1. Optimizing Your Message With Advanced Analytics Thursday March 19 th, 2015 Paul Maiste & Brett Mowry

2. 2 Paul Maiste Ph.D. President Lityx LLC Brett Mowry Managing Partner ESSE One Presenting

3. 3 Are you currently customizing your message to the needs of your customers? Vote

4.  Introductions  Approach to Optimizing Your Message  Case Study #1: Segmentation Approach  Case Study #2: Predictive Analytics Approach Agenda 2

5. Amazon optimizes every aspect of every message 5 Optimizing Your Message: What do we mean?

6. 6 Strategy & Process Competitive and consumer Insight Product planning Campaign planning Financial analysis Advanced Analytics Structured (A/B) Testing Segmentation Predictive Analytics Attribution & Optimization Data & Technology Web Email CRM Ad Serving   To optimize your message and results, advanced analytics needs to come together with data, technology, strategy and process.

7. Structured (A/B) Testing Segmentation Predictive Analytics Attribution & Optimization 7 Solutions Vary in Outputs and Complexity

8. 8 What area of applying advanced analytics do you struggle with the most? Vote

9. 9 Case Study #1 Segmentation Approach

10. Online Segmentation Case Study 10  Financial Service company had a customer segmentation that they wanted to apply online.  The segmentation had deep insights on product preference and messaging that, if applied, was believed to drive sales.  Process, analytics and technology were brought together to implement a solution that quickly drove a 10% increase in profits.

11. 11 “Engine” combines data sources to identify when a user is a customer Customer receive experience with customized offer, messaging, etc. 3 rd party data Segmentation Modeling Implementation Technology Segmentation “Engine” Customized landing page experience Search engines or display partners + Segment 3 Segment 1 Segment 2 Consumer clicks through + Build out ‘Segmentation Engine’ and Customize Experiences Segment 3

12. 12 Segmentation model attributes were implemented based on predictiveness, reach and potential legal concerns Financial Demographic Purchase Behavior Minimum PreferredPredictive/Desired In-market: Credit Products, Cards, Rewards 7900k High-end Travel, Travel Rewards Card Holder 9 HE: 3.8MM RE: 2.5MM Past Purchases: Business Travel84.5MM In-market: Financial Products/Services 4 312.2MM In-market: Autos427.8MM Vehicle Budget Range: $40K+122.6k HHI: $100K+ 5 1271.4MM Net Worth: $250k+102MM Median Home Value86.5MM States: DC, CT, MA4-6 760k-3MM Cohort: Affluent Baby Boomer12252k Cohort: Baby Boomers (55-63) 2 66.6MM Age: 50-64 2 5 50-59: 17MM 60-64: 6.7MM Attribute 1 Use: Change of Copy Use: Different Offer Use: Product Order Index Reach

13. 13 “Engine” combines data sources to identify when a user is a customer Customer receive experience with customized offer, messaging, etc. 3 rd party data Segmentation Modeling Implementation Technology Segmentation “Engine” Customized landing page experience Search engines or display partners + Segment 3 Segment 1 Segment 2 Consumer clicks through + Initial test results drove a 10% lift of in profits. Additional testing continues to improve performance +3% +15 % +10 % Segment 3

14. 14 Case Study #2 Predictive Analytics

15.  Optimize email campaigns so that a targeted product message is featured. Emails are cheap, but intrusive. Maximize open and click through rates while minimizing unwanted email traffic.  Data integration included website behavior along with prior digital communication behavior and response. Multi channel data integration  Online retail industry. 15 Predicting Product Preference Case Study

16.  Point-in-time data capture is key.  Content metadata is necessary to categorize behavioral activities. Reviews, articles  Prior marketing communications.  Recency and frequency across activities completes the picture. 16 Data Sources Single View of Prospect Demographics Prior Communications Website Behavior

17. Data ElementNotes Total Articles Read – Category AOverall interest in Category A content Total Articles Read – Category BOverall interest in Category B content Articles Read Past 30 – Category ARecent interest in Category A content Articles Read Past 30 – Category BRecent interest in Category B content Articles Read 31-90 Days – Category ANot-as-recent interest in Category A content Articles Read 31-90 Days – Category BNot-as-recent interest in Category B content Recency - Articles Read – Category ADays since last viewed Category A content Recency – Articles Read – Category BDays since last viewed Category B content Velocity – Articles Read – Category ARecent trend uptick/downtick in Category A Velocity – Articles Read – Category BRecent trend uptick/downtick in Category B Category PreferenceCategory most often viewed 17 Sample Data Elements Continue for different content types (video, whitepaper, etc), categories (beyond just A,B), periods (91-180, 180+).

18.  Buying behavior within product categories was driven by: Recency of last website session Prior email click behavior (number clicks, recency) Length of time on prospect file (since first website visit) Interest (based on website activity) in related category Basic demographics (e.g., income category) 18 Power of Predictive Analytics

19.  Targeting emails to interests resulted in improvement in response and uptake. 19 Results Drive Improved Uptake Product uptake lift relative to random product presentation and untargeted emails 4x 2x

20.  Update process can be on a weekly basis. More or less frequent depending on nature of product and amount of communication  Track model performance over time and refresh when degradation begins. 20 Ongoing Implementation Process Compile Fresh Data from All Sources Apply Model to Get Scores Target Email Based on Scores

21. 21 THANK YOU! Brett Mowry Managing Partner, ESSE One brett@esseone.com 773-791-1546 Paul Maiste President, Lityx LLC. maiste@lityx.com 410-919-8093

22. 22 Appendix

23. 23 Case Study #3Optimization

24.  Given a budget and historical performance data, what is the optimal number of impressions to purchase across. Site category Geography Ad dimensions Creative 24 Online Advertising Optimization

25.  Typical Constraints Budget Contractual Testing Inventory  The optimal answer can be very different for different goals 25 Constraints and Objectives  What are we optimizing for? Conversion rate Click thru rate Total conversions Cost per click (or conversion)

26. 26 The objective impacts the decision  Depending on the business objective, budget allocation shift geographically. Minimizing CPC favors heavy Atlanta targeting, while Maximizing Clicks distributes budget more equally.