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MotiveQuest and Big Data 4.16.13 slideshare version
 

MotiveQuest and Big Data 4.16.13 slideshare version

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A perspective on Big Data presented by Online Anthropologist David Rabjohns, CEO MotiveQuest at the Kellogg Innovation Network (KIN) Catalyst in April 2013.

A perspective on Big Data presented by Online Anthropologist David Rabjohns, CEO MotiveQuest at the Kellogg Innovation Network (KIN) Catalyst in April 2013.

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  • Hi, My name is David and I am a Big data-a-holic.I have been in the big data business for ten yearsUntil 5 years ago I was drowning in the stuff. Has anybody else here felt that way?Well Finally I hit rock bottom and had an epiphany.
  • Today I am going to share what I learned in that moment1 big insight about how to deal with big data2 Real world Businesscases4 tips for you to take home and use
  • But first another story.OK you all know the story of the emperors new clothes.Two weavers promise an emperor a new suit of clothes that is invisible to those unfit for their positions, stupid, or incompetent….
  • …when the Emperor parades before his subjects in his new clothes, a child cries out, "But he isn't wearing anything at all!“I believe that many of the high fashion big data gurus are just like those tailors, promising a suit of clothes that doesn’t really exist. It reminds me of the days of CRM. Big promise painful delivery.But first what is big data?
  • Most people pedaling big data today are more like the weavers than the child.
  • What is BIG data?
  • Big data, its simplest form, is just that, lots and lots of data.It took us from the birth of the world till 2003, when I started my company, to generate five exabytes of data ..
  • …we now create the same amount every 2 days. That is a lot of web, Facebook, twitter, forum, sales stuff to wade through
  • For example if Facebook was a country they would be the third largest country in the world.Facebook has 1bn users after only 8 years.That is bigger than the USA.
  • Twitter is at 400m tweets per day.
  • When Obama won the last election itset Twitter's record for the most new tweets-per-second, reaching 327,000 tweets a second at its peak.
  • At its core Big data is all about people andconnections
  • The continuation of a trend that started with the industrial revolution.
  • Age storyAgriculturalIndustrialSocial
  • It can be overwhelming.How many of you have Big data/social data systems at your offices?How many of you have mastered them?
  • OK so here is the secret. Ready
  • The ugly, emperor tailor myth, is that you don’t need all that data.You don’t need big data at all.You actually need the opposite.
  • What you really need is smart data.
  • What is smart data? Smart data is just the right amount of data to solve the specific problem that you have today.
  • I have learned this working on business problems for some of the largest Fortune 500 companies. It is an insight that has helped:- Grow market shareStem customer defections andOpen new sources of revenue
  • How do I know this?It all started when I, stupidly, started a big data company in my basement in 2003.At the time people hadn’t heard of big data or social media and didn't even know what a blog was. Facebook hadn’t been invented and Twitter wasn’t even a twinkle in it parents eye.Yes my friends thought I was nuts..
  • For the first 3 years from 2003 to 2006 I thought that my job was to find and wrap my arms around all the data I could.We built software that could collect and classify the data. We looked at Buzz, sentiment, influence, likes, etc.But it felt unsatisfying. We had so much data and we didn’t know what data mattered most.Luckily my office was in Evanston, IL the home of Northwestern University. So I walked over there to find some smart people to help me.
  • The smart person I found was a brilliant professor and statistician named Jakkie Thomas. We started on a 2 year journey to see if any of the metrics that we had mattered a damn when it came to sales and share.
  • We found online advocacy correlates with offline sales 97% of the time on average.
  • But to move to action you need to understand where the advocacy comes from
  • So what are the 4 secrets you can use when you go home?
  • It would have been easy for Sprint to feel sorry for itself. The brand was without the iPhone as AT&T gleefully added an avalanche of subscribers from other carriers. AT&T’s biggest weakness was its unreliable network, but that was Verizon’s territory. Sprint was playing at a huge disadvantage when it came to model offerings and network perceptions. This disadvantage made it easy for Verizon and AT&T to poach Sprint customers and sent the brand’s defection (churn) rates soaring.  Sprint understood it needed to reassess its approach to its customers and figure out a way to combat their steady exodus, knowing that no single model would be their savior.  GOAL: Sprint needed to dramatically reduce the number of customers leaving the network.
  • We aggregated carrier advocacy conversation – messages where one brand was being actively recommended over another – to understand what drove recommendation and retention.Initially we harvested over 10 million motivationally representative consumer conversations over a 14 month period, from major message forums, discussion boards, and newsgroups in the Cellular and Tech category.  Nextwe organized the data so that three distinct participant groups could be isolated and compared with each other in terms of content and patterns of behavior – churn driver, advocate and individual participant.  Thenour strategists applied proprietary linguistic analysis software tools to these conversations, building custom models to gauge passion, sentiment, and to identify the fundamental drivers that underscore behavior relative to decision-making and subsequent behavior in the context of the cellular carriers. Finally we developed an approach to segment community members by their role in driving others towards or away from Sprint. For example, Sprint advocates throughout the data period were isolated with their conversations analyzed to understand what drove loyalty. Conversely those leaving the brand had their conversations analyzed separately to contextualize the appeal of Sprint’s competitors. This behavioral approach allowed us to focus on Sprint’s sphere of influence. An obvious panacea could have been the iPhone, but what else do cellular advocates care about and is it something that Sprint can change in the more near term?
  • ADVOCATES’ REASON TO STAYWe identified the carrier advocates for each brand and analyzed their conversations. What was getting them excited? Why were they recommending one carrier or another? It quickly became clear through the naturally occurring advocacy conversation that there were three primary reasons to stay loyal to a carrier:  Customer ServiceModels CoverageThis focus on advocates’ reasons to stay shifted Sprint’s view of the challenge. By focusing more closely on what creates advocates vs.. what attracts new customers, the entrant was able to provide a roadmap for addressing churn by shoring up current customers. Those customers, serving as the brand’s grassroots supporters, would in turn be more likely advocate for Sprint, recommending the carrier to others and reverse churn’s rising tide. When we quantified the advocates’ reasons to stay by looking for opportunities, Sprint’s challenge became starkly illustrated. In the topics that drove most advocacy conversation – customer service, models and network – Sprint found itself behind the category leaders. 
  • Which reason to stay should be the focus? Sprint’s model line-up had a number of dynamic options in the pipeline, particularly ones leveraging the Android OS. But focusing on models is a dangerous foundation for long term advocacy growth as new models just leapfrog each other in terms of buzz and attention. Coverage is another tricky area given so much is based on perception and location.The best opportunity for focus became customer service. It was the biggest driver of carrier advocacy – even ahead of models and coverage – and Sprint found itself a distant fourth behind Verizon, T Mobile and AT&T. It was an area Sprint needed to overhaul and quickly. Without a proper customer service approach, the arrival of the new smartphone models would inevitably suffer. The shift from cell phones to mobile computers requires even more technical support and Sprint didn’t have AppleCare to bolster their customer service perception like AT&T. The Evo and Epic could be the best phones in the market, but if they couldn’t be properly serviced there would be no meaningful reason for customers to stay with Sprint.
  • DEFINING CUSTOMER SERVICE Once we understood customer service was the key to protecting Sprint’s base, we needed to understand how consumers define service across categories. We analyzed customer service conversations in financial, telecom/electronics, travel, automotive and retail conversations, looking for patterns and like themes. We found that there were seven clusters of conversation that define a customer service experience. People wanted service that was:Knowledgeable Proactive Efficient Trusted Good attitudePersonalized Convenient
  • Need to say something about this slide or lose it
  • The ResultsThe results of focusing on “reasons to stay” and revamping Sprint’s customer service offerings have had wide ranging effects. In December 2010, a Consumer Reports satisfaction survey of carriers ranked Sprint second, indicating that the brand had surpassed Verizon in many aspects of customer service. Consumer Reports called Sprint’s turnaround “remarkable” given the brand ranked last in the survey less than two years prior. A May 2011 report by the American Customer Satisfaction Index showed even greater results, ranking Sprint first in terms of customer satisfaction.
  • So what are the 4 secrets you can use when you go home?
  • If you do these 4 things (in any dataset) you are much more likely to end up with something that you can use to drive action.
  • So, in conclusion, the moral of the story is, don’t believe all the hype focus on the small things that matter – think small - and don’t let yourself be the one that is caught in your underwear.
  • Thanks

MotiveQuest and Big Data 4.16.13 slideshare version MotiveQuest and Big Data 4.16.13 slideshare version Presentation Transcript