{"id":3405,"date":"2014-07-13T17:01:38","date_gmt":"2014-07-13T11:31:38","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=3405"},"modified":"2018-09-21T20:20:16","modified_gmt":"2018-09-21T14:50:16","slug":"association-analysis-retail-case-study-example-part-4","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/association-analysis-retail-case-study-example-part-4\/","title":{"rendered":"Association Analysis – Retail Case Study Example (Part 4)"},"content":{"rendered":"
\n
\"Edward<\/a>

Edward Scissorhands – by Roopam<\/p><\/div>\n

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<\/a>,\u00a0Part 2<\/a>,\u00a0and Part 3<\/a>). \u00a0In the last part, we discussed exploratory data analysis (EDA:\u00a0Part 3<\/a>). In this article\u00a0we will talk about association analysis, a helpful technique to mine interesting patterns in customers’ transaction data. Association analysis can be used as a handy tool for extended exploratory data analysis. By the way, association analysis is also the core of market basket analysis or sequence analysis. Later in the article, we will use association analysis in our case study example to design effective offer catalogs for campaigns and also online store design (website).<\/p>\n

Scissorhands<\/span><\/h2>\n

I must have been 9 or 10 years old when in our school we had our first craft lecture. Craft lectures are called SUPW<\/em> in India, it’s an abbreviation\u00a0\u00a0for ‘Socially Useful Productive Work’<\/em>. As a part of the first lecture, each student was provided with an A4 sized color paper and a pair of scissors. In the first lecture excited kids with no direction discovered that they could cut a sheet in a virtually infinite number of ways. It was neither socially useful nor productive work, and created a lot of wasted paper. A more apt long form of SUPW<\/em> in this case is\u00a0‘Some Useful Paper Wasted’<\/em>. Later with a more directed effort we discovered that there are so many cool shapes hidden in a piece of paper as long as scissors are used wisely.<\/p>\n

This is precisely the kind of experience many analysts have when they come across customers’ transaction data in companies. There is wealth of information about customer behavior hidden in this data but it is hard to figure out where to start. Transaction data can be sliced, diced and grouped in infinitely many ways similar to a piece of paper dissected with scissors. The key in both these above cases is direction.<\/p>\n

Hollywood Image of Data Analysis<\/span><\/h2>\n

\"A-Beautiful-Mind\"<\/a>Let me describe a typical\u00a0Hollywood visual for data analysis, a man standing in front of a giant\u00a0screen with data (sequence of numbers) floating all over the screen. This man will detect patterns in this data on the fly. This is a powerful image but completely untrue. The above technique of stare at data and hope to find patterns is guaranteed\u00a0to generate all\u00a0noise and very little signal. Even the great code breakers like John Nash and Alan Turing will fail if they try to find patterns in data using this Hollywood technique.<\/p>\n

The point I am trying to drive at here is that data analysis is a highly planned activity. \u00a0As an analyst never touch your data before you have a proper\u00a0plan of action (hypotheses etc.) in place. Having said this there are always going to be times as an analyst, when you have to enter uncharted territories of data to find patterns. In these cases, I will recommend you rely on machine learning algorithms or create your own modified algorithms specific to your requirements. In my opinion, machines are any day better than us humans at this task. Association analysis powered by the\u00a0Apriori<\/em> algorithm is one such\u00a0technique to mine transaction data. Let’s explore association analysis in the next part.<\/p>\n

Association Analysis<\/span><\/h2>\n

Association analysis, as you will discover soon, is primarily\u00a0frequency analysis performed on a large dataset. Since datasets for most practical problems are large you need clever algorithms like Apriori<\/em> to manage association analysis.Let’s consider a much smaller\u00a0transaction dataset to learn about association analysis. Here, each row or transaction number represents market baskets of customers. For the subsequent products columns, 1 represents ‘bought the product in that transaction’, whereas, 0 stands for ‘did not buy’.<\/p>\n\n\n\n\n\n\n\n\n
Transaction #<\/td>\nShirts<\/td>\nTrousers<\/td>\nTies<\/td>\n<\/tr>\n
001<\/td>\n1<\/td>\n1<\/td>\n1<\/td>\n<\/tr>\n
002<\/td>\n0<\/td>\n1<\/td>\n0<\/td>\n<\/tr>\n
003<\/td>\n1<\/td>\n0<\/td>\n1<\/td>\n<\/tr>\n
004<\/td>\n1<\/td>\n0<\/td>\n1<\/td>\n<\/tr>\n
005<\/td>\n1<\/td>\n1<\/td>\n0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

There are a few association analysis metrics (i.e. support, confidence, and lift) that are really helpful in deciphering information hidden in this kind of dataset. Let us explore these metrics and understand their usage. Support\u00a0for purchase of shirts and ties together in association analysis is defined as:<\/p>\n

\"\textup{Support}=\frac{\textup{\normalsize<\/pre>\n
For our data there are 3 transactions with both shirts and ties (shirts\u2229ties) out of total 5 transactions.<\/p>\n
\"\textup{Support}=\frac{\normalsize<\/pre>\n
60% is a fairly high value for support and you will rarely find such high values for support in real world examples. For\u00a0real world problems with several product groups, support of 1% or at times even lower depending upon the nature of your problem is also useful.<\/p>\n
Confidence for association is calculated using the following formula:<\/p>\n
\"\textup{Confidence}=\Large\frac{\textup{\normalsize<\/pre>\n
In our dataset, there are 3 transaction for both shirts and ties together out of 4 transactions for\u00a0shirts. The calculation for confidence for our dataset is:<\/p>\n
\"\textup{Confidence}=\frac{\large<\/pre>\n
Again\u00a0you will rarely find such high value of confidence for most real world problems unless there are appealing combo offers on two products. A good value of confidence is again problem specific.<\/p>\n
A third useful metric for association analysis is lift; it is defined as:<\/p>\n
\"\textup{Lift}=\frac{\textup{\normalsize<\/pre>\n
Expected confidence in the above formula is presence of ties in the overall dataset i.e. there are 4 instances of ties purchase out of 5.<\/p>\n
\"\textup{Lift}=\frac{{\normalsize<\/pre>\n
The value for lift, 125%, shows that purchases of the ties improve when the customers buy shirts. The question you are asking here is that if the customer buys a shirt, does his chance of buying ties go up i.e. value of lift above 100%. Let us use our knowledge about association analysis for the case study example we have been working on.<\/p>\n
Retail Case Study Example – Association Analysis<\/span><\/h2>\n
\"Dress_Shirt_and_Tie_Combos_Men_Nordstrom\"<\/a>DresSMart Inc., where you are the Chief Analytics Officer & Business Strategy Head, is\u00a0an online retail store for clothes and apparel. They showcase different products, brands, and styles. You know association analysis works best when performed separately on different customer segments (read about customer segmentation<\/a>). However, you have decided to do a quick association analysis on the data available in your company.<\/p>\n
With your data for formal shirts and ties we explored in the above example, you got support of 0.2% with confidence of 12% and lift of 509%. This implies that though there are fewer percentage records of transactions with both ties and shirts, once the customers buys formal shirts his chances of buying a tie goes up five fold.<\/p>\n
DresSMart provides the option to it’s customers to return\u00a0the undamaged product back within 30 days with full refund. You did a further investigation of customers who are buying ties along with shirts and found that product return rates of the ties for these transactions are also 3 times more than the other return rates. This is an indicator that customers are struggling to choose matching ties while placing the orders online along with shirts. There is a need to improve this process on the company’s website. The idea is to reduce product return rate while exploiting the full opportunity for cross selling ties with shirts.<\/p>\n
You have found some good clues to improve the\u00a0profitability of your company through exploratory data analysis tools. Now you want to prepare and address the original objectives (Part 2<\/a>) to improve profitability for campaign efforts. You will delve into serious modeling for this task next time around.<\/p>\n
Sign-off Note<\/span><\/h4>\n
Hope you enjoy being\u00a0Edward Scissorhands with your data! See you soon with the next part of this case study example where we will explore more about decision tree algorithms.<\/p>\n","protected":false},"excerpt":{"rendered":"
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 association analysis, a helpful technique<\/p>\n
Read More…<\/a><\/p>\n","protected":false},"author":1,"featured_media":3404,"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":"\nAssociation Analysis - Retail 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 Association Analysis 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\/association-analysis-retail-case-study-example-part-4\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Association Analysis - 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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":3318,"url":"https:\/\/ucanalytics.com\/blogs\/exploratory-data-analysis-retail-case-study-example-part-3\/","url_meta":{"origin":3405,"position":1},"title":"Exploratory Data Analysis (EDA) – Retail Case Study Example (Part 3)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"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\u2026","rel":"","context":"In "Marketing Analytics"","block_context":{"text":"Marketing Analytics","link":"https:\/\/ucanalytics.com\/blogs\/category\/marketing-analytics\/"},"img":{"alt_text":"penalty shootout","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/07\/penalty-shootout.jpg?resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/07\/penalty-shootout.jpg?resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2014\/07\/penalty-shootout.jpg?resize=525%2C300 1.5x"},"classes":[]},{"id":3973,"url":"https:\/\/ucanalytics.com\/blogs\/model-selection-retail-case-study-example-part-7\/","url_meta":{"origin":3405,"position":2},"title":"Model Selection – Retail Case Study Example (Part 7)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Model Selection This is a continuation of our retail case study example for campaign and marketing analytics. 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":3195,"url":"https:\/\/ucanalytics.com\/blogs\/marketing-analytics-retail-case-study-example-part-2\/","url_meta":{"origin":3405,"position":3},"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":3405,"position":4},"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":[]},{"id":2783,"url":"https:\/\/ucanalytics.com\/blogs\/in-conversation-with-eric-siegel-author-predictive-analytics\/","url_meta":{"origin":3405,"position":5},"title":"In Conversation with Eric Siegel: Author ‘Predictive Analytics’","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"In Conversation with.. Today we are starting a new series on YOU CANalytics called 'in conversation with'. In this series we will talk to the leaders and experts of predictive analytics and big data to gain deeper insight into the field. Dr. Eric Siegel Our first guest for the series\u2026","rel":"","context":"In "Events & Interviews"","block_context":{"text":"Events & Interviews","link":"https:\/\/ucanalytics.com\/blogs\/category\/events-and-interviews\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2015\/12\/Slide15.jpg?fit=290%2C210&ssl=1&resize=350%2C200","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts\/3405"}],"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=3405"}],"version-history":[{"count":0,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts\/3405\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/media\/3404"}],"wp:attachment":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/media?parent=3405"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/categories?post=3405"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/tags?post=3405"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}