{"id":1326,"date":"2021-12-23T06:44:46","date_gmt":"2021-12-23T06:44:46","guid":{"rendered":"https:\/\/mintea.blog\/?p=1326"},"modified":"2021-12-23T06:44:56","modified_gmt":"2021-12-23T06:44:56","slug":"1326","status":"publish","type":"post","link":"https:\/\/mintea.blog\/?p=1326","title":{"rendered":"[Quote] CRM-using-Predictive-Analytics"},"content":{"rendered":"

Path: Cloud \/ 000. Research \/ Analytics & BI<\/p>\n

Source: CRM-using-Predictive-Analytics.pdf<\/p>\n

\"Diagram\n\nDescription<\/p>\n

\"Table\n\nDescription \"Diagram\n\nDescription \"Diagram\n\nDescription \"Graphical<\/p>\n

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2.3.5 Data transformations and enrichment (Data Preparation)<\/strong><\/p>\n

Predictors should by no means be confounded with the target attribute. Predicting a target event using attributes directly related with it is pointless. For instance, using the usage of a product to predict its ownership might lead to a model with astonishingly but erroneously high classification accuracy since we use a variant of the target attribute as a predictor.<\/p>\n

In most classification applications, analysts end up with tens or even hundreds of candidate predictors. However, some or even most of them turn out to be unrelated to the target event and of trivial predictive efficiency. Specific classification algorithms, such as Decision Trees, integrate internal screening mechanisms to select and use only those attributes which are relevant to the target event. other algorithms such as neural networks, Bayes networks, or Support vector Machines lack this feature.<\/p>\n

The principal component analysis (PCA<\/strong>) identifies sets of continuous fields and extracts components which can be used as inputs in subsequent classification. Apart from simplification, PCA also offers an additional benefit. The extracted components are uncorrelated, a great advantage when dealing with statistical models such as logistic regression which are sensitive to multicollinearity (the case of correlated predictors).<\/p>\n

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2.8 Acquisition Modelling<\/p>\n

2.9 Cross-selling Modelling<\/p>\n

2.10 Offer Optimization Modelling<\/p>\n

2.11 Deep-selling Modelling<\/p>\n

2.12 Up-selling Modelling<\/p>\n

2.13 Voluntary Churn Modelling<\/p>\n

Sample chart<\/p>\n

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\"Diagram\n\nDescription \"Diagram\n\nDescription<\/p>\n

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Path: Cloud \/ 000. Research \/ Analytics & BI Source: CRM-using-Predictive-Analytics.pdf   2.3.5 Data transformations and enrichment (Data Preparation) Predictors should by no means be confounded with the target attribute. Predicting a target event using attributes directly related with it is pointless. For instance, using the usage of a product to predict its ownership might … Continue reading [Quote] CRM-using-Predictive-Analytics<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":1327,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[60],"tags":[32,61,62,55,26,54],"class_list":["post-1326","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-book-quotes","tag-analytic","tag-book-quotes","tag-crm","tag-customer-analytic","tag-data","tag-data-mining"],"_links":{"self":[{"href":"https:\/\/mintea.blog\/index.php?rest_route=\/wp\/v2\/posts\/1326","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/mintea.blog\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mintea.blog\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mintea.blog\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mintea.blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1326"}],"version-history":[{"count":2,"href":"https:\/\/mintea.blog\/index.php?rest_route=\/wp\/v2\/posts\/1326\/revisions"}],"predecessor-version":[{"id":1336,"href":"https:\/\/mintea.blog\/index.php?rest_route=\/wp\/v2\/posts\/1326\/revisions\/1336"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/mintea.blog\/index.php?rest_route=\/wp\/v2\/media\/1327"}],"wp:attachment":[{"href":"https:\/\/mintea.blog\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1326"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mintea.blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1326"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mintea.blog\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1326"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}