{"id":3129,"date":"2014-06-22T13:49:42","date_gmt":"2014-06-22T08:19:42","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=3129"},"modified":"2017-05-22T14:48:43","modified_gmt":"2017-05-22T09:18:43","slug":"marketing-analytics-retail-case-study-part-1","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/marketing-analytics-retail-case-study-part-1\/","title":{"rendered":"Marketing Analytics – Retail Case Study Example (Part 1)"},"content":{"rendered":"
\n

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 huge interest. However, the learning from this case could be extended to many other industries.<\/p>\n

As a part of this series for marketing analytics, we will talk about identifying opportunities among the existing customer base for cross\/up sell. We will design experiments and A\/B testing schemes to learn about managing campaigns. We will also explore other scientific ways to learn more about the customer for organizations to strengthen their business relationship with their customer base. But before we start we need to understand a bit more about the science of..<\/p>\n

Incentives and Influence<\/span><\/h2>\n
\"Social<\/a>

Social Behavior & Marketing Analytics – by Roopam<\/p><\/div>\n

OK, in this section of the article I have a task for you. You want people to cut down on their electricity consumption by switching from air conditioners to ceiling fans. I have listed down a set of reasons you could offer to them through advertisements. Arrange the following reasons in order of their influence on most people to cut down on energy consumption.<\/p>\n

    \n
  1. Benefit to Society<\/strong> – share the saved power with deprived sections of the society<\/li>\n
  2. Save Money<\/strong> – cut down on electricity bill<\/li>\n
  3. Follow Others<\/strong> – <\/strong>everyone else is doing it and so should you<\/li>\n
  4. Protect Environment<\/strong> – reduce the burden of power generation on the environment<\/li>\n<\/ol>\n

    Have you taken a stab at the task? Then read ahead!<\/p>\n

    The above question is a part of research work carried out by\u00a0Dr.\u00a0<\/span>Robert<\/span>\u00a0Cialdini, author of international best-selling book ‘Influence:\u00a0<\/em><\/span>The Psychology of Persuasion<\/em>‘. He did a survey and asked people the same question about what will motivate them among the above\u00a0factors to save electricity. The following is the result from the telephonic survey:<\/p>\n

    ‘Protect Environment’<\/em>\u00a0was ranked on the top followed by ‘Benefit to Society<\/em>‘ and ‘Save Money<\/em>‘. This leaves ‘Follow Others<\/em>‘ at the bottom of the list to influence people to reduce their electricity consumption. In a survey, you capture the\u00a0order of responders’ aspirations. The results from the survey carried out by Dr. Cialdini make sense since who wants to follow the herd. We are all free thinking creatures, aren’t we? But the question is whether people\u00a0behave the\u00a0way they think.<\/p>\n

    To test whether people’s money is where their mouth is, Dr. Cialdini did a set of experiments. He placed\u00a0a message on the doorknobs of\u00a0people’s houses with either one of the four reasons to save electricity. Then he measured the electricity consumption patterns for all these people. The results will surprise you for sure. The group that\u00a0received ‘Follow others<\/em>‘ message outperforms every other group\u00a0by a huge and significant margin for reduced consumption of electricity.<\/p>\n

    The roots for the success of ‘Follow Others<\/em>‘ strategy are in evolution. From the evolutionary standpoint, for species ‘Follow Others<\/em>‘ is a far more superior strategy for survival than any other. Imagine a prehistoric human in a dilemma to choose one of the two roads in front of him: one the road well traveled and safe, and other with little information about it. There is high risk on the road less\u00a0traveled, and\u00a0\u00a0risk taking is an aberrant behavior from the evolutionary point of view.<\/p>\n

    If you still have doubts about the results from Dr. Cialdini’s research and the evolutionary argument, try to fight\u00a0the temptation of looking in the same direction where a group\u00a0of people staring at some incident on\u00a0the street.<\/p>\n

    Marketing Analytics<\/span><\/h2>\n

    You like it or not but humans involuntarily behave like other similar humans around them. We all follow an involuntary pattern and this pattern detection is precisely the idea behind marketing analytics. If your next door neighbor buys a new car you are immediately tempted to buy a new car. You may not believe how we live our lives around these constant involuntary temptations and mostly succumb to them. The task for marketing analytics is to identify groups of people similar in their attributes to learn more about them. This method is also referred to as look-alike-modeling. This helps businesses\u00a0to\u00a0devise a sound marketing strategy for different groups of people based on their needs and consumption pattern.<\/p>\n

    To get\u00a0a quick feel for workings of\u00a0marketing analytics, consider the following schematic. You start with the unexplored customer portfolio and then using data mining and advanced statistical tools try to identify hidden but interesting patterns in customer spends behavior. Eventually, you superimpose business knowledge and rules on top of this classified\u00a0portfolio to formulate sound business & marketing strategy for business growth.<\/p>\n

    \"Marketing<\/a>

    Marketing Analytics Modeling: Schematic by Roopam<\/p><\/div>\n

    We will get a better understanding of the above process in the following case study.<\/p>\n

    Case Study Example – Marketing Analytics<\/span><\/h2>\n

    You have recently joined in as the chief analytics officer &\u00a0business strategy head at an online shopping store called DresSMart Inc. that specializes in apparel and clothing. One day you had the chief marketing officer of the company come rushing to you office looking unusually worried\u00a0. The board of directors has given him hefty targets for sales and\u00a0slashed\u00a0his marketing budgets into half at the same time. \u00a0You immediately identify that you are dealing with a common business problem of improving business revenue with reduced cost. You have also realized that this is a great opportunity for you to establish analytics practices in the company since there is a quick opportunity for you to improve the P&L (income statement).<\/p>\n

    Additionally, the CMO informed you that last year they had carried out marketing campaigns with different offers on the product catalog. A direct mailing product catalog was sent to some hundred thousand customers from the base of over a couple of million customers last year with the response rate of 4.2%. The direct mailers were later followed up with SMS and email messaging.<\/p>\n

    To explain your strategy to the CMO, you drew a quick and dirty campaign P&L statement on the white board in your office. The following is a version of your drawing<\/p>\n

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

    The objective essentially is to maximize cumulative value generated by customers while minimizing total mailer cost. You explained that the analysis will have an impact on the variable component of the campaign i.e. you will reach out to the right set of customers and generate maximum value. Additional there is an intangible benefit of this exercise i.e. reduced customer dissatisfaction from unsolicited offers.<\/p>\n

    The CMO left you as a much less worried man than when he entered your office. However, you know that you had your work cut out, and you need to think of the right\u00a0approach to solve this problem. You are up for the challenge!<\/p>\n

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

    Coming back to incentives and influence, you have enough incentives to work on the above problem since it will establish you and your practice in the company. Additionally, you will also influence the right people in the company since you will improve the income statement of the company. The primary task\u00a0while initiating an analytics project is to clearly define the end goals\/objectives of the exercise. The projects that influence the financials of a company goes a long way. Hence it is a best practice to link the end goals\/objectives of\u00a0your\u00a0projects to the financial influence. See you soon with the next part of this case study example.<\/p>\n","protected":false},"excerpt":{"rendered":"

    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 huge interest. However, the learning<\/p>\n

    Read More…<\/a><\/p>\n","protected":false},"author":1,"featured_media":3130,"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":"\nMarketing Analytics - Retail Case Study Example - Part 1<\/title>\n<meta name=\"description\" content=\"This is a marketing analytics case study example from online retail that will illustrate the power of data science in sales and marketing process.\" \/>\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\/marketing-analytics-retail-case-study-part-1\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Marketing Analytics - 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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":3318,"url":"https:\/\/ucanalytics.com\/blogs\/exploratory-data-analysis-retail-case-study-example-part-3\/","url_meta":{"origin":3129,"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":4357,"url":"https:\/\/ucanalytics.com\/blogs\/next-best-action-retail-case-study-example-part-11\/","url_meta":{"origin":3129,"position":2},"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":3973,"url":"https:\/\/ucanalytics.com\/blogs\/model-selection-retail-case-study-example-part-7\/","url_meta":{"origin":3129,"position":3},"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":3405,"url":"https:\/\/ucanalytics.com\/blogs\/association-analysis-retail-case-study-example-part-4\/","url_meta":{"origin":3129,"position":4},"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":4274,"url":"https:\/\/ucanalytics.com\/blogs\/regression-model-retail-case-study-part-10\/","url_meta":{"origin":3129,"position":5},"title":"Regression Model – Retail Case Study Example (Part 10)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"This article is a continuation of our marketing analytics case study example for campaign management solutions. In this case, we started with the following\u00a0two\u00a0goals to build models to identify Most responsive customers Most revenue generating customers Problem definition: \u00a0Part 1\u00a0&\u00a0Part 2 Description: Part 3 Association: Part 4 Classification: Part 5,\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\/10\/Regression-Train-by-Roopam.jpg?fit=448%2C320&ssl=1&resize=350%2C200","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts\/3129"}],"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=3129"}],"version-history":[{"count":0,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts\/3129\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/media\/3130"}],"wp:attachment":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/media?parent=3129"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/categories?post=3129"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/tags?post=3129"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}