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Guha Athreya, Sr. Manager, Customer Analytics - AbsolutData spoke on the topic “Application of Innovative Analytics in Business” at Predictive Analytics World, SFO. He showcased innovations in ...

Guha Athreya, Sr. Manager, Customer Analytics - AbsolutData spoke on the topic “Application of Innovative Analytics in Business” at Predictive Analytics World, SFO. He showcased innovations in business applications of analytics, designed to guide the CXOs to deal with the increasingly complex customer environment. The presentation also included case studies and lessons learned.

AbsolutData is a global leader in applying analytics to drive sales and increase profits for its customers. AbsolutData has built strong expertise and traction with Fortune 1000 companies across 40 countries. We specialize in big data, high end business analytics, predictive modeling, research, reporting, social media analytics and data management services. AbsolutData delivers world class analytics solutions by combining their expertise in industry domains, analytical techniques and sophisticated tools.

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Applications of innovative analytics in business Presentation Transcript

  • 1. © Absolutdata 2014 Proprietary and Confidential Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco www.absolutdata.com April 25, 2014 Applications of innovative analytics in business Applications To Guide CxO’s In Dealing With The Increasingly Complex Customer Environment
  • 2. © Absolutdata 2014 Proprietary and Confidential 2 CRM ME Data Visualization & Reporting Market Research Big Data Absolutdata Snapshot Company Overview  Founded in 2001, Absolutdata is a pioneer in delivering consulting-oriented advanced analytics services to a global client base  It helps clients understand their customers better by statistical data analysis and delivering key analytics that help enhance their own value  Senior management from McKinsey, Kraft, Pfizer, Mitsubishi, Nielsen, GE, and HSBC  400+ employees across San Francisco, Los Angeles, New York, Dubai, Singapore, London and Delhi Corporate Philosophy Mission To empower forward-looking organizations to reach new heights of business performance through the optimal use of data Services Provided
  • 3. © Absolutdata 2014 Proprietary and Confidential 3 The Attribution Problem In a Multi Channel, Multi Device Ecosystem, adding the reported impact of all on-line and off-line channels gives a number higher than company’s revenue Find the True Value 30% 45% 60% 20% 100 ???145
  • 4. © Absolutdata 2014 Proprietary and Confidential 4 The Big Reasons: Traditional Approaches Do Not Work Because Revenue is impacted by many drivers beyond just Media Investments Media Investments channels work together, but each channel reports all of the synergies Media  On-line  Offline Other Marketing  CRM  Direct Marketing  Pricing  Distribution External  Competition  Technology  Demographic shits  Macro economics Operations  Supply  CSAT Revenue Traditional MMM approaches result in over-fitting History and unstable Prediction 0 500 1,000 1,500 1/4/10 4/4/10 7/4/10 10/4/1 0 1/4/11 4/4/11 7/4/11 10/4/1 1 1/4/12 4/4/12 7/4/12 10/4/1 2 BookingsperWeek (‘000) Actual bookings Predicted bookingsR2 = 90% 2010 2011 2012 2013 PredictedHistory
  • 5. © Absolutdata 2014 Proprietary and Confidential 5 Phase 1- Market Mix Model Absolutdata’s Solution Approach to Revenue Attribution Holistic approach incorporates all drivers Appropriate level of modelling sophistication - OLS, HB etc )( Daily Signups Baseline sales Regression Coefficient Level of Media Effort Regression Coefficient Other Factors e.g. Temperature Phase 2- Structural Equation Models Phase 3 – Discrete Choice Attribution Assess Synergies, Refine Attribution TV GI Search Clicks Email Campaign Print Affiliates Display Over all Signups+ + + + + TV Impacts TV Impacts Secondary Relationships Cooke Data, Triangulation Attribution’s % impact of each media channel drives daily proportions Cookie data captures Unique ID activity and measure recency and frequency )) ((
  • 6. © Absolutdata 2014 Proprietary and Confidential 6 Our expertise in driving actionable results in a complex market environment Russia Ukraine Brazil China Philippines Poland Serbia Argentina Australia Malaysia Turkey Spain USA India UAE Romania Puerto Rico UK Indonesia Saudi Arabia South Africa Denmark Germany France Thailand Egypt Belgium Mexico Japan Vietnam Building A Global Knowledge Base Across Verticals  200+ Market Mix Studies in past 12 months  Pricing Analysis Studies  Portfolio Simulation Consumer Durables Telecom Electronic Goods Retail Banking & Financial Online Services
  • 7. © Absolutdata 2014 Proprietary and Confidential 7 Case Study in Hospitality – Phase 1 Results Bringing disparate data sources together Building a Master Model DATA TYPES DESCRIPTION Bookings Transient and Total Bookings Media Online & Offline Spends Competition - Online & Offline Spends Emails Delivered, Clicked etc. Competition - # of Campaigns Promotions HHonors Quarterly Promotions Rate Sales Competition Loyalty Promotions Other Drivers ADR, # of Hotels and Rooms, Occupancy, SALT etc Competition ADR, # of Hotels and Rooms, Occupancy etc Macro Economics GDP, CPI, Unemployment etc Step 1: Model built on Weekly Transient Bookings for Jan’10 – Dec’12 1/4/10 4/4/10 7/4/10 10/4/10 1/4/11 4/4/11 7/4/11 10/4/11 1/4/12 4/4/12 7/4/12 10/4/12 BookingsperWeek(‘000) Actual bookings R2 = 87% 2010 2011 2012 Step 2: Model robustness checked by predicting the first two months of 2013 1/1/13 1/8/13 1/15/13 1/22/13 1/29/13 2/5/13 2/12/13 BookingsperWeek(‘000) 2013: Jan-Feb Step 3: Bookings translated to associated revenue for subsequent attribution analysis
  • 8. © Absolutdata 2014 Proprietary and Confidential 8 Case Study in Hospitality – Phase 2 Results, Phase 3 in Progress Phase 2: Model used to understand Historical Impact Phase 3: Terrific deep insights inform planning and ROI Optimization HWW Media Impact $ 708 MM HWW Impact of Other Drivers $ 1,434 MM Competition Impact $ -138 MM Base Line $ 10.4 BN Cumulative Effect of All Past Marketing Efforts Incremental 2.0 BN 53.1% 20.5% 14.1% 6.0% 4.1% 2.9% 0% 23% 45.6% 12.3% 8.8% 5.5% 4.5% 0.1% 0% 10% 20% 30% 40% 50% 60% Search TV Affiliates Display PR E-mail Print Primary Attribution After Secondary Attribution (Actual Contribution from Model) Impact of Secondary Relationship on Search= - 30% Impact of Secondary Relationship on TV= +25%
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