Marketing Analytics Ch. 4

Front 1

Good positioning

Back 1

Can be described with one word
Volvo Safety

Front 2

Differentiation

Back 2

How the product or service is different

Front 3

Positioning

Back 3

Set of strategies to ensure that differences occupy a distinct and important position in the minds of customers

Front 4

Clutter

Back 4

25,000 products in grocery store 150 models of cars 100,000 prescription drugs
bad for positioning

Front 5

Position using perceptual map -- perceptual map

Back 5

Graphical representation of competition
-- perception of what customers think

Front 6

Perceptual Maps -- Distances

Back 6

Perceived distance between brands

Front 7

Perceptual Maps -- Vectors

Back 7

Denotes attributes: “Full-Bodied” Vectors have Direction & Magnitud

Front 8

Perceptual Maps -- Axes

Back 8

Dimensions used by customers to differentiate (premium (x) & heavy (y))

Front 9

Snake Diagram

Back 9

Alternative; Implies equal weighting of attributes

Front 10

Applications of Perceptual Maps - New Products

Back 10

Blank areas signify opportunities Market for really cheap, medium body beer?
But: “Gap in the Market not equal to Market in the Gap”
(might be a reason why ppl do not pursue the gap)

Front 11

Applications of Perceptual Maps -- Image

Back 11

Wal-Mart known for low prices or selection/convenience?

Front 12

Applications of Perceptual Maps -- Competitor

Back 12

Identify who to compete against
Competitor might not communicate differentiation well
What makes Gateway PCs unique?

Front 13

Applications of Perceptual Maps -- Market Insight

Back 13

Insight to competitive structure and positioning Might not agree with manager’s perceptions--

Front 14

Perceptual Mapping Techniques: Attribute-Based -- Identify

Back 14

Identify set of products and attributes Competitive Set: All laptops or all PCs? Attributes: Features, Performance, Durability, ...

Front 15

Perceptual Mapping Techniques: Attribute-Based -- Data

Back 15

Obtain perception data Surveys among homogeneous groups

Front 16

Perceptual Mapping Techniques: Attribute-Based -- Method

Back 16

Select a perceptual mapping method Data Matrix: Customer perceptions on attributes Factor analysis to extract relevant data --

Front 17

Perceptual Mapping Techniques: Attribute-Based -- Interpret

Back 17

Interpreting factor analysis output; determine axes -- directly measurable (speed, light, weight, etc **performance products)

Front 18

Perceptual Maps -- Similartiy Based

Back 18

Image prodcuts

Front 19

Perceptual Maps -- Attribute Based

Back 19

Functional Products

Front 20

Joint Space Maps -- External Analysis

Back 20

Shows ideal points

Front 21

Joint Space Maps - Simple Joint Space

Back 21

Shows Preference Vectors

Front 22

Perceptual Maps -- Attribute Based -- what do the vectors reporesent

Back 22

Each vector corresponds to a cluster of consumers that respond a similar way

Front 23

Perceptual Mapping Techniques: Similarity-Based -- Identify

Back 23

Identify alternatives of interest Competing products or services
(used if hard to distinguish based on attributes ie perfume)

Front 24

Perceptual Mapping Techniques: Similarity-Based -- Matrix

Back 24

Develop matrix of similarities Ask customers to rank: 1 = most similar  9 = least similar

Front 25

Perceptual Mapping Techniques: Similarity-Based -- Map

Back 25

Develop perceptual map Computer-generated: KYST, MDSCAL

Front 26

Perceptual Mapping Techniques: Similarity-Based -- Dimensions

Back 26

Determine minimum number of dimensions Minimize math “stress” associated with various dimensions

Front 27

Perceptual Mapping Techniques: Similarity-Based -- Interpret

Back 27

Close in map  Close in perception Determine meaning of axes (ask customers)

Front 28

Joint-Space Maps -- Limitation

Back 28

Perceptual Maps: No information on desirable positions
(Alternative to perceptual maps)

Front 29

Joint-Space Maps -- Joint Space

Back 29

Perceptions + Preferences on same map

Front 30

Joint-Space Maps -- Ideal Point

Back 30

Optimum point Goldilocks principle: “Just Right” Example: Food seasoning

Front 31

Joint-Space Maps -- Vector

Back 31

Unidirectional More is Always Better: Speed Less is Always Better: Breakdowns

Front 32

Simple Joint-Space Maps -- Ideal Brand

Back 32

Hypothetical ideal brand Customers evaluate in attribute-based model Respondent-specific Simply another alternative to evaluate

Front 33

Simple Joint-Space Maps -- Distance

Back 33

Measure distance from Ideal to Actual Distance equivalent to Preference

Front 34

Simple Joint-Space Maps -- Alternative

Back 34

“Preference vector”
(hypothetical ideal brand, who prefers this?)

Front 35

External Analysis Using PREFMAP3 -- PREFMAP3

Back 35

Mapping model assuming different preferences for different people Starts with existing perceptual map with products Adds ideal brand or preference vector for each respondent

Front 36

External Analysis Using PREFMAP3 -- Input

Back 36

N x m matrix: N = customers; m = alternatives

Front 37

External Analysis Using PREFMAP3 -- process

Back 37

PREFMAP3 locates ideal point for each customer on map Algorithm minimizes distance to preferred brands

Front 38

External Analysis Using PREFMAP3 -- Interpret

Back 38

Largest group in circle “1”: “Ideal Market Share”

Front 39

Incorporating Price in Perceptual Maps -- Typical Way

Back 39

Include price as one of many attributes

Front 40

Incorporating Price in Perceptual Maps -- Alternative

Back 40

Divide coordinates of map by price Convert to “per-dollar” coordinates