The Cold Hard Data on Corporate Social Media Investment

The Cold Hard Data on Corporate Social Media Investment

Introduction

Is there a return on investment from social media?

I hear this question frequently, usually from frustrated brand managers or from executives who have to foot the bill for the social media budget. While there are success stories (I know a brand that acquires customers on Facebook for $0.50) it seems that most firms struggle with this question.
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Forget the Phone: Social Media is a More Accurate Way to Predict Vote Results

Forget the Phone: Social Media is a More Accurate Way to Predict Vote Results

Introduction

Previously I developed a model to predict who would get voted off American Idol based on what people were saying on Twitter[1].

To do this, I created a random “focus group” of American Idol fans who are active on Twitter. Each week I looked to see who this group was talking about. Since raw counts can sometime be misleading, I used a simple model for conversation as an exogenous variable for the autoregression. Essentially, it says that each week new people should be talking about successful contestants. So even if a contestant has a large number of mentions, if those mentions are coming from the same group of people, then the contestant is in danger of being voted off.

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Older Crowd Loves Hortons, While Youth Flock to Starbucks and McDonald’s

Older Crowd Loves Hortons, While Youth Flock to Starbucks and McDonald’s

Introduction

In my previous series of posts on Canada’s coffee wars, I looked at how Starbucks, Tim Hortons and McDonald’s were viewed province-by-province[1], analyzed how their distinct marketing strategies were working[2], and studied brand overlap in the marketplace[3]. To complete this study, I will now analyze whether or not they are targeting the same market.

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The Social Data You’re Getting May Only be a Fraction of the Story

The Social Data You’re Getting May Only be a Fraction of the Story

Introduction

I’m often asked why I go to so much trouble to create my Twitter datasets using sampling techniques rather than the more common Twitter Stream API[1] . For those who don’t know, the Twitter Stream API is the standard way that most analysts get their Twitter data. It taps into the Twitter Firehose (a live stream of all current tweets on Twitter) and filters it based on keywords, user accounts and location. Using Twitter streams, analysts can get very targeted tweets in real time.

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Footnotes    (↵ returns to text)

  1. https://dev.twitter.com/docs/api/streaming
Want to Make Your Post Go Viral? It’s All About the Second-Degree Network

Want to Make Your Post Go Viral? It’s All About the Second-Degree Network

Introduction

One main reason organizations use social media is the hope that a post or link will go viral. Networks like Twitter make this extremely easy, allowing people to share interesting content with their friends. But in practice it is very difficult to “make” something go viral. It requires coming up with a message people want to share, having enough people actually see the message, and ensuring that this message is spread efficiently through the network.
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