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Predictive Intelligence

Presentation made to the AMA that shares ways to predict outcomes with just enough data necessary to get a quick start to finding a workable solution.

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Predictive Intelligence

  1. 1. March 25, 2014 Charles Sayers Experience Innovation SapientNitro csayers@sapient.com predictiveintelligencemaking better business decisions with just enough data
  2. 2. what we’ll look at  coping with data volume and velocity  dashboards: the good, the bad, the ugly  the guesswork behind most predictions  activity : straw modeling  activity : data by design © 2014 SapientNitro
  3. 3. we’re generating a LOT of data REALITY © 2014 SapientNitro
  4. 4. % of the data in the world today was created in the last 2 years alone. - IBM - © 2014 SapientNitro
  5. 5. This is the volume of photos uploaded to Flikr in a single day © 2014 SapientNitro
  6. 6. we’re generating too much, too fast PROBLEM © 2014 SapientNitro
  7. 7. © 2014 SapientNitro dataisaccumulatedfaster thanourabilitytounderstandit ibm
  8. 8. We are drowning in information and starving for knowledge. - John Naisbitt © 2014 SapientNitro
  9. 9. innovations in wearables and biometrics will drive a surge in physiological data © 2014 SapientNitro a new wave of data is on the horizon
  10. 10. © 2014 SapientNitro 190,000people with deep analytic sills as well as 1.5millionmanagers and analysts will be needed by 2018 to fill jobs in Big Data
  11. 11. © 2014 SapientNitro we can only process so many numbers at one time PROBLEM
  12. 12. © 2014 SapientNitro % structuredandunstructureddata of analytics projects will deliver insights based on by 2015, more than
  13. 13. therearemanydatasolutions tohelpyoumanageandmine © 2014 SapientNitro REPORTING ANALYSIS MONITORING PREDICTION what happened? why did it happened? what’s happening now? what might happen? predictive analytics dashboards and scorecards OLAP and visualization tools query, reporting and search tools BUSINESS VALUE COMPLEXITY BUSINESS INTELLIGENCE TECHNOLOGIES
  14. 14. Data types most beneficial for  service and sales improvement Data types most beneficial for  market and trend analysis Internal Databases External Website Content Social Media Content Internet‐Connected Device  Data Location‐based Data Internal Log Files RFID Data Sensor‐Generated Data Competitive Intelligence Customer Segment Data Market Condition Models  and Data Sales Results and Forecast  Data Internal Customer Data Third Party Market Data Point of Sale Data Combined in an actionable, real‐time context,  data can drive better market analysis and improve  customer intelligence SOURCE: “Big Data and the Democratisation of Decision,” Alteryx weneedtousedatatosolve problems,notfindproblems © 2014 SapientNitro
  15. 15. thevalueofintelligence companies that use predictive intelligence median ROI 145% annual customer retention +6% companies that don’t use predictive intelligence median ROI 89% annual customer retention -1% VS. © 2014 SapientNitro
  16. 16. trending data patterns of continuous change • sales • conversion • retention • journey dependent variables controllable factors that influence change • marketing • pricing • branding • response kpi’sthatinfluencethefuture behavioral data patterns of contextual interaction • churn • dwell time • engagement • task completion uncontrollable factors that influence change • weather • economy • seasonality • disasters independent variables © 2014 SapientNitro
  17. 17. what’sthedifference? analytics understanding the factual past intelligence VS. anticipating the near future © 2014 SapientNitro
  18. 18. dashboards © 2014 SapientNitro
  19. 19. 3 types of dashboards 1. Strategic 2. Operational 3. Analytic © 2014 SapientNitro
  20. 20. © 2014 SapientNitro strategic simple, concise; contains aggregate metrics representing overall health (Executive Dashboards, Financial Dashboards, Planning Dashboards)
  21. 21. © 2014 SapientNitro operational monitor real time operations; track and alert deviations from the “norm” (Network Monitors, Health Monitors, Manufacturing Dashboards)
  22. 22. © 2014 SapientNitro analytical interdependent, interactive; provide ability to futurecast and test what-if scenarios (Campaign Response, Sales Forecast, Investment Prioritization)
  23. 23. anatomyofapredictivedashboard analysis reportingmonitoring prediction © 2014 SapientNitro
  24. 24. anatomyofapredictivedashboard analysis reportingmonitoring prediction © 2014 SapientNitro
  25. 25. © 2014 SapientNitro insight is the key to predictive intelligence
  26. 26. whichcomesfirst, thedataortheinsight? © 2014 SapientNitro
  27. 27. most predictions begin with a guess © 2014 SapientNitro
  28. 28. predictive guessing © 2014 SapientNitro making structured and informed guesses using common sense and insight
  29. 29. predictiveguessing usesunconventionalrelationships © 2014 SapientNitro
  30. 30. predictiveguessing doesn’trequirealotofdata © 2014 SapientNitro
  31. 31. predictiveguessing isaboutfindingtherightcontext © 2014 SapientNitro
  32. 32. whatifwe’re smarterthanwethink? © 2014 SapientNitro
  33. 33. straw modeling ACTIVITY 1 is about creating a visual hypothesis based on readily accessible data, common sense and intuition © 2014 SapientNitro
  34. 34. guestimation worksheet Storesales -15 -10 -5 0 5 10 15 20 25 -10 -5 0 5 10 Net margin sales margin Abercrombie & Fitch Amazon.com Best Buy The Gap Home Depot JC Penney Macy’s Nordstrom’s Sears Target howwelldidtheseretailersperform? 1 2 3 4 5 6 7 8 9 10 © 2014 SapientNitro
  35. 35. takeaways © 2014 SapientNitro • “accuracy” is influenced by data we (think) we already have • initial straw models often point to logical starting points • all guesses should be validated by real data • it’s important to know what we (and others) are good at guessing
  36. 36. data by design ACTIVITY 2 allows us to design educated perspectives that point us to relevant data clusters © 2014 SapientNitro
  37. 37. list5innovationsyourcompanyis consideringforinvestment © 2014 SapientNitro innovations 1 2 3 4 5
  38. 38. readiness H/M/L howwouldyouscoreeach innovationbybusinessreadiness? innovations 1 2 3 4 5
  39. 39. readiness H/M/L howwouldyouscoreeach innovationbyvaluetocustomer? © 2014 SapientNitro innovations 1 2 3 4 5 value H/M/L
  40. 40. businessreadiness customer value Plotour innovations usingthis3X3 basedon readinessand value(H/M/L) © 2014 SapientNitro
  41. 41. PRACTICAL INNOVATIVE EMERGINGEXPERIMENTAL Experimental In-branch experiences involving technologies and approaches that are in the early stages of development and exploration Practical Market-ready solutions already available in- branch to always-on consumers Emerging Promising in-branch experiences that will require greater investment in infrastructure to enable Innovative Future-facing experiences that offer greatest opportunity in-branch differentiation both the long- and short-term readiness value thisisourstraw model © 2014 SapientNitro
  42. 42. whatdatawillweneedtovalidate ourguess? © 2014 SapientNitro readiness value
  43. 43. taking the guesswork out of guesswork FINAL THOUGHTS © 2014 SapientNitro
  44. 44. 6 characteristics of valuable data 1. Fast Acquire, analyze and adapt in real-time 2. Centralized Unify data repository and enable universal access 3. Actionable Collect what you can use (not just what you can collect) 4. Diverse Combine multiple data sources – across mode, place, device, time and action 5. Retained Data has value beyond original anticipation. Don’t throw it away! 6. Scalable Plan for exponential growth
  45. 45. capturethespark Accept the fact that our best guesses will require further refinement and validation. © 2014 SapientNitro
  46. 46. modelsimply Complexity is confusing. Get to the point. © 2014 SapientNitro
  47. 47. stayfocused Concentrate on finding actionable data © 2014 SapientNitro
  48. 48. getdirty Predicting the future is a messy business. You won’t call everything right. Maybe some of it will hit the mark. Maybe none of it. It’s always better to err on the side of action, than lose by doing nothing. © 2014 SapientNitro
  49. 49. avoiddeflation Don’t suck the air out of every prediction. Strive to strengthen the strength of your insights and rationale. © 2014 SapientNitro
  50. 50. © 2014 SapientNitro questions
  51. 51. thank you Charles Sayers Experience Innovation SapientNitro csayers@sapient.com March 25, 2014 predictiveintelligence making better business decisions with just enough data

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