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Gauging Consumer Behaviour via Social Analytics
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Gauging Consumer Behaviour via Social Analytics

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    Gauging Consumer Behaviour via Social Analytics Gauging Consumer Behaviour via Social Analytics Presentation Transcript

    • Gauging Consumers Gauging Consumers Through Through Their Online Behaviour Their Online Behaviour Hareesh Tibrewala Hareesh Tibrewala hareesh@socialwavelength.com hareesh@socialwavelength.com
    • Consumer Insights • Object of marketing / advertising / PR…is to finally influence consumer behavior in a way that it is beneficial for your brand • For the first time in the history of brand-kind, there is an opportunity to track consumer behavior – In real time – On a large scale – Access to authentic information • Social Analytics derived from Social Media Listening
    • Listening to these consumer conversations has become increasingly important
    • Customer Relationship Management (CRM) • • • Consumers are using social media platforms to share their opinions about brands In case a consumer puts up a complaint about a brand, it is important for the brand to engage with the consumer and to be seen as a responsive brand In case a consumer puts up a positive review about the brand, brand should engage with the consumer and use this opportunity to generate favorable brand advocacy Negativ Negativ e e Complain t Brand Response Customer Response Positive Positive
    • Understanding Brand Sentiment • Listening to what consumers are talking about a brand, helps understand and map consumer sentiment Sentiment Analysis for two airline carriers Indigo Airlines Vertical Positive • Kingfisher Airlines Not only can one map the sentiment for one’s own brand, one can do it for competitors brand as well Booking and Customer Care Neutral 0% 20% Negative SIM Score 80% -0.60 Positive Neutral 0% 29% Area of Concern Negative SIM Score 71% -0.41 Understanding consumer sentiment in the marketplace can help create actionable product / communication / customer service strategies 40% 26% 34% 0.32 13% 37% 63% -0.13 On-ground Services • General Feedback 33% 8% 58% -0.17 0% 55% 45% 0.09 On-board Services 26% 26% 48% 0.04 8% 79% 21% 0.67 Punctuality 38% 7% 55% -0.10 0% 3% 97% -0.94 Corporate 25% 73% 3% 0.95 3% 27% 73% -0.44 Ranking Metric Aspects (verticals) of the air carriers Share of each sentiment Area of Concern
    • PR Crisis Alert • • • PR Crises now-a-days generally tend to start from social media and then at some point of time hit mainstream media Monitoring social media platforms on an ongoing basis can help identify an emerging crises A timely response management system can help prepare for the crises and ensure that a major negative PR event gets averted Mainstream Media Extremely High Internet Publications High Rapid Sharing Moderate Initial Conversations First appearance Low Harmless 0 hours 5 hours 8 hours 15 hours 24 hours
    • Identifying Sales Opportunities • Just as brands are looking for customers, the customers are also looking for products • Social Media helps identify situations where a potential customer may be looking for your brand • One can then guide the conversation with that customer into a sales opportunity
    • Generating Business Intelligence • • • • • Listening to conversations on Social Media allows brands to capture data which they would have otherwise missed This could have been data about their product, brand, service, category or industry This data – which is conversations among people, can be scrutinized to extract business intelligence This could be predictive information about sales, a perception matrix about your brand or product, among other types of intelligence This is actionable intelligence, which you can use to take more informed decisions
    • Radian6 – The Enterprise Monitoring Tool • • • • Great Pedigree – A part of Salesforce.com, a $20bn market cap company – Global presence Robust Technology – Fetches conversations from depth of digital universe – Real-time data discovery – Relationships with Twitter, Facebook etc for live feeds Market Leader in Monitoring Tools – Clients include Pepsi, Dell, L'Oreal, Fuji Film, UPS, 3M, Commonwealth Bank, KLM, Queen’s University, Mayo Clinic, Edelman, Golin Harris, Bissel, Crocs, Intuit, Durex, Airwick, Clearasil, Nurufen, Reckit Benckiser, Bell Aliant, Southwest Airlines, Microban, Dettol, and many more Excellent User Interface and Reports – Customisable UI – Real time analytics
    • Monitoring Tool AAmix of social media conversations both mix of social media conversations both relevant and irrelevant to our search relevant and irrelevant to our search To extract relevant conversation Human Intelligence To convert data into actionable intelligence Purely relevant conversations Purely relevant conversations
    • Predicting Personality Traits • Paper published by Kaggle.com (2012) Carried out an experiment that involved analyzing 2927 Twitter user handles • Profile attributes of the handle as well historic Twitter data was analysed • 586 different features were studied • – Friends, Followers, Number of tweets, Number of RTs – Average number of followers of my followers, use of predefined words – Use of pronouns (“I”, “We”)
    • Predicting Personality Traits • More attributes – – – – Use of swear words Use of numerals in the Tweet References to family and friends Emotions expressed Were able to predict and correlate behaviour of a person with the words the person uses • In spite of the fact that a person may be very careful about what he Tweets, it is the choice of words that he uses to communicate that gives away his personality •
    • Predicting Outbreak of Diseases • Project by the US Centre for Disease Control and Prevention • By looking at spike in search times in Google results is able to predict outbreak of Flu or Dengue epidemic (Google.com/ FluTrends) • By looking at Twitter and other conversations on social networks is able to track diseases and natural disasters (Mappyhealth.com)
    • Screen Shot 2013-12-05 at 10.01.51 PM.png
    • Twitter Mood Predicts Stock Market • Research done by Johan Bullen and 3 other researchers are University of Cornell • “Using tools like OpinionFinder and GPOMS, which measures mood in terms of 6 dimenions (Calm, Alert, Sure, Vital, Kind, Happy), we cross validated market swings with mood swings We find an accuracy of 87.6% in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6%.” • New Business Model : Zingfin.com
    • BlueFins.com : Social TV
    • Online Market Research • Client profile • – More than 100 communities of diabetes patients / care givers were identified – More than 3000 conversations over a 90 day period were mined, classified and analyzed – This analysis was used to help the brand gain insights into factors that influence the buying pattern – Global pharmaceutical brand • Solution Challenge – Wanted to some insights pertaining to factors that influence the buying pattern for patients with diabetes • Outcome – Research done on social platforms corroborated findings from a traditional market research exercise which was also commissioned by the brand
    • Questions? If you need a copy of this presentation, please leave your business card. We will email it to you. Hareesh Tibrewala Jt. CEO, Social Wavelength. hareesh@socialwavelength.com Company blog: blog.socialwavelength.com LinkedIn: linkedin.com/in/hareeshtibrewala