Simple Steps to Personalize Marketing and Boost Revenue

1. Simple Steps to Personalize Marketing and Boost Revenue Personalizing Offersto Get Customers Buying Peter Moloney CEO, Loyalty Builders, Inc. CONFIDENTIAL

2. If You Could Make Exactly the RightOffer to Every Individual Customer,Why Wouldn’t You? Email Campaign: November, 2015 Target Group: Offers specific to each customer Control Group: Static offers, same to all customers

3. Most Common Answer: “It’s too hard to do”For consumer products and services Customers: 5K to 5M or more Products: 100 to 100K or more Low-touch sales Regular purchasing by most loyal customers

4. The Right Offer Requires Predictions About Each Individual Customer Who are the best customers for each product? What are the best products to offer each customer? When will each customer make a purchase (or not)? What lifecycle stage is the customer heading toward? What incentive/discount will be most profitable? Who is worth your marketing $$, and who is a waste of time? Lifecycle Predictions Product Interests

5. The Automation of Marketing Communication with Customers: “How” Last 10 years… “When” Last 5 years… “What” Next… Ironically, the more automated marketing has become, the more customers try to protect themselves from it

6. Trials with our clients confirm:It’s About the Message (“What”) • Fewer, but more relevant messages out-perform bombardment • It’s not “free” when irrelevant messages become annoying • Messaging by segment is not as effective as to the individual • Present activity is just one indicator of intent and doesn’t scale Is this customer: Interested to buy golf clubs? Dreaming about the future? Checking out a friend’s clubs? Browsing for gift ideas? Checking out this retailer? A loyal customer?

7. Automating The Last Mile: “What” to Offer • Limit source data to minimum (use best data) • Constrain objectives (e.g., best recommendation) • Use best data science (machine learning, automated modeling) • Deliver actionable results (e.g., campaign lists) • Package it as a “service”

8. Using Transaction Data: More Reliable, Effective & Far Simpler • First party data available for all customers • Most reliable and accurate data on customers • Typically available over many years • Most accurate predictor of future purchases • No privacy concerns • Easy to source – No data integration • No ambiguity about what the data means In 97 studies, simpler modeling techniques proved more accurate 81% of the time, and reduced errors 27% (S. Armstrong, Wharton, 2015)

9. Save 80% of Time, Cost, Complexity: Translate scores and “insights” into marketing actions Translate scores and “insights” into marketing actions Integrate as much data about your customers as possible Find “predictor” attributes and score customers Marketing Campaigns Data Preparation Data Modeling Data/Insight Interpretation Campaign Planning Offer Selection DiscountLevel Creative Design Campaign Lists Execute Campaign Data/Insight Interpretation Campaign Planning Offer Selection DiscountLevel Creative Design Campaign Lists Sourcing Extraction Cleansing Enrichment Data Modeling Transformation Integration Storage Governance Data Mining Data Sampling Algorithm Selection Scoring Functions Rules Testing

10. List File Driving Variable Content Campaign List from Loyalty Builders: Individualized image and message Individualized discount Individualized product offers

11. Even a Little Lift Makes a Big Difference Individualized Business as Usual This $0.42 lift over previous methods delivered $314,700 of additional revenue At 500K individualized emails per month, this translates to $2.6 million additional revenue per year

12. QUESTIONS? peterm@loyaltybuilders.com

13. Focus on Revenue Lift Via more relevant offers : LIFECYCLECENTRIC CUSTOMER CENTRIC PRODUCT CENTRIC Offer a product to the right set of customers. Apply the right level of resources and incentives to each customer Personalize offers to EACH customer MATURITY MODEL

14. Work on Predictive Metrics for Each Customer Product Recommendations: Lifecycle Predictions:

15. Validated Accurate and EffectiveProven over hundreds of live campaigns Withhold from analysis the purchase data of most recent period Compare predicted purchasing to actual purchasing in most recent period

16. Loyalty Builders’ Cloud Service Predictive Analytics-as-a-Service Deliverables Subscriber-Side Longbow(Self-service SaaS tool) E-Commerce CRM Metrics DB LB Data Delivery Service Predictions by Customer Data Import Customer PurchaseData from Subscriber Campaign & Program Execution Lists Campaign Execution Lists Campaign Platform Analytics Platform Cust ID TX Date $ Amount Product ID Summary Reports Predicted Metrics for Each Customer

17. Over 800,000 emails sent 250,000 emails in control group Customized emails produced 3x to 4x more revenue Over 65% more buyers for Dark and Decaf vs. Control group Actual Results: $490,000 Revenue Lift This campaign was nominated for most innovative marketing campaign award by MITX.