think a critical skill, however, is learning how to talk to the right customers.”

To figure out who to talk to, Dan Wolchonok used behavioral analytics to narrow down his customer base. Rather than get a random subset, or allow only the loudest customers to have their voices heard, Wolchonok sought out the exact customers he needed to solve his most pressing product issues.

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causation vs correlation

The Difference Between Correlation and Causation

Correlation and causation are two of the most important concepts to understand if you want to create growth.

Ben Yoskovitz, Founding Partner at HighlineBeta, explains the difference between correlation and causation by stating “correlation helps you predict the future, because it gives you an indication of what’s going to happen. Causality lets you change the future.”

Knowing the difference between the two goes a long way in ensuring that your business decisions are based on hard facts and measurable variables.

Making decisions based on assumptions means you run the risk of jeopardizing the success that you’re working hard for. It’s not intentional but before you make your next decision, consider whether your actions are based on assumptions or proven facts.

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how-to-ride-the-wave-of-big-platform

How to Ride the Wave of a Big Platform

As we’ve said time and again, even if you have a great product, simply launching it and hoping for the best won’t get you far. One way to drive engagement and growth is with the help of a platform that already has access to millions of people.

Baremetrics, a subscription analytics service, took this approach when they partnered with Stripe, an online payments platform. For them, timing was everything. They got in at the right time and rode Stripe’s massive wave of popularity to grow their business.

Partnering with Stripe allowed Baremetrics to get out and in front of millions of potential customers. They’re constantly adapting their strategies to keep growing and meeting the needs of their growing customer base.

You can do the same thing to grow if you choose the right platform, partner at the right time and use it to solve customer pain. But don’t stop there, you’ll need to keep looking for opportunities to grow your customer base.

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how-to-structure-growth-teams

How to structure your growth team

Growth is so fundamental to startups that Y Combinator founder Paul Graham makes them virtually equivalent in his legendary essay, “Startup = Growth.

That’s why it’s such a mistake for startups to delay putting together a growth team responsible for that growth.

As Wealthfront VP of Growth Andy Johns points out, “startups will build a really robust finance organization, but not a team with the responsibility to measure, understand and improve the flow of users in and out of the product and business.”

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161213-4-ways-bias-sneaks-into-your-mobile-analytics

4 Ways Bias Sneaks Into Your Mobile Analytics

In 2013, Helen Turnbull described feeling uneasy when she saw a woman was piloting her flight instead of an older, white man. Dr. Turnbull (who has a PhD in internalized oppression) actually considered getting off to wait for the next plane.

Biases are predispositions to one thing over another, and they affect everything from casual conversations to hiring decisions. If Dr. Turnbull falls prey to the wiles of bias, so do all of us.

Though most of us understand biases in the world around us — does your workplace have left-handed scissors? — it can sometimes be tricky to spot them in statistics. Numbers don’t lie, right?

Well, numbers might not, but we do. We need to be conscious about eliminating bias no matter what we’re doing — including when we’re interpreting our data.

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