open analytics talk -developments and challenges in social media measurement
TRANSCRIPT
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Copyright © 2013 Porter Novelli Inc. All rights reserved. CONFIDENTIAL AND PROPRIETARY MATERIALS OWNED BY PORTER NOVELLI INC.
Developments and Challenges in Social Media Measurement
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Agenda
Who is this guy?
Déjà vu all over again
A game of Chutes and Ladders
Light at the end of the tunnel?
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Who is this guy?
• 20 years research/analytics experience
• Focus on media: Turner Networks, MySpace, Yahoo, media/ad agencies
• Quantitatively focused:
• MMMs• Segmentation Analysis• Campaign Attribution• Behavioral Targeting• Fan/Follower Valuation
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Who is this guy?
• The Public Relations discipline took hold of social marketing
• Porter Novelli’s client base is global, which leads to some interesting social media analytics opportunities
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Déjà vu all over again
• Dirty data in the social space
• Inappropriate methodologies
• Vendors that do not care about data quality
• No industry standards
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Déjà vu all over again
• Data is spam laden
• All tweets are not created equal
• Interactions across social channels mean something different
• Does an emoji connote sentiment? Does it generate influence? How much influence does it generate?
• What is influence worth? What is reputation worth?
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Déjà vu all over again
• Because of the sheer volume of data, trying to make sense of this has led some firms down very strange roads
• A common approach is to sample the social conversation, and infer quantitative conclusions
• This is in defiance of the Central Limit Theorem
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Déjà vu all over again
• On my arrival into the public relations industry, I took as many vendor meetings as I could. My findings:
• All data vendors have the “best” sentiment scoring engine … though the criteria for this claim is unknown
• Vendor-side spam filtering is ineffective
• The interest across vendors is creating prettier charts with vibrant colors, rather than data quality
“magic beans”
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Déjà vu all over again
• There are several groups trying to develop some industry standards around social media measurement, but as of now, there are no accepted standards
• The best we have at the moment are the Barcelona Principles
• Will social media ever get to the same level of standards as the IAB/WAA on online media measurement?
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Chutes and Ladders
• “Every thing is measurable”
• The reason that standards were developed on the web analytics side was due to the investment
• Public relations wants more marketing dollars
• Standards are coming out, but are they strong enough?
Where: E = excused from flyingI = insanityR = requests an evaluation
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Chutes and Ladders
• Is the objective of the social analytics qualitative insights mining, measurement, or both?
• If sampling leads to inappropriate or insufficient conclusions what are the measurement options?
• In the web analytics world, we take spam filtration for granted; in social, relevance is everything.
• Every social analytics program is going to have error … some known and some unknown.
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Light at the end of the tunnel?
• There are platforms that allow a full analysis of text … some are robust and offer easy ways to integrate text and other data into one reporting platform
• The solution that we have developed is using an open source text analytics platform, so we effectively built our own solution
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Light at the end of the tunnel?
• People talk about brands, products and services using a specific ontology
• “Sick” connotes “good” for some categories, “bad” for others
• Most vendors who provide sentiment scoring across the entire universe of conversation are not able to account for these differences
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Light at the end of the tunnel?
Process:
• Pull in data from multiple sources
• Build dictionary and grammar rules
• Categorize text by conversation category and sentiment based on rules (human and machine learning algorithms)
• Human scoring and validation
• Dump results to UI
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Best Practices
• Any vendor who talks about “best” sentiment engine – based on what?
• Know your data
• Get as close to the source as you can
• Solutions custom to your needs are always better than out-of-the-box
• Beware of pretty Uis
• Good governance of data and analytics
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16
Questions?