Marketing Analytics II

Transcription

1 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 1 Marketing Analytics II Chapter 3A1: Segmentation: Segmentation 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

2 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 2 Outline/ Learning Objectives Topic Description Introduction Techniques Examples Overview of market segmentation, targeting, and positioning Overview of different segmentation techniques A Priori and Post Hoc segmentation technique examples

3 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 3 STP: Segmentation, Targeting, Positioning STP Segmentation Segmentation: Subdividing general markets into distinct segments with different needs, and which respond differently to marketing efforts. -Increased customer satisfaction -Increased marketing effectiveness Targeting Targeting: Selection of market segments. Cannot service every possible segment. Positioning Positioning: Activities to make consumers perceive that a brand occupies a distinct position relative to competing brands.

4 STP Advantages Concentration of Force Focus core competencies on relevant market segments Customer Satisfaction Consumers get what they want STP Advantages Profitability Different groups place different values on similar goods Competitive Advantage Hertz: focus on airport rentals Enterprise: focus on local rentals Niche Marketing Specific segments with specific needs Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 4

5 Sample Market Segments Quality-Oriented Segment Rolex Swiss Watches Durability-Oriented Segment Briggs and Riley Travelware Sample Market Segments Cost-Oriented Segment GEICO Insurance Style-Oriented Segment Apple Computers Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 5

6 Segment Selection Criteria Internal Homogeneity Individuals in group respond similarly External Heterogeneity One group different from another Segment Selection Criteria Size Large enough to be profitable Parsimony As few segments as possible Accessibility Easy to reach with marketing Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 6

7 Response Variable Categories Functional Performance; Reliability; Durability Service and Convenience Time savings; Convenience Response Variable Categories Usage Usage scenario; Usage rate Financial Cost savings; Revenue gain Psychological Trust; Esteem; Status Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 7

8 Segmentation Identifier Variables Demographics Demographics Age; Income Geographics Country; Region; City Consumer Identifier Variables Business Identifier Variables Industry; Company size Geographics Company location Psychographics Situational Lifestyle; Interests Specific applications; Order size Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 8

9 Segmentation Variables Y Axis Response Variables Dependent Variable Relationship between independent and dependent variables Independent Variable Identifier Variables X Axis Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 9

10 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 2 Market Segmentation: A Priori vs. Post Hoc A Priori Post Hoc Market Research And Analysis Latin: From Before Segments defined before primary market research and analysis Latin: After This Segments defined after primary market research and analysis

11 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 3 A Priori Market Segmentation Process Segmentation Variables Sample Design Collection Segmentation Technique Marketing Programs Step Segmentation Variables Sample Design Collection Segmentation Technique Marketing Program Description Response Variable: Usage rate, etc. Identifier Variable: Age; Income; etc. Large surveys: Often use random sample Small surveys: Often use non-random Online survey tools: SurveyMonkey, etc. Cross-tab; Regression; etc. Leverage information known about segment

12 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 4 Market Segmentation: Segmentation Descriptive To describe similarities and differences between groups Predictive To predict relationship between independent and dependent variables

13 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 5 Market Segmentation: Analytic Techniques Segmentation Methods A Priori Post Hoc Descriptive Predictive Descriptive Predictive Hierarchical Partitioning Clustering Cross-Tabulation Regression Ward s K-Means Conjoint

14 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 2 Cross Tabulation: Process Gather Market Examine Market Construct Cross-Tab Table Interpret Cross-Tab Table Step Gather Market Examine Market Construct Cross-Tab Table Interpret Cross-Tab Table Description Conduct survey to gather response var. info. as well as identifier variable information Consider relationships between response variable and identifier variables Use purpose-built tool, or do manually Consider how to apply results

15 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 3 Cross Tabulation Example: Acme Restaurants surveys local community during local town fair. Goal is to get information for cross-tab segmentation. Step 1.Gather Market Gather response variable information (Frequency) as well as identifier variable information: Annual income; Age; Occupation

16 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 4 Cross Tabulation Step 2.Examine Market Examine relationship between response variable (frequency) and identifier variables -Frequency definitely varies by income -Frequency does not appear to vary by age -Frequency varies by occupation, but information is redundant with income

17 Cross Tabulation + many other respondents Step 3. Construct Cross-Tab Table -Use commercial statistics software package such as SPSS and MarketSight (or do it manually) -A Priori Segmentation: Use pre-known bands of independent variable (in this case, Income) -Count the number of respondents dining out 4 times per month that make $10K-$49K/yr, etc. -Divide by total to get percentages Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 5

18 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 6 Cross Tabulation Step 4. Interpret Cross-Tab Table -Segment 1: Dining Misers: Low income individuals who dine out rarely -Segment 2: Dining Medians: Mid-income individuals who dine out occasionally -Segment 3: Dining Mavens: High-Income individuals who dine out frequently (our target)

19 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 7 Regression-based Segmentation: Process Gather Market Examine Market Execute Regression Analysis Interpret Regression Results Step Gather Market Examine Market Execute Regression Analysis Interpret Regression Results Description Conduct survey to gather response var. info. as well as identifier variable information Consider relationships between response variable and identifier variables Use Excel Analysis ToolPak Plug in Part-Worths as regression coefficients

20 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 8 Regression-based Segmentation Example: Acme Automobiles wishes to identify segments purchasing used automobiles Step 1.Gather Market Gather response variable information (Spending) as well as identifier variable information: Income

21 Regression-based Segmentation Step 2. Examine Market Seek relationships between response variable and identifier variables A Priori Segmentation: Use pre-known bands of independent variable (in this case, Income) Alternative: Sort by response variable (dependent variable); Notice gaps in spending Can use techniques such as K-Means to automate this process Next step: Find out relationship between income & spending for each segment Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 9

22 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 10 Regression-based Segmentation: Excel Process Verify Linearity Launch Analysis Select Regression Analysis Input Regression Spending $10,000 $9,000 $8,000 $7,000 $6,000 $5,000 $20,000 $25,000 $30,000 Income 3A. Verify Linearity Microsoft Excel: Least Squares Algorithm Good to plot out data to check if linear

23 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 11 Regression-based Segmentation: Excel Process Verify Linearity Launch Analysis Select Regression Analysis Input Regression Excel Home Analysis A B C D E F G 3B. Launch Analysis

24 Regression-based Segmentation: Excel Process Verify Linearity Launch Analysis Select Regression Analysis Input Regression Analysis Analysis Tools OK Regression 3C. Select Regression from Analysis Tools Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 12

25 Regression-based Segmentation: Excel Process Verify Linearity Launch Analysis Select Regression Analysis Input Regression Regression Input Y Range Input X Range x Labels Constant is Zero x Confidence Level: 95 % OK 3D. Input Regression Y Range: Dependent Variable (Response Variable) X Range: Independent Variables (could have multiple X variables) Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 13

26 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 14 Regression-based Segmentation: Excel Results R-Squared, the Coefficient of Determination Also known as Goodness of Fit, from 0 (no fit) to 1 (perfect fit)

27 Regression-based Segmentation: Excel Results Results, Segment 1 Spending = ( ) * Income Spending = (Intercept) + (Income Coefficient) * (Income) Spending (Buyer 1) = ( ) + ( ) * ($24,000) = $7,427 Spending (Buyer 2) = (7, ) + ( ) * ($52,000) = $24,073 Spending (Buyer 3) = (25,186.44) + ( ) * ($180,000) = $66,068 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 15

28 Segmentation: Cluster Analysis Cluster Analysis Hierarchical Methods Example: Ward s Ward s Method: Agglomerative hierarchical clustering Groups clusters in hierarchy, from bottom up Result is a tree-like diagram (dendogram) Partitioning Methods Example: K-Means K-Means: Specify K, the number of final clusters to expect Execute K-Means algorithm Forms groups based on distance from centroid Mathematics and algorithms of Cluster Analysis are complex; Use cluster analysis built into SAS, SPSS, and other packages Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 16

29 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 17 Market Segmentation: Conjoint Analysis Step Attribute Selection Bundle Definitions Description Collection Part-Worths Calculation Marketing Execution Attribute Selection Select characteristics of product/service customers find relevant Example: Attributes of Screen Size, Processor Speed, Battery Life Bundle Definitions Define candidate products by varying characteristics into bundles Example: Bundles include Laptop A, Laptop B, Laptop C Collection Part-Worths Execution Survey customers on their preferences for different bundles Calculate desire for each attribute, based on bundle evaluation data Different segments desire different characteristics

30 Market Segmentation: Other Techniques Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 18

31 Stephan Sorger 2015: Marketing Analytics: Segmentation: Segment: 19 Check Your Understanding Topic Description Introduction Overview of market segmentation, targeting, and positioning Techniques Overview of different segmentation techniques Examples A Priori and Post Hoc segmentation technique examples