Statistical concept generation techniques Product and Innovation Management Topic 6

What are these techniques? 1. Determinant gap map 2. Overall similarity (OS) perceptual gap map 3. Attribute ratings (AR) conjoint analysis

Determinant gap map Identify the two most determining attributes The two attributes that are most important and (2)differentiating Managerial expertise and judgement is used to plot products on a map Speedy and cost efficient Discrepancy between managerial perception and customer perception? Eg： Candy bar/ breakfast cereal 事实上并不健康

Determinant gap map – Carbonated drinks example 0.8 Dr Pepper Tango 0.4 Coke Pepsi 7-Up 0.0 Diet Tango 不同 左边都是可乐喜好的。右边。。 下面都是 Horizontal-cola flavour vertical- dietness Diet Pepsi -0.4 Diet 7-Up Diet Coke -0.8 -1.5 0.0 1.5

Overall similarity perceptual gap map Respondent customers are asked to judge how similar or dissimilar different major products in the same market are, using their own criteria Likert scale questions are used Use multidimensional scaling technique to draw the map

Overall similarity perceptual gap map – Beer example 2.0 Becks 1.0 Corona Budweiser Budvar Carsberg Grolsch 0.0 Harp San Miguel Horizontal-Initial taste and strength Vertical-After taste and strength -1.0 Holsten Stella Artois -2.0 -2.0 -1.0 0.0 1.0 2.0

Likert-scale questions for overall similarity perceptual gap map Very dissimilar Very similar Becks v Budweiser 1 2 3 4 5 6 7 Budweiser v Carlsberg Carlsberg v Corona …… Becks v Stella Artois

Example of data output for one respondent customer Becks Budvar Budweiser Carlsberg Corona Grolsch Harp Holsten San Miguel Stella Artois 5 6 7 4 2 3 1

Attribute ratings conjoint analysis A product to be developed is represented by a bundle of attributes. ppt22 All possible combinations of all of the determinant attributes Visualization of each possible combination through a card or a static picture of the prototype Identification of which combinations would be most preferred by customers Conjoint analysis Attributes （characteristics） 3 attributes Colour - red,black,blue I have 3 levels of this colour attribute

How to decide on the attributes for conjoint analysis? 1. Features 2. Functions 3. Benefits New toothpaste

An introductory example – let’s generate a new coffee product concept (1st attribute) Utility value

An introductory example – let’s generate a new coffee product concept (2nd attribute) Utility value

An introductory example – let’s generate a new coffee product concept (3rd attribute) Utility value

Detailed work example – prepared salsa Three determinant attributes: Spiciness, Thickness, Colour Spiciness: Mild, Medium-hot, Extra-hot 3 Thickness: Regular, Thick, Extra-thick 3 Colour: Green, Red 2 How many different types of salsa that can be considered? 3x3x2=18 3x3x2=18 Conjoint analysis Attributes （characteristics） 3 attributes Colour - red,black,blue I have 3 levels of this colour attribute

Preference ranking by one of the respondents Spiciness Thickness Colour Actual ranking Predicted ranking Mild Regular Red 4 Green 3 Medium-hot 10 6 8 Extra-hot 15 16 Thick 2 1 5 13 11 Extra-thick 7 9 14 12 17 18 用100个人去得到 最后17这个result Regression analysis Y=a+bx

Analyse preference ranking data Use ‘monotone analysis of variance’ to identify and quantify common patterns within the rank order data Estimate the utilities of each level of each attribute for each respondent -> (1) Can tell which levels of the determinant attributes are preferred -> (2) Can tell which attributes are treated as more important than others 1.

Which levels of the spiciness attribute are preferred? -1.774 0.105 1.667+1.774=3.441 1.667

Which levels of the thickness attribute are preferred? -1.074 0.913 1.074+0.913=1.987 0.161

Which levels of the colour attribute are preferred? 0.161 -0.161 Colour – 0.161+0.161=0.322 3.441+1.987+0.322=5.75

Which attributes are treated as more important than others? 59.8% 34.6% Spiceness=3.441/5.75 Thickness=1.987/5.75 Colour=0.322/5.75 5.6%

Two issues on the applicability of attributes rating conjoint analysis 1. How well does the attribute ratings conjoint analysis model incorporating utility scores predict actual customer preference patterns? 2. What if we have many more attributes and/or levels? 把备注的全部乘起来。 432 combinations 100respondent Prepared salsa Spiceness – 3 Thickness – 3 Colour – 2 Type of container – glass VS plastic – 2 Size of container – family VS individual – 2 Type of ingredients – organic VS non-organic 2 Brand name – 3 different brand name. 3

Four hurdles to be crossed for effective conjoint analysis 1. Can the product be specified as a bundle of attributes? 2. How can our company identify the determinant attributes of the product? 3. Are the respondents familiar with the product and its determinant attributes? 4. Can our company act on the conjoint analysis results? 2.How to identify the 3 to 4 attributes out of 10 potential attributes? Determinant contribute Important Differentiating 3. New to the world products Radical VS incremental innovations 4.

Application of virtual prototypes in conjoint analysis Enabled by improvements in virtual reality computer and video technology Respondents are brought into a virtual buying environment that simulates the information typically available in a realistic buying environment By using surrogate travel technology, respondents ‘walk around’ a dealer showroom and look at virtual, computer-generated prototypes By using video monitor and laser videodisk player, respondents see advertisements, read relevant information, hear statements from salespeople, and word-of-mouth comments from customers

Question time! 1. What are the key differences among the three statistical concept generation techniques taught today? What are the main/major differences 2. What is the major limitation of the three techniques? Statistical techniques (topic 6)– incremental innovations Non-statistical techniques (topic 5) – both redical and incremental innovations 3. Which of the three techniques is better? Conjoint analysis A gap in the map doesn’t ..

Q1 criteria data Determinant gap map managers Cheaper/fast/High risk Overall similarity perceptual gap map (ppt6) consumers Conjoint analysis Most detail specific