ENTRIES TAGGED "metrics"

Computing Twitter Influence, Part 1: Arriving at a Base Metric

The subtle variables affecting a base metric

This post introduces a series that explores the problem of approximating a Twitter account’s influence. With the ubiquity of social media and its effects on everything from how we shop to how we vote at the polls, it’s critical that we be able to employ reasonably accurate and well-understood measurements for approximating influence from social media signals.

Unlike social networks such as LinkedIn and Facebook in which connections between entities are symmetric and typically correspond to a real world connection, Twitter’s underlying data model is fundamentally predicated upon asymmetric following relationships. Another way of thinking about a following relationship is to consider that it’s little more than a subscription to a feed about some content of interest. In other words, when you follow another Twitter user, you are expressing interest in that other user and are opting-in to whatever content it would like to place in your home timeline. As such, Twitter’s underlying network structure can be interpreted as an interest graph and mined for insights about the relative popularity of one user when compared to another.
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The True Cost of Lemonade

Learn to resist vanity metrics

One of the things we preach in Lean Analytics is that entrepreneurs should avoid vanity metrics—numbers that make you feel good, but ultimately, don’t change your behavior. Vanity metrics (such as “total visitors”) tend to go “up and to the right” but don’t tell you much about how you’re doing.

Many people find solace in graphs that go up and to the right. The metric “Total number of people who have visited my restaurant” will always increase; but on its own it doesn’t tell you anything about the health of the business. It’s just head-in-the-sand comforting.

A good metric is often a comparative rate or ratio. Consider what happens when you put the word “per” before or after a metric. “Restaurant visitors per day” is vastly more meaningful. Time is the universal denominator, since the universe moves inexorably forwards. But there are plenty of other good ratios. For example, “revenue per restaurant visitor” matters a lot, since it tells you what each diner contributes.

What’s an active user, anyway?

For many businesses, the go-to metric revolves around “active users.” In a mobile app or software-as-a-service business, only some percentage of people are actively engaged. In a media site, only some percentage uses the site each day. And in a loyalty-focused e-commerce company, only some buyers are active.

This is true of more traditional businesses, too. Only a percentage of citizens are actively engaged in local government; only a certain number of employees are using the Intranet; only a percentage of coffee shop patrons return daily.

Unfortunately, saying “measure active users” begs the question: What’s active, anyway?

To figure this out, you need to look at your business model. Not your business plan, which is a hypothetical projection of how you’ll fare, but your business model. If you’re running a lemonade stand, your business model likely has a few key assumptions:

  • The cost of lemonade;
  • The amount of foot traffic past your stand;
  • The percent of passers-by who will buy from you;
  • The price they are willing to pay.

Our Lean lemonade stand would then set about testing and improving each metric, running experiments to find the best street corner, or determine the optimal price.

Lemonade stands are wonderfully simple, so your business may have many other assumptions, but it is essential that you quantify them and state them so you can then focus on improving them, one by one, until your business model and reality align. In a restaurant, for example, these assumptions might be, “we will have at least 50 diners a day” or “diners will spend on average $20 a meal.”

The activity you want changes

We believe most new companies and products go through five distinct stages of growth:

  • Empathy, where you figure out what problem you’re solving and what solution people want;
  • Stickiness, where you measure how many people adopt your solution rather than trying it and leaving;
  • Virality, where you maximize word-of-mouth and references;
  • Revenue, where you pour some part of your revenues back into paid acquisition or advertising;
  • Scale, where you grow the business through automation, delegation, and process.

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Joshua Bixby on the business of performance

Joshua Bixby on the business of performance

Why businesses should care about speed.

In this Velocity Podcast, Strangeloop's Joshua Bixby discusses the business of speed and why web performance optimization is an institutional need.

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Moneyball for software engineering

Moneyball for software engineering

How metrics-driven decisions can build better software teams.

Don't dismiss "Moneyball" just because it began in the sports world. Many of the system's metrics-based techniques can also apply to software teams.

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Mobile metrics: Like the web, but a lot harder

Mobile metrics: Like the web, but a lot harder

Flurry's Sean Byrnes on the challenges of mobile analytics.

Flurry's Sean Byrnes talks about the intricacies of mobile analytics, the metrics app developers care about most, and the problems that stem from Android fragmentation.

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