Sherman Mohr, CEO Understanding Data Mining in the Social Media Age I’m sharing this research and comments in association with a presentation made Nov 21 st at the Association for Career and Technical Educators in Nashville, TN 2014. It ’ s slide orientation coincides with the presentation that exist on Slideshare.net at http://www.slideshare.net/shermanmohr/understanding-data-mining-in-the-social-media-marketing- age Intro Slide: Hi and welcome to Nashville! How many of you have visited Music city in the past? Have you noticed some changes? After 25 years here, I can attest, there are some rapid changes taking place. Changes that are likely fresh in your experience are our hotel rates. Wow! I did a study for an article I wrote on the Sharing Economy, specifically Uber, Lyft and Airbnb here in Nashville and found some interesting stats. A significant event coordinator in Nashville shared with me some time ago that our new Music City convention center increased ou r city’s capability to host events to the extent Nashville qualified for 77% of the conventions in the world. However, Nashville only truly qualifies for 32% of those conventions due to a shortage of hotel rooms. This means, an online search for hotel rooms will generally turn up rates that are matching San Francisco and Manhattan. It’s far too true, I’ve researched the side by side comparison. How many of you go online to read a review prior to purchasing an item or having work done on your house or car? What are some of the ways you research services or products? Expedia, Hotels.com, Branded sites? 58% of Americans perform online research about the products and services that they are considering purchasing. Source: Jim Jansen, Pew Research Center’s Internet and American Life Project, 2010 Over 1 million people view tweets about customer service every week. Roughly 80% of those tweets are negative or critical in nature. Source: Touch Agency Bright Local states that trust in online reviews are believed by only 13% of the readership in 2014 while in 2011 it was 33%. We’re growing more skeptical. I couldn’t find the statistical source but it is said that people only believe 25% of what is shared with them by strangers. Mark Twain said “There are lies, damn lies, and statistics” with that in mind, I may have a 25% chance of your believing what I share with you today. Slide 2: This is when the internet and the online world of data became real to me. My university economics professor Walter Johnson, at Mizzou, cited terrifying specifics about the accuracy of missiles during lectures and I didn’t pay much attention. 27 years later I’m pulling up Google maps and I see the satellite images detailing what side of the house and office I happen to be located in or at least where my phone
Sherman Mohr, CEO is located. You’ll see how this relates to our data mining discussion in just a bit. Have you paid attention to what your phone provides to marketers? We’re going to discuss some of those details today. Our preferences, comments, sharing, and online community involvement is being analyzed. The analysis is so subtle that most participants don't even notice. I cover in this discussion how marketers are gathering, extracting, analyzing and then building advertising campaigns around social media participation. In some cases the data gathering is nefarious, those efforts get the headlines. Most of us marketing types seek to exchange something of value in return for your giving up the details of your life. We want to play within ethical boundaries and perform better for our companies and clients. Slide 3: My motivation around data. My company, Shared Spirits, Inc. is a data gathering business model. Without boring you too much, allow me to explain my interest in the data gathering and data mining business. A couple of years ago, a friend and I were sitting at a bar having a drink and he asked if I’d ever seen a particular game on Facebook? I said I had and asked; Why? He said he wanted to do the same thing except in real life. In other words, he had a practical application of something that had been virtualized in the form of a game. In this particular case, the application surrounded the ability to buy a person a drink from any venue in the world that happened to be in our network and send that drink to a contact so it may be received, redeemed, and tracked all on the smart phone. Why would we want to do this? It’s all about the data. Casual drinkers are some of the most affluent people in the market place spending over 1% of their annual incomes on adult beverages. 144,000,000 million Americans attest to having had a drink in the last seven days. The current way spirits, wine, and beer brands measures success is through case counts. Our app will provide brands a way to build relationships specifically with their buyers, all through our technology of course. As a result of this activity over the last three years, I’ve become far more interested in the way marketing messages are built into technology. I’m a b eliever in the premise that marketing now has to be delivered inside a utilitarian skin so to speak. Some image advertising will never go away of course but advertising that doesn’t serve to build real relationship along with the ROI will be missing the bo at. Slide 4: What is data mining? Dr. Bruce Ratner reminds us that there is a problem with the definition of data mining. Data mining is not at all well - defined. He states; “Today’s data mining is a high -concept: having elements of fast action in its development, glamour as it stirs the imagination for the unconventional and unexpected, and a mystic that appeals to a wide audience that knows curiosity feeds human thought. I g oogled “definition of data mining” and received a gross (vis-à-vis net) number of 40,300,000 definitions! (Curiously, the first entry was “Data mining is
Sherman Mohr, CEO derogatory … ”) To have a sound working assumption for the task at hand, I netted the “gross” google - number to 4,030. (This netting in and of itself, coincidentally reflects that the definition of google’s search engine optimization is also ill-defined.) Suffice it to say that data mining is an ill-defined concept, as 4,030 definitions are clearly not needed to unambiguously explain the concept. Unprecedentedly, the data mining concept early on (circa 1970s/early 1980s) did not have, and currently does not have the scholarly cause to take form. I conclude that data mining is an ill-defined concept. And, I declare that the net number of definitions suggests there are discipline-specific data mining definitions; but how many are there: 18, 36, 54, … ? [2] Regardless of an agreed number of disciplines, 4,030 divided by the “agreed - number” presents data mining p roper or data mining discipline-specific as an ill-defined concept. “ Dr. Ratner goes on to explain, “Today, statisticians accept data mining only if it embodies Tukey’s EDA Paradigm. (exploratory data analysis )[3, 4] They define data mining as any process that finds unexpected structures in data and uses the EDA framework to insure that the process explores the data, not exploits it. Note the word “unexpected,” which suggests that the process is exploratory, rather than a confirmation that an expected structure has been. By finding what one expects to find, there is no longer uncertainty as to the existence of the structure. Statisticians are mindful of the inherent nature of data mining and try to make adjustments to minimize the number of spurious structures identified. In data mining the statistician has no explicit analytical adjustments available, only the implicit adjustments affected by using the EDA paradigm itself.” Tukey himself summed up his definition of EDA as follows: "If we need a short suggestion of what exploratory data analysis is, I would suggest that: 1. it is an attitude, AND 2. a flexibility, AND 3. some graph paper (or transparencies, or both)." https://www.stat.berkeley.edu/~brill/Papers/EDA11.doc Traditional marketing and sales researchers view sales data in macro-economic ways. In other words, sales and marketing pros from traditional schools of thought don’t always believe in the digital vie w of our world. They tend to remain focused on the print, TV, billboard, and radio versions of the narrative that companies want shared. I classify traditional marketing as pretty much anything that is “pushed” onto the listener or reader. If the audience wasn’t asked to participate in the results or the campaign in some proactive way, that’s a sure sign that the advertising was traditional. Slide 5: Data gathering in traditional marketing and traditional market research. Traditional marketing research often involves assessing the overall market for a good or service, surveying consumers about their likes and dislikes, and conducting focus groups to gauge consumer responses to a new product. The growth of information technology has transformed market research, with a growing number of analysts learning about consumer preferences and buying habits by mining massive sets of quantitative data and employing complex algorithms to uncover patterns and correlations that enable more effective marketing. While data mining emphasizes extracting predictive information about customers and sales from large databases, traditional marketing research focuses on identifying factors that influence the buying decisions of households and organizations. Relevant data is then collected, often through sales data,
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