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Be a Big Data Voodoo Daddy or Mama)
 

Be a Big Data Voodoo Daddy or Mama)

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A few chapters on how to weave the the current big data / marketing technology hairball into a tapestry of customers, each as unique as a thumbprint

A few chapters on how to weave the the current big data / marketing technology hairball into a tapestry of customers, each as unique as a thumbprint

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  • News: what’s happening in the world Cultural and consumer trends: each datapoint represents a person’s attitudes Corporate trends: what are world events, cultural and consumer trends doing to marketers’ agendas? Tool Pool – a thematic map of the tech players diving into the marketing tech spaceDemos – 1: small data; 2: larger data Test methodology – Under the dashboard, what’s going on? What do analysts do? Use cases – 1: small data; 2: larger data Ways to test your own data: a few analyst tools 7 Quiz Questions – Basics about data quality 5 Public sector use cases – big data put to practical use Future events, resources – for people following the topic; resources cited in this presentation. This preso is available as a clickable .pdf so you can dive into any topic discussed here. Let’s look at the news.
  • Next we will look at corporate news affecting big data in marketing
  • But there is hope. It’s now front and center. I subscribe to a dozen periodicals, and every single one of them has a headline each week on the subject of Big Data. The Boston Sunday Globe has a “Globe Magazine” which is usually filled with puff pieces. Society events, dating advice, beautiful homes, oh…and big data. Oct 14 cover article is about retail grocery chains analyzing consumer behavior to refine their niche and better target their customers. What’s next: Tiger Beat? People Magazine? Or…..
  • Emerging stages - Big data has actually been a topic in larger enterprises for some time. It’s just moving down market, as we create more and more data that’s useful to organizations of all sizes. Mainly departmental – many of the tools you’ll see discussed here are, relatively speaking, silo solutions, and many address the online datastream but not how to combine it with offline data from POS, mail, retail receipts, and other behaviors not manifested in the digital sphere. Intuition – experience based judgment – you need human circuit breakers to avoid running off the rails. We still encounter executives who decide that since an email campaign worked well today, we should send one again tomorrow - not considering inbox fatigue. Data challenges – quality data is everyone’s biggest challenge. Do you trust the data under your dashboard? Is that colorful meter’s needle pointing in the right direction? If not, and it’s discovered too late, your exec team loses trust in the dashboard, and then where are you? Talent shortage – time and again, the Forrester and IDG surveys show CMO saying they are understaffed, or the people with the right skills are scarce.
  • Twenty year span of changing attitudes. Anybody born after 1980 doesn’t have the benefit of this hindsight.
  • Millenials are concerned about security of account information, but they balance that concern with optimism that we’ll use this new power only to do good. The trust we’ll tailor the buying experience to the preferences they’ve been telegraphing in their digital behavior. And plenty of shining, aspirational examples exist. How did the world find out about the raid on Bin Laden’s compound? How did the neighboring countries of North Africa unite in revolt (Arab Spring)? Four years ago I struck a Faustian bargain with an event management company (GSMI). At the time, I was DirMktg for CuraSoftare, a Risk Mgt SW co. I helped emigrate from S. Africa to exploit the US market, where most of their target market is headquartered (Delaware). I / we had build such an audience in under a year based on our thought leading webinars in which we highlighted some breakthrough thinking on the subject of risk management, the foundation for our product framework, that we had an entire industry following us. We found that we were the ones putting the cheeks in the seats for GSMI’s entire risk mgt conference. Wait, it’s our audience, why pay to be a Sponsor?
  • Now that we’ve look at consumer trends in attitudes about Big Data, Let’s look at some Corporate trends
  • Some of these tools are better than others for how well, how reliably they help you solve business problems. Shortly we’ll look at a basic methodology you can apply directly to data – with or without one of these tools layered on top – to determine how well you are solving a business question.
  • Whether you are looking directly at the data, or laying a Cool Tool from the Pool on top of a set of data, you still have to follow some sort of methodology. In fact, I suggest when you evaluate any candidate from the Cool Tool Pool, that you use this data analysis methodology and ask how well it follows the methodology. If you can clearly understand how well it does this, you will then be able to determine how much time it will save, how much faster it will get you a reliable answer, and ultimately the ROI case you can build for adopting that cool tool.

Be a Big Data Voodoo Daddy or Mama) Be a Big Data Voodoo Daddy or Mama) Presentation Transcript

  • Be a Agenda / Menu 1
  • Be a Big Data Voodoo Daddy Agenda / Menu# BDVD # FutureM 2
  • Be a Big Data Voodoo Daddy (or mama) Agenda / Menu # FutureM 3# BDVD 3
  • # BDVD Ed Alexaner Ed Alexander, Managing Consultant @fanfoundry Agenda / Menu# BDVD # FutureM 4
  • Agenda / Menu ( = link button ) What is it? News and Views Cultural and consumer trends Corporate Trends Technology Landscape (the Cool Tool Pool) Demo Time A Test Methodology (BADIR) Use Cases Ways to test your own data Get Better Data (7 Quiz Questions) Public & Private Sector Data Mashups Get Real (time) Summary, Future Events, Resources # BDVD # FutureM 5
  • Defining “big data” – the four V’s: # BDVD # FutureM 6
  • If DataCouldTalk…# BDVD # FutureM 7
  • If ( ) DataCouldTalk…# BDVD # FutureM 8
  • Challenges – tooling up to: • Capture, combine and curate • Store, search and share • Analyze and visualize # FutureM 9 # BDVD 9
  • Cross-channel marketing challenges 35% - Managing campaign execution across multiple channels 33% - Understanding customer interactions across channels 25% - Controlling marketing budgets that depend on IT collaboration Source: “ The Key to Successful Cross-channel Marketing”, an Oct. 2012 Forrester / ExactTarget survey of 211 US marketers # FutureM 10 # BDVD 10
  • Opportunities • Internet search • Business informatics • Medical research • Genomics • Astronomy • Aviation • Meteorology • Finance # FutureM 11 # BDVD 11
  • Sources – 2 new quintillion bytes / day • Sensors • Mobile devices • Cameras • Microphones • Social graph – UGC # FutureM 12 # BDVD 12
  • The news, in general… The worst economic crash in 75 years A world economy with no place to hide “Always on” connectivity Widespread distrust of business Activist shareholders and special interest groups How does it impact your marketing agenda? # BDVD # FutureM 13
  • Big Data in the news… # BDVD # FutureM 14
  • What next? (kidding!) # BDVD # FutureM 15
  • What next? (kidding!) Special Big Data Issue # FutureM 16 # BDVD 16
  • What next? Not kidding! Sunday magazine article - upshot: • It’s about big data, not Wal-mart • The customer has all the power Example: Kroger (coupon response) • 70% of targeted • 3.4% of mass mailed Analysts & Techs Quoted: • Kantar Retail • Symphony IRI Group • Catalina Marketing Modiv Media’s “Scanit!” device • 89 Degrees # FutureM 17 # BDVD 17
  • The corporate view: big data in marketing Emerging stages – some business sectors have gone mainstream; Marketing is tooling catching up Mainly departmental - not much data integration or sharing Intuition based on business experience is still a driver; data analytics plays a supporting role Data challenges persist: accuracy, consistency, access, realtime Talent shortage - challenges business to apply results Culture’s role: orgs with a “culture of measurement “ succeed # BDVD # FutureM 18
  • The corporate view: big data in marketing Bloomberg Business Week Research Services # FutureM 19 # BDVD 19
  • The corporate view: big data in marketing Bloomberg Business Week Research Services # FutureM 20 # BDVD 20
  • The corporate view: big data in marketing1. CXOs now paying attention. Why?• Compete – lead up, catch up, patch up PR• Add Predictive Intelligence – detect, adapt, seize opportunity• Optimize - avoid leaving money on the table2. Elusive answers are suddenly more attainable everywhere Operations, Sales, Marketing, Customer Care, R&D, etc.3. Transformation can now be justified with data + judgment• Managers are now analysts who produce & consume data• Managers leverage business savvy to interpret and act on data4. Priorities can be tuned• Identify top few “needle mover” opportunities and focus on them• Decision support can gain visibility based on proven results 21 # BDVD # FutureM 21
  • Cultural trend: Data-driven, custom communication # BDVD # FutureM 22
  • Cultural trend: Data-driven, custom communication1992: sad :(PointCastIntrusiveIn your faceOff-targetPoor quality # BDVD # FutureM 23
  • Cultural trend: Data-driven, custom communication1992: sad :( 2002: mad ):PointCast “Push sux”Intrusive SubversiveIn your face IntrusiveOff-target SpookyPoor quality Invasive # BDVD # FutureM 24
  • Cultural trend: Data-driven, custom communication1992: sad :( 2002: mad ): 2012: rad! :)PointCast “Push sux” I want my MDVIntrusive Subversive WelcomeIn your face Intrusive ExpectedOff-target Spooky PreferredPoor quality Invasive …but secured? *MDV: Massive Data Visualization # BDVD # FutureM 25
  • The new consumer demand: “I want my MDV”: We’re always on, and doing it now - • Showrooming • Facebooking • GPS navving • Socializing – Foursquare, Twitter, Instagram, etc. • Shopping & Banking • Customer care • Audience & Community building • World blending (ex: QR, text, POS, Call Center # BDVD # FutureM 26
  • The new consumer demand: “I want my MDV”: Millenials are Digital Natives – mobile, social and always on They blur the lines between the digital and physical world They are less concerned about what’s going on with their data * By 2020, they will account for 50% + of retail spending Post-millenials are growing up digital * They seek trust, transparency and authenticity # FutureM 27 # BDVD 27
  • Corporate Trends # BDVD # FutureM 28
  • Big Datas Shifting Focus: Transaction > Engagement Personal Systems Analog Transaction Engagement Experiential Fulfillment Circa Pre-1950s 1950+ 2000+ 2005+ 2010+ Reliability & Continuous Sense and Agility and Intention Design Point stability improvement response flexibility driven Challenge Human Computing Social Contextual Individual Comm. Style Analog Systems Dictatorial Conversational Role tailored Personalized Multi-channel, Bionic, Social-led, UX Physical Machine based real time portable omni-media Time / space Speed Governed Just in time Real time Right time continuum Corporate & Personal, Reach Physical Corporate Value chains Internet one to oneInformation & structured Immersive Self-aware, Word of mouth Knowledge flows Knowledge records & data information embedded Social Tangentially Fundamentally Pervasively Ubiquitously Water cooler orientation social social social social Intelligence Human based Hard coded Business rules Predictive Pattern based Loyalty, Social Community & Examples assembly line Payroll, ERP, CRM reward, games, relationship social business context managementSource: R Wang & Insider Associates, LLC. # FutureM 29 # BDVD 29
  • # BDVD # FutureM 30
  • # FutureM 31# BDVD 31
  • Gartner: 72% have a “CMTO” today # BDVD # FutureM 32
  • http://www.emarketer.com/Article.aspx?R=1008909# BDVD # FutureM 33
  • ( What, no real time? ) 72% http://www.emarketer.com/Article.aspx?R=1008909# BDVD # FutureM 34
  • Technology Landscape (Cool Tool Pool) DAM SEO Email Testing & Search & PPC ads Marketing Optimization VIdeo Landing Site add-ins Web sites Pages Marketing E-commerce SM Ads Automation Webinars Targeting Display adsCRM Community Personalization SM marketing Call center B2B Data Multi-channel Gamification Analytics Mobile Databases Design Creative Chat Big Data Events Video ads Datasets PR APIs Surveys Collaboration Cloud Business Customer Loyalty Intelligence Experience Location Agile # BDVD # FutureM 35
  • Technology Landscape (Cool Tool Pool) # BDVD # FutureM 36
  • Stretch Goals for Cool Tools1. Rapid time to value - always on, omni-channel, user chummy for staff and customers2. Point and click customization - user-driven, brain dead simple3. 360 degree customer view – every salient data source linked, integrated and secure4. Real time visibility - instant refresh for all customer-facing and decision making (tactical) occasions5. Clean data - easy for all users to maintain, inspect and fix6. High adoption - self-training, guided navigation, less clutter7. Extended success – new capabilities & advantages8. Broad community - best / better practice sharing – each one teach one # FutureM 37 # BDVD 37
  • The payoff: central data + cool toolsStrategic Goals1. Boost productivity and efficiency• Centrally accessible, multichannel marketing data• Serves across addressable marketing channels• Easier to find and act on than data trapped in silos. 2. Reduce costs, improve marketing productivity Centralized multi-channel marketing data:• Improves ability to target and glean subscriber intelligence• Improves efficiency of data intelligence tasks• Improves organizational alignment 3. Enhance customer segmentation and personalization• Consistent view into multichannel customer data• Improve segmentation, 1:1 personalization, relevance # FutureM 38 # BDVD 38
  • The payoff: central data + cool toolsTactical goals• Campaign analytics and testing• Optimization, Acquisition, Lead Generation• Predictive Modeling – what is your killer niche?• Segmentation / Personae – who acts how?• Attribution precision – across channels, online and offline• Valuation of social media• Design testing (multivariate testing) • Websites • Emails • Offers • Messages # BDVD # FutureM 39
  • It’s Demo Time!# BDVD # FutureM 40
  • Framing the Discussion (Surprise!)It’s not about data & dashboards, it’s about culture & context.Ask: how can data help solve problems and guide decisions?1. Decide which challenges you’d like to address. Examples: reducing customer churn ● improving sales reducing inventory cost ● improving upsell / cross sell improving service ● improving user experience2. Develop a use case – customers, partners, departments, staff3. Run a pilot project – involve those end-users4. Invest in ways that will help meet your challenges. # BDVD # FutureM 41
  • A Test Methodology: BADIR Business Analysis Data Insights Recommend Question Plan Collection Solutions # BDVD # FutureM 42
  • A Test Methodology: BADIR Business Analysis Data Insights Recommend Question Plan Collection Solutions Sidebar: Use BADIR not only to test and report on data, but to vet those Cool Tools. Ask: Does that “cool tool” help break down silos? Does it support integration of processes and data? Okay, moving on… # FutureM 43 # BDVD 43
  • A Test Methodology: BADIR Business Analysis Data Insights Recommend Question Plan Collection SolutionsVague: Hypothesis: Specific: Choices: How do yourHow should I What business Only collect The right findings answerimprove my beliefs will we the data you methodologies the businessmarketing test, and how? need and techniques question?spend?Specific:How can Iidentifyunderservedcustomers? # BDVD # FutureM 44
  • Case Study #1: Business Analysis Data Insights Recommend Question Plan Collection SolutionsVague: Hypothesis: Specific: Choices: How do yourHow should I What business Only collect The right findings answerimprove my beliefs will we the data you methodologies the businessmarketing test, and how? need and techniques question?spend?Specific:How can Iidentifyunderservedcustomers? # BDVD # FutureM 45
  • Case Study #1: Business Analysis Data Insights Recommend Question Plan Collection SolutionsVague: Hypothesis: Specific: Choices: How do yourHow should I What business Only collect The right findings answerimprove my beliefs will we the data you methodologies the businessticket sales? test, and how? need and techniques question?Specific:How can Iidentifyproductiveticket salesinitiatives? # BDVD # FutureM 46
  • Case Study #1: Business Analysis Data Insights Recommend Question Plan Collection SolutionsVague: Hypothesis: Specific: Choices: How do yourHow should I What business Only collect The right findings answerimprove my beliefs will we the data you methodologies the businessticket sales? test, and how? need and techniques question?Specific: Hypotheses:How can I 1. Will an early bird discount sell tickets?identify 2. Will a promo code help sell tickets?productive 3. Will a promo code stimulate referrals who buy?ticket sales 4. Will people still buy at full price?initiatives? Let’s analyze current data # BDVD # FutureM 47
  • Case Study #1: Business Analysis Data Insights Recommend Question Plan Collection SolutionsVague: Hypothesis: Specific: Choices: How do yourHow should I What business Only collect The right findings answerimprove my beliefs will we the data you methodologies the businessticket sales? test, and how? need and techniques question?Specific: Hypotheses: QTY PCTHow can I 1. Will an early bird discount sell tickets? . . . . . . . . . 231 28%identify 2. Will a promo code help sell tickets? . . . . . . . . . . . 149 19%productive 3. Will a promo code stimulate referrals who buy? 262 32%ticket sales 4. Will people still buy at full price?. . . . . . . . . . . . . . 168 21%initiatives? 810 # BDVD # FutureM 48
  • Case Study #1: Data Insights Collection QTY PCT 231 28% 149 19% 262 32% 168 21% 810 # FutureM 49 # BDVD 49
  • Case Study #1: Data Insights Collection QTY PCT 231 28% 149 19% 262 32% 168 21% 810 # BDVD # FutureM 50
  • Case Study #1: Data Insights Collection QTY PCT 231 28% 149 19% 262 32% 168 21% Community 810 # FutureM 51 # BDVD 51
  • Case Study #1: Business Analysis Data Insights Recommend Question Plan Collection SolutionsVague: How do yourHow should I findings answerimprove my the businessticket sales? question?Specific: QTY PCTHow can I 231 28%identify 149 19%productive 262 32%ticket sales 168 21%initiatives? Community 810 # FutureM 52 # BDVD 52
  • Case Study #1: Business Analysis Data Insights Recommend Question Plan Collection SolutionsNext up:MultichannelattributionBehavioralScoring Hypotheses: QTY PCTSocial Sharing 1. Will an early bird discount sell tickets? . . . . . . . . . 231 28%impact 2. Will a promo code help sell tickets? . . . . . . . . . . . 149 19% 3. Will a promo code stimulate referrals who buy? 262 32%Geo/Pop/Wealth 4. Will people still buy at full price?. . . . . . . . . . . . . . 168 21% 810 # FutureM 53 # BDVD 53
  • Case Study #1: Business Analysis Data Insights Recommend Question Plan Collection SolutionsNext up:MultichannelattributionBehavioralScoringSocial SharingimpactGeo/Pop/Wealth # FutureM 54 # BDVD 54
  • Case Study #1: Business QuestionNext up:MultichannelattributionBehavioralScoringSocial SharingimpactGeo/Pop/Wealth # FutureM 55 # BDVD 55
  • Case Study #2: Catalog Retailers (national brands) # BDVD # FutureM 56
  • A Marketing Optimization Map PLANNING OPTIMIZATION WEB SERVICES ENGAGEMENT MOREM Analytics Optimization Response Internal CA Dashboards Management OR External NK S Reporting RequestE Chat U ManagementT M WebE E Offer ConsumerR Offer Portal Catalog Data R Messaging + Data Catalogs Adapters + Demos & Lifestyle + Life-Stage CUSTOMER ECOMMERCE + Purchase Behaviors DW SYSTEMS + Security & Preferences AND POS Enhancement Client Systems Data # BDVD # FutureM 57
  • Testing your data # BDVD # FutureM 58
  • Ways to test your own dataMultivariate Testing - testing more than one element of an offer,website, email etc. in a live environment. Multiple A/B tests.Grail quest: optimize content across channels and contacts Content Contacts ChannelsLimits:• Time – to obtain statistically valid samples• Complexity – although tooling helps greatly• Computing power – although Cloud apps / hosting helps # BDVD # FutureM 59
  • Where to test?Online is easiest (but offline can be tested, too) Email: • Open, click & convert rates Website: • Landing page conversions • User registration pages • E-commerce checkout processes Offline: POS, Call Center, Catalog, Brochure, Signage, Layout # BDVD # FutureM 60
  • What to test?Effect or response to changes in Physical Appearance Elements• Copy• Layout• Images• Colors (backgrounds, etc.)Effect or response to changes in Content Elements• Price points• Purchase incentives• Premiums• Trial periods # BDVD # FutureM 61
  • Testing’s biggest challenge:Complexity – it happens quickly! Example: To test 3 different images in 3 different locations, you need to test how many possible combinations? a) 9 b) 18 c) 27 # BDVD # FutureM 62
  • Testing’s biggest challenge:Complexity – it happens quickly! Example: To test 3 different images in 3 different locations, you need to test how many possible combinations? a) 9 b) 18 c) 27 # BDVD # FutureM 63
  • Test toolsBrowser side (page tagging)Examples (visit www.whichmvt.com for more) :Server Side (DNS proxy, or hosted in your data center)Examples: # BDVD # FutureM 64
  • Test methodsDiscrete Choice / Choice Modeling (complex)Vary the attributes or content elementsQuantify impact of combinations on outcomesDiscover interaction effectsOptimal DesignIterations and waves of testingConsider relationships, interactions, constraints across elementsTaguchi MethodsReduce variations yet obtain statistically valid test results # BDVD # FutureM 65
  • Get better data # FutureM 66 # BDVD 66
  • 7 Quiz Questions for Better Data1. What data should I have? Look at your core mission, values, vision, strategy • What 5 things will impact the business in the coming year? o Ex: Will weather patterns affect L. L. Bean’s winter sales? • What are revenue drivers – quarterly, annually, channelwise? o Can new big data sources yield competitive advantage? • What are the “subjective” success criteria? Sales? CRV? Lift? Decide what matters, and set objectives from that. # BDVD # FutureM 67
  • 7 Quiz Questions for Better Data2. What metrics should I have? • Define Measurable goals - R&D, Marketing, Support, Sales, Ops, Finance, Engineering, HR etc. • Determine the right metrics. • Make certain you have the tools to measure them. # BDVD # FutureM 68
  • 7 Quiz Questions for Better Data3. What stands in the way? Get clarity and agreement on how to measure goal attainment. Example: “Better customer service” is a bit too nebulous • Metrics with inaccurate or incomplete data • Metrics that are complex or difficult to explain • Metrics that complicate operations or create excessive overhead • Metrics that cause people to act at cross purposes with the firm. An outsider should be able to audit if objectives were met. # BDVD # FutureM 69
  • 7 Quiz Questions for Better Data4. How can I get data and measurements on demand? SaaS apps can help you connect dataflow to analysis. Just beware the locked spreadsheet. • Salesforce.com: good for sales and dealflow • HubSpot: good for web marketing • Quickbooks, Excel: linked via xml app to data flow for instant financial / accounting updates and reports Departmental dashboards can enable weekly, daily, hourly or realtime trendspotting and fast course corrections. # BDVD # FutureM 70
  • 7 Quiz Questions for Better Data5. How can I empower everyone with on-demand insights? Create a Culture of measurement. • Maintain transparency to avoid surprises • Celebrate wins as they occur • Keep people properly motivated and on the same page Link rewards to the right performance measures All this makes it easier to work toward common, unified, clearly understood goals. # BDVD # FutureM 71
  • 7 Quiz Questions for Better Data6. Where to I start? Start at the top. • Set a strong example for people to follow • Publicize goals and keep your own progress visible • Demonstrate commitment to attaining shared goals • Pick the 5 most important goals and get the salient data Even if your targets were “off” at the outset, demonstrate success toward something, even if it’s just better intelligence. Pilot projects are learning labs. # BDVD # FutureM 72
  • 7 Quiz Questions for Better Data7. What should I do differently today? Continually question, re-evaluate and refine. • External factors can affect progress toward goals at any time. • External factors can affect goal setting at any time. • External factors can affect goal selection at any time. • Cultural factors can affect generation and use of data insights Determination is good, just keep it aimed productively. # BDVD # FutureM 73
  • Public & PrivateSector Mashups # FutureM 74 # BDVD 74
  • 5 Public Sector Mashups1. Hurricane Risk Calculator Houston, TX Source: • NWS + historic data Use: • Neighborhood-level risk prediction http://risk.rtsnets.com • Predict flood, wind & power outages • Aids go/no go evacuation decisions # BDVD # FutureM 75
  • 5 Public Sector Mashups2. Better Earthquake Detection Quake-Catcher Network, CA Source: • Laptop accelerometer data http://qcn.stanford.edu Use: Improve on seismographic data • More location specific • Vastly cheaper • Free (laptop drop protection) • Easy to install in desktop PCs # BDVD # FutureM 76
  • 5 Public Sector Mashups3. Containing Diseases CDC, Atlanta, GA Source: • Google & Twitter search trends http://cdc.gov Use: • Speed disease detection • Enable response precision • Prevent & contain outbreaks • Eliminate SARS-like recurrences • Save lives • Support virality research # BDVD # FutureM 77
  • 5 Public Sector Mashups4. Predictive Policing Mountain View, CA Sources / mashup: • Foreclosures, school schedules, past crimes, bus schedules, library visits, weather conditions Use: • Predict likely crime occurrences • Focus police intervention efforts # BDVD # FutureM 78
  • 5 Public Sector Mashups5. Homeland Security F.A.S.T Module, Washington, D.C. Sources: • Human suspect readings • Pulse, speech, CV, etc. • Bio, Interpol, other databases Use: • Predict malintent • Gather suspect intelligence # BDVD # FutureM 79
  • Private / Commercial Mashups1. Vine Whisperers Fruition Sciences, Napa, CA Sources: • Sensors implanted in vines • Weather and irrigation readings Use: • Upload sensor readings to cloud database • Conserve water and improve vineyard yields • Build expertise in irrigation and crop management # FutureM 80 # BDVD 80
  • The world is your mashupUser experience (UX) – web, mobile, social, print, POS, etc. Meta data – session info, device features Connectors, apps, processors, Cool Tools “plus” Mashup data – public, leased, licensed Proprietary data – customers, partners, inventory, assets # FutureM 81 # BDVD 81
  • Get real (time) # FutureM 82 # BDVD 82
  • Real Time Direct Marketing Tools"Sales for Service" app Lead Nurturingcustomer interaction data from call ctr & POS Lead Scoringtailors offers quickly upon purchase / conversionimproves cross / upsell programs and offer targetingincludes: offer repository, biz rules engine, contacthistory DB, predictive analyticsTurns call center from a cost to a profit center (Email marketing) API to SFDC consolidates response in CRM(ID web visitors by IP)slices by: biz size, vertical, industry, geo(crowdsourced DBs) Find people and companiesTechprospex (ID tech used by B2B company) customer analyticsDrills down by model, version improves & automates sales response # FutureM 83 # BDVD 83
  • Real Time Direct Marketing Tools Persona triggers Lead Lists Email Customer Analytics BI / Prospect Intelligence # BDVD # FutureM 84
  • Q: Who owns it? Persona triggers Lead Lists Sales EmailMarketing Customer Analytics BI / Prospect Intelligence # FutureM 85 # BDVD 85
  • Q: Who owns it? Persona triggers Lead Lists Sales EmailMarketing Customer Analytics Example: BI / Prospect Intelligence # FutureM 86 # BDVD 86
  • A: It’s jointly owned Marketing WWDDD ?Call centerCatalogEvent CommunitiesMobile ChannelsPOS CRM Storage,Print Support Integration,Social Service Access,Web Privacy, Security Sales IT # FutureM 87 # BDVD 87
  • A: It’s jointly owned CMO Main input: customer Storage, Integration, Access, Privacy, Security CIO Main input: technology # FutureM 88 # BDVD 88
  • A: It’s jointly owned • Partner with internal functions – Sales, Marketing, I.T. o Let business needs drive infrastructure decisions • What goals do they share? o Drive change and innovation o Manage and mitigate risk and opportunity o Develop competitive advantage (customer insight) # FutureM 89 # BDVD 89
  • Admit what you don’t know • Convenient sample sizes are not necessarily predictive • A small fraction of all data is digitized; most is unstructured • Data may reduce some biases, but creates others • Competitive advantage ideas: a) Generate data in new ways b) Gather data in new ways c) Combine data in ways nobody else has • Permit judgment to color your data interpretation # FutureM 90 # BDVD 90
  • Summary• Overlay outside data on your own to gain new insights• Engage Sales, I.T., support etc. for a 360 degree business view• Invest in “Cool Tools” and silo-busting capability• Benchmark your competitive space• Solve your customer’s problems, and it will solve yours• Make data quality everyone’s easy chore• Acknowledge what you don’t know, and let judgment in # FutureM 91 # BDVD 91
  • Future Events and ResourcesA DMA / NCDM Dec. 2012 Event # BDVD # FutureM 92
  • ReferencesTechAmerica FoundationPutting Big Data and Advanced Analytics to Work (McKinsey)The Logic behind Retailers’ Mercurial Pricing (HBR)The Current State of Business Analytics: Where do We Go from Here?(SAS / Bloomberg Business Week Research Services)Top 16 Tools to Create InfographicsTackling Multichannel Attribution (John Young, Epsilon)Predictive Analytics WorldTaming the Big Data Tidal Wave (Bill Franks, Teradata) # BDVD # FutureM 93
  • ResourcesAnalysis and Data Visualization Tools # FutureM 94 # BDVD 94
  • Thank you! .com +1 (781) 492-7638 (USA East) @fanfoundry 95