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Part II
Data Management and Technology

Chapter 7
Database and Customer Data Development

© Taylor & Francis 2016

7.1 Introduction

• Organizations have become extremely proficient at generating data
• Mass infusion of data has created unlimited opportunities for building relationships with customers
• The Internet has accelerated data generation and has challenged organizations to determine how to leverage this data in an effort to sustain and grow relationships with their customers

© Taylor & Francis 2016

7.2 Data Defined

• Primary data
• Acquired from the original source
• Secondary data
• Acquired from some party other than the party from which the data represents
• Derived data
• Information created from other data
• Individual data
• Attributed to a specific person
• Household data
• View of data from a household perspective

© Taylor & Francis 2016

7.3 Data Capture

• Touch points
• One of the key steps of the customer data integration process
• New touch points presenting challenges (e.g. IoT, Mobile, GEO)
• Real-time versus batch
• Marketers need data to be captured and disseminated at different situations
• Data captured may need to be processed and action taken as soon as possible
• Marketer may not need to know information until a trend or pattern has emerged
• Organization and data management
• Internal versus external
• How much data?

© Taylor & Francis 2016

7.4 Data Transformation

• Convert data into information
• Information aging
• Convert information into knowledge

DATA → INFORMATION → KNOWLEDGE

© Taylor & Francis 2016

7.5 Business Intelligence (BI) and Business Analytics (BA)

• Data mining—review historical information in an effort to generate business intelligence
• Descriptive analytics—review historical for the purpose of understanding what happened and why
• Predictive analytics—answers the question “what will happen and when may it happen?”
• Streaming analytics—real-time process which captures data through interpretation of data filtering complex event processing
• Prescriptive analytics—provides the “why” as well as suggested options to ensure the “what and when” will occur and enhance, modify, or prevent the occurrence

© Taylor & Francis 2016

7.5 Support Systems

• Decision support systems
• Software systems designed for a specific purpose
• Easy to use with graphical user interfaces
• Can be partially or fully automated
• Executive information systems
• Designed to provide information for higher-level decision making through the use of dashboards
• Enterprise resource planning systems
• Integrate most, if not all, business functions

© Taylor & Francis 2016

7.5 Location and Access Considerations

• Decision on data location dependent upon respective BI and BA activity
• Operational data store supports dynamic business activity—telemarketing, Web, mobile, P.O.S, IoT
• Data warehouse is an optimal entity for any analytical activity requiring static and inclusive data—(e.g. data mining, predictive analytics)
• Data marts are efficient entities due to inclusion of relative data and access via very specific software

© Taylor & Francis 2016

7.5 Location and Access Considerations

• Big data/data lakes are foundations for unstructured data (e.g. digital customer interactions, social conversations, emails, IoT endpoint sensors, videos, audio clips)
• Unstructured data access two ways
• Exploration—data mining
• Enhance previously defined business issue
• Cloud used to describe entity location

© Taylor & Francis 2016

7.5 Other Analytical Techniques

• Recency, frequency, and monetary (RFM)
• Not a “true” data mining approach
• Uses historical information from three data categories and the user makes an assumption that past behavior is a good predictor of future behavior
• Decision trees
• Leaves represent classifications, and branches represent conjunctions of features that lead to those classifications
• Created by splitting the source set into subsets based on an attribute value test
• More complex than RFM
• Helps turn complex data representation into a much easier structure

© Taylor & Francis 2016

7.5 Data Mining

• Cluster analysis
• Place customers/prospects into groups such that everyone in the group has similar traits
• Categories include demographics, psychographics, behavioral, geographics
• Other data mining techniques
• Artificial neural network, business intelligence (BI), data stream mining, fuzzy logic, nearest neighbor algorithm, pattern recognition, relational data mining, text mining, chi-Square, t-test, regression, correlation

© Taylor & Francis 2016

7.5 Data Mining

• Data mining benefits
• Better understanding of customers and prospects supports relationship-building efforts
• Measurable
• Fatigue prevention
• Precipitate new opportunities
• Fraud detection and identification of nonfavorable behavior

© Taylor & Francis 2016

7.5 Data Mining

• Data mining challenges
• Organizational obstacles to attaining data
• Cost versus benefit
• Ability to capture data
• Giving customer/prospect perception of invasiveness
• Privacy issues
• Sustained secondary availability

© Taylor & Francis 2016

7.6 Enabling CRM

• Industry examples illustrate how data capture, transformation, and mining help enable CRM
• Manufacturer tools products
• Entertainment and hotel
• Financial services
• Infant formula manufacturer
• Apparel cataloger
• Hotel and travel
• Retail grocery
• Small business
• Fraud detection and other nonfavorable behavior