Marketing Analytics II Chapter 7B: Product Analytics: Decision Trees Stephan Sorger Disclaimer: All images such as logos, photos, etc. used in this presentation are the property of their respective copyright owners and are used here for educational purposes only © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 1www.stephansorger.com

Outline/ Learning Objectives TopicDescription Decision Tree Models Select among multiple product development projects Portfolio Allocation Decide in which product/service to place investments Product/Service Metrics Track success of products and services © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 2www.stephansorger.com

Decision Tree Models Gather Relevant Data Calculate Random Node Values Calculate Decision Node Values Select Winning Alternative Establish Decision Choices TopicDescription Decision ChoicesList out alternatives Relevant DataGather data for each alternative Random Node ValuesCalculate values at random nodes Decision Node ValuesCalculate values at decision nodes Uses results from random node calculations Winning AlternativeSelect alternative with highest net expected value © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 3www.stephansorger.com

Decision Tree Models Typical Development Project Selection Scenario Step 1: Establish Decision Choices Decision Node Random Node Scenario C. Enhance Existing Product Develop New Product Development Project Decision A. Use Standard Budget AverageStrongPoor B. Use Reduced Budget AverageStrongPoorAverageStrongPoor © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 4www.stephansorger.com

Decision Tree Models Step 2: Gather Relevant Data: Choice 1, Standard Budget Step 2: Gather Relevant Data: Choice 2, Reduced Budget © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 5www.stephansorger.com

Decision Tree Models Step 2: Gather Relevant Data: Choice 3, Develop Existing Product Step 2: Gather Relevant Data: Costs for Each Alternative © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 6www.stephansorger.com

Decision Tree Models Step 3: Random Node Value: Alternative 1: New Product, Standard Budget © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 7www.stephansorger.com

Decision Tree Models Step 3: Random Node Value: Alternative 2: New Product, Reduced Budget Step 3: Random Node Value: Alternative 3: Enhance Existing Product © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 8www.stephansorger.com

Decision Tree Models Step 4: Calculating Decision Node Values Step 5: Select Alternative with Highest Net Expected Value Winner: “Develop New Product, Standard budget” C. Enhance Existing Product Develop New Product Development Project Decision A. Use Standard Budget AverageStrongPoor B. Use Reduced Budget AverageStrongPoorAverageStrongPoor EV = $326,000 Cost = $200,000 Net EV = $126,000 EV = $152,000 Cost = $100,000 Net EV = $52,000 EV = $63,000 Cost = $40,000 Net EV = $23,000 Net EV = $126,000 © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 9www.stephansorger.com

BCG Matrix: Product Portfolio Allocation Enter Data Assign Rating Assign Status Allocate Resources List Products © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 10www.stephansorger.com

Product/Service Metrics Product/ Service Sales Input Table: Total Revenue by Month, Products A, B, and C Product/ Service Sales Input Table: Revenue in Different Markets by Month, Product A © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 11www.stephansorger.com

Product/Service Metrics January Time December Product C: Seasonal Product B: Declining Product A: Steady Rise Product/ Service Sales: Revenue Trends © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 12www.stephansorger.com

Product/Service Metrics Revenue A B C A B C A B C Market 3 Market 2 Market 1 Product/ Service Sales: Market Adoption © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 13www.stephansorger.com

Product/Service Success Quadrants 50% Gross Margin Product A Product C Product B Product D Super StarsNiche Stars Mass MarketConcern Areas Revenue Product/ Service Profitability: Product Success Quadrant Tool: Graphical Format Adapted from product profitability analysis tool by Demand Metric; Used with permission © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 14www.stephansorger.com

Product/Service Success Quadrants Illustrative method to group products/ services by profitability © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 15www.stephansorger.com Gross Margin (Percentage) = (Revenue – COGS) / Revenue * 100%

SEM Attribute Preference Test GoogleSearch Box Left Nav. Featured Ads Organic Search Results Ads Vacuum Carpets Fast Turbo-Vortex design Delivers 2x the suction! Vacuum Drapes Easily EZ-DRAPE attachment Cleans curtains with ease! Hey Allergy Sufferers! Hyper-HEPA filter Removes 1-micron particles A B C TestClicksBuys A24012 B300 2 C4 0 Pay Per Click Ads such as Google AdWords Apply Search Engine Marketing (SEM) to test attribute preferences © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 16www.stephansorger.com

Check Your Understanding © Stephan Sorger 2015: Marketing Analytics: Product Analytics: Tree 17www.stephansorger.com TopicDescription Decision Tree Models Select among multiple product development projects Portfolio Allocation Decide in which product/service to place investments Product/Service Metrics Track success of products and services over time