univ. of az global racing symposium 2015 - digital strategies

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University of Arizona Global Racing Symposium “DIGITAL MARKETING STRATEGIES” Leveraging The “Back-End” Tools Dec. 8, 2015

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Page 1: Univ. of AZ Global Racing Symposium 2015 - Digital Strategies

University of Arizona Global Racing Symposium“DIGITAL MARKETING STRATEGIES”

Leveraging The “Back-End” ToolsDec. 8, 2015

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Agenda• What is “Big Data”?

- Creating a Frame of Reference- The “4 V’s” – Drivers of Big Data

• Social Media Data Sources & Technology Landscape

• Data Types: Structured vs. Unstructured

• Consumer Insights & Today’s Systems- Implementing Unstructured Data- Data Visualization

Social Media Influencers- The “Klout” Score- Identifying Social Media Influencers- Measuring Influencer Value

• Identifying Racing Data

• Key Takeaways

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What is Big Data?Data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a reasonable amount of time.

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Data Frame of ReferenceMegabyte (MB) - A good sized novel.

Gigabyte (GB) - 1600 books. About 300 MP3s.

Terrabyte (TB) – 1.6M books. 30 weeks worth of high-quality audio.

Petabyte (PB) – 160M books.

Exabyte (EB) – 3000 times the entire content of the Library of Congress.

Zettabyte (ZB) – 1 billion Terrabytes; Two hundred billion DVDs.

Yottabyte (YB) – 1 trillion Terrabytes.

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The “Four V’s” – Drivers of Big Data

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Data Sources

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Social Media Landscape

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Marketing Technology Landscape

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Structured vs. Unstructured - DefinedData is classified as either Structured or Unstructured.

• Structured Data refers to information that resides in a traditional row-column database—like Excel.

• Unstructured Data refers to information that doesn't reside in a traditional row-column database.

NOTE: Experts estimate that 80 to 90 percent of the data in any organization is unstructured.

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Examples of Structured DataStructured Data usually refers to information that resides in a traditional row-column database—like Excel. Here the data is stored in fields in a database

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Examples of Unstructured DataUnstructured Data files often include text and multimedia content. Examples include email messages, word docs, videos, photos, audio files, presentations, etc. This data doesn't fit neatly in a database.

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Consumer InsightsToday’s systems can structure the unstructured, then correlate key internal data with the relevant social media universe – revealing new, actionable insights.

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Implementing Unstructured DataBig Data ToolsSoftware like Hadoop or Oracle Endeca can process both unstructured and structured data that are extremely large, very complex and changing rapidly.

Data Integration ToolsCombine data from disparate sources to be analyzed from a single application & the capability to unify structured and unstructured data.

Search and Indexing ToolsThese tools retrieve information from unstructured data files such as documents, Web pages and photos

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Data VisualizationData visualization is the presentation of data in a pictorial or graphical format.

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Social Media InfluencersA Social Influencer is one who:• Has the maximum followers• Can influence others easily• Creates and shares content regularly

Benefits of identifying Social Influencer:• Leverage 3rd-Party credibility (others)• Expand the message & the business

How to Identify the Influencers:• Scores like “Klout” score are available to measure the influence of

someone in social media

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The Klout Score• Klout is a digital service that uses social media analytics to rank its users

according to online social influence via the "Klout Score“

• Klout measures influence by using data points from various sites– Twitter, Facebook, Google+, LinkedIn, Instagram etc., and Klout itself.– Count, follower count, retweets, list memberships, influential follower

retweets unique mentions. Information is blended with data from other social network followings & interactions to come up with the Klout Score.

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Identifying Social Media InfluencersWhen developing an influencer outreach campaign, make

sure you’ve got a good “READ” on the situation!

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Measuring Influencer ValueSeveral Key Performance Indicators (KPIs) can be combined into meaningful

ratios to help measure audience activity and engagement.

• Sentiment• Re-tweets• Forward to a friend• Social media sharing• Comments• Like or rate something• Reviews• Contributors and active contributors• Page views• Unique visitors• Traffic from social networking sites• Time spent on site• Response time

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Identifying Racing Data• Wagering volumes, on-track/off-track/on-line• Loyalty program information• Attendance• Non-wagering revenues (F&B, Parking, Merchandise)• Social Media sites, racing blogs• Odds• Race quality/types• Results• Handicapping data• Performance history• Wager types: win, place, show, exotics• Weather• Seasonality• Track condition• Special events

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Key Takeaways• Marketing Analytics is the primary opportunity-driver of business growth

going forward. • “Dip your toe in the water now!”

• Unstructured data will reveal new value and actionable insights.• Operators need to take the long-view and invest to grow the sport.

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REFERENCE INFORMATION

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Social Media – Behavioural SegmentsDepending on the Goal, one can select the specific segment of users. Social Analytics Tools help to identify the following from Social Data:

• Influencers• Recommenders• Detractors

The above combined with customer and transaction data would give necessary insights into customer behavior.

The social users can also be segmented by demographics, geographies, which would provide information that might not be captured in CRM.

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Social Communication Metrics

• Alerts (register and response rates / by channel / post click activity)

• Bookmarks• Comments• Downloads• Email subscriptions• Fans (become a fan of something / someone)• Favorites (add an item to favorites)• Feedback (via the site) • Followers (follow something / someone)• Forward to a friend• Groups (create / join / total number of groups / group

activity)• Install widget (on a blog page, Facebook, etc)• Invite / Refer (a friend)• Key page activity (post-activity)• Love / Like this (a simpler form of rating something)• Messaging (onsite)• Personalization (pages, display, theme)

• Posts• Profile (e.g. update avatar, bio, links, email, customization,

etc)• Print page• Ratings Registered users (new / total / active / dormant /

churn) • Report spam / abuse Reviews Settings Social media

sharing / participation (activity on key social media sites, e.g. Facebook, Twitter, Digg, etc)

• Tagging (user-generated metadata) • Testimonials • Time spent on key pages • Time spent on site (by source / by entry page) • Total contributors (and % active contributors) • Uploads (add an item, e.g. articles, links, images, videos) • Views (videos, ads, rich images) • Widgets (number of new widgets users / embedded

widgets) • Wish lists (save an item to wish list)

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Sentiment Analysis

Sentiment Analysis – uses natural language processing, text analysis and computational linguistics to identify and extract subjective information in source materials. It aims to determine the attitude of a writer with respect to some topic or the overall contextual polarity of a document.

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Sentiment KPI’s• Total Conversation about a topic, activity or initiative• Number of Positive Conversations about topic, activity or initiative• Number of Negative Conversations about a topic, activity or initiative• Ratio or Percentage of Negative Conversations/Total Conversation • Ratio or Percentage of Positive Conversations/Total Conversations

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Data Source Gnip DataSift DescriptionTwitter √ √ Microblog site

Facebook √ √ Social networking site

Sina Weibo √ Chinese microblog site

WordPress √ √ Open-source blog tool & CMS

Intense Debate √ √ Blog comments & host

Tumblr √ √ Multimedia microblog

Foursquare √ Social network for mobile

Yammer √ Enterprise social network

LexisNexis √ Legal & public records database

Google+ √ √ Social network & authorship tool

YouTube √ √ Video sharing website

Bitly √ √ URL shortening service

Instagram √ √ Photo & video sharing

NewsCred √ Content & syndication platform

Reddit √ √ Entertainment & news website

Wikipedia √ Collaboratively edited internet encyclopedia

Daily Motion √ √ French video sharing website

Topix √ Tokyo stock price index

IMDb √ Internet movie database

Disqus √ Blog comment hosting

Estimize √ Community of stock analysts & traders

Sitrion √ Social & collaboration software

Flickr √ Image & video hosting

MetaCafe √ Video sharing – short form

Panoramio √ Geolocation photosharing

Photobucket √ Image & video hosting

Plurk √ Social network microblog

Stackoverflow √ Q&A site for computer programming

Vimeo √ Video sharing website

VK √ Russian social network

Stocktwits √ Social media sharing for investors & traders

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Page 27: Univ. of AZ Global Racing Symposium 2015 - Digital Strategies

Thank You!

Sean Frisby

[email protected] ・ www.brandtenet.com

520-288-6910