{"id":1951,"date":"2018-03-27T19:52:23","date_gmt":"2018-03-27T19:52:23","guid":{"rendered":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/?post_type=chapter&#038;p=1951"},"modified":"2024-04-25T02:48:48","modified_gmt":"2024-04-25T02:48:48","slug":"goals-of-data-analysis","status":"publish","type":"chapter","link":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/chapter\/goals-of-data-analysis\/","title":{"raw":"Goals of Data Analysis","rendered":"Goals of Data Analysis"},"content":{"raw":"<div class=\"textbox learning-objectives\">\r\n<h3>Learning Objectives<\/h3>\r\n<ul>\r\n \t<li>Recognize the goals of market basket analysis, targeting promotions, and assortment planning<\/li>\r\n<\/ul>\r\n<\/div>\r\n<img class=\"alignright wp-image-2547 size-full\" src=\"https:\/\/s3-us-west-2.amazonaws.com\/courses-images\/wp-content\/uploads\/sites\/2986\/2018\/03\/13184548\/Screen-Shot-2018-04-13-at-11.42.16-AM-e1534181447989.png\" alt=\"Shopping cart full of groceries\" width=\"308\" height=\"248\" \/>\r\n\r\nMarket basket analysis gives clues as to what a customer might have bought if the idea had occurred or been suggested to them. Other terms used are \u201cimpulse purchasing\u2019 or \u201ccross selling\u201d to describe this consumer purchasing behavior.\r\n\r\nThe availability of detailed information on customer transactions has led to the development of techniques that automatically look for associations between items that are stored in the database. An example is data collected using bar-code scanners in supermarkets. Such \u2018market basket\u2019 databases consist of a large number of transaction records. Each record lists all items bought by a customer on a single purchase transaction. Managers would be interested to know if certain groups of items are consistently purchased together. They could use this data for store layouts to place items optimally with respect to each other, they could use such information for cross-selling, for promotions, for catalog design, and to identify customer segments based on buying patterns.\r\n\r\nMarket basket analysis can be used as a first step in deciding the location and promotion of goods inside a store or on a web page. If, as has been observed, purchasers of Barbie dolls are more likely to buy candy, then high-margin candy can be placed near to the Barbie doll display. Customers who would have bought candy online might be tempted with Barbie doll images popping up on web page margins. The infamous \u201cwould you like fries with that\u201d phrase is an example of the association between products that market basket analysis can reveal.\r\n\r\nThe computational complexity involved in calculating the results of market basket analysis is a challenge met only with DW and data mining techniques. With data warehouses storing billions of transaction lines, so-called \"big data\" tools are needed to draw meaningful conclusions. Special techniques involving filtering or aggregating parts of the transaction database are commonly used to create performance algorithms to allow some level of interactivity, such as what-if queries and scenario creation in business intelligence applications.\r\n\r\nMarket basket analysis is a strong tool in the retailers\u2019 arsenal to increase sales using the latest data analysis techniques. Once out of reach, sifting through mountains of data to draw empirical conclusions can lead to effective assortment plans\u2013determining the appropriate product mix\u2014and promotional opportunities to cross-sell.\r\n<div class=\"textbox tryit\">\r\n<h3>Practice Questions<\/h3>\r\nhttps:\/\/assess.lumenlearning.com\/practice\/cda22095-d7f0-4c4f-9f16-a45b2ebabcc5\r\n<\/div>","rendered":"<div class=\"textbox learning-objectives\">\n<h3>Learning Objectives<\/h3>\n<ul>\n<li>Recognize the goals of market basket analysis, targeting promotions, and assortment planning<\/li>\n<\/ul>\n<\/div>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignright wp-image-2547 size-full\" src=\"https:\/\/s3-us-west-2.amazonaws.com\/courses-images\/wp-content\/uploads\/sites\/2986\/2018\/03\/13184548\/Screen-Shot-2018-04-13-at-11.42.16-AM-e1534181447989.png\" alt=\"Shopping cart full of groceries\" width=\"308\" height=\"248\" \/><\/p>\n<p>Market basket analysis gives clues as to what a customer might have bought if the idea had occurred or been suggested to them. Other terms used are \u201cimpulse purchasing\u2019 or \u201ccross selling\u201d to describe this consumer purchasing behavior.<\/p>\n<p>The availability of detailed information on customer transactions has led to the development of techniques that automatically look for associations between items that are stored in the database. An example is data collected using bar-code scanners in supermarkets. Such \u2018market basket\u2019 databases consist of a large number of transaction records. Each record lists all items bought by a customer on a single purchase transaction. Managers would be interested to know if certain groups of items are consistently purchased together. They could use this data for store layouts to place items optimally with respect to each other, they could use such information for cross-selling, for promotions, for catalog design, and to identify customer segments based on buying patterns.<\/p>\n<p>Market basket analysis can be used as a first step in deciding the location and promotion of goods inside a store or on a web page. If, as has been observed, purchasers of Barbie dolls are more likely to buy candy, then high-margin candy can be placed near to the Barbie doll display. Customers who would have bought candy online might be tempted with Barbie doll images popping up on web page margins. The infamous \u201cwould you like fries with that\u201d phrase is an example of the association between products that market basket analysis can reveal.<\/p>\n<p>The computational complexity involved in calculating the results of market basket analysis is a challenge met only with DW and data mining techniques. With data warehouses storing billions of transaction lines, so-called &#8220;big data&#8221; tools are needed to draw meaningful conclusions. Special techniques involving filtering or aggregating parts of the transaction database are commonly used to create performance algorithms to allow some level of interactivity, such as what-if queries and scenario creation in business intelligence applications.<\/p>\n<p>Market basket analysis is a strong tool in the retailers\u2019 arsenal to increase sales using the latest data analysis techniques. Once out of reach, sifting through mountains of data to draw empirical conclusions can lead to effective assortment plans\u2013determining the appropriate product mix\u2014and promotional opportunities to cross-sell.<\/p>\n<div class=\"textbox tryit\">\n<h3>Practice Questions<\/h3>\n<p>\t<iframe id=\"assessment_practice_cda22095-d7f0-4c4f-9f16-a45b2ebabcc5\" class=\"resizable\" src=\"https:\/\/assess.lumenlearning.com\/practice\/cda22095-d7f0-4c4f-9f16-a45b2ebabcc5?iframe_resize_id=assessment_practice_id_cda22095-d7f0-4c4f-9f16-a45b2ebabcc5\" frameborder=\"0\" style=\"border:none;width:100%;height:100%;min-height:300px;\"><br \/>\n\t<\/iframe>\n<\/div>\n\n\t\t\t <section class=\"citations-section\" role=\"contentinfo\">\n\t\t\t <h3>Candela Citations<\/h3>\n\t\t\t\t\t <div>\n\t\t\t\t\t\t <div id=\"citation-list-1951\">\n\t\t\t\t\t\t\t <div class=\"licensing\"><div class=\"license-attribution-dropdown-subheading\">CC licensed content, Original<\/div><ul class=\"citation-list\"><li>Goals of Data Analysis. <strong>Authored by<\/strong>: Bob Danielson. <strong>Provided by<\/strong>: Lumen Learning. <strong>License<\/strong>: <em><a target=\"_blank\" rel=\"license\" href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\">CC BY: Attribution<\/a><\/em><\/li><\/ul><div class=\"license-attribution-dropdown-subheading\">Public domain content<\/div><ul class=\"citation-list\"><li>Shopping cart graphic. <strong>Authored by<\/strong>: CDC. <strong>Provided by<\/strong>: Wikimedia Commons. <strong>Located at<\/strong>: <a target=\"_blank\" href=\"https:\/\/commons.wikimedia.org\/wiki\/File:Shopping_cart_with_food_clip_art.svg\">https:\/\/commons.wikimedia.org\/wiki\/File:Shopping_cart_with_food_clip_art.svg<\/a>. <strong>License<\/strong>: <em><a target=\"_blank\" rel=\"license\" href=\"https:\/\/creativecommons.org\/about\/pdm\">Public Domain: No Known Copyright<\/a><\/em><\/li><\/ul><\/div>\n\t\t\t\t\t\t <\/div>\n\t\t\t\t\t <\/div>\n\t\t\t <\/section>","protected":false},"author":62559,"menu_order":19,"template":"","meta":{"_candela_citation":"[{\"type\":\"original\",\"description\":\"Goals of Data Analysis\",\"author\":\"Bob Danielson\",\"organization\":\"Lumen Learning\",\"url\":\"\",\"project\":\"\",\"license\":\"cc-by\",\"license_terms\":\"\"},{\"type\":\"pd\",\"description\":\"Shopping cart graphic\",\"author\":\"CDC\",\"organization\":\"Wikimedia Commons\",\"url\":\"https:\/\/commons.wikimedia.org\/wiki\/File:Shopping_cart_with_food_clip_art.svg\",\"project\":\"\",\"license\":\"pd\",\"license_terms\":\"\"}]","CANDELA_OUTCOMES_GUID":"81bff414-5390-4d05-9997-f04991448088, b75678d6-a33e-41bb-8d1d-1bd61f850c25","pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-1951","chapter","type-chapter","status-publish","hentry"],"part":1910,"_links":{"self":[{"href":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/wp-json\/pressbooks\/v2\/chapters\/1951","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/wp-json\/wp\/v2\/users\/62559"}],"version-history":[{"count":16,"href":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/wp-json\/pressbooks\/v2\/chapters\/1951\/revisions"}],"predecessor-version":[{"id":6465,"href":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/wp-json\/pressbooks\/v2\/chapters\/1951\/revisions\/6465"}],"part":[{"href":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/wp-json\/pressbooks\/v2\/parts\/1910"}],"metadata":[{"href":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/wp-json\/pressbooks\/v2\/chapters\/1951\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/wp-json\/wp\/v2\/media?parent=1951"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/wp-json\/pressbooks\/v2\/chapter-type?post=1951"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/wp-json\/wp\/v2\/contributor?post=1951"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/courses.lumenlearning.com\/wm-retailmanagement\/wp-json\/wp\/v2\/license?post=1951"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}