Product and Service Analytics Disclaimer: All 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 Marketing Analytics: Product Analytics 7.1www.StephanSorger.com

Conjoint Analysis © Stephan Sorger Marketing Analytics: Product Analytics 7.2www.StephanSorger.com Attribute Levels - Screen Size: 5 inch, 7 inch, 10 inch Bundles - Different combinations of attributes Attributes - Operating system, screen size, battery life Profiles - Specific bundles preferred by segments Part-Worths - Values placed on particular attributes Conjoint Analysis - Technique to examine trade-offs consumers make to understand their preferences Conjoint Analysis for Tablet Device

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.3www.StephanSorger.com Collect Preference Data Code Data for Analysis Calculate Attribute Part-Worths Apply Conjoint Results Prepare for Conjoint StepDescription Prepare for ConjointIdentify evaluation attributes Select levels for each attribute Form bundles (candidate “products”) Get Preference DataSurvey consumers for their preferences Code Data Prepare data for analysis by coding it Calculate Part-WorthsCalculate preference for each attribute Apply ResultsInterpret to assess market size and segmentation

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.4www.StephanSorger.com Select Attribute Levels Form Candidate Bundles Identify Evaluation Attributes Conjoint Analysis Preparation TopicDescription Identify Evaluation AttributesReview available consumer evaluation sources General sources: Amazon.com, Epinions.com, etc. Specialty sources: CoffeeGeek, Home-Barista Conduct survey of top attributes (next slide) Select Attribute LevelsApply knowledge gained from study of category Form Candidate BundlesCombine various attribute levels to form bundles

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.5www.StephanSorger.com Example: Acme Espresso Machines

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.6www.StephanSorger.com Acme Espresso Machine Attribute Levels Attribute Levels for Non-Numeric Values

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.7www.StephanSorger.com Candidate Bundles, also known as “Cards”

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.8www.StephanSorger.com Rank Ordering Rating Scale Pairwise Comparison Data Collection Techniques TopicDescription Pairwise ComparisonRespondents compare pairs of options Advantage: Respondents find easy to evaluate Disadvantage: Requires many comparisons Rank OrderingRespondents place options in rank order: 1 – 100 Advantages: Fast Disadvantages: Respondents find it difficult Rating ScaleRespondents rate each option independently Advantages: Works well with Excel Disadvantages: Must provide rating scale

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.9www.StephanSorger.com Pairwise Comparison

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.10www.StephanSorger.com Pairwise Comparison

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.11www.StephanSorger.com Rating Scale

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.12www.StephanSorger.com Sample Respondent Preference Results Sample Respondent Segmentation Identification Results

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.13www.StephanSorger.com Coding Process

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.14www.StephanSorger.com Sample Respondent Results, Coded into Binary for Easier Machine Computation Binary Coding with Three Levels

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.15www.StephanSorger.com Sample Respondent Results, with Redundancies Removed Remove redundancies to prevent linear dependency problems

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.16www.StephanSorger.com Excel HomeData…… Data Analysis ABCDEFG Regression Input Y RangeOK Input X Range Labels Constant is Zero Confidence Level: % 95x x Launching Data Analysis in ExcelEntering Data in Regression Dialog Box

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.17www.StephanSorger.com Microsoft Excel Regression Results Preference = Constant + A1 * Speed 1 + A2 * Capacity 1 + A3 * Price 1 Preference = * Speed * Capacity * Price 1

Conjoint Analysis: Process © Stephan Sorger Marketing Analytics: Product Analytics 7.18www.StephanSorger.com Market Simulation Market Segmentation Conjoint Application TopicDescription Market SegmentationCorrelate conjoint data with segmentation data (Demographic, Geographic, Behavioral, Psychogr.) High part worth utility for speed  “Used at work” Market SimulationCollective voice of hundreds of potential customers Simulate market reception to new machine First choice rule: Respondents choose 1 product Market share: % of respondents with high utility

Decision Tree Models © Stephan Sorger Marketing Analytics: Product Analytics 7.19www.StephanSorger.com 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

Decision Tree Models © Stephan Sorger Marketing Analytics: Product Analytics 7.20www.StephanSorger.com 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

Decision Tree Models © Stephan Sorger Marketing Analytics: Product Analytics 7.21www.StephanSorger.com Step 2: Gather Relevant Data: Choice 1, Standard Budget Step 2: Gather Relevant Data: Choice 2, Reduced Budget

Decision Tree Models © Stephan Sorger Marketing Analytics: Product Analytics 7.22www.StephanSorger.com Step 2: Gather Relevant Data: Choice 3, Develop Existing Product Step 2: Gather Relevant Data: Costs for Each Alternative

Decision Tree Models © Stephan Sorger Marketing Analytics: Product Analytics 7.23www.StephanSorger.com Step 3: Random Node Value: Alternative 1: New Product, Standard Budget

Decision Tree Models © Stephan Sorger Marketing Analytics: Product Analytics 7.24www.StephanSorger.com Step 3: Random Node Value: Alternative 2: New Product, Reduced Budget Step 3: Random Node Value: Alternative 3: Enhance Existing Product

Decision Tree Models © Stephan Sorger Marketing Analytics: Product Analytics 7.25www.StephanSorger.com 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

BCG Matrix: Product Portfolio Allocation © Stephan Sorger Marketing Analytics: Product Analytics 7.26www.StephanSorger.com Enter Data Assign Rating Assign Status Allocate Resources List Products

Product/Service Metrics © Stephan Sorger Marketing Analytics: Product Analytics 7.27www.StephanSorger.com 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

Product/Service Metrics © Stephan Sorger Marketing Analytics: Product Analytics 7.28www.StephanSorger.com January Time December Product C: Seasonal Product B: Declining Product A: Steady Rise Product/ Service Sales: Revenue Trends

Product/Service Metrics © Stephan Sorger Marketing Analytics: Product Analytics 7.29www.StephanSorger.com Revenue A B C A B C A B C Market 3 Market 2 Market 1 Product/ Service Sales: Market Adoption

Product/Service Success Quadrants © Stephan Sorger Marketing Analytics: Product Analytics 7.30www.StephanSorger.com 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

Product/Service Success Quadrants © Stephan Sorger Marketing Analytics: Product Analytics 7.31www.StephanSorger.com Illustrative method to group products/ services by profitability

SEM Attribute Preference Test © Stephan Sorger Marketing Analytics: Product Analytics 7.32www.StephanSorger.com 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 SEM to test attribute preferences