{"id":3318,"date":"2014-07-05T17:29:28","date_gmt":"2014-07-05T11:59:28","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=3318"},"modified":"2016-10-13T14:46:29","modified_gmt":"2016-10-13T09:16:29","slug":"exploratory-data-analysis-retail-case-study-example-part-3","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/exploratory-data-analysis-retail-case-study-example-part-3\/","title":{"rendered":"Exploratory Data Analysis (EDA) – Retail Case Study Example (Part 3)"},"content":{"rendered":"
\n
\"Exploratory<\/a>

Exploratory data analysis for Soccer – by Roopam<\/p><\/div>\n

For the last couple of weeks we have been\u00a0working on a marketing analytics case study example (read Part 1<\/a> and Part 2<\/a>). In the last part (Part 2<\/a>) we defined a couple of advanced analytics objectives based on the business problem at an online retail\u00a0company called DresSmart Inc. In this part, we will perform some exploratory data analysis as a part of the same case study example. But before that let’s explore the power of\u00a0exploratory data analysis (EDA) to\u00a0reveal hidden facts about the greatest game on the planet – soccer or football.<\/p>\n

Soccer\u00a0– Exploratory Data Analysis<\/span><\/h2>\n

Soccer is undoubtedly the most popular game on the planet with over 200\u00a0nations having their official soccer teams. No other game has such a universal appeal with millions of hardcore followers. \u00a0Every detail of soccer\u00a0is analyzed by the players, the coaches and the support staff. Despite\u00a0this, a careful exploratory data analysis of the game could unravel match-winning\u00a0secrets\u00a0about the greatest game, as you will see in\u00a0the next two example case studies.<\/p>\n

Penalty Kicks<\/span><\/h4>\n

Let’s relive the first knockout (pre-quarterfinal) match of the Soccer World Cup 2014 between Brazil and Chile. The scores were level at 1-1 at the end of allotted 90 minutes. Even the extra half an hour could not conclude the match with the scoreboard still reading 1-1. This led the match towards penalty shoot-outs to break the tie. After the Brazilian player, Neymar, scored the goal in the penultimate penalty kick, Brazil \u00a0were 3-2 ahead in the penalty shootouts. Chile still has a penalty kick left from Gonzalo Jara and the opportunity to extend the tie further – but if he misses Chile’s campaign will be over in the competition. What should\u00a0Gonzalo Jara do to extend the tie?<\/p>\n

\"penalty<\/a>

Gonzalo Jara’s Kick – Source: irishtimes.com<\/p><\/div>\n

On average, at this level around 75% penalty kicks convert to goals. The odds, by this definition, are highly in favor of\u00a0Gonzalo Jara. Where should he kick the ball to improve his odds further? All the\u00a0fans, coaches, and players will say kick the ball in either corner, away from the goalkeeper who is standing in the center of the goal. They will also advise never to shoot the ball at the dead center towards the goalkeeper. A group of researchers asked the same question and did the exploratory data analysis of penalty kicks at the elite level of soccer. Goalkeepers usually go by their instincts when the ball is kicked at them with undecipherable pace. They either jump towards their left (57% of times) or right (41% of times). This leaves them at the center just 2% of times to stop the ball hit right towards them. Hence, a kick hit dead towards the center of the goal has significantly higher chances of conversion to goal then kicks on either corner at the same height.<\/p>\n

Back to Gonzalo Jara, he hits the ball towards his right, in the direction of the diving goalkeeper as shown in the picture above. He misses the shot, the ball\u00a0hits the goal post and ricochets away from the goal. As a result, Chile got\u00a0knocked out of the world cup and Brazil advanced to the next stage. In\u00a0Gonzalo Jara’s defense, the conversion rate for crucial penalty kicks like this one (to avoid elimination) drops to 44%. Yes, pressure is another beast to which\u00a0even the best succumb.<\/p>\n

\"\"<\/a><\/p>\n

Corner Kicks<\/span><\/h4>\n

In another case, a few years ago Manchester City’s soccer team was struggling with corner kicks and hence decided to do some exploratory data analysis to differentiate\u00a0effective corner kicks from\u00a0ineffective. The team of analysts analyzed hundreds of videos of corner kicks from the premier league. After their analysis, they found that in-swinging kicks towards the goal were far more effective and dangerous than the out-swinging kicks. They took their findings to\u00a0Roberto Mancini, the coach of \u00a0Manchester City team at that time. Mancini, who has played and followed the game since his childhood, rejected the findings outrightly. He recalled all those memorable and picture perfects goals by great headers of out-swingers. On the other hand, clumsy goals of in-swingers hardly created a lasting impression on the spectators’ mind. Mancini, it turned out, was wrong. All that looks great and memorable is not always optimal.\u00a0This is a great case for how simple but sincere exploratory data analysis can\u00a0challenge the deeply ingrained beliefs developed over centuries (yes, soccer is a really old game).<\/p>\n

Exploratory Data Analysis – Retail Case Study Example<\/span><\/h2>\n

Back to our case study example (read\u00a0Part 1<\/a>\u00a0and\u00a0Part 2<\/a>), in which\u00a0you are\u00a0the chief analytics officer &\u00a0business strategy head at an online shopping store called DresSMart Inc. You are helping out the CMO of the company to enhance the company’s campaigns’ results. For the last few days, you are playing around with data as a part of exploratory data analysis. The following is one of the several interesting results and patterns you have noticed in the data. When you analyzed the distribution of customers across a number of product categories (men’s shirt, casual trousers, formal skirts etc.) purchased by each customer you found the following pattern.<\/span><\/p>\n

\"Exploratory<\/a>

Exploratory data analysis – marketing analytics case study (retail)<\/p><\/div>\n

The above distribution looks more or less as expected. However, there is an interesting peak for customers purchasing more than 50 product-categories. Who are these customers? Why are they buying so many product categories for their usage? You further analyzed this small set of customers and found that they are growing at a faster rate than the other set of customers. Since the inception of the company 7 years ago, the percentage of customers purchasing 50+ product categories in a year has exponentially gone up (currently at 2.1%). This set of customers also contributes to about 23% of all the sales for\u00a0DresSMart Inc. The following graphs are part of your above analysis.<\/p>\n

\"Exploratory<\/a>

Exploratory data analysis<\/p><\/div>\n

So, what is going on here? You further analyzed the patterns and size(s) of clothes these customers are buying and noticed they are buying the same style\u00a0in different sizes. Aha! Now you know them, these are small neighborhood retailers using\u00a0DresSMart Inc as a wholesaler. The following is what you concluded from the above analysis<\/p>\n

    \n
  1. There is no point sending these retailers the same retail product catalog and campaign as to retail customers<\/li>\n
  2. There is an opportunity to\u00a0strengthen business ties with these mom-&-pop retailers and in turn, improve profitability of your company through a separate business program<\/li>\n<\/ol>\n

    Additionally, your further analysis revealed that order\u00a0fulfillment or\u00a0delivery\u00a0patterns (delivery quantity \/ chargers etc.) \u00a0for these retailers are similar to other customers. Your company is incurring additional cost for these customers in delivery. You could plan the overall supply chain much better keeping these small retailers in the equation. This exploratory data analysis has given you ideas for more low hanging fruits to improve company’s profitability.<\/p>\n

    Sign-off Note<\/span><\/h4>\n

    Exploratory data analysis is a powerful tool. A diligent EDA is an absolute must to put your advanced business analytics in the right direction. EDA provides a great opportunity to test your simple business hypotheses and hunches before jumping into a rigorous model building. Coming back to soccer, we are approaching the final stages of the World Cup. Enjoy the last few games and may the best team lift the prized trophy.<\/p>\n","protected":false},"excerpt":{"rendered":"

    For the last couple of weeks we have been\u00a0working on a marketing analytics case study example (read Part 1 and Part 2). In the last part (Part 2) we defined a couple of advanced analytics objectives based on the business problem at an online retail\u00a0company called DresSmart Inc. In this part, we will perform some<\/p>\n

    Read More…<\/a><\/p>\n","protected":false},"author":1,"featured_media":3301,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[1,59],"tags":[7,42,6,72,10],"jetpack_publicize_connections":[],"yoast_head":"\nExploratory Data Analysis (EDA) - Case Study Example<\/title>\n<meta name=\"description\" content=\"Case study example for an online retail store: In this part we will illustrate the power of Exploratory Data Analysis (EDA) in marketing analytics.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ucanalytics.com\/blogs\/exploratory-data-analysis-retail-case-study-example-part-3\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Exploratory Data Analysis (EDA) - 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In the previous two parts, we discussed a couple of decision tree algorithms (CART and C4.5)\u00a0for classification. Recall a previous case study example on\u00a0banking and risk management where we discussed logistic regression\u00a0which is\u2026","rel":"","context":"In "Marketing Analytics"","block_context":{"text":"Marketing Analytics","link":"https:\/\/ucanalytics.com\/blogs\/category\/marketing-analytics\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/09\/photo.jpg?fit=1200%2C1029&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/09\/photo.jpg?fit=1200%2C1029&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/09\/photo.jpg?fit=1200%2C1029&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/09\/photo.jpg?fit=1200%2C1029&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/09\/photo.jpg?fit=1200%2C1029&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":3405,"url":"https:\/\/ucanalytics.com\/blogs\/association-analysis-retail-case-study-example-part-4\/","url_meta":{"origin":3318,"position":1},"title":"Association Analysis – Retail Case Study Example (Part 4)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"This is a continuation of the case study example of\u00a0marketing analytics we have been\u00a0discussing for the last few articles. You can find the previous parts at the following links\u00a0(\u00a0Part 1,\u00a0Part 2,\u00a0and Part 3). \u00a0In the last part, we discussed exploratory data analysis (EDA:\u00a0Part 3). In this article\u00a0we will talk about\u2026","rel":"","context":"In "Marketing Analytics"","block_context":{"text":"Marketing Analytics","link":"https:\/\/ucanalytics.com\/blogs\/category\/marketing-analytics\/"},"img":{"alt_text":"A-Beautiful-Mind","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/07\/A-Beautiful-Mind.jpg?resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/07\/A-Beautiful-Mind.jpg?resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/07\/A-Beautiful-Mind.jpg?resize=525%2C300 1.5x"},"classes":[]},{"id":3129,"url":"https:\/\/ucanalytics.com\/blogs\/marketing-analytics-retail-case-study-part-1\/","url_meta":{"origin":3318,"position":2},"title":"Marketing Analytics – Retail Case Study Example (Part 1)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Today we are going to start a new case study example on YOU CANalytics. In this case study example, we will examine different facets of marketing analytics and customer relationship management (CRM). We will use the example of online retail to explore more about marketing analytics - an area of\u2026","rel":"","context":"In "Marketing Analytics"","block_context":{"text":"Marketing Analytics","link":"https:\/\/ucanalytics.com\/blogs\/category\/marketing-analytics\/"},"img":{"alt_text":"Marketing Analytics Modeling","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/06\/Marketing-Analytics-Modeling-Blog.jpg?resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/06\/Marketing-Analytics-Modeling-Blog.jpg?resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/06\/Marketing-Analytics-Modeling-Blog.jpg?resize=525%2C300 1.5x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/06\/Marketing-Analytics-Modeling-Blog.jpg?resize=700%2C400 2x"},"classes":[]},{"id":633,"url":"https:\/\/ucanalytics.com\/blogs\/data-visualization-case-study-banking\/","url_meta":{"origin":3318,"position":3},"title":"Data Visualization – Banking Case Study Example (Part 1)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"A Scientist & An Artist A few weeks ago while wandering around in Florence, the birthplace of the Renaissance, I could not escape the thought of Leonardo da Vinci : the greatest polymath of all times. Leonardo\u2019s illustrious resume contains titles such as painter, inventor, physicist, astronomer, engineer, biologist, anatomist,\u2026","rel":"","context":"In "Banking Risk Case Study Example"","block_context":{"text":"Banking Risk Case Study Example","link":"https:\/\/ucanalytics.com\/blogs\/category\/risk-analytics\/banking-risk-case-study-example\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/09\/Data-Visualization.jpg?fit=1098%2C1059&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/09\/Data-Visualization.jpg?fit=1098%2C1059&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/09\/Data-Visualization.jpg?fit=1098%2C1059&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/09\/Data-Visualization.jpg?fit=1098%2C1059&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/09\/Data-Visualization.jpg?fit=1098%2C1059&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":3195,"url":"https:\/\/ucanalytics.com\/blogs\/marketing-analytics-retail-case-study-example-part-2\/","url_meta":{"origin":3318,"position":4},"title":"Marketing Analytics – Retail Case Study Example (Part 2)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Last week we started a case study from the online retail industry to learn more about marketing analytics (Read Part 1). Before we continue with the same case, let me share a few factors that enhance the quality of analysis for marketing or customer analytics. The obvious factors, of course,\u2026","rel":"","context":"In "Marketing Analytics"","block_context":{"text":"Marketing Analytics","link":"https:\/\/ucanalytics.com\/blogs\/category\/marketing-analytics\/"},"img":{"alt_text":"Noir - by Roopam","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/02\/photo.jpg?fit=713%2C1024&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/02\/photo.jpg?fit=713%2C1024&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/02\/photo.jpg?fit=713%2C1024&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/02\/photo.jpg?fit=713%2C1024&ssl=1&resize=700%2C400 2x"},"classes":[]},{"id":4357,"url":"https:\/\/ucanalytics.com\/blogs\/next-best-action-retail-case-study-example-part-11\/","url_meta":{"origin":3318,"position":5},"title":"Next Best Action – Retail Case Study Example (Epilogue)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"This article is the epilogue to our case study example about campaign and marketing analytics for an online retail company. You could read the previous parts at Problem definition: \u00a0Part 1\u00a0&\u00a0Part 2 Description: Part 3 Association: Part 4 Classification: Part 5, Part 6,\u00a0Part 7,\u00a0Part 8 Estimation: Part 9, Part 10\u2026","rel":"","context":"In "Marketing Analytics"","block_context":{"text":"Marketing Analytics","link":"https:\/\/ucanalytics.com\/blogs\/category\/marketing-analytics\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/11\/Free-Will.jpg?fit=315%2C448&ssl=1&resize=350%2C200","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts\/3318"}],"collection":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/comments?post=3318"}],"version-history":[{"count":0,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts\/3318\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/media\/3301"}],"wp:attachment":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/media?parent=3318"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/categories?post=3318"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/tags?post=3318"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}