CHAPTER 11: BUILDING A CUSTOMER – CENTRIC ORGANIZATION – CUSTOMER RELATIONSHIP MANAGEMENT

CUSTOMER RELATIONSHIP MANAGEMENT (SCM)

 

 

   Today, most competitors are simply a mouse-click away, and this intense competition is forcing firms to switch from sales-focused business strategies to customer-focused business strategies. Customers are one of a firm’s most valuable assets, and building strong loyal customer relationship is a key competitive advantage.
   CRM is means of managing all aspect of a customer’s relationship with a an organization to increase customer loyalty and retention and an organization’s profitability.

CRM enables an organization to :

• - Provide better customer service 
• - Make call centers more efficient 
• - Cross sell products more effectively 
• - Help sales staff close deals faster 
• - Simplify marketing and sale processes 
• - Discover new customers 
• - Increase customer revenues 

RECENCY, FREQUENCY, and MONETARY VALUE

Organizations can find their most valuable customers through “RFM” Recency, Frequency, Monetary value
• How recently a customer purchased items ( RECENCY )
• How frequently a customer purchased items ( FREQUENCY )
• How much a customer spends on each ( MONETARY VALUE )

 

THE EVOLUTION OF CRM 

• CRM  reporting technology – help organizations identify their customer across other applications
• CRM analysis technologies – help organizations segment their customers into categories such as best and worst customers
• CRM predicting technologies – help organizations make predictions regarding customer behavior such as which customers are at risk of leaving
Three phases in the evolution of CRM include reporting, analyzing and predicting 

THE UGLY SIDE OF CRM 

Identify the primary forces driving the explosive growth of customer relationship management: 

• The primary forces driving the explosive growth of CRM include Automation/ Productivity/ Efficiency, Competitive Advantage, Customer Demand / Requirements, Increase Revenues, Decrease Costs, Customer Support, Inventory Control, Accessibility 

CUSTOMER RELATIONSHIP MANAGEMENT’S EXPLOSIVE GROWTH 

Forecasts for CRM Spending ( in billions ) 

Compare operational and analytical customer relationship management:

• Operational CRM – supports traditional transaction processing for day-to-day font-office operations or systems that deal directly with the customers. For example, marketing, shipping.
• Analytical CRM – supports back office operations and strategic analysis and includes all systems that do not deal directly with the customers.
The primary difference between operational CRM and analytical CRM is the direct interaction between the organization and its customers

OPERATIONAL CRM AND ANALYTICAL CRM 

Define the relationship between decision making and analytical customer relationship management: 

• Analytical CRM solutions are designed to dig deep into a company’s historical customer information and expose patterns of behavior on which a company can capitalize. Analytical CRM is primarily used to enhance and support decision making and works by identifying patterns in customer information collected form the various operational CRM systems. 

CUSTOMER RELATIONSHIP MANAGEMNET SUCCESS FACTORS

- CRM success factors include:

• Clearly communicate the CRM strategy 
• Define information needs and flows 
• Build an integrated view of the customer 
• Implement in iterations- avoid big-bang approach 
• Scalability for organization growth 

CHAPTER 10 - EXTENDING THE ORGANIZATION-SUPPLY CHAIN MANAGEMENT

SUPPLY CHAIN MANAGEMENT

• The average company spends nearly half of the every dollars that it earns on production
• In  the past, companies focused primarily on manufacturing and quality improvement to influence their supply chains.

BASICS OF SUPPLY CHAIN

The supply chain has three main like :

1. Materials flow from suppliers and their "upstream" suppliers at all levels
2.Transformation of materials into semi finished and finished products through the organization's   own production process
3.Distribution of products to customers and their "downstream" customers at all levels

• Organizations must embrace technologies that can effectively manage supply chains.

 

 

 

 

 

INFORMATION TECHNOLOGY'S ROLE IN THE SUPPLY CHAIN

• IT's primary role is to create integration or tight process and information linkages between functions within a firm.

 

 

 

 

 

 

VISIBILITY

 

• Supply chain visibility- the ability to view all areas up and down the supply chain
• Bullwhip effect- occurs when distorted product demand information passes from one entity to the next throughout the supply chain

 

CONSUMER BEHAVIOR

 

• Companies can respond faster and more effectively to consumer demands through supply chain enhances
• Demand planning software -generates demand forecasts using statistical tools and forecasting techniques

COMPETITION

 

• Supply chain planning (SCP) software -uses advanced algorithms to improve the flow and efficiency of the supply chain
• Supply chain execution (SCE) software -automates the different steps and stages of the supply chain.

 

 

 

 

SPEED

 

• Three factors fostering speed

 

 

SUPPLY CHAIN MANAGEMENT SUCCES FACTORS

 

 

 

 

 

• SCM industry best pratices include :
• Make the sale to suppliers
• Wean employees off traditional business practices
• Ensures the SCM systems supports the organizational goals
• Deploy in incremental phases and measure and communicate success
• Be future oriented

SCM SUCCES STORIES

 

• Top reasons why more and more executives are turning to SCM to manage their extended enterprises 

 

 

 

 

SCM SUCCES STRORIES



 

• Numerous decision support system (DSSs) are being built to assist decision makers in the design and operation of integrated supply chains
• DSSs allow managers to examine performances and relationships over the supply chain and among :
• -Supplier, Manufacture, Distributors and Other factors that optimize supply chain performance

 

 

 

 

 

 

 

 

 

 



 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

CHAPTER 9 -ENABLING THE ORGANIZATION - DECISION MAKING

CHAPTER 9: ENABLING THE ORGANIZATION -DECISION MAKING

 

DECISION MAKING

Reasons for growth of decision-making information systems.

• People need to analyze large amounts of information.
• People must make decisions quickly.
• People must apply sophisticated analysis techniques, such as modeling and forecasting, to make good decisions.
• People must protect the corporate asset of organizational information.

MODEL

-A simplified representation or abstraction of reality.

-IT system is an enterprise.

TRANSACTION PROCESSING SYSTEMS

• Moving up through the organizational pyramid users move from requiring transaction information to analytically information.

• Transaction Processing System - The basic business system that serves the operational level (analysts) in an organization.
• Online Transaction Processing (OLTP) - The capturing of transaction and event information using technology to (1) process the information according to defined business rules, (2) store the information, (3) update existing information to reflect the new information. more to save the information.
• Online Analytically Processing (OLAP) - The manipulation of information to create business intelligence in support of strategic decision making. do the information for decision making. more to analysis.

 

DECISION SUPPORT SYSTEMS (DSS)

 

Models information to support managers and business professionals during the decision-making process (MANAGERS)

Describe the three quantitative models typical used by decision support systems:

• Three quantitative models used by DSS's include:

Sensitivity analysis. - A special case of what-if analysis, is the study of the impact on the other variables when one variables is changed repeatedly.

What-if analysis. - Checks the impacts of a change in a variable or assumption on that model.

Goal - seeking analysis. - Finds the inputs necessary to achieve goal such as a desired level of output.

 Interaction between a TPS and a DSS

EXECUTIVE INFORMATION SYSTEMS

A specialized DSS that supports senior level executives within the organization (EXECUTIVES)

•  Most EISs offering the following capabilities:

Consolidation - The aggregation of data from simple-\ roll-ups to complex groupings of interrelated information.

Drill-down - Enables users to view details, and details of details, of information.

Slice-and-dice -  The ability to look at information from different perspectives.

Interaction between a TPS and EIS 

•  Digital dashboard - integrates information from multiple components and present it in a unified display.

 ARTIFICAL INTELLIGENCE (AI)

The ultimate goal of AI is the ability to build a system that can mimic human intelligence.

List and describe four types of artificial intelligence systems:

❤Four most common categories of AI include;

Expert System – Computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems. Eg: Playing Chess.

Neural Network – Attempts to emulate the way the human brain works. Eg: Finance industry uses neural network to review loan applications and create patterns or profiles of applications that fall into two categories – approved or denied.

Fuzzy Logic – A mathematical method of handling imprecise or subjective information. Eg: Washing machines that determine by themselves how much water to use or how long to wash.

Genetic Algorithm – An artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem. Eg: Business executives use genetic algorithm to help them decide which combination of projects a firm should invest.

Intelligent Agent – Special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users which is Multi-agent systems and Agent-based modeling.Eg:  Shopping bot Software that will search several retailer’s websites and provide a comparison of each retailers’s offering including price and availability.

DATA MINING

Data-mining software includes many forms of AI such as neural networks and expert systems.

Common forms of data-mining analysis capabilities include;

• Cluster Analysis.
• Association Detection.
• Statistical Analysis.

CLUSTER ANALYSIS.

• Cluster Analysis – A technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible.
• CRM systems depend on cluster analysis to segment customer information and identify behavioral traits. Eg: Consumer goods by content, brand loyalty or similarity.

 

 

 

ASSOCIATION DETECTION

• Association Detection – Reveals the degree to which variables are related and the nature and frequency of these relationships in the information.
• Market Basket Analysis – Analyzes such items as Web sites and checkout scanner information to detect customers’ buying behavior and predict future behavior by identifying affinities among customers’ choices of products and services. Eg: Maytag uses association detection to ensure that each generation of appliances is better than the previous generation.

 

STATISTICAL ANALYSIS

• Statistical Analysis – Performs such functions as information correlations, distributions, calculations, and variance analysis.
• Forecast – Predictions made on the basis of time-series information.
• Time-series Information – Time-stamped information collected at a particular frequency. Eg: Kraft uses statistical analysis to assure consistent flavor, color, aroma, texture, and appearance for all of its lines of foods.

CHAPTER 8 - DATA WAREHOUSE

What is Data Warehouse?

Ø  Defined in many different ways, but not rigorously

-          A decision support database that is maintained separately from the organization’s operational database.

-          A consistent database source that bring together information from multiple sources for decision support queries.

-          Support information processing by providing a solid platform of consolidated, historical data for analysis.

History of Data Warehousing

Ø  In the 1990’s executives became less concerned with the day-to-day business operations and more concerned with overall business functions

Ø  The data warehouse provided the ability to support decision making without disrupting the day-to-day operations, because;

-          Operational information is mainly current – does not include the history for better decision making

-          Issues of quality information

-          Without information history, it is difficult to tell how and why things change over time

Data warehouse fundamentals

Ø  Data warehouse – A logical collection of information – gathered from many different operational databases – that supports business analysis activities and decision-making takes

Ø  The primary purpose of a data warehouse is to combined information throughout an organization into a single repository for decision-making purposes – data warehouse support only analytical processing

Data warehouse model

Ø  Extraction, transformation and loading (ETL) – A process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse.

Ø  Data warehouse then send subsets of the information to data mart.

Ø  Data mart – contains a subset of data warehouse information.

Multidimensional Analysis and Data Mining 

Ø  Relational Database contains information in a series of two-dimensional tables.

Ø  In a data warehouse and data mart, information is multidimensional, it contains layers of columns and rows

-          Dimension – A particular attribute of information

Ø  Cube – common term for the representation of multidimensional information

Ø  Once a cube of information is created, users can begin to slice and dice the cube to drill down into the information.

Ø  Users can analyze information in a number of different ways and with number of different dimensions.

Ø  Data Mining – the process of analyzing data to extract information not offered by the raw data alone. Also known as “knowledge discovery” – computer-assisted tools and techniques for sifting through and analyzing vast data stores in order to finds trends, patterns and correlations that can guide decision making and increase understanding

Ø  To perform data mining users need data-mining tools

-          Data-mining tool – uses a variety of techniques to finds patterns and relationships in large volumes of information. Eg: retailers and use knowledge of these patterns to improve the placement of items in the layout of a mail-order catalog page or Web page.

Information Cleansing or Scrubbing

Ø  An organization must maintain high-quality data in the data warehouse

Ø  Information cleansing or scrubbing – A process that weeds out and fixes or discards inconsistent, incorrect or incomplete information

Ø  Occurs during ETL process and second on the information once if is in the data warehouse

Ø  Contract information in an operational system

Ø  Standardizing Customer  name from Operational Systems

Ø  Information cleansing activities

-          Missing Records or Attributes

-          Redundant Records

-          Missing Keys or Other Required Data

-          Erroneous Relationships or References

-          Inaccurate Data

Ø  Accurate and complete information

Business Intelligence 

Ø  Business Intelligence – refers to applications and technologies that are used to gather, provides access, analyze data and information to support decision making efforts

Ø  These systems will illustrate business intelligence in the areas of customer profiling, customer support, market research, market segmentation, product profitability, statistical analysis, and inventory and distribution analysis to name a few

Ø  Eg; Excel, Access

Question?

1.       Describe the roles and purposes of data warehouse and data marts in an organization

2.       Compare the multidimensional nature of data warehouses (and data  marts) with the two-dimensional nature of databases

3.       Identify the information of ensuring the cleanliness of information throughout an organization

4.       Explain the relationship between business intelligence and a data warehouse

CHAPTER 7 - STORING ORGANIZATIONAL INFORMATION - DATABASE

RELATIONAL DATABASE FUNDAMENTALS

Information is everywhere in an organization

Information is stored in databases

·         Database – maintains information about various types of objects (inventory), events (transactions), people (employees), and places (warehouses)

Database models include:

·         Hierarchical database model – information is organized into a tree-like structure (using parent/child relationships) in such a way that it cannot have too many relationships

·         Network database model – a flexible way of representing objects and their relationships

·         Relational database model – stores information in the form of logically related two-dimensional tables

Entities and Attributes

Entity – a person, place, thing, transaction, or event about which information is stored

Attributes (fields, columns) – characteristics or properties of an entity class

Keys and Relationships

Primary key – a field (or group of fields) that uniquely identifies a given entity in a table

Foreign key – a primary key of one table that appears an attribute in another table and acts to provide a logical relationship among the two tables

RELATIONAL DATABASE ADVANTAGES

Database advantages from a business perspective include

·         Increased flexibility

·         Increased scalability and performance

·         Reduced information redundancy

·         Increased information integrity (quality)

·         Increased information security

Increased Flexibility

A well-designed database should:

•         Handle changes quickly and easily

•         Provide users with different views

•         Have only one physical view

Physical view – deals with the physical storage of information on a storage device

•         Have multiple logical views

Logical view – focuses on how users logically access information

Increased Scalability and Performance

A database must scale to meet increased demand,  while maintaining acceptable performance levels

•         Scalability – refers to how well a system can adapt to increased demands

•         Performance – measures how quickly a system performs a certain process or transaction

Reduced Information Redundancy

•          One of the primary goals of a database is to eliminate information redundancy by recording each piece of information in only one place

•          Databases reduce information redundancy

Redundancy – the duplication of information or storing the same information in multiple places

•          Inconsistency is one of the primary problems with redundant information

Increase Information Integrity (Quality)

•         Information integrity – measures the quality of information

•         Integrity constraint – rules that help ensure the quality of information

Increased Information Security

•         Information is an organizational asset and must be protected

•         Databases offer several security features including:

Password – provides authentication of the user

Access level – determines who has access to the different types of information

Access control – determines types of user access, such as read-only access

DATABASE MANAGEMENT SYSTEMS

software through which users and application programs interact with a database

DATA-DRIVEN WEB SITES

A data-driven Web site is an interactive Web site kept constantly updated and relevant to the needs of its customers through the use of a database. Data-driven Web sites are especially useful when the site offers a great deal of information, products, or services. Web site visitors are frequently angered if they are buried under an avalanche of information when searching a Web site. A data-driven Web site invites visitors to select and view what they are interested in by inserting a query, which the Web site then analyzes and custom builds a Web page in real-time that satisfies the query. The figure displays a Wikipedia user querying business intelligence and the database sending back the appropriate Web page that satisfies the user’s request.

Data-Driven Web Site Business Advantages

•          Development: Allows the Web site owner to make changes any time—all without having to rely on a developer or knowing HTML programming. A well-structured, data-driven Web site enables updating with little or no training.

•          Content management: A static Web site requires a programmer to make updates. This adds an unnecessary layer between the business and its Web content, which can lead to misunderstandings and slow turnarounds for desired changes.

•          Future expandability: Having a data-driven Web site enables the site to grow faster than would be possible with a static site.  Changing the layout, displays, and functionality of the site (adding more features and sections) is easier with a data-driven solution.

•          Minimizing human error: Even the most competent programmer charged with the task of maintaining many pages will overlook things and make mistakes. This will lead to bugs and inconsistencies that can be time consuming and expensive to track down and fix. Unfortunately, users who come across these bugs will likely become irritated and may leave the site. A well-designed, data-driven Web site will have ”error trapping” mechanisms to ensure that required information is filled out correctly and that content is entered and displayed in its correct format.

•          Cutting production and update costs: A data-driven Web site can be updated and ”published” by any competent data entry or administrative person. In addition to being convenient and more affordable, changes and updates will take a fraction of the time that they would with a static site. While training a competent programmer can take months or even years, training a data entry person can be done in 30 to 60 minutes.

•          More efficient:  By their very nature, computers are excellent at keeping volumes of information intact. With a data-driven solution, the system keeps track of the templates, so users do not have to. Global changes to layout, navigation, or site structure would need to be programmed only once, in one place, and the site itself will take care of propagating those changes to the appropriate pages and areas. A data-driven infrastructure will improve the reliability and stability of a Web site, while greatly reducing the chance of ”breaking” some part of the site when adding new areas.

•          Improved Stability: Any programmer who has to update a Web site from ”static” templates must be very organized to keep track of all the source files. If a programmer leaves unexpectedly, it could involve re-creating existing work if those source files cannot be found. Plus, if there were any changes to the templates, the new programmer must be careful to use only the latest version. With a data-driven Web site, there is peace of mind, knowing the content is never lost—even if your programmer is.

Integrating Information among Multiple Databases

•          Integration – allows separate systems to communicate directly with each other

Forward integration – takes information entered into a given system and sends it automatically to all downstream systems and processes

Backward integration – takes information entered into a given system and sends it automatically to all upstream systems and processes

•          One of the biggest benefits of integration is that organizations only have to enter information into the systems once and it is automatically sent to all of the other systems throughout the organization

•          This feature alone creates huge advantages for organizations because it reduces information redundancy and ensures accuracy and completeness

•          Without integrations an organization would have to enter information into every single system that requires the information from marketing and sales to billing and customer service

Integrating Information among Multiple Databases

Building a central repository specifically for integrated information

•          The above figure displays an example of customer information integrated using this method

•          Users can create, read, update, and delete in the main customer repository, and it is automatically sent to all of the other databases

•          This method does not follow the business process when building the integrations

•          Business-critical integrity constraints still need to be built to ensure information is only ever entered into the customer repository, otherwise the information will become out-of-sync