Big Skinny Case Analysis Essay examples

2485 Words Jun 21st, 2013 10 Pages
2013

CONTENTS

EXECUTIVE SUMMARY 2

PROBLEM STATEMENT 3

DATA ANALYSIS 3

KEY DECISION CRITERIA 4

ALTERNATIVE ANALYSIS 4

RECOMMENDATIONS 7

ACTION AND IMPLEMENTATION PLAN 9

Executive Summary

In 2010, Big Skinny CEO Kiril Alexandrov was looking to transcend from retail distribution and print advertising to the world of online marketing to achieve maximum growth. The retail sales pitch was an easy one, as Alexandrov focused on the value of the wallet and the impulsiveness of consumers (Benjamin & Kominers, 2012). Unfortunately, translating this type of sales pitch was much harder to do in the world of cyberspace. Big Skinny centered their online marketing
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Big Skinny also ran into glitches with their current online marketing campaigns that brought unwanted negative attention and resentment towards the company.
Data Analysis When Big Skinny transcended into the world of online marketing, it had to develop a way to attract visitors to the website while attempting to convince these visitors to buy wallets. Since most of their wallets were being sold at trade shows or retail stores that centered on a straight-forward approach regarding impulse and value, the translation of this strategy to the internet proved to be a tall task. Big Skinny looked at various means of advertising such as display ads, algorithmic search, sponsored search, A/B Testing and social media. Display ads offered a two-frame animation; however, the click-through rate of general display ads in 2009 was only .1% (Bejamin & Kominers, 2012). Algorithmic searches use algorithms that the search engine deems most relevant to the user’s query. The websites that most resemble the query appear the highest on the search engine’s list. Sponsored searches use keywords that the advertisers specify that they want to target. These are mostly sold on a “per-click” basis; however the company loses money if the clicks aren’t converted into sales. A/B testing is a marketing technique that shows different advertisements to different users to compare the response rates between the two.

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