A real-time application based on predictive Data Mining algorithms, supporting the selection of offers, products and/or additional services, as well as the selection of channels of communication with customers.
Whisper marketing and influencer marketing are important elements of many marketing campaigns. Dissemination of product information by a friend in specific target groups is much more powerful than advertising targeted directly at all people in a given community. It is natural, that we trust the recommendation of a friend rather than the marketing message of the company. Therefore, it is important to identify people who have the potential to influence their community.
Creating a marketing campaign to gain leads may involve several problems, which are problemtic and tedious to solve. Thus, we want to build an algorithm that will maximize the expected revenue taking into account the limitation concerning number of offers sent to each lead in one message.
Freq is a tool for visual data exploration using the Advanced Miner software. The advantages of Freq are innumerable, however, at the beginning, it is worth mentioning the following: quick overview of the attributes and calculation of their statistics, visual comparison of attributes, possibility of limiting data to specific classes, as well as export to Excel spreadsheets. In this post we will present the possibilities of Freq through operations on simple databases.
Imagine that you have built a predictive model with R and you would like to predict in real time, whether it is profitable to grant a loan and you wonder how to publish the script written in the R language as a Web Service.
According to Forrester, it costs 5x more to acquire new customers than it does to keep current ones. Another study shows that returning customers spend 30% more than new customers and are more willing to purchase new items from the offer of the vendor.
Among the many decisions you’ll have to make when building a predictive model is whether your business problem is either a classification or an approximation task. It’s an important decision because it determines which group of methods you choose to create a model: classification (decision trees, Naive Bayes) or approximation (regression tree, linear regression).
In this part of the tutorial you will learn more about definition and types of lift curves, accumulated LIFT with percentage scale how to construct a LIFT curve. You will gain more information about the accumulated LIFT with percentage scale and other types of LIFT curves.
The ROC curve is one of the methods for visualizing classification quality, which shows the dependency between TPR (True Positive Rate) and FPR (False Positive Rate).
Tutorial: How to establish quality and correctness of classification models? Part 3 – Confusion Matrix
Confusion Matrix is an N x N matrix, in which rows correspond to correct decision classes and the columns to decisions made by the classifier. The number ni,j at the intersection of i-th row and j-th column is equal to the number of cases from the i-th class which have been classified as belonging to the j-th class.
How to determine the quality and correctness of classification models? Part 2 – Quantitative quality indicators
In this part of tutorial we will discuss derived quality indicators and show how to select the appropriate indicator using as an example churn analysis.