Analyzing the Athletic Shoe Market for SHOE FIEND You are the marketing analyst for SHOE FIEND, an online athletic shoe store. To date, your company has done little formal marketing research about athletic shoe buyers in the United States. You recently ran a series of reports about the shoe buying habits of several US consumer segments. At this time, you have decided to focus on the 5 best-selling shoe brands on your website Adidas Asics Nike, New Balance, and Reebok After looking through the MMR report options you decided that the most fitting question for your purposes was "Did you buy (SHOE BRAND in the last 12 months?" The report is designed to compare the shoe buying habits of consumers across several different consumer characteristics gender, age, and internet use in addition, you also created three sub-segment schemes that combined two different segmentation variables Gender and Age (Men 18-34 and Women 18-34) and internet use and Age (Heavy Internet users 18-34 years old) Click the icon to view the results of the report Click the Kon to view the additional information First, review the data from the report. You will use this information to make some inferences about the brand preferences of the different segments Follow this you will combine the information in the report with some financial assumptions provided by SHOE FIEND to make some recommendations for future marketing tactics Task 1: Correct the Table Unfortunately it appears that someone accidentally left some of the calculated values blank in the table. Luckily, you know you can use the other available information in the table to calculate the missing values The Market Segment Size (%) for Heavy internet users is % (Round to one decimal place) 0 Total Men Women Adults 18-34 Men 18-34 Women 18-34 Internet 1 (Heavy Users) Adults 18-34 & Heavy Internet 35,491 36,108 45,259 21,455 236,196 114.009 122.187 71,599 100 48.3% 51.7 30.3 15.0 153 9.1 Market Segment Size (000s) Markel Segment Size (%) ADIDAS Estimated Count (0005) % of Total % within Mkt Seg who Bought last year Index 7,083 17.235 100 73 9,224 53.5 8,011 46.5 3,979 23.1 3,104 180 4,300 24.9 2,487 14.4 8.1 6.6 9.9 8.6 9.5 11.6 112 154 100 111 90 136 118 130 11,166 100 4,994 44.7 6.172 55.3 3,014 27.0 1,333 119 1,681 15.1 2,420 21.7 914 82 4.7 4.4 42 3.8 4.7 5.3 4.3 100 93 107 89 79 98 113 90 ASICS Estimated Count (000s) % of Total % within Mkt. Seg. who Bought last year Index NIKE Estimated Count (0005) % of Total % within Mkt. Seg who Bought last year Index NEW BALANCE Estimated Count (000s) % of Total 49,158 100 24,673 50.2 24,485 49.8 20.196 41.1 22.8 9,005 18.3 24.9 12,800 26.0 7.145 14.5 21.6 20.0 282 31.5 28.3 33.3 20.8 100 104 96 136 152 120 136 160 25,013 100 12,131 48.5 12,882 51.5 4.253 17.0 2,693 10.8 1,560 6.2. 4,745 19.0 1.653 6.6 20.8 21.6 200 28.2 31.5 24.9 28.3 33.3 100 104 96 136 152 120 136 160 25,013 100 12,131 48.5 12,882 51.5 4,253 17.0 2,693 10.8 1,560 6.2 4,745 19.0 1,653 6.6 70 WECTIT TVIKL seg WHO Bought last year Index NEW BALANCE Estimated Count (000s) % of Total % within Mkt Seg who Bought last year Index REEBOK Estimated Count (000s) % of Total % within Mkt. Seg who Bought last year Index 10.6 10.6 10.5 5.9 76 4.3 10.5 7.7 100 100 100 56 72 41 99 73 10,434 100 4,957 47.5 5,477 52.5 2.852 27.3 1,705 16.3 1,147 11.0 2,146 20.6 917 88 4.4 43 4.5 40 4.8 32 4.7 43 100 98 101 90 109 72 107 97