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2016 | nr 240 | 204
Tytuł artykułu

Hybrydowe modele predykcyjne w marketingu relacji

Warianty tytułu
Hybrid Predictive Models in Relationship Marketing
Języki publikacji
Praca ma charakter teoretyczno-empiryczny, co wpływa na zawartość czterech kolejnych rozdziałów. Dwa pierwsze zawierają analizę dorobku naukowego w zakresie problematyki budowy modeli predykcyjnych w marketingu relacji z podziałem na poszczególne fazy cyklu życia klienta i etapy modelu ACURA (acquisition, cross-sell, up-sell, retention, advocacy). Dwa kolejne rozdziały mają charakter empiryczny, a znajdują się w nich przykłady hybrydowych modeli predykcyjnych oparte na drzewach klasyfikacyjnych i regułach asocjacyjnych. Głównym celem pracy jest porównanie statycznych hybrydowych modeli predykcyjnych z modelami podstawowymi użytymi w trakcie hybrydyzacji w trzech fazach cyklu życia klienta. (fragment tekstu)
The book presents hybrid predictive models in relationship marketing. These models relate to the customer life cycle (acquisition phase, development phase and retention phase) and to the popular model ACURA. The need to analyse customer data is emphasized by all schools of relationship marketing. It is recognised that the relatively easy access to data combined with advanced models and analytical tools enable offers to be better tailored, targeting to be more effective, competitive advantage to be increased and customer retention to be improved. Analytical tasks are part of analytical CRM, database marketing, business intelligence and data-driven marketing, which can all to some extent be regarded as synonyms. Building predictive models goes beyond the services sphere (telecommunications industry, financial services) and relates to FMCG and durable goods. Combining models and analytical tools is now common practice when building predictive models in many research areas. The greater time the analytical procedure requires is often compensated by higher prediction accuracy, a reduced class imbalance problem and the ability to deliver clear patterns from complex structure datasets. Researchers distinguish between hybrid models (also known as two-step classification models, cascade models, integrated models, and cross-algorithm ensembles) where different analytical tools are combined and ensemble models (also known as committees) where the same analytical tools are combined. The author treats hybridisation as combining supervised models with unsupervised ones and combining classical statistical analytical tools with those derived from data mining and machine learning. When descriptive models (associations, sequences) are applied for predictive purposes, hybridisation is based on the sequential use of different tools that provide a clear interpretation of the results. The first hybrid predictive model refers to customer acquisition and was built by combining decision trees (C&RT algorithm) with k-means algorithm and Kohonen networks. The model is based on a real dataset from a cosmetics company's advertising campaign that uses a social networking website. The dependent variable has two categories: "the user clicked on the banner ad" and "the user ignored the advertising". After identifying the profile of Internet users who clicked on the banner ad, the company optimized the campaign by displaying it only on accounts of users with certain characteristics. The results clearly show that the hybrid approach has an advantage over the basic decision tree model with respect to all performance measures - accuracy, recall, precision and lift in the first two deciles. In the second hybrid predictive model - a churn model - decision trees (C&RT algorithm) were combined with logistic regression. During the experiment two datasets were used - both related to churn analysis and downloaded from popular online repositories. There are four advantages of the hybrid C&RT-logit approach: in-depth interpretation of the phenomenon, the identification of a stronger relationship between the independent variables and the dependent one, the unique probabilities of belonging to the category of dependent variable, and higher values of pseudo R2 measures when compared to a basic logistic regression model. The next two hybrid models presented in the book are based on association rules and sequential rules. Both refer to a phase of development of relations, that is, to cross-sell and up-sell. The first one is based on a real transaction dataset of a service company. The hybridisation procedure was carried out here in two ways. In the first approach, an algorithm searched for transactional rules in subsets (segments) that were delivered during RFM analysis. In the second approach, a large number of rules was clustered by using a k-means algorithm and SOM. The variables that were used during cluster analysis were the three popular performance measures: support, confidence and lift. Hybrid market basket analysis allows for faster selection of interesting transactional patterns. The second hybrid cross-sell and up-sell model combines sequential rules with social network analysis. The analysis looked at consumer behaviour during visits to an e-shop website offering clothing for women. It is an example of web usage mining, in particular web clickstream analysis. Due to the large number of rules, SNA was proposed, and its main goal was to effectively visualise the results. The final form of the model (the size and structure of the network) is affected by how two measures - support and confidence - are binarised. The book presents several alternative models with binarisation at the point of the median, arithmetic mean and upper quartile. (original abstract)
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