For: H&M Hennes & Mauritz AB Prepared by: Ran Huo, Senior Consultant from RH Consulting LLC Date: 4/21/2016

Conjoint Analysis

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Executive Summary Key Challenges:

• H&M is facing intense competition from its direct competitors. Among the four Appeal retail brands we studied, H&M stands at the third place in terms of market share, and is closely followed by Forever 21. H&M is struggling with how to increase its current market share.

• Optimizing H&M’s Marketing Mix is a good solution to its current market share issue. In order to optimize its Marketing Mix, based upon the result of our Conjoint Analysis, we recommend H&M to increase its TPU1 by optimizing six features (Travel distance, Price, Display Placement, Sales Associate/Customer Interaction, Brand identity, and Channels of promotion) that we covered in our study.

• In implementing our recommendations, H&M might face dilemmas and restrains both internally and externally. However, if H&M could successfully implement our recommendation, we predict a 6% increase for H&M’s market share.

Recommendations:

• Fix the most important feature – travel distance first. Provide free shuttle bus service to shorten the perceived travel distance for H&M’s target customers. If budget allows, build new stores close to high population density area.

• Tradeoff the losing utility2 and leverage the two sweet spots (Price and Sales Associate/Customer Interaction) to increase utility while saving money for other investment.

• Switch to promotional channel that carries higher utility and boost H&M’s TPU.

• Flank attack ZARA to reinforce H&M’s brand identity.

• Make sure to keep H&M’s TPU above the entry barrier.

1 TPU is Total Product Utility. It is the sum of all seven features’ utility. Please see Appendix 2 for the detailed explanation. 2 Utility is the result we get from the Conjoint Analysis. It is also named as coefficients. Utility is a good indicator for customers’ preference and intention to purchase. Higher utility indicates higher possibility for customer to purchase.

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Table of Content I. Introduction.............................................................................................................................................................................................................................................................................3II. Overview................................................................................................................................................................................................................................................................................3III. Conjoint Analysis Findings & Recommendation...................................................................................................................................................................................................3

A. 18 Service Profiles.........................................................................................................................................................................................................................................................3B. Overall Feature Importance........................................................................................................................................................................................................................................3

1. Findings:........................................................................................................................................................................................................................................................................42. Recommendations:.....................................................................................................................................................................................................................................................4

C. Feature Type – Value Structure.................................................................................................................................................................................................................................41. Findings:........................................................................................................................................................................................................................................................................42. Recommendation:.......................................................................................................................................................................................................................................................5

D. Total Product Utility.....................................................................................................................................................................................................................................................51. Findings:........................................................................................................................................................................................................................................................................62. Recommendations:.....................................................................................................................................................................................................................................................6

E. Market Share & Simulation.........................................................................................................................................................................................................................................61. Findings:........................................................................................................................................................................................................................................................................62. Recommendations:.....................................................................................................................................................................................................................................................6

F. Intercept Interpretation – entry barrier.....................................................................................................................................................................................................................71. Findings:........................................................................................................................................................................................................................................................................72. Recommendations:.....................................................................................................................................................................................................................................................7

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I. Introduction Through a bid process, RH Consulting LLP won a contract by the Appeal Retail Brand, H&M to conduct a comprehensive research study in order to understand target customers’ value system, to predict target customers’ intention to purchase, and to optimize its Marketing Mix. Based on the findings and discussions from the meeting with H&M, our consulting firm decided to conduct the Conjoint Analysis3. The data gathering process was majorly qualitative through focus groups discussion, client input, and survey study results. The data analyzing process was majorly quantitative through applying the statistical model of Multiple Linear Regression on StatPlus. The primary objectives for the consultation are including: • Complete an integrative analysis through Conjoint Analysis • Present our findings through “Overall Feature Importance Pie Chart” and “Utility Bar Graph”. • Understand the “Overall Feature Importance” and “Utility” for each feature and level to customers • Make clear recommendations and tactics on how to optimize its Marketing Mix and increase its TPU & Market Share. II. Overview H & M Hennes & Mauritz AB (H & M) is a global clothing retailer based in Sweden, and principally retails a comprehensive choice of clothing, footwear and accessories for men, women and children besides offering a wide range of cosmetics and home textiles4. However, according to the SWOT analysis, H&M is facing the external threat from “Intense competition”. H&M faces intense competition from other fashion retailers across various markets. Some of the competitors have longer operating history, greater brand recognition, more established customer and supplier relationships and greater financial resources than H&M.5 Rising competition may eventually lead to H&M’s shrinking margin, market share, and low retention rate. In addition, H&M is also facing the threat from “Changes in fashion trend”. According to the SWOT analysis (2014), inability of the company to change or update its collection, according to the fashion trends and varying customer preferences may negatively affect its brand image.6 In an attempt to help H&M overcome the threats by optimizing its Marketing Mix, RH Consulting conducted a Conjoint Analysis. III. Conjoint Analysis Findings & Recommendation A. 18 Service Profiles

The Conjoint Analysis is based on the result of a survey study of samples from H&M’s target market. The survey study included 18 service profiles 7 for our respondents to rate. Each of the service profile represents an alternative for H&M to optimize its Marketing Mix. B. Overall Feature Importance We calculated the “Feature Importance” 8for each feature and plotted the result into a pie chart. Figure 1 shows us the “Overall Feature Importance”.9

3 Please see detailed process of how we conduct Conjoint Analysis in Appendix 2. 4 H & M hennes & mauritz AB : Retail - company profile, SWOT & financial report.(2014). (). Basingstoke: Progressive Digital Media 5 H & M hennes & mauritz AB : Retail - company profile, SWOT & financial report.(2014). (). Basingstoke: Progressive Digital Media. 6 H & M hennes & mauritz AB : Retail - company profile, SWOT & financial report.(2014). (). Basingstoke: Progressive Digital Media. 7 Service profiles come from the “Orthogonal Design” which covered in-depth in the Appendix 2. 18 service profiles are the unique combination of 7 levels and 3 features. The “Orthogonal Design” guarantees that the 18 service profiles are completely independent, and each level of the feature has been tested for the same amount of times. Please also see Appendix 11 for the details of Service Profiles. 8 Please see appendices for the detailed calculation. We apply the equation: !"#$#%&' !"#$#"%!!"#"$%$ !"#$#"%

!"#$% to calculate the “Feature Importance”.

Figure 1: Overall Feature Importance

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1. Findings: The most important feature for customers is the “Travel Distance” with 20.1% of “Feature Importance”. In the second place, there is a tie between “Average Item Price” and “Display Placement” both with 19.6%. “Sales Associate/Customer Interaction” stands at the fourth place with 17.6%. “Brand Identity” stands at the fifth place with 10.6%. “Channels of promotions” comes after “Brand Identity” with 8.0% of “Feature Importance”. Finally, the least important feature is “Shopping Purpose” with only 4.5% of “Feature Importance”. For a mature industry, it is good for us to learn that price is not the most important feature or differentiator for our target customers. In addition, price is equally important as “Display Placement”, which is one of the new features H&M want to test. It is also good for us to see the other new feature “Shopping purpose” plays the least important role in terms of consumers’ level of happiness. It prevents H&M from wasting money on fixing this feature. 2. Recommendations: Based upon the findings of “Overall Feature Importance”, we recommend H&M to invest money on optimizing features in accordance with the ranking based upon “Feature Importance”. For example, H&M could allocate 20.1% of its “Marketing Mix” budget on optimizing the retail stores’ “Travel distance” feature. 19.6% of the budget will be allocated to optimizing the pricing. Currently most of H&M retail store’s average travel distance is about 10 miles. Changing the physical location of existing retail stores is not feasible. Hence, we recommend H&M to shorten the perceived travel distance. A “Guerilla marketing” strategy10 is recommended. H&M could provide free round-trip shuttle buses service. The shuttle buses will be featured with paintings of H&M’s logo with a tagline “H&M: There is no distance between you and us.” Ideally, H&M could shorten the perceived travel distance to 0 mile. By doing so, it could increase the utility by 0.28.11 H&M could also consider building new physical stores near the high population density area. However, it is extremely expensive for H&M, and is only recommended when H&M’s budget allows. C. Feature Type – Value Structure By conducting Conjoint Analysis, after we plotting the re-scaled utility12 value of each value, we draw our “Utility Bar Graph”13 (Figure 2). 1. Findings:

Trade-off14: Due to the in-store space limitation, H&M have to change from “On Rack” display to “On Shelf” display, which will give up the 0.19 utilities.

Linearity/Rationality For the feature of “Travel Distance”, for every 10 miles increase in travel distance, utility falls by 0.31.15 H&M’s current retail locations’ traveling distances are between 0 mile to 10 miles.

9 Appendix 9 shows how we create the “Overall Feature Importance” pie chart. 10 Guerilla marketing refers to creative, staged events that draw attention to a company or brand such as Oscar Mayer’s “Weinermobile” – a sizable hot dog car that travels all over the United States and gets customers engaging with the company’s products. Cited from: Mourey, James (2013-08-24). Urge: Why You Really Want What You Want (And How To Make Everyone Want What You've Got). 11 Travel distance 10 miles = 0.48, travel distance 0 miles = 0.76. Difference = 0.76 – 0.48 = 0.28. Since it is expensive for H&M to shorten the realistic travel distance, H&M could ideally shorten the perceived distance to 0 mile. 12 We re-scale the original utility for each level based on a 0-1 scale in order to get rid of the negative value. Please see appendix for detailed explanation. 13 Please see Appendix 2 and Appendix 10 for how we draw the Utility Bar Graph 14 Due to certain reason, it forces company to switch to level with lower utility. To maintain utility neutrality, we need to find levels to trade-off the losing utility. 15 The feature of travel distance is quantitative in nature. In addition, 0 miles to 10 miles: 0.76-0.48 = 0.28; Travel distance 10 miles to 20 miles: 0.48-0.15 = 0.33. Difference is 0.05 < 0.1. So it is considered a straight-line relationship between change in levels and change in utility.

Figure 2: Conjoint Analysis: Utility Bar Graph

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Sweet-spot/Irrationality We find the following levels that indicates consumers’ exhibiting irrational behaviors: • For the “Average Item Price” feature, the middle level $49.99 carries the highest level of utility of 0.74.16 According to H&M’s price book, the current midpoint of item price is around $21. • For the feature of “Sales Associate/Customer Interaction”, the more attentive customer services Sales Associates provide, the lower level of happiness customers experience.17 2. Recommendation:

Trade-off: In order to give back the losing 0.19 utilities, we recommend H&M to change the current “Go to the office” brand identify to a “Go on a date” one, which carries 0.2 more utilities.18 In order to successfully changing the brand identity, we recommend H&M to launch a social media campaign called “H&M: A Romantic Choice for you and your loved one.” H&M will promote its couples’ outfits on its social media sites. As part of the campaign, H&M also encourage couples to upload their romantic moment pictures on their social media sites and hash tag “H&M Our Moment”. The campaign will help H&M to change into “Go on a date” brand identity, hence tradeoff the losing utility.

Linearity: Again, H&M is recommended to shorten the perceived travel distance for its target customers by providing free shuttle bus service. The less distance its customers are needed to travel, the much happier its customers are.

Sweet Spot: Let’s see how H&M could leverage the two sweet spots we presented in our findings. For the “average item price”, according to Figure 2, the medium price level of $49.99 provides the highest utility (most happiness to customers).

We recommend H&M to raise their “average item price” by 60%, from $21 to $33.6. If H&M does so, it could increase its utility by 0.1, to 0.62.19 By raising 60% of the price, H&M could not only increase its margin, but also increase the level of happiness for its customers.

For the “Sales Associate/Customer Interaction”, we could see that more attentive customer service will lead to lower utility, we recommend H&M to decrease the numbers of sales associates on floor. H&M is committing on providing its customers with the highest level of customer service like “Be my stylist”. However, we could see that customers give it the lowest utility as 0.15. We assume customers who visit H&M enjoy more “alone” time and would like to find the unexpected item themselves. We recommend H&M to change from “Be my stylist” level of service to “Help me when needed” to grant more freedom and “alone” time for its customers. A “Scavenger hunt” campaign is an applicable solution. At the entrance of every retail store, there will be a banner said “H&M Scavenger Hunt: Secret treasures are waiting for you to discover”. Customers are encouraged to find the unexpected items themselves. Sales associates will provide help only when the customers ask. By doing so, H&M will increase its utility by 0.27, to 0.42. H&M could save a considerable amount of labor cost without jeopardize the utility for its customers.

By trading off the losing utility, fixing the travel distance issue, and leveraging the two sweet spots, H&M could not only dramatically increase its ROI, but also increase its customers’ happiness level, which will eventually be translated into higher market share and customer loyalty. D. Total Product Utility The Total Product Utility20 for all the 2,187 service profiles21 has been calculated. Please see figure 3 for the “Best TPU” service profile and “Worst TPU” service profile.

16 It is irrational because more expensive option makes customers happier than less expensive option does.17 It is irrational because customers are much happier getting less attentive customer services. 18 According to figure 2, “Go to the office” brand identity carries 0.15 utilities, while “Go on a date” brand identity carries 0.35 utilities. By changing into “Go on a date” brand identity, H&M could expect 0.35-0.15=0.2 increase in utility, which will tradeoff for the losing 0.19 utility. The difference is less than 0.1. 19 According to figure 2, the utility for “$9.99” is 0.45, and the utility for “$49.99” is 0.74. Since we are using a linear regression model, we could apply the interpolation equation: 𝑦! = !!!!! (!!!!!)

(!!!!!) + 𝑦!. As a result, the utility for

$21 should be: 𝑦! = !".!!!.!! (!.!"!!.!")

(!".!!!!.!!) + 0.45 = 0.52. The utility for $33.6 should be 0.62. Hence 60% increase in price will lead to 0.1 increases in utility.

20 Total Product Utility is the sum of utilities for each product/service profile. 21 Based on the result from focus group, we came with 7 features with 3 levels each feature. It will totally generate 3!= 2,187 combinations of features and levels. Each combination is a service profile. Please see the appendices for the detailed explanation.

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1. Findings: The “Best TPU” is 3.66, whereas the “Worst TPU” is 0.65. In addition,

H&M’s current TPU is 2.06, which is in the good shape. However, in order to overcome the external threats, we recommend H&M to strategically improve its TPU over time. 2. Recommendations: • Besides the recommendation we made previously, we recommend H&M to also change its current channel of promotion from “Direct Mail” to “Digital”.

o By doing so, H&M could increase its utility by 0.24. 22 o We recommend H&M to take good advantage of its social media sites to distribute digital promotional materials. o Email campaign is also an acceptable form of digital promotion. H&M could email its customers who registered or subscribed its email list. o Mobile promotion is an increasing trend of digital promotion. With Apple’s passbook app, H&M could create mobile coupon for its customers to download into their passbook. Whenever they are within the 1-mile radius of H&M retail location, their IPhones will notice them the available coupon to use in H&M.

• We also recommend H&M to try to maximize its TPU as much as possible. “Best TPU” profile provides perfect guidelines. However, H&M should also keep the margin in mind. Sometimes maximizing TPU will be accompanied with the increasing cost. We recommend H&M to try to find its “Optimal TPU” profile, which will maximize its TPU, maximize its margin, and minimize its overall cost. Unfortunately, we don’t have enough data. E. Market Share & Simulation 1. Findings:

In order to learn how competitors perform in terms of market share, we did an extensive research on competitors’ service offerings. We studied three direct competitors of H&M including: Ralph Lauren, Forever 21, and ZARA. Figure 4 shows the current market share percentage among the four retailers. H&M has 21% of market share23 and stands at the third place. ZARA has the highest TPU of 3.29 (0.37 lower than the Best TPU) and highest Market Share of 33%. 2. Recommendations: • We recommend H&M to implement what we recommended24 in the previous

22 According to figure 2, Direct mail = 0, digital = 0.24. The difference = 0.24 – 0 = 0.24. 23 Market share is calculated by applying the equation: !"# !" !"#$%!"#

!"#$% !"# !" !"" !"#$%&"!'. H&M’s Market Share = !.!"

!.!"!!.!"!!.!"!!.!" = !.!"

!.!" = 21%. Here we assume the seven features we came up with drive the market share.

24 Figure 5 shows the six features we recommend H&M to adjust, and their impacts on simulated market share.

Figure 3: Best, Worst, & Current TPU

Figure 4: Market Share & TPU

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section as soon as possible. o According to figure 5, by adjusting the features, H&M will increase its TPU by 0.9, which will translate into a 6% increase in market share. o H&M now win over Ralph Lauren by 3% market share and stands at the 2nd place. o In addition, the gap between H&M and ZARA is decreased by 8%.25

• As we could see from figure 4 and figure 526, the H&M’s improved service profile has overlap with ZARA. Both of their brand identities now are “Go on a date”. We recommend H&M to flank attack ZARA-Market Leader.

o We recommend H&M to launch a TV commercial to reinforce its “Go on a data” brand identity. Since H&M recently changed its brand identity to “Go on a date”, it needs more IMC tools to reinforce its newly changed brand identity. o The TV commercial will feature H&M and ZARA’s brands. The purpose is to make customers to misattribute in considering that H&M is a better choice than ZARA in terms of romantic, casual, and dating appeals.

o Ideally, the reinforcement of H&M’s brand identity will force ZARA to change to new brand identify that carries lower utility to customers. F. Intercept Interpretation – entry barrier 1. Findings: According to Appendix 8, the original utility for our intercept27 is 1.87 and the re-scaled utility is 1. It is higher than other features. As a result, it indicates that the Appeal Retail market is highly mature. The intercept is also useful because it sets up the entry barrier for the market. If you would like to play in the market, your minimum TPU should be higher than the value of intercept. In our case, it is 1. The new retailer has to have at least a TPU of 1, in order to enter and survive in the market. 2. Recommendations: H&M’s current service profile carries a TPU of 2.06, which is in a good shape. However, with the external thread from competitors and fashion trend, plus the internal restrains such as budget, space, location, and etc., H&M might encounter problem in implementing our recommended service profile. Since Conjoint Analysis only shows a static picture of H&M’s current position, H&M’s market share and utility might fluctuate over time. In facing the situation of utility and market share fluctuation, we highly recommend H&M to at least keep its TPU above the intercept of 1. This is the bottom line for a retailer to play in the market. We also recommend H&M to actively look for ways to trade off any losing utility and to keep eye open for sweet spots to leverage.

25 Before implementing the recommendation, the market share gap between ZARA and H&M was 33% - 21% = 12%. After implementing the recommendations, now the market share gap between ZARA and H&M is 31% - 27% = 4%. The market share gap decreases by 12% - 4% = 8%. 26 We created figure 5 based on the data from Appendix 12.27 Intercept is an indicator of market maturity. The higher the value of intercept, the more mature the market is.

Figure 5: Simulated Market Share

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Appendix 1: About the research

Our research design is based on Conjoint Analysis. Conjoint models predict an individual’s preference for a stimulus object to clarify the relative importance of each attribute by decomposing the overall preference measure. Conjoint analysis thus allows the researcher to determine the individual’s part-worth utility for each attribute under study.28 According to Dolan (1990), Conjoint estimates an individual’s “value system”, which specifies how much value a consumer puts on each level of each of the attributes. If we know an individual’s value system, we can predict which of a set of available alternatives he will buy. 29 In addition, Conjoint asks the consumer to make judgments about products overall and then uses mathematical analysis to uncover the value system, which must be behind the preference judgments.30 By conducting Conjoint Analysis, instead of asking target consumers to rate separate attributes of the products, consumers will rate the realistic “full profile” of each product/service option as they would in the real world. Based upon the result from Conjoint Analysis, we could better predict consumers’ intention to purchase of the products/services.

Appendix 2: Research Process Step 1: Segment & Problem Based on the result of segmenting & targeting, H&M decides that their target segment looks like: • Demographic:

o Gender: Male & Female o Age: 18-34 o Generation: Millennial & Gen Z o Ethnicity: Caucasian, Asian, African-American, Hispanic-American

• Geographic: o US. Midwest o City or Metro size: 1,000,000 to 6,000,000 o Density: Urban & Suburban Area

• Psychographic: o Fashion oriented lifestyle

• Behavioral: o User Status: Potential & First time user o User Rate: Medium to Heavy user o Loyalty Status: Split Loyals & Shifting Loyals

Problem: Optimizing Marketing Mix According to the SWOT analysis conducted by Canadean (2014), H&M’s external threats are including “Changes in fashion trends” and “Intense competition”. Some of its competitors have longer operating history, greater brand recognition, more established customer and supplier relationships and greater financial resources than H&M. 31 In addition, the company’s performance may be impacted by the frequent evolution in the fashion trends. The rate of change in fashion trends has been increasing over the years, thereby forcing companies such as H&M to constantly update its product offerings with the latest market trends.32 In order to stand against intense competition and catch up with the rapid changing fashion trend, H&M decides to optimize their Marketing Mix.

28 Hiroak, N. (2015). Price Elasticity of Ticket Demand in the Professional Basketball League in Japan: A Case Study of Simulating Ticket Purchase Rates Using Conjoint Analysis. Sport Marketing Quarterly, 24(4), 246-257. 29 Dolan,R.J. (1990). Conjoint Analysis: A Manager’s Guide. Harvard Business School Cases, 1. 30 Dolan,R.J. (1990). Conjoint Analysis: A Manager’s Guide. Harvard Business School Cases, 1. 31 H & M hennes & mauritz AB : Retail - company profile, SWOT & financial report.(2014). (). Basingstoke: Progressive Digital Media. 32 H & M hennes & mauritz AB : Retail - company profile, SWOT & financial report.(2014). (). Basingstoke: Progressive Digital Media.

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Step 2: Focus Group & Client Input for Features and Levels In order to capture more reliable data, we needed to learn consumers’ language. We decided to conduct 4 focus groups, with 4 moderators to manage each group. We would like our focus groups to talk about actual products, such as “where and why they purchase the products”. We combined the result from focus groups with the input from H&M. Finally, we came up with 7 features with 3 levels each. (Please see the Appendix 3). Within the 7 features, there are two new features H&M would like to test including “Display Placement” and “Shopping Purpose”. Again, in order to capture more reliable data, the features we came up with are highly differentiating features. Moreover, the features and levels are specific and described without vague words. Step 3: Choose Orthogonal Design After we had our 7 features with 3 levels each, we chose the right experimental design, which is Orthogonal Design. If we didn’t choose any design, our respondents have to rate 3!, which equals to 2,187 service profiles. By choosing Orthogonal Design, our respondents only have to rate 18 profiles of sample service profiles. The reason why we could only ask our respondents to rate 18 profiles is because these 18 profiles (combinations of features and levels) are totally un-correlated and independent to each other. We could consider these 18 profiles as the samples that represent the total population of 2,187 service profiles. Here comes the question, how do we get the 18 profiles? We went to our statistician and he used his statistic handbook to pull out the Orthogonal Design (Please see Appendix 4). The Sawtooth software could also help us to generate this Orthogonal Design. As we could see here, the Orthogonal Design is a combination of 0’s and 1’s, with 0 implies absence of level, 1 implies presence of level. The 14 columns represent levels from each feature. Every feature has one level missing, which is (0,0), and it doesn’t matter which one to drop. Every single row is a service profile. As we could see, the entire Orthogonal Design contains 18 service profiles (18 rows of 0’s and 1’s). The Orthogonal Design also makes sure each level get tested the same amount of times. Step 4: Create Code Sheet In order to describe the 18 profiles we generated from Orthogonal design, we created a Code Sheet (Please see Appendix 5). As we mentioned above, every feature has one level missing, which is (0,0). By using the Code Sheet, it allows us to describe each service profile, which is by interpreting each row of unique combination of 0’s and 1’s. For example, for the first service profile in the Orthogonal Design, it contains 14 0’s. In order to describe the profile, we used the Code

Appendix 3: 7 features & 3 levels for H&M

Appendix 4: The Orthogonal Design

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Sheet as a reference. Average Item Price for profile number 1 is (0,0), which is neither “$9.99” nor “$49.99”. It means the Average Item Price for profile number 1 is $99.99. We applied the rules to the following 6 features, and we had the “Full Profile” of service number 1:

• Average Item Price (0,0) = $99.99 • Channels of promotion (0,0) = In-store promotion • Display Placement (0,0) = Bin Rummaging • Sales Associate/Customer Interaction (0,0) = “Be my stylist” • Traveling Distance (0,0) = 20 miles. • Brand Identity (0,0) = “Go to the office” • Shopping purpose (0,0) = “I need to try it on”

We then applied the rule to the following 17 profiles, and we had the total of 18 “Full Profile” descriptions of services that are ready for our target consumers to rate.

Step 5: Design Survey After we had the 18 “Full Profile” descriptions of services, we conducted a survey to collect data from larger size sample. Our survey was designed as 5-7 minutes. Like our focus group, participants were brand-blinded. We used close-end questions on our survey, and our survey consisted of three sections: • Screener Questions:

o Are you between the ages of 25 – 34? o Are you currently live in the U.S. Midwest? o The participants needed to answer “yes” to both questions in order to proceed to the next section of the survey. Otherwise, they will be not qualified to respond to the survey.

• 5-7 Behavioral Questions: o How much do you typically spend when you are shopping in an Appeals retailor? (Closed – ended multiple choices question). o How often do you visit an Appeals retailor? (Check all that apply) o We would like to assess consumer behaviors within the entire Appeals retailor category.

• Conjoint Analysis Questions: o We provided a specific and clear instruction on the Conjoint Analysis section. o For example: In the following section, you will see 18 realistic descriptions of Appeal Retail Stores. When reading the descriptions, please realize that all 18 retail stores are carrying same highest quality of products. Despite the quality of products, these 18 retail stores are independent with each other on the following 7 features:

§ Average Item Price: the average price you normally pay for each item § Channels of promotion: the channels where you always receive your promotions (Coupon, discount, catalogue, and etc.) § Display placement: How retailors display their products (on rack, on shelf, or in bins) § Sales Associate/Customer Interaction: How in-store sales associate interact with their customers § Travel Distance: How far customers need to travel to visit the store. § Brand Identity: The styles of appeals that each retail store carries (casual, formal)

Orthogonal Design Code Sheet for H&M

Average Item Price ($9,99, $49,99, $99,99) • X1 = 0,1 (0 = Not “$9,99”, 1 = “$9,99”) • X2 = 0,1 (0 = Not “49,99”, 1 = “$49,99”)

Channels of promotion (Digital, Direct Mail, In-store promotion) • X3 = 0,1 (0 = Not “Digital”, 1 = “Digital”) • X4 = 0,1 (0 = Not “Direct Mail”, 1 = “Direct Mail”)

Display Placement (On rack, On shelf, Bin Rummaging) • X5 = 0,1 (0 = Not “On rack”, 1 = “On rack”) • X6 = 0,1 (0 = Not “On shelf”, 1 = “On shelf”)

Sales Associate/Customer Interaction (Leave me alone, Help me when I need, Be my stylist)

• X7 = 0,1 (0 = Not “Leave me alone”, 1 = “Leave me alone”) • X8 = 0,1 (0 = Not “Help me when I need”, 1 = “Help me when I need”)

Traveling distance (0 miles, 10 miles, 20 miles) • X9 = 0,1 (0 = Not “0 miles”, 1 = “0 miles”) • X10 = 0,1 (0 = Not “10 miles”, 1 = “10 miles”)

Brand Identity (stay at home, go on a date, go to the office) • X11 = 0,1 (0 = Not “stay at home”, 1 = “stay at home”) • X12 = 0,1 (0 = Not “go on a date”, 1 = “go on a date”)

Shopping purpose (I know what I want, I need to browse, I need to try it on) • X13 = 0,1 (0 = Not “I know what I want”, 1 = “I know what I want”) • X14 = 0,1 (0 = Not “I need to browse”, 1 = “I need to browse”)

Appendix 5: Code Sheet

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§ Shopping Purpose: The different purposes of customers who visit the store (window shopping, know what to buy). o As you read the descriptions of each of the 18 retail stores, please rate the extent to which you would like to purchase on the scale with 1 means “would definitely never consider purchasing there” and 7 means “would definitely consider purchasing there”. o Respondents will be presented one retail store a time, and totally they will rate the entire 18 retail stores.

• Demographic & Psychographic Questions o At the end of the survey, we asked 5-7 demographic & Psychographic questions such as income level, ethnicity, gender, education, employment, Scio-economic status, and etc. o The information we collected will be used for creating marketing campaign.

We created and launched the survey via Qualtrics and used a filling company called “Cint”. Step 6: Collect Data The Conjoint Analysis Survey requires fewer amounts of respondents than other types of survey. We asked 15 respondents from our target consumer segment to take the survey. The reason why we only asked 15 respondents is because they will generate more than enough data points for us to analyze. At the same time, it could save H&M a considerable amount of money, because fewer respondents require less money. We had 15 respondents who read and rated 18 profiles. Each profile contains 14 levels. As a result, 15 respondents will generate 14 × 15 × 18 = 3,780 data points. Step 7: Analyze Data – Multiple Regression Analysis After getting the results back from our filling company, we ran Multiple Linear Regression on StatPlus using both the ratings and Orthogonal Design. P-level: The table below shows us the summary of output. As we know, the null hypothesis for linear regression model (y = kx + b, b is constant) is the slope equals zero (k=0), which means the changing of independent variable will not affect the dependent variable. In another word, the independent variable is not correlated with the dependent variable. In order to predict the intention to purchase, we want our independent variable (combination of features) impact the dependent variable (intention to purchase). We would like our p-value to be less than the significance level 5%, so that we could “not accept the null hypothesis”. As we could see, our p-value is lower than 5% (significance level), which indicates that less than 5% chances my data is random (lowly correlated). Adjusted R Square: Let’s take a look at the other important indicator, which is “Adjusted R Square” (please see Appendix 7). R-square is a statistic metric, which indicates how close the data is fitting into the regression model. In another word, it means, “How much meaning is in the data” or “How much meaning do I capture”. We would like our R-square to be as high as possible. However, high R-square might not because of the better fit of the model. Whenever a new predictor added into the model, the R-square will increase. Hence, the high R-square might be due to the noise of data. This is also the reason why we focus on the “Adjusted R-square”, which has been adjusted and compared to the regression model. In addition, the “Adjusted R-square” is always lower than the “R-square”. It only increases under the circumstance that the newly added predictor indeed improves or better fits the regression model. Ideally we would expect our “Adjusted R-square” to be more than 30%. However, in our case, we had a 22% “Adjusted R-square”. It doesn’t mean our study is flaw and unreliable. Since the Conjoint Analysis is to predict consumers’ intention to purchase, it is acceptable to have a low “Adjusted R-square”, simply because human behavior is hard to predict.

Appendix 6: Summary Output 1

Appendix 7: Summary Output 2

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Coefficients/Utility: The other thing we want to look at is the “coefficients” of our levels (please see Appendix 8). Here the values of coefficients are the utility in Conjoint Analysis, which represent the levels of happiness for customers. Higher value means happier customers. As we could see, some levels’ utility values are negative. Regularly, negative value indicates that this particular value hurt your business. However, in Conjoint Analysis, negative value indicates that this level is less important or simply less helpful for improving your business (customers’ intention to purchase). Re-scaled Utility: Next step, we want to have a much-visualized way to look at these levels’ utilities. As a result, we created a bar graph to visualize our data. In order to create the bar graph, firstly, we plugged in all the levels from our Code Sheet, including our “0,0” levels. As we could see from Appendix 8, we added 7 “0,0” levels to the table, and we set their utilities as “0”, because they are the base levels that we compared to. In order to create an aesthetic and easy to read bar graph, we need to get rid of the negative values. As a result, we re-scaled the utilities based on a 0 – 1 scale. Firstly, we calculated the range of our utilities, which is 1.87 – (- 0.33) = 2.2. In order to re-scale the utility, we apply the equation: !"#$#%&' !"#$#"%!!"#"$%$ !"#$#"%

!"#$%. For

example, for the feature and level of “Travel distance: 0 mile”, when we apply the equation: !.!!!(!!.!!)!.!

, we got our re-scaled utility for this feature and level as 0.75. We followed the same rule and re-scaled the other features and levels (please see the “Re-scaled Utilities” column). After re-scaling the features and levels, we sorted the data from highest to lowest.

Overall Feature Importance Pie Chart: Then we put “rank order” to clump the three levels from same feature together. As we could see from Appendix 9, we clumped levels by features. The next step, we would like to know the importance of each feature. We then calculated the range for each feature (please see the “feature importance” column). In order to learn the percentage of feature importance, we summed all features’ ranges, which are 3.02. Then we apply the equation: !"#$%&" !"#$%&'()*

!"# !" !"" !"#$%& to each feature. We finally got our percentage of feature importance (please see the percentage of

Feature Importance column). We used the result of “Percentage of Feature Importance” to create the Pie Chart we showed previously. Utility Bar Graph: We then sorted the data again from highest to lowest (please see Appendix 10). As you could see, we used the table of

“Organizing to Plot Utility Bar Graph” to draw the Utility Bar Graph as we showed. Step 8: Interpret Results & Solve Problem After we completed our “Utility Bar Graph”, we concluded five most important elements from the “Conjoint Analysis”, including: Overall Feature Importance, Feature Type – value structure, TPU (best, worst, and optimal), Market share and simulation, and Intercept Interpretation – entry barrier

Appendix 8: Original Utility/Coefficients & Re-scaled Utility

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Appendix 11: 18 Service Profiles

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Appendix 12: Current VS. Simulated Market Share