CHAPTER 11

CHAPTER 1 - BUILDING A CUSTOMER-CENTRIC ORGANIZATION - CUSTOMER RELATIONSHIP MANAGEMENT 

CUSTOMER RELATIONSHIP MANAGEMENT (CRM)

CRM enables an organization to;

Ø   Provide better customer service

Ø  Make call centers more efficient

Ø  Cross sell products more effectively

Ø  Helps sales staff close deals faster

Ø  Simplify marketing and sales processes

Ø  Discover new customers

Ø  Increase customer revenues

RECENCY, FREQUENCY AND MONETARY VALUE

An organization can find its most valuable customers by using a formula that industry insiders call FRM;

Ø  How recently a customer purchased items (recency)

Ø  How frequently a customer purchased items (frequency)

Ø  How much a customer speeds on each purchased (monetary value)

THE EVALUATION OF CRM

CRM reporting technologies help organizations identify their customers across other applications. CRM analysis technologies help organizations segment their customers into categories such as best and worst customers. CRM predicting technologies help organizations predict customer behavior, such as which customers are at risk of leaving. 

THE UGLY SIDE OF CRM: WHY CRM MATTERS MORE NOW THAN EVER BEFORE

CUSTOMER RELATIONSHIP MANAGEMENT’S EXPLOSIVE GROWTH

USING ANALYTICAL CRM TO ENHANCE DECISION 

Ø  Operational CRM – supports traditional transactional processing for day-to-day front-office operations or systems that deal directly with the customers

Ø  Analytical CRM – supports back-office operations and strategic analysis and includes all system that do not deal directly with the customers

CUSTOMER RELATIONSHIP MANAGEMENT SUCCESS FACTORS

CRM success factors include;

Ø  Clearly communicate the CRM strategy

Ø  Define information needs and flows

Ø  Build an integrated view of the customer

Ø  Implement in iterations

Ø  Scalability for organizational growth

USING ANALYTICAL CRM TO ENHANCE DECISION

Operational CRM and analytical CRM

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chapter 10

CHAPTER 10 - EXTENDING THE ORGANIZATION - SUPPLY CHAIN MANAGEMENT

Supply chain management 
-The average company spends nearly half of every dollar that it earns on production

- In the past, companies focused primarily on manufacturing and quality improvements to influence their supply chains

BASICS OF SUPPLY CHAIN

SCM – the management of information flows between and among stages in a supply chain to maximize total supply chain effectiveness and profitability

The supply chain has three main links.

1.       Materials flows from suppliers and their upstream suppliers at all levels

2.       Transformation of materials into semi-finished products, or the organization’s own production processes

3.       Distribution of products to customers and their downstream customers at all levels

INFORMATION TECHNOLOGY’S ROLE IN THE SUPPLY CHAIN

 Information technology’s primary role in SCM is creating the integrations or tight process and information linkages between functions within a firm such as marketing, sales, finance, manufacturing, and distribution – and between firms, which allow the smooth, synchronized flow of both information and product between customers, suppliers and transportation providers across the supply chain

VISIBILITY 

·         Supply Chain Visibility is the ability to view all areas up and down the supply chain. Changing supply chains requires a comprehensive strategy buoyed by information technology. Organizations can use technology tools that help them integrate upstream and downstream, with both customers and suppliers.

·         The bullwhip effect occurs when distorted product demand information passes from one entity to the next throughout the supply chain.

CUSTOMER BEHAVIOR

·         The behavior of customers has changed the way businesses complete. Customers will leave if a company does not continually meet their expectations. They are more demanding because they have information readily available, they know exactly what they want, and they know when and how they want it.

·         Demand planning software generates demand forecasts using statistical tools and forecasting techniques. Companies can respond faster and more effectively to consumer demands through supply chain enhancements such as demand planning software.

·         Once an organization understands customer demand and its effect on the supply chain it can begin to estimate the impact that its supply chain will have on its customers and ultimately the organization’s performance.

COMPETITION

·         Supply chain planning (SCP) software uses advanced mathematical algorithms to improve the flow and efficiency of the supply chain while reducing inventory. SCP depends entirely on information for its accuracy.

·         Supply chain execution (SCE) software automates the different steps and stages of the supply chain. This could be as simple as electronically routing orders from a manufacturer to a supplier.

SPEED 

·         These systems raise the accuracy, frequency and speed of communication between suppliers and customers, as well as between internal users.

·         Another aspect of speed is the company’s ability to satisfy continually changing customer requirements efficiently, accurately and quickly.

SUPPLY CHAIN MANAGEMENT SUCCESS FACTORS

SCM industy best practices include:

1. Make the sale to suppliers

2. Wean employees off traditional business practices 

3. Ensure the SCM system supports the organizational goals 

4. Deploy in incremental phases and measure and communicate success

5. Be future oriented

SCM Success Stories

Numerous decision support systems (DSSs) are being built to assist decision makers in the design and operation of integrated supply chains

DSSs allow managers to examine performance and relationships over the supply chain and among:

• suppliers
•  Manufacturers
• Distributors
• Other factors that optimize supply chain performance

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chapter 9

 CHAPTER 9 - ENABLING THE ORGANIZATION - DECISION MAKING

Decision Making

Ø  Reasons for Growth of Decision Making Information System

-          People need to analyze large amounts of information – Improvements in technology itself, innovations in communication, and globalization have resulted in a dramatic increase in the alternatives and dimensions people need to consider when making a decision or appraising an opportunity

-          People must make decisions quickly – Time is of the essence and people simply do not have time to sift through all the information manually

-          People must apply sophisticated analysis techniques, such as modeling and forecasting, to  make good decisions – Information systems substantially reduce the time required to perform these sophisticated analysis techniques

-          People must protect the corporate asset of organizational information – Information systems offer the security required to ensure organizational information remains safe.

Ø  Model – A simplified representation or abstraction of reality

Ø  IT systems in an enterprise

Transaction Processing System

Ø  Moving up through the organizational pyramid users move from requiring transactional information to analytical information

Ø  Transaction processing system – the basic business system that serves the operational level (analysis) in an organization

Ø  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

Ø  Online analytical processing (OLAP) – the manipulation of information to create business intelligence in support of strategic decision making

Decision support systems

Ø  Decision support system (DSS) – models information to support managers and business professionals during the decision-making process

Ø  Three quantitative models used by DSSs include;

1.       Sensitivity analysis – the study of the impact that changes in one (or more) parts of the model have on other parts of the model

2.       What-if analysis – checks the impact of a change in an assumption on the proposed solution

3.       Goal-seeking analysis – finds the inputs necessary to achieve a goal such as a desired level of outputs

What-if analysis

Goal-seeking analysis

Executive information system 

Ø  Executive information system (EIS) – A specialized DSS that supports senior level executives within the organization

Ø  Most EISs offering the following capabilities;

-          Consolidation – involves the aggregation of information and features simple roll-ups to complex groupings of interrelated information

-          Drill-down – enables users to get details, and details of information

-          Slice-and-dice – looks at information from different perspectives

Ø  Interaction between a TPS and an EIS

Ø  Interaction between a TPS and a DSS

Ø  Digital dashboard – integrates information from multiple components and presents it in a united display

Artificial intelligence (AI)

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

Ø  Intelligent system – various commercial applications of artificial intelligence

Ø  Artificial intelligence (AI) – simulates human intelligence such as the ability to reason and learn

Ø  Four most common categories of AI include;

1.       Expert system – computerized advisory programs that imitate the reasoning processes of experts in solving difficult problems

2.       Neural network – attempts to emulate the way the human brain works

o   Fuzzy logic – a mathematical method of handling imprecise or subjective information

3.       Genetic algorithm – an artificial intelligent system that mimics the evolutionary, survival-of-the-fittest process to generate increasingly better solutions to a problem

4.       Intelligent agent – special-purposed knowledge-based information system that accomplishes specific tasks on behalf of its users

Data Mining

Ø  Data-mining software includes many forms of AI such as neutral 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 consistence flavor, color, aroma, texture, and appearance for all of its lines of foods.

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Chapter 8

CHAPTER 8 - ACCESSING ORGANIZATIONAL INFORMATION - 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

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