Inspire 2013 - Practical Predictive Analytics- Boston Consulting Group


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Join Cornelius Kaestner, Principal at Boston Consuting Group and Dr. Dan Putler from Alteryx in this informative and practical guide to predictive analytics, using the built-in modular functionality in Alteryx. It's time to leap into the future guided by a proven set of best practices that will help you illuminate what's going to happen, and know what to do now. He will be joined by Cornelius Kaestner who will share BCG’s “real world” experience with predictive analytics as the company continues to expand its use of the predictive tools and R capabilities built into Alteryx

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Inspire 2013 - Practical Predictive Analytics- Boston Consulting Group

  1. 1. Practical Predictive AnalyticsThe Stepping Stones to SuccessDan Putler (Alteryx)Cornelius Kaestner (The Boston Consulting Group)March 6, 2013
  2. 2. We have shaped business thinking for 50 years... Growth- Share Matrix Experience Curve Time-based Competition ?Trading up/Trading down Change Monster Adaptive Advantage Positional durability Emotional High Low Information flow Industry boundaries 1950 1955 1960 1965 1970 Manageable 1975 1980 1985 1990 1995 2000 2005 Data overload Exabytes 1800 Clear Blurred Digital Information 1600 Functional 1400 1200 1000 DVD, RFID, Digital TV, MP3 players, Digital cameras, Camera phones, VoIP, Moore’s Law (indexed)1 Available Storage 800 Medical imaging, Laptops, 600 Datacenter applications, Games, Satellite images, GPS, ATMs, Scanners, 400 Sensors, Digital radio, DLP theaters, Telematics, Peer-to-peer, Email, Instant messaging, Videoconferencing, 200 CAD/CAM, Toys, Industrial machines, Security systems, Appliances 0 Technical 2005 2006 2007 2008 2009 2010 2011
  3. 3. ...including thinking on the value of Big Data
  4. 4. Our thought leadership resonates with our clients Global revenue (Indexed, 1990=100) 2,000 1,500 +15% 1,000 500 0 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012
  5. 5. Capturing value from Big Data: our framework Big Data Navigation Strategy Advanced Strategic Platform analytics Analytics Analytics Enterprise Big Data Business Model Data Business Information transformation Transformation Creation Management Alteryx supports our Strategic Analytics efforts
  6. 6. Key principles for our Strategic Analytics efforts Work with clients who are open to new analytical methods Follow demand: known challenges our clients are looking to solve Focus on challenges with significant upside potential Start where decent data exists Invest where we can learn the most Seek pragmatic, implementable solutions instead of perfectly pure analytics
  7. 7. Retail Example: Optimizing circulars Approach: SKU / event promo analysis Insight and impact Up to 50% of promotions have no impact on sales or margin Incremental Margin Significant opportunities to improve value creation, e.g., • 4% sales opportunity • 7% margin opportunity • 10% flyer cost reduction Additional insight from the analysis • At one retailer, stores were not consistently executing promotions • At another, we could identified vendors Incremental Sales who consistently underfunded promotions
  8. 8. Our approach for Strategic Analytics at BCG Identify talent to Small team to drive the effort, each with combination of skills drive Strategic • Business understanding to recognize actionable solutions Analytics • Analytical aptitude and technological savvy to leverage tools Critical tools available to all (Alteryx, Tableau) Enable our • 125 Alteryx users enabled in the last 6 months organization Remote processing available for larger data sets Seek out opportunities to test new methods Encourage Invest in learning opportunities experimentation Involve clients in the experimentation Codify and Formalize our lessons learned into products share wins Look for opportunities to apply products at other clients
  9. 9. The Lay of the Land• Predictive analytics is GREAT!...• …but predictive analytics is a scary thought for a lot of managers • Lots of math • Potentially a lot of expense • Can you believe the numbers that come out of the fancy models?• How do you even get started?
  10. 10. Two Approaches to Getting Started• Hire an outside firm • No fixed costs • Take advantage of the outside firm’s expertise• Do-it-yourself • Lower variable costs • Greater opportunities to learn and understand the capabilities and limitations of predictive analytics • Allows for a closer connection and integration with existing business processes• Many organizations conduct a mixture of in-house and outsourced predictive analytics projects
  11. 11. Four Steps to In-House Predictive Analytics Success• Start small and take a “learning by doing” approach• Develop an initial list of possible predictive analytics projects that address frequent and important business decision in your organization• Select projects from the initial list that make use of well-known metrics for predicting outcomes• Compare the results of a new predictive analytics-based business process to the incumbent process used to make a decision
  12. 12. Four Steps to In-House Predictive Analytics Success• Start small and take a “learning by doing” approach• Develop an initial list of possible predictive analytics projects that address frequent and important business decision in your organization• Select projects from the initial list that make use of well-known metrics for predicting outcomes• Compare the results of a new predictive analytics-based business process to the incumbent process used to make a decision
  13. 13. The Virtues of Starting Small• An initial low financial commitment with respect to both software and personnel • You likely already have Alteryx licenses if you are in this room• The organization is able to develop internal expertise in predictive analytics that it can leverage in the future• The organization develops a better understanding of what is and is not possible with predictive analytics• It provides the ability to assess the possible benefits from using predictive analytics to drive business processes, but in a limited way that limits the downside risk• Several successful small projects builds managerial confidence in the approach, enhancing organizational buy-in
  14. 14. What do you Need to Start Small?• One or two current staff members with the willingness to take on a new challenge, have a basic set of computer skills, and are given some time to experiment with the methods• Appropriate software • You likely already have Alteryx licenses if you are in this room • Our Predictive Analytics – Essentials online course can provide a jump-start • The analysis tool pack in Excel has been used by a number of organizations to get started• What about advanced statistical and data mining training? • It helps, but an understanding of the business and the willingness to learn matters more • Asking the right question is a lot more valuable than using the best analysis method
  15. 15. Develop a List of Business Questions PA can Inform• A useful way to start is with your organization’s key performance indicators (KPIs) and then determine how predictive analytics can help address the business decision that underlie the KPIs• OK let’s use an example to make this concrete • Congratulations you are now the General Manager of a major league baseball team • In this job, what are your KPIs? • What decisions can you make in order to deliver on those KPIs? • What information can we use to inform these decisions?
  16. 16. Use Well-Known Metrics to Select Projects• In many cases there are (fairly) well-known metrics that can be taken advantage of to select projects from the list of potential projects • Relying on others past experience in selecting predictor variables can really shorten the time it takes to develop a useful predictive analytics model • Recency, frequency, and monetary value (or RFM) is a well known example from direct marketing that works well for cross selling applications to existing customers • Web searches to find relevant articles, blog posts, slide decks, and other resources can really help• Should web searches fail, thinking through the information that is available at the time a decision is made (as opposed to what is available with 20-20 hindsight) is a useful thought experiment that can be used to develop possible metrics
  17. 17. Back to Baseball• We know that scoring runs is a critical element in winning baseball games, and we know we can draft or acquire players based on statistics (metrics) that, as a team, will lead to scored runs. What are the available statistics?• Common baseball batting statistics on individual players available on a historical basis: • Hits • Walks (Base on balls and hit by a pitch) • Strikeouts • Singles • Doubles • Triples • Homeruns • RBIs • Batting average / plate appearances (at bats)
  18. 18. Back to Baseball• The statistics are for individuals, but what happens when we combine them into a team? • What is better, having a set of players in the batting order that can only get to first base on a walk or hitting a single, but do so at every at bat, or a set of players in the batting order that only hit home runs, but have a one in three chance of doing so at each at bat?• We just saw the common statistics, are there metrics we can construct from them that can be more informative? • The wisdom of Bill James and SABRmetrics: On Base Percentage + Slugging Percentage• The question: Can we use this information as the basis for drafting or acquiring players?
  19. 19. Compare Models to Traditional Business Processes• Testing and experimentation are an essential part of the use of predictive analytics tools • The goal of these tests is to objectively compare the performance of the predictive analytics-based business process to the traditional business process• Why? • Many managers don’t trust models, but they are very comfortable with comparing one group with another to see if there is a noticeable difference between the two of them • Favorable results in these tests increase managers’ trust in predictive analytics• How? • A/B testing: Explicitly creating treatment (those who are addressed using a predictive analytics-based process) and control (those who are addressed using traditional business processes) groups and then compare results • Retrospective testing: Use two time periods and compare differences in outcomes based on traditional business processes and those predicted as best by a model
  20. 20. Thank You!
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