Gravity Summit 2010 PeopleBrowsr

Intelligent Stream Miningand Strategic Response Solutions

“Real-time Internet conversations occur on many platforms from blogs to social media sites and new web superstars like

Twitter.

Tools that you have used in the past, like Google or their email alerts, cannot keep

pace with these conversations.”

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Our Core Strategy

Build Web Apps that look into the Data Mine:

Live Data Mine of Conversations and

Mentions

Social Search Engagement

Work FlowReal Time & Historical Analysis

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PeopleBrowsr Clients

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Advertising Stream

Print Radio TV Social Media

Media Spend

$$$Social MediaIs an efficient channel

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Inside PeopleBrowsrAnalytics

Fan Pages

Mentions, RTs, …

Comments, Sharing,…

Profiles, Comments, …

Status Updates, Comments, …

Pictures, Comments, …

Connections, Comments

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Inside PeopleBrowsr

• 360 days of data

• 1 TB of data/month and growing

• FILTER Tweets, BIOs, Location

• Full Feed from Twitter with greater API access for large scale campaigns

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Problems we Solve:

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• How do Traditional Channels effect Social Media

• Viral Sentiment and Customer Service Issues

• How do we influence the Conversation

• Filter High Velocity Streams for Actionable Messages

• How to build an engaged Audience

• Converting Social Media into Sales

• Calculating ROI

Media Spend on Twitter

Media Spend on Twitter

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The PeopleBrowsr Cycle• Historical and Ongoing Sentiment Analysis

• Campaigns to Build Followers

• Whitelist and Campaigns to Direct Message

• Reporting on ROI for traditional and Social Media

• Custom Monitoring Dashboards

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SuperBowl

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Analytics:

• Effect of Traditional Media on Social Media

• Mechanical Turk to measure accurate Sentiment

•Metrics to measure Success:

• Total Mentions

• Positive Mentions

By Volume

Mullen and Radian6

SuperBowl

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Results:

• 103,158 Total Mentions

• Sampled 1000 Tweets from Every Brand and used Mechanical Turk Human Sentiment to analyse

• Polarised:

• 50% Positive

• 28% Neg

• 18% Neutral

SuperBowl

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Positive Sentiment Chart

SuperBowl

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Positive Sentiment Chart

SuperBowl

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SuperBowl

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SuperBowl

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SuperBowl

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SuperBowl

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O’Reilly and PeopleBrowsr Analysis:

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Correlation of Tweets and Ads

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Doritos and Bud Light ran a series of themed ads

Doritos ran more ads and received more positive sentiment tweets

Top Brands – By SentimentGoogle and Snickers had most positive sentiment

Both show a longer tail of tweet interest

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Low Sentiment BrandsPre-game controversy for Focus on the Family ad

Fewer tweets than other brands in study

GoDaddy ran racy ads

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No Sentiment BrandsCoca Cola and Budweiser generated more neutral sentiment than other

brands

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Korean Cars

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Kia generated more tweet volume while running fewer ads

SuperBowl

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By Volume

Mullen and Radian6

O’Reilly and PeopleBrowsrO’Reilly and PeopleBrowsr

By VolumeBy Positive

http://www.slideshare.net/peoplebrowsr/superbowl-3231030Contact me for an xls of the Brands Tweets JodeeRich@gmail.com

Hollywood

Company Size: $200 Million in Revenue

Social Media Spend: $20K

Goal: Evaluate impact of non-Traditional Media on the Social Media sphere

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Hollywood

Solution: 180 day Historical Analysis of Posts overlayed on TV Ad spend and other channels

Performance:

• Identified key performer as branded video content

• 50% fluctuation on engagement based on time of message release

• Targeted influencers RTed a combined 879 times

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Hollywood Sentiment Campaign

Listen to your Audience: • Monitor Twitter for mentions of the movie and its stars• Find all negative comments about the movie• Order your results by influence

Build Relevant Followers: • Follow most relevant people• Wait for follow back

Make Contact: • Acknowledge – Let complainers know they have been heard• Engage with the influencers

Transform enemies in your best advocates: • Run positive sentiment campaigns

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Filtering a high Velocity StreamSeek an effective way to measure brand sentiment

accurately. The goal is to find a list of influencers speaking in both positive and

negative terms and engage.

Risks: Stream includes spam, affiliates, and other non-relevant mentions .

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Velocity

10,000Mentions/day

Filtering Process

1. Build a unique @name list from the extract

2. Build spam @names with PB spam alogorithm

3. Build affiliates list

4. Review Spam and Affiliate @names - reinsert false positives

5. Finalise Spam and Affiliate list

6. Wash spam and Affiliate list against the full stream to produce a non-Spam and Non-Affiliate cleaned Daily Stream

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Data Mine | Analytics | Brand Engagement | Messaging

Sentiment Analysis

95% accuracyVs 70-80% automation alone

SENTIMENTOne Build a live streamTwo Analyze with a special dictionaryThree Increase quality with Real-time human re-sorting Mechanical Turk

HISTORICAL ARCHIVEOne Identify Keyword @nameTwo Set Time Length (30d)Three Search archive (360d)

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one we build a live stream for an industry or a group using @Names or Keywords. Eg: Foo Camp attendees and the companies that they represent or Airlines.

How it works

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How it works

two we then analyze that stream with a special dictionary for the industry or group and sort the results into buckets.

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How it works

three increase quality with real time human re-sorting

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Eg: Quality Improvement on June Airlines data

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Custom Dashboards and Reporting

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Social Media Metrics for ROI

Metrics on “Time for Engagement”

Best Time for Engagement with Followers

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Sneak Preview: T2 – Next Gen

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• Combine Search and Posting

• Provide inline Content

• Contextual Ads as a Post is created

• HyperConnected

• HyperLocal

• Integrated with other Services • Yelp• Amazon• Open Table

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T2 prototype

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T2 prototype

Intelligent Stream Miningand Strategic Response Solutions

@WingDude | JodeeRich@gmail.com

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