Snappos is a hypothetical online shoe retailer (like Zappos). Snappos has only recently begun their email marketing efforts. They have collected 250 email addresses and have been sending bi-weekly emails to their entire email list for the last eight weeks. They have made no efforts to clean their email list or segment it or optimize it in any way. You have a database of information about the 16 sent emails and the resulting action of each of the 250 members of the email list. Information about the emails: The coding key for the email dataset is as follows: Time of Day Category Key 1 2 3 4 10 5 6 7 8 9 10 Early Morning Late Afternoon Afternoon Evening Offer BOGO 20% off 30% off 50% off New products Free shipping Women's athletic shoes Women's dress flats Women's high heels Women's sandals Women's slippers Women's boots Men's athletic shoes Men's sandals Men's dress shoes Men's boots Each of the 16 emails had the same layout with three visual fields: TOD Each field featured one of ten possible shoe categories. Emails were sent at various times of the day. The subject line of each email featured an offer and highlighted the product category in field 1. Email 1 has the following properties: Offer F1 F2 1 F3 23 4 5 3 1 6 This means this email was sent in the evening. The subject line talked about new products, specifically new women's high heels. Women's high heels were pictured in field 1; women's athletic shoes were pictured in field 2; and women's boots were pictured in field 3. Email Response: Below is the response key. 0 Delivered, unopened 1 2 3 4 5 6 Hard bounce Soft bounce Delivered, opened Field 1 clicked Field 2 clicked Field 3 clicked Most emails were delivered, but unopened (which is typical of email lists). Emails could also bounce via hard or soft bounce or be delivered (but result in no clicks). Recipients who opened the email and clicked on one of the fields are coded 4, 5, or 6 to indicate which field they clicked. Make sure that you understand the data and then, answer the following questions after analyzing the data. (You can do the analysis in Excel). 1. Which respondents should be removed from the email list? 2. What is the best time of day to send emails? 3. Which offers generate the best response? 4. Snappos is looking to segment this email list. Should it put men in one segment and women in the other and only send emails featuring the proper gendered shoe to those lists? 5. Snappos will soon be running a sale on women's high heels. Which recipients on the list should receive this email?