Open analytics talk -Developments and Challenges in Social Media Measurement
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Open analytics talk -Developments and Challenges in Social Media Measurement
- 1. Copyright © 2013 Porter Novelli Inc. All rights reserved. CONFIDENTIAL AND PROPRIETARY MATERIALS OWNED BY PORTER NOVELLI INC. Developments and Challenges in Social Media Measurement
- 2. Agenda Who is this guy? Déjà vu all over again A game of Chutes and Ladders Light at the end of the tunnel?
- 3. 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
- 4. 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
- 5. Déjà vu all over again • Dirty data in the social space • Inappropriate methodologies • Vendors that do not care about data quality • No industry standards
- 6. 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?
- 7. 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
- 8. 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”
- 9. 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?
- 10. 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 flying I = insanity R = requests an evaluation
- 11. 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.
- 12. 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
- 13. 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
- 14. 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
- 15. 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
- 16. 16 Questions?
Editor's Notes
- This talk is called Developments and Challenges in Social Media Measurement. While the traditional format is I speak, you listen or go through emails, and there are questions at the end, I do like a good discussion on this topic. The area of social media measurement is still under construction, so if you want to chime in – by all means feel free. I’m going to talk about my journey through this space, and a solution I developed … but I am happy to provide suggestions for folks who are just starting out in trying to solve these problems at the end of the discussion. One thing I would like to mention as well, is that text analytics can solve more problems than just how people are engaging with your company’s brands or products. They can be useful for mining insights in product development, customer care, and some other interesting applications, and what we’ll be talking about for the next 30 minutes or has a relationship to these other areas as well.