The document discusses developing a marketing analytics platform to optimize marketing campaigns and measure sales response. It involves: 1) Collecting various data sources like call volumes, website behavior, surveys to understand customers. 2) Identifying key performance indicators like awareness, consideration, intent, loyalty that have predictive linkage to sales. 3) Building analytics solutions like dashboards and reports to convert data into actionable insights for marketing programs and sales. 4) Taking an integrated and analytical approach using various tools like regression, genetic programming, structural equation modeling, and simulators to examine relationships and identify patterns in the data.

1. Developing a Marketing Analytics Platform

2. Optimization Group Mission

3. Our Approach

4. Parameters for learning and measuring Sales response to campaigns Call volume / reasons to call Website behavior Ongoing voice of customer research Periodic email surveys and polls Internal employee research Independent benchmarking Census and credit bureau data Psychographic profiles Relevant data from across company What people do What we can append What people tell us

5. Analytically, we identify a handful of KPIs with predictive linkage to sales! Awareness  Consideration  Intent Brand impact and engagement Resonance and understanding Loyalty and advocacy Leadership Customer Satisfaction Establish baseline Recommend to others (Net Promoter) Ease of use Cancel? (Defection models) Renew? (repeat purchase models) Positioning strength Messaging (persuasion/motivation) Spending levels Media mix Inventory levels Time to serve Quality/defects Brand Health Customer View Operations Marketing Program Sales/ Market Share

6. Building an Analytics Solution

7. Dashboard Examples

8. Daily Reporting Dashboard

9. Weekly Reporting Dashboard

10. “ What If” Simulator

11. In Summary… Marketing Analytics … not just “market research” Primary data + secondary data Integrate and synthesize data from throughout the company – not just research data but also sales, marketing plan investments, etc. Using proven tools and templates… We frame data and convert to actionable information

12. Contacts Jeff Ewald Founder and CEO E: jewa [email_address] ationgroup.com T: 248.459.1194 Tom Thompson Business Development Director E: [email_address] T: 734.748.5460

13. Intersecting marketing, science and technology™

14. Why is our analytic/modeling approach different? Utilize many tools -- selecting the best approach for each task Proprietary tools and data platforms permit examination of more variables Typically 3 orders of magnitude more Identifies complex relationships and patterns in the data Interactions Curvilinear functions (multi-order polynomials) Modeling in the real-world environment

15. Many Tools … Many Approaches

16. Statistical Regression $10 $9 $5 $4 $7 $8 $3 $2 $1 $6 R R R N R N N N R Goal: Minimize deviations between data and assumed model form Most appropriate when analyst has good hard data Test preconceived hypotheses based on relatively good understanding of the space or issue

17. Genetic Programming $10 $9 $5 $4 $7 $8 $3 $2 $1 $6 R R R N R N N N N R Goal: Create model form that fits the data This is software that literally programs itself, based on the data it is testing. Our proprietary GP software product is GMAX™

18. Structural Equation Models Structural Equation Modeling is used to examine alternative data structures to better understand the relationships among various dependent and independent constructs SEMs allow for creation of hypothetical variables (called latent variables) – which effects are estimated from the other, observed variables

19. Simulators Allows decision maker to view outcomes under a wide range of scenarios “ Stress test” the decision Improves understanding of relationships between variables (internal and external) Various types from simple equations, to Monte Carlo simulation to sophisticated Agent based modeling