Conjoint Experiment ReportEarlyRiders

Team 12: Yanxuan (Katrina) Li/ Zijun Wang/ Mengxu Wang/ Wei-Shih Chung/Tusha Kateja

Executive Summary

• From the cluster analysis, there are three distinct segments, and the targeted products are:• Classic Rider: fairly price sensitive, like 18”, rocking and glamorous horse. ($139.99, 18’’,

Rocking, Glamorous)• Giant Lover: less price sensitive, like 26”, rocking and glamorous horse. ($139.99, 26’’,

Rocking, Glamorous)• Bouncing Rider: most price sensitive segment, like 26”, bouncing and racing horse. ($119.99,

26’’, Bouncing, Racing)• Different gender have significant different preference on attributes:

• Male: More price sensitive, prefer 26”, bouncing and racing horse ($119.99, 26”, Bouncing, Racing)

• Female: Less price sensitive, prefer 26”, rocking and glamorous horse ($139.99, 26”, Rocking, Glamorous)

• Marketing Recommendation:• Drop both current product

• Product 13: 18" Rocking Glamorous Horse ($139.99)• Product 5: 18" Rocking Racing Horse ($139.99)

• Launch Product 15 and 2, and target different marketing message towards family with boys and girls• Product 15: 26" Rocking Glamorous Horse ($139.99)• Product 2: 18" Bouncing Racing Horse ($119.99)

• Closely monitor competitor’s product price, once competitor lower its price, start to product the same product.• Product 8: 26" Rocking Racing Horse ($119.99）

Cluster Analysis

Segment

Constant

Price Height Motion Style

1 (Black) 37.9 12.0 -6.5 9.7 0.12 (Red) 43.1 8.8 16.5 7.2 11.03 (Green)

29.0 22.4 6.1 -9.8 -6.5

Segment Utility Table

Segment Characteristics- Classic Rider: Consumer in segment 1 is

fairly price sensitive, like 18”, rocking and glamorous horse.

- Giant Lover: Consumer in segment 2 is the less price sensitive segment, they like 26”, rocking and glamorous horse.

- Bouncing Rider: Consumer in segment 3 is the most price sensitive segment, they like 26”, bouncing and racing horse.

20%

30%

50%

3 Cluster

123

Intercept Price Height Motion Style

-20

-10

0

10

20

30

40

5037.9

12

-6.5

9.7

0.1

43.1

8.816.5

7.211

2922.4

6.1

-9.8 -6.5

Cluster Means

123

Segmentation

Segment Cluster Name Utility1 (Black) Classic Rider 41.1

2 (Red) Giant Lover 66.8

3 (Green) Bouncing Rider 25.3

Business Concern:

• Price higher for less price sensitive segment (1&2) and Price lower for more price sensitive segment (3) to get more profit.

• Costs of different height and motion features are different, so we should find a balance between keeping their preferred height and motion features and variable costs.

• Make sure that different segments will still choose products for their segments after we increase price in segments with lower price sensitivity.

• Finally, we use market simulator to trade off between utility and cost and test the scenarios to ensure largest profit.

Competitor’s Concern:

Competitor’s product: $139.99 26’’ Rocking RacingUtility generated by competitor’s product in three segments:

We should add or modify products that specifically target each segment to generate higher utility than competitor’s product and increase market penetration.

Targeted Profiles

Segment Profile Price Height Motion Style Utility VariableCost

MarginContribution

Classic Rider

13 139.99 18’’ Rocking Glamorous

47.7 33 78.99

Giant Lover

15 139.99 26’’ Rocking Glamorous

77.8 41 70.99

Bouncing Rider

4 119.99 26’’ Bouncing Racing 57.5 35.1 66.99

Products 13, 15 and 4 are best for each segment:• All consumers have higher utility in these products than in competitor’s product.• Preference in attributes other than price is enough to keep them in assigned segments.• Since segment 1 & 2 have lower price sensitivity, and we can extract more consumer surplus

while keeping them in the assigned segment because they can get more utility from their assigned products than from other products in market.

• We run regression to test whether different age prefer different attributes. From the table, we can see that not all the attributes are statistical significant

• Only “Price”, “Height”, “Height*ageD” and “Motion*ageD” are significant different.• As a result, it is not necessary to run another regression to find out which attributes

matters the most to different age. • Thus, no product line insight can be found using age.

Price Height Motion Style Price*ageD Height*ageD

Motion*ageD Style*ageD

P-value *** *** *** ***

Significant Yes Yes No No No Yes Yes No

*summary(lm(ratings~desmat*ageD))

A Priori Segmentation (Age)

We run regression to see whether gender have significant different toward age. And As the table in the right, all the attributes are significant different. (P-value<0.05)Therefore, we try to run specify the profile for different gender, female and male. The conclusion is shown below:• male is more price sensitive • Both segment prefer 26” toy, but

female like it more.• Female prefer rocking horse while

male prefer bouncing horse.• Female prefer glamourous horse while

male prefer racing horse.7

Price Height Motion Style

P-value *** *** *** ***

Significant

Yes Yes Yes Yes

Gender Constant

Price Height Motion Style

Female 39.9283 11.68 12.54 4.20 6.64

Male 32.8681 18.22 3.69 -3.00 -2.7

*summary(lm(ratings~desmat*genderD))*To test whether each gender has significant preference attributes

*summary(lm(ratings~desmat,subset=ageD==1))*summary(lm(ratings~desmat,subset=ageD==0))*To find out the coefficient for each segment.

A Priori Segmentation (Gender)

By using the coefficient to calculate the utility of each segment, we can discover that female and male have totally different preference toward toy horse. Thus, we recommend to launch 15for female, 2 for male. Methods of choosing profile:1.Higher utility (make sure each segment won’t buy products from other segments)2.Higher profit (to extract as more consumer surplus as we can)

Although Both segment prefer 26 inches horse, because male are much more sensitive to price, if we provide cheaper price, male will also buy 8

Group Profile

Price Height

Motion Style Cost Profit

Female 15 $139.99(0) 26”(1) Rocking(1) Glamourous(1)

$33 $106.99

Male 2 $119.99(1) 18”(0) Bouncing(0)

Racing(0) $21 $98.99

Preferred Products for Different Gender Segment

Simulate market shares for different product‐line scenarios based on the previous segmentation methods. Using the first choice rule to forecast the market shares in different scenarios, and calculate the total profit for the company under different scenarios.

Market Simulations

(Market Share) Comp. (P7)

Comp. (P8)

Own (P13)

Own (P5)

New (P4)

New (P15)

New (P2)

Total Profit

0 Status Quo 63.6% 15.4% 21.0% $75,024.00

1 Segmentation 1(Cluster Analysis)

3.3% 11.7% 55.9% 29.1% $209,428.00

2 Segmentation 2 (By Gender)

2.7% 30.4% 66.9% $247,036.0

0

3 Adding All Seg. 2.0% 6.1% 41.5% 27.3% 23.1% $197,328.0

0

4 Comp. Lower Price (Seg. 2)

35.0% 20.3% 44.7% $151,752.0

0

5 Comp. Lower Price

(Produce the Same)

35.0%

(Half&Half)

20.3% 44.7% $170,252.00

• Status Quo: Currently, EarlyRider is providing Product 13 (18" Rocking Glamorous Horse) and Product 5 (18" Rocking Racing Horse), and the competitor is providing Product 7 (26" Rocking Racing Horse). All three products in the market are priced at $139.99. The total profit of EarlyRider is $75,024.

• Scenario 1: The cluster analysis found three segments, and according to the previous analysis, the best targeted products are Product 13 ($139.99 18" Rocking Glamorous Horse), 4 ($119.99 26" Bouncing Racing Horse), and 15 ($ 139.99 26" Rocking Glamorous Horse). In this scenario, we offer all those three products and drop Product 5. Result in a total profit of $209,428. This is a drastic profit increase because the new products have take around 60% market share from our competitor.

• Scenario 2: Considering the best targeted products for different gender, and dropping both current product. Product 15 ($139.99 26" Rocking Glamorous Horse) and 2 ($119.99 18" Bouncing Racing Horse) can take more market share from the competitor and result in $247,036 total profit for EarlyRider.

• Scenario 3: If adding all targeted products in the product line, it will futher decrease the competitor’s market share. However, the cannibalization effect result a less profitable situation for EarlyRider.

• Scenario 4: If the competitor lower the price of its product to $119.99, it can be labeled as Product 8 in the product profile. If we continue selling the best target products for different gender, the total profit will be $151,752 since the competitor take around 30% market share in total from our products.

• Scenario 5: However, if we try to produce the same product as competitor and sell at the same price. We can assume that we will talk away half of the market share. It will give us $170,252 total profit.

*Underlined assumption and calculation steps related to the product profiles and product costs are in the appendix.*Other market simulation scenario tested but not included due to low returns.

Market Simulations (Cont.)

• EarlyRider should drop both Product 13 and 5– Product 13: 18" Rocking Glamorous Horse

($139.99)– Product 5: 18" Rocking Racing Horse ($139.99)

• Launch Product 15 and 2, and target different marketing message towards family with Boys or Girls– Product 15: 26" Rocking Glamorous Horse

($139.99)– Product 2: 18" Bouncing Racing Horse ($119.99)

• Closely monitor competitor’s product price, once competitor lower its price, start to product the same product, which is Product 8– Product 8: 26" Rocking Racing Horse ($119.99）

Competitor’s Price Dropping Reaction

This dynamic Go-To-Market strategy is taking the competitor’s reaction into consideration. At first, we can have 97.3% market share and $247,036 annual profit. If competitor lower the price of their product, we will start to produce the same product and leverage the existing brand equity to direct compete for market share (assuming 50% MS). We can still have 82.5% market share and $170,252 total profit.

Potential Marketing Plan

Appendix

Alternative Segmentation: Two Segments

Segment

Constant

Price Height Motion Style

1 (Black) 29.0 22.4 5.1 -9.8 -6.5

2 (Red) 41 10.1 7.5 8.2 6.7

Segment Most Preferred Product

- Bounce Lover: Profile 4, 119.99, 26’’, Bouncing, Racing

- Rock Star: Profile 16, 119.99, 26’’, Rocking, Glamour

14

Segment Profile Price Height Motion Style Utility VariableCost

MarginContribution

Bounce Lover

4 119.99 26’’ Bouncing Racing 56.5 29 66.99

Rock Star 15 139.99 26’’ Rocking Glamorous

63.4 41 70.99

Targeted Profile

Competitor’s Concern

Segment Cluster Name

Utility

1 (Black) Bounce Lover 24.3

2 (Red) Rock Star 56.7

Competitor’s product: $139.99 26’’ Rocking RacingProducts 4 and 15 are best for each segment:• All consumers have higher utility in these products

than in competitor’s product• Since segment 2 have lower price sensitivity, and we

can extract more consumer surplus while keeping them in the assigned segment and not switching to the competitor ‘s product

• However, as long as competitor choose to decrease their price as response, the utility of Rock Star will increase to 66.8 and they will switch to competitor’s product

Alternative Segmentation: Two Segments

• From the cluster plot of the two cluster approaches, we found out the customers were divided into three groups apparently. Therefore, separating them into three segments seems to be the ideal way to target different types of customers better.

• If we choose to separate customers only into two groups, we would have to provide one product to a group of customers who have different needs. And since one product cannot satisfy the needs of most customers, their utility of this product would be lower. Thus, we can add another product line to specifically target the heterogenous subgroup.

• Additionally, adding another product line could help us to face the possible price decrease of our competitor better. From the previous analysis, we revealed that if we provide three products, then the utility of each segment of customers would be high and so that they would not get higher utility from our competitor even if they decrease their price. However, if we only launch two product, then customers’ utility would be lower and they would have higher utility from our competitor and switch from our product if our competitor decrease their price.

• We chose the product profiles basically based on the rationale that we would meet the features customers needed most and we picked a higher price to enlarge contribution margin if they are not heavily price sensitive. We also adjusted the the features of our product to ensure that each segment would buy the product designed for them and would not buy our other products or competitor’s products.

Rationale to Choose Three Segments and Product Profiles

*Two Segments

*Three Segments

• Assume the current toy horses market will only served by EarlyRider and one competition in the projected future. Both company will only offer toy horses different in the following four attributes: price, height, motion, and style. Each attribute have only two factors of levels. The levels for retail price are $119.99 and $139.99, for height are 18" and 26", for motion are rocking or bouncing, and for style are glamorous or racing. It means that both company together can potentially offer 16 different kinds of toy horses.

• Market Overview: Assume that the total annual market size is 4,000 units.• Revenue Structure: Wholesale prices are $111.99 for the $139.99 product retail price and $95.99 for the

$119.99 product retail price. (For cleaner result, use $112 and $96 in the calculation)• Cost Structure:

– Each product in the product line results in fixed costs of $20,000 per year.

Product Variable Costs

18" Rocking

$33

26" Rocking

$41

18" Bouncing

$21

26" Bouncing

$2916

Market Simulation Assumption

• Total Profit = ∑((Whole sale price – Variable Cost)*Market Share*Market Size) – Fix cost*Owned product line

17

Market Simulation Assumption

18

Relevant R code

Relevant R code

THANKS!