Data, BIG and SMALL that s

Transcription

1 Data, BIG and SMALL that s Collecting, Protecting and Profiting from Big and Small Data My Background Strategy Tech Data People Process 1

2 The evolution of retail and its data Stone Age » Language Credit Data» Logistics, and» Logic (good and bad)» Limited and closely held Contact and Demographics 2000 Infinity! Customers Rule! Age of Industry Factories Rule!» Factories» Facts and Figures» Fraternities (good and bad) Behavioral Data Modeled Brands & Bytes Data Rule!» Birth of the database» Best Practices» Big Data (good and bad) From this To this!! 2

3 Gartner s Digital Marketing Transit Map 3

4 Data is the new oil! Consumer Data Data and metadata created from other Big Data Customer Data Facts, data and metadata created by and about the actual people who buy your products and/or services. Type Volume Value Volunteered Observed Inferred Personal Data: The Emergence of a New Asset Class World Economic Forum 2011 Initiative Unlike real oil, there s no shortage! Purchase History Credit and Loyalty Location and Staffing Site traffic and registration Warranty and Repair Product Attributes Market Data Customer Care Promotion / Response History 4

5 Big Data tanks are filling fast Customer and transactional data pour into one or more tightly controlled tanks The higher the grade, the more precious the crude Small Data Big Data 5

6 Unlike oil, data quickly spoils Do you know where your data came from? Does it have an expiration date? How often do you do a sniff-test of your data - at the customer level? How often do you refresh the spices that enhance your data? SMALL data has a BIG impact Personal Facts Shared with trusted few Permission Respect when you collect and connect Preferences Implied and Explicit Capturing, cleansing, and constantly re-confirming this Small Data is critical to turning oily data into 6

7 The theoretical View: The challenges is to integrate the right customer information across multiple dimensions critical to a holistic customer view Attitudes & Preferences Purchase History / Product Affinity Credit & Loyalty Accounts Channel Interactions Geographics & Demographics Prioritize by value and readiness Execution and Measurement Analysis and Optimization Media / Distribution Channel Broadcast Events and Brand Promotions Promotions Loyalty Marketing Merchandise and Operational Direct Mail Telemarketing / Text Messaging Personalized Site/Offer/ & Product Assortment Paid Search / Banner Ads / Affiliate Referrals In-store / POS Promotion High Value Minimal Gaps High Value Larger Gaps Lower Value Larger Gaps 7

8 Value (increases exponentially) BUSINESS requires many grades of fuel Customer Loyalty Product Assortments Localization Clientelling Dynamic Site Experience Account Management Customer Service Real-time Offers 1-to-1 Personalized Marketing Customer Segmentation Versioned Marketing Channel Optimization Non-Personalized Direct Marketing The Customer Data Maturity Model 5% 15% Ability to track sales 25% 40% Basic Customer Enriched Customer In-Depth Customer 5% 10% Enriched Prospect Basic Inaccurate Customer Data Prospect Maturity (increases with both length and breadth of relationship) 8

9 Fueling the marketing engine Real-world Examples and Lessons Learned 9

10 Broadcast & Public Media Events, Charity & Endorsements Targeted Marketing Social Media & Search Offers Reviews Discounts Markdowns Brick & Mortar Web & Phone Recovery Retention Accelerating the Path to Purchase and Loyalty Permission and Profiles Personalization Surveys Rewards Customer Lifecycle Brand Awareness Product Consideration Purchase Decision Financing Delivery Customer Satisfaction Brand Loyalty Engagement and Loyalty First get permission! The 3 R s 1. Reason Appropriate for the situation and relationship. 2. Reciprocity Recognition, Receipts, Rewards 3. Respect Consistency, Cadence and Choice. 10

11 Ask and ye shall receive! - > 2 Million new s, with Permission and a Preference either Operational (ereceipts), Promotional, or both. - Created or confirmed association between Tender and Account. (Small, profile data) - Dramatic Increase in Response (> 30% of s opened) and much higher Average Spend on next event for Customers with ereceipt Opt-In. - 10x ROI in first year! Data Driven Content and Offer Personalization Aka Hyper-Personalization Challenge: Produce and optimize reach for over 500,000 different versions of a 10MM Circulation promotional catalog. 11

12 What was the big deal? The most versions ever produced for a DM Campaign was 8. Goal was to do 8 4 versions!! Personalization across all 64 pages - including covers, product spreads, and offers - unique product and offers combos for every 20 households! Promises made before true scale of production, print, and mail logistics were known and optimized model for version assignment was even tested. Clearly stated on cover that the book was custom created for the recipient. If the customer didn t like the book, it implied that we didn t really know them! What worked and what didn t Strengths were highlighted Model Segmentation and Optimization Campaign Management Print Production Lessons were learned Content Creation, Attribution and Management Legacy Circulation expectations Co-op funding and product placement restrictions Operationalization of Personalized Offers at POS Purchase History vs. Preferences and Life-stage 12

13 Rate Spend in $ Relevancy A major factor in spend regardless of channel. Clicks Only Omni-Channel Bricks Only e-marketable Customers < 5 Million ~ 6 Million >10 Million s per week or 2 Open Rate 12.7% 13.2% 10.3% Click Through Rate 3.6% 3.9% 2.1% Online Purchase Rate 0.30% 0.28% 0.0% Open and Clicks are nice spend is better! 18,00% $180,00 16,00% 14,00% 12,00% 10,00% 8,00% 6,00% 4,00% Channel % of Audience % Open Or Click Avg Online Spend $160,00 Avg Total $140,00 Spend $120,00 Only 9% 4% $0.15 $1.66 $100,00 and DM 4% 6% $0.50 $11.31 $80,00 , Reminder , DM 22% 10% $1.26 $21.23 $60,00 , Reminder 64% 7% $0.39 $3.29 $40,00 2,00% 0,00% no score Relevancy Higher is better Open Rate Click Through Rate Spend $20,00 $- 13

14 Currency and Accuracy is ESSENTIAL Customer Experience In-Store Scheduling - Best Associates with Best Customers Replenishment vs. Customer Convenience Voice of the Customer - Automated Post- Purchase Net Promoter Pay online & Pick-up in Store Clientelling, Permissions and Preferences 14

15 Data Ecosystems - BIG and SMALL! 15