Effective CRM using Predictive Analytics (eBook)

Antonios Chorianopoulos (Autor)

eBook Download: EPUB

2015
John Wiley & Sons (Verlag)
9781119011576 (ISBN)

Infos

A step-by-step guide to data mining applications in CRM.

Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques.

The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes.
In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise.

Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel.  Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications.

 

Key Features:

Includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues.

Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel.

Accompanied by a website featuring material from each case study, including datasets and relevant code.

 

Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM. 

 

Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers.  The book will also be useful to academics and students interested in applied data mining.

 

Antonios Chorianopoulos, Alpha Bank Greece.

Textauszug

1
An overview of data mining: The applications, the methodology, the algorithms, and the data

1.1 The applications

Customers are the most important asset of an organization. That’s why an organization should plan and employ a clear strategy for customer handling. Customer relationship management (CRM) is the strategy for building, managing, and strengthening loyal and long-lasting customer relationships. CRM should be a customer-centric approach based on customer insight. Its scope should be the “personalized” handling of the customers as distinct entities through the identification and understanding of their differentiated needs, preferences, and behaviors.

CRM aims at two main objectives:

1. Customer retention through customer satisfaction
2. Customer development

Data mining can provide customer insight which is vital for these objectives and for establishing an effective CRM strategy. It can lead to personalized interactions with customers and hence increased satisfaction and profitable customer relationships through data analysis. It can offer individualized and optimized customer management throughout all the phases of the customer life cycle, from acquisition and establishment of a strong relationship to attrition prevention and win-back of lost customers. Marketers strive to get a greater market share and a greater share of their customers. In plain words, they are responsible for getting, developing, and keeping the customers. Data mining can help them in all these tasks, as shown in Figure 1.1.

Figure 1.1 Data mining and customer life cycle management.

Source: Tsiptsis and Chorianopoulos (2009). Reproduced with permission from Wiley

More specifically, the marketing activities that can be supported with the use of data mining include:

Customer segmentation

Segmentation is the process of dividing the customer base in distinct and homogeneous groups in order to develop differentiated marketing strategies according to their characteristics. There are many different segmentation types according to the specific criteria/attributes used for segmentation. In behavioral segmentation, customers are grouped based on behavioral and usage characteristics. Although behavioral segments can be created using business rules, this approach has inherent disadvantages. It can handle only a few segmentation fields, and its objectivity is questionable as it is based on the personal perceptions of a business expert. Data mining on the other hand can create data-driven behavioral segments. Clustering algorithms can analyze behavioral data, identify the natural groupings of customers, and suggest a grouping founded on observed data patterns. Provided it is properly built, it can uncover groups with distinct profiles and characteristics and lead to rich, actionable segmentation schemes with business meaning and value.

Data mining can also be used for the development of segmentation schemes based on the current or expected/estimated value of the customers. These segments are necessary in order to prioritize the customer handling and the marketing interventions according to the importance of each customer.

Direct marketing campaigns

Marketers carry out direct marketing campaigns to communicate a message to their customers through mail, Internet, e-mail, telemarketing (phone), and other direct channels in order to prevent churn (attrition) and drive customer acquisition and purchase of add-on products. More specifically, acquisition campaigns aim at drawing new and potentially valuable customers from the competition. Cross/deep/up-selling campaigns are rolled out to sell additional products, more of the same product, or alternative but more profitable products to the existing customers. Finally, retention campaigns aim at preventing valuable customers from terminating their relationship with the organization.

These campaigns, although potentially effective, when not refined can also lead to a huge waste of resources and to the annoyance of customers with unsolicited communication. Data mining and classification (propensity) models in particular can support the development of targeted marketing campaigns. They analyze the customer characteristics and recognize the profile of the target customers. New cases with similar profiles are then identified, assigned a high propensity score, and included in the target lists. Table 1.1 summarizes the use of data mining models in direct marketing campaigns.

Table 1.1 Data mining models and direct marketing campaigns

Source: Tsiptsis and Chorianopoulos (2009).

Business objective

Marketing campaign

Data mining models

Getting customers

Acquisition: finding new customers and expanding the customer base with new and potentially profitable customers

Acquisition classification models can be used to recognize potentially profitable prospect customers by finding “clones” of existing valuable customers in lists of contacts

Developing customers

Cross selling: promoting and selling additional products/services to existing customers Up selling: offering and switching customers to premium products, other products more profitable than the ones that already have Deep selling: increasing usage of the products/services that customers already have

Cross/up/deep-selling classification models can reveal the existing customers with purchase potentials

Retaining customers

Retention: prevention of voluntary churn, with priority given to presently or potentially valuable customers at risk

Voluntary attrition (churn) models can identify early churn signals and discern the customers with increased likelihood of voluntary churn

When properly built, propensity models can identify the right customers to contact and lead to campaign lists with increased concentrations of target customers. They outperform random selections as well as predictions based on business rules and personal intuitions.

Market basket and sequence analysis

Data mining and association models in particular can be used to identify related products, typically purchased together. These models can be used for market basket analysis and for the revealing of bundles of products/services that can be sold together. Sequence models take into account the order of actions/purchases and can identify sequences of events.

1.2 The methodology

The modeling phase is just one phase in the implementation process of a data mining project. Steps of critical importance precede and follow the model building and have a significant effect in the success of the project. An outline of the basic phases in the development of a data mining project, according to the Cross Industry Standard Process for Data Mining (CRISP-DM) process model, is presented in Table 1.2.

Table 1.2 The CRISP-DM phases

Source: Tsiptsis and Chorianopoulos (2009). Reproduced with permission from Wiley.

1. Business understanding

2. Data understanding

3. Data preparation

Understanding of the business goal Situation assessment Translating the business goal to a data mining objective Development of a project plan

Considering data requirements Initial data collection/exploration and quality assessment

Selection of required data Data acquisition Data integration and formatting (merge/joins, aggregations) Data cleaning Data transformations and enrichment (regrouping/binning of existing fields, creation of derived attributes, and KPIs: ratios, flag fields, averages, sums, etc.)

4. Modeling

5. Model evaluation

6. Deployment

Selection of the appropriate modeling technique Especially in the case of predictive models, splitting of the dataset into training and testing subsets for evaluation purposes Development and examination of alternative modeling algorithms and parameter settings Fine-tuning of the model settings according to an initial assessment of the model’s performance

Evaluation of the models in the context of the business success criteria Model approval

Create a report of findings Planning and development of the deployment procedure Deployment of the data mining model Distribution of the model results and integration in the organization’s operational CRM system Development of a maintenance–update plan Review of the project Planning of next steps

Data mining projects are not simple. They may end in business failure if the engaged team is not guided by a clear methodological framework. The CRISP-DM process model charts the steps that should be followed for successful data mining...

Produkt-Details

• Erscheint lt. Verlag: 26.10.2015
• Sprache: englisch
• Themenwelt: Informatik ► Datenbanken ► Data Warehouse / Data Mining
• Themenwelt: Mathematik / Informatik ► Mathematik ► Finanz- / Wirtschaftsmathematik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Technik
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Marketing / Vertrieb
• Schlagworte: Business & Management • Business Statistics • Computer Science • Consumer Behavior • CRM Applications • Customer Relationship Management • Database & Data Warehousing Technologies • Data Mining • Data Mining for Excel • Data Mining Statistics • Datenbanken u. Data Warehousing • IBM SPSS Modeler • Informatik • Marketing • predictive analytics • Predictive Modeling • RapidMiner • Segmentation • Statistics • Statistik • Verbraucherverhalten • Wirtschaft u. Management
• ISBN-13: 9781119011576 / 9781119011576