Creating Business Value - Use Cases in CPG/Retail

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Meetup Prezo Oct 9, 2013

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Creating Business Value - Use Cases in CPG/Retail

  1. 1. Business Value Consulting for a PREDICTIVE and AGILE Enterprise STRATEGY + ANALYTICS + TECHNOLOGY ENABLING BIG DATA TRANSFORMATIONS FOR CONTINUOUS ADVANTAGE ™ rightedge ™ rightedge.com
  2. 2. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Rightedge™ Confidential & Intellectual Property Material cannot be reproduced or distributed in any form without express written permission.
  3. 3. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION C r e a t i n g B u s i n e s s Va l u e - U S E C A S E S I N C P G / F M C G / R e t a i l / E - C o m m e r c e
  4. 4. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION AGENDA ①  Industry Big Data Challenges (30 mins) ②  4 Use Cases (40 mins) •  CPG •  FMCG •  Retail •  E-Commerce ③  Use Case Takeaways (15 mins) ④  Closing Thoughts (10 mins) ⑤  Q & A (25 mins)
  5. 5. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 5© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 5 Industry Big Data Challenges
  6. 6. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Which industries are creating Data?
  7. 7. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Big Data in Consumer Context Chart based on IDC and UC Berkeley Data Growth Estimates, Adapted from Source: IDC & CosmoBC.com: http://techblog.cosmobc.com/2011/08/26/data-storage-infographic/ Petabyte PC Internet Time MobileMainframe Terabyte Data Volume Exabyte Zettabyte Machine 2011 Transactions M 2 M Interactions Consumers Patterns/Trends Behaviors Activities Internet of Things Mobile Apps U G C Social Networks Sales of Goods & Services People Machines Markets
  8. 8. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Which Verticals Are Impacted? May 2011 B2C Sectors 1.  Generate/Have/Need lots of “customer” & “machine” data 2.  Need to use it to compete, grow & profit while reducing cost to serve 3.  Demand/consumption estimates are crucial due to high volume low margin plays and resource optimization 4.  Margins under pressure due to Consumerization - Consumer has more Info to make a choice than what companies know Utilities is another sector to consider
  9. 9. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Velocity Variety Volume Ability to Make Sense of Data in Real-Time To Take IMMEDIATE Action Big Data Analytics For B2C Companies Billions of Signals/Events Terabytes to Petabytes to Exabytes Structured, Semi-Structured, Unstructured Business Value Actionable Insights Leading To Superior Outcomes Variance Sparse, Missing, Partials, Inaccurate Market Share Revenue Margins Growth Rate …
  10. 10. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Enabling Context Driven Decision-Making What B2C Companies Need NOW? 1 2 3 Predictive analytics Real-time analytics Investigative analytics Predict What is going to happen so I can plan ahead or pre-empt Know What is Happening Now so I can respond or adjust ASAP Analyze What & Why it Happened so I can learn, refine, experiment
  11. 11. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 11© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 11 Use Cases In CPG, FMCG, Retail, E-Commerce
  12. 12. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Key Elements of Business Strategy Complex & Dynamic Interplay of CUSTOMERS MARKETS PRODUCTS In Exchanging VALUE Industry Sector economics Products CustomersMarkets
  13. 13. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Key Elements of Business Strategy Profitable Customer = WTP – CTS WTP – Willingness To Pay CTS – Cost To Serve VALUE EXCHANGE Industry Sector economics Products CustomersMarkets
  14. 14. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Consumer Packaged Goods, Fast Moving Consumer Goods Type of Good consumed every day by average consumer ü  Replenished frequently ü  Perishable ü  Price Sensitive ü  Highly competitive ü  High market saturation ü  Low switching costs
  15. 15. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION CPG/FMCG Key Success Factors Type of Good consumed every day by average consumer ü  Replenished frequently ü  Perishable ü  Price Sensitive ü  Highly competitive ü  High market saturation ü  Low switching costs ①  Product Innovation (R & D) ②  Brand Marketing ③  Flexible Manufacturing ④  Strong Distribution Network ⑤  Pricing Prowess ⑥  Advertising & Promotions
  16. 16. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION CPG/FMCG Challenges Big Data Drivers - Social, Mobile, Internet Usage ①  High Volume, Low Margin Business – Dog Fight for Market Share & Profitability ②  Brand Stickiness in Consumer Path to Purchase ③  Keeping track of Individual (& Group) Consumer Lifestyle & Behavior Shifts ④  Reaching the Consumer at the right time (purchase cycle) with the right message ⑤  Responding to Consumer & Market Signals As Soon As Possible
  17. 17. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION CPG/FMCG Key Use Cases ①  Consumer Path to Purchase ②  Consumer Awareness of Brand & Loyalty ③  Consumer Income Levels & Shifts ④  Consumer Spending Patterns/Trends ⑤  Consumer Choices & Availability at POS Profitable Customer = WTP – CTS WTP – Willingness To Pay CTS – Cost To Serve VALUE EXCHANGE
  18. 18. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION
  19. 19. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Retail/E-Commerce
  20. 20. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Retail/E-Commerce Goods Sold in Stores, Online Only or Both ü  Product Selection ü  Price Ranges ü  Visitor Experience ü  Location & Access ü  Highly competitive ü  Store Overhead ü  Low switching costs
  21. 21. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Retail/E-Commerce Key Success Factors ①  Product Assortment ②  Store Location & Experience ③  Online & In-Store Marketing ④  Strong Merchandising ⑤  Pricing Prowess ⑥  Advertising & Promotions Goods Sold in Stores, Online Only or Both ü  Product Selection ü  Price Ranges ü  Visitor Experience ü  Location & Access ü  Highly competitive ü  Store Overhead ü  Low switching costs
  22. 22. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Retail/E-Commerce Challenges Big Data Drivers - Social, Mobile, Internet Usage ①  High Volume, Low Margin Business – Dog Fight for Customer Share of Wallet ②  Comparison Shopping (Price, Assortment) ③  Store “Show rooming effect” ④  Inventory Level Management – Avoid Understock or Overstock ⑤  Consumer Shopping Experience (Online, In-Store, Both)
  23. 23. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Retail/E-Commerce Key Use Cases ①  Consumer Path to Purchase ②  Shopping Cart Abandonment ③  SKU Level Demand Forecasting ④  Pricing Optimization ⑤  Consumer Product Switching at POS ⑥  Consumer Shopping Experience Profitable Customer = WTP – CTS WTP – Willingness To Pay CTS – Cost To Serve VALUE EXCHANGE
  24. 24. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 24© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 24 Retail Markdown Optimization
  25. 25. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Markdown Optimization Project •  Department Store, $17 Billion Revenue •  1000+ Stores, 100,000 SKUs at each store •  Last 10 years experienced a gradual decline in gross-margin •  especially on permanently marked down merchandise.
  26. 26. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Business Goal Client wanted to get more precise with their markdown strategy 1.  Develop optimal depth and timing of markdowns based on the store-level inventory and anticipated future demand. (Forecasting Model) 2.  Reduce frequency of markdowns as this has significant impact on store labor. (Optimization Model)
  27. 27. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Modeling Challenges Forecasting Model Challenges •  Short-life cycles for fashion products •  High volume of data at the store level •  High levels of promotional activities Optimization Model Challenges •  Store level inventory •  Price elasticity •  Baseline forecast, while accounting for numerous business constraints.
  28. 28. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Data Pooling Typically data is too noisy and sales are insufficient at the lowest level (PC9/Store/Week) Pooling data to higher level provides better estimates of model parameters (seasonality, trend, marketing effects) Sales Price
  29. 29. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Business Value Delivered Expected to generate $90 Million annually in margin improvements through more precise clearance markdowns
  30. 30. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION FOODS BACKGROUND •  One of the fastest-growing & innovative retailers based out of Britain (with over 800 stores in UK & £2.5 Billion in Sales) serving Frozen (Foods/Grocery/Snacks/Produce) & Chilled Items •  I.F. own legacy forecasting system is considered to be basic and ill-suited to promotions (% OFF, BOGO, BAGB, Display, Coupons, Flyers, TV Commercials/Media etc.) •  A more accurate forecast would enable inventory reduction while maintaining adequate service levels •  Some additional sales uplift might also be achieved in specific categories. Next steps •  Assess the economic value of better forecasting using our technology for Iceland Foods at SKU/STORE/WEEK level
  31. 31. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 31© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 31 E-Commerce Pricing Optimization
  32. 32. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION ACME Computers: A Pricing Tool for Used Parts Cost / Margin Demand / Supply Technology Life Cycle Competition Inventory / Elasticity §  Who are selling a part? §  At what price? §  New or refurbished? §  In stock or backorder? §  What else do they sell? How do we get the above data? §  Website scrubbing §  APIs o  Google Shopping API o  Semantics3 API o  Indix Price API o  Invisible Hand API Wants a model that will automatically adjust prices for Newport’s used computer parts
  33. 33. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION IdentifyPeerGroupACME Computers: A Pricing Tool for Used Parts HDD and more …Adapter Battery CPU Cable Memory System Board and more … Experiment to vary price over time Determine elasticity Adjust prices based on inventory and elasticity Base Price Weighted Price Distribution Categorize Parts
  34. 34. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 34© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 34 Takeaways
  35. 35. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Big Data Technologist Lens Business Value Analyst Lens Decision Maker Lens Big Data Views KPIs •  Real-time •  Interactive •  Batch •  Predictive •  Descriptive •  Prescriptive •  Revenue •  Margin •  Market Share KVBI™ Models Queries F.A.I.T.H F.A.I.T.H
  36. 36. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Tied Together by F.A.I.T.H™ Methodology F A I T H Framing the business problem, formulating biz case, strategizing on scenarios Analysis & Modeling of the business problem with KVBI™, Relevant Data Insights Extraction, Interpretation and Validation Timely Action & Visual Reporting (using Technology) Harvesting Yield & KPI Monitoring for Closed Loop Feedback
  37. 37. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Get F.A.I.T.H™ Certified Strategy + Analytics + Technology = Business Value F A I T H CONSISTENT. ITERATIVE. REPEATABLE. CLOSED-LOOP. Create, Grow, Build Data-Driven Decision-Making Mindset
  38. 38. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Big Data Technologist Lens Business Value Analyst Lens Decision Maker Lens Bootcamps Value KPIs •  Real-time •  Interactive •  Batch •  Predictive •  Descriptive •  Prescriptive •  Revenue •  Margin •  Market Share KVBI™ Models Queries F.A.I.T.H Bootcamp #1: Intro to Data-Driven Decision-Making Bootcamp #2: Intro to Business Analytics Bootcamp #3: Intro to Big Data Technologies Develop Business Sense Develop Technology Sense Develop Analytical Sense
  39. 39. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 39© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 39 Closing Thoughts
  40. 40. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Key Decision Areas (B2C Companies) ①  Consumer Behavior – Path-To-Purchase, Loyalty (repeat purchase) ②  Dynamic Segmentation (Micro – Operational, Macro - Strategic) ③  Prediction and Recommendation (Relevant, Timely Offers/Advt.) ④  Constant Experimentation with Various/Variant Offerings ⑤  Cross-sell, Up-Sell Opportunity Realization
  41. 41. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Keys To Superior Outcomes (B2C Companies) ①  Fragmented & Highly Variant Path-To-Purchase Data ②  Data Sharing is Crucial - Manufacturers, Distributors, Retailers ③  Real-time (Micro) Segmentation & Targeting is becoming necessary ④  Buying & User Experience - key drivers for Brands & Retail mindshare ⑤  Demand Forecasting & Modeling becoming more important
  42. 42. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Applying advanced analytics in consumer companies http://bit.ly/1b8dlKS
  43. 43. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION Thank You! Balu Rajagopal balu@rightedge.com Questions ? Comments ? Please Email Me.
  44. 44. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. PRIVATE & CONFIDENTIAL. NOT FOR DISTRIBUTION 44© Copyright 2013 Pivotal. All rights reserved. ‹#›© COPYRIGHT 2013 RIGHTEDGE. ALL RIGHTS RESERVED. 44 Q & A
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