Author Archives: Aditya Vempaty

Future of apps

The Future of Apps Is Apps With Analytics

A few weeks ago, TechCrunch ran an excellent post called “The Future of Apps Should Be Better Apps.” In it, Anshu Sharma responds to the Wall Street Journal’s pitch that chatbots are going to replace apps with a resounding “No, not that easily.”

Sharma makes a solid point. It could be that we end up chatting with our phones more than tapping them. It could be that we transition to the mobile web, and we might even end up somewhere in between web and native apps.

But it won’t be the most hyped up or flashiest future that wins the day. It’ll be the solution that does the best job of solving the complex issues that apps have. That’s not going to happen overnight. It’s going to happen as the result of small discoveries and a gradual movement towards more user-friendly computing.

We may be agnostic on “what comes next.” What we do know is that analytics is going to pave the way.

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Trump post

What Donald Trump Can Tell You About Your Data

When should a young company start looking at its analytics? If Donald Trump is any indication, they should start early and often.

Well into 2016, virtually the entire media establishment was in agreement on the model they were using to understand the GOP race. In it, Donald Trump was a non-starter. He had no endorsements and no support from the traditional players in the Republican party. Even 538’s Nate Silver, the once-whiz kid who correctly predicted 50 out of 50 states in the 2012 general election, wrote him off.

What went wrong was a classic Bayesian misstep. Instead of updating their priors to account for new data, the pundits and commentators clung to them. They saw the contrary evidence; they just chose not to give it weight. What’s more surprising is that not even Nate Silver, the modern-day king of Bayes, was immune.

“Numbers have no way of speaking for themselves,” as he warned in his book The Signal and the Noise, “Data-driven predictions can succeed—and they can fail. It is when we deny our role in the process that the odds of failure rise.”

Analytics is not all about having all the data. It’s not about building the right models—or priors—because you’re not going to guess correctly every time. It’s about being flexible with your priors in the face of new evidence.

If we can learn anything from Trump’s rise, it’s that you’re better off getting a grip on your data early, being fluid about what it means, and revising your priors before they have a chance to dig in.

Otherwise, you’ll get blindsided—just like America did.

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You Say You're Data-Driven

You Say You’re Data-Driven, We Call Bullshit

Word on the street is that data is important. And we know that you know it’s important–in fact, based on the State of Analytics 2016 report, 86% of industry professionals believe analytics is critical to their decision-making process.

But setting a couple of KPIs and checking your DAUs is still a far cry away from being truly “data-driven.” So many people and companies now use that term to describe their processes, it’s pretty much become meaningless.

Princess Bride meme

Inigo Montoya does not approve.

Few businesses actually understand what it means to have a fully integrated culture of data. If you can’t quickly and easily get actionable insights from your data, unite your company around that insight, and make product or business decisions from it, describing yourself as “data-driven” would be a blatant misuse of the word. As it happens, 74% of businesses say they want to be data-driven, but only 29% say they are good at connecting analytics to action.

Forrester Diagram

To know if you are truly a data-driven company, you should answer yes to these three critical questions:

  1. Is everyone aligned around a single core metric?
  2. Can everyone access the data they need?
  3. Can everyone get insights from their data?

Key word here? Everyone. Not just the analysts and data scientists, not just growth marketers, not just the C-Level execs. Everyone.

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Mobile Retention Tactics

20 Crazy Effective Mobile Retention Tactics

As a mobile analytics company, we live and breathe app retention. We see it as the true foundation of growth, without which your app will quickly sink into the ground.

Churn, the natural and eternal enemy of the entrepreneur, is a foe that can come from anywhere—apathy, competitors, clunkiness, you name it. Fighting it is a constant battle, waged from the very moment users download your app and through every single interaction they have with it. To stand a chance of success, you need to chip away at it all over, improving every angle of your service and product.

To help you, here are 20 techniques to improve your mobile retention: from improving the onboarding process, to ways to enhance your product, to re-activating users on the fringes, and everything in between.

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Is Retention Like Teenage Sex?

There’s a great quote about big data from Dan Ariely, Professor of Behavioral Economics at Duke University, that’s been circulating for the last couple years: “Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”

That got us thinking – big data isn’t the only thing that’s like teenage sex; by Ariely’s analogy, this is particularly true of user retention too. We talk about how important retention is and we all think we do it, but we don’t have a set definition for what it is and we don’t have a clear framework for driving retention. Think about it for a moment: the concept of retention has been around over 15 years, yet most people still don’t truly understand what it is, why it matters and how to do it right.

We’re going to shed light on this topic by reviewing what retention is, why it matters and how you can track your retention metrics. By the end of this post, you will:

  1. Have a fundamental understanding of retention.
  2. Learn how you can set up a framework to assess your app’s retention.

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