CRM_Note_3_Analytics

CRM_Note_3_Analytics - CRM Note Set 3: Predicting...

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Database Marketing and Observable Customer Metrics CRM Note Set 3: Predicting Individual Level Customer Behavior
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Example 1: Catalog(s)
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Agenda / Goals Basics: Database Marketing Observable Metrics RFM and Decile Analyses Tuscan Lifestyles Case Advanced: Statistical Analysis of Panel Data Linear Regression Logistic Regression Diamonds in the Data Mine Introduce Charitable Contributions Data Introduce Online Grocer Data Applied Work / Customer Analytics Assignment http://www.youtube.com/watch?v=ZxHPevujqZM&feature=related
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CRM Analytics Data warehouses may contain massive amounts of customer and market data. How do we go from data to CRM? How do we convert data to marketing policy? Data Analysis Decision Support Goals Understanding Relationships Predicting Response
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Customer Metrics Observable Measures Decisions to buy RFM CLV / LTV (long term value) Loyalty? Unobservable Constructs Customer perceptions Attitudes (satisfaction) Behavioral intentions Loyalty? What Firms Do What Consumers Think What Consumers Do What Firms Get
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What Customers Do Observable Customer Decisions Purchases Expenditures Rejections Complaints Open / Click on Email Communications Requests for Information Transaction Histories
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Database(d) Marketing What do we know about  a specific customer? = + How should we market Based on what we know? How do we expect a given customer to react?
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Database Marketing The fundamental question is what information is useful / needed to predict behavior Who to offer? What to offer? Probability of buying as a function of DATA Dynamic Consequences?
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Data Analysis: Basics Decile and RFM Analysis
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Decile Analysis Pick a variable of interest (e.g. Recency, frequency, monetary or other, such as LTV) Sort or rank the database from best to worst on that variable For deciles, divide into 10 equal sized groups top group is decile 1, next is decile 2, etc. note: sometimes the ‘top group’ is 10 and the bottom is labeled 1. It doesn’t matter as long as you know which is which! (and are consistent in whether a ‘1’ is best or worst) More generally, can specify quintiles (5 groups) or any other n -tile Now, can summarize other variables of interest by n- tile ex: customer LTV, response to test mailing, marketing dollars, etc.
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Premise of RFM: Past behavior is predictive of the future Recency = how long ago the customer last made a purchase Frequency = how many purchases the customer has made Monetary = how much each customer has spent in total Appropriate for existing customers and widely used for segmenting and targeting Prospects typically segmented by behavioral and demographic characteristics
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Sample Database Charitable Contributions A non-profit organization that uses direct mail to solicit additional
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CRM_Note_3_Analytics - CRM Note Set 3: Predicting...

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