Data Warehousing
& Data Mining
Wolf-Tilo Balke Silviu Homoceanu
Institut für Informationssysteme
Technische Universität Braunschweig http://www.ifis.cs.tu-bs.de
• Last week:
– Clustering
• Flat: K-means
• Hierarchical: Agglomerative, Divisive
– Clustering high-dimensional data
• CLIQUE
• This week..
Summary
12. Decision Support Systems (DSS)
DSS Applications:
12.1 Marketing Models
12.2 Supply Chain Management
12. Decision Support Systems
• Decision-making is the process of making choices. It includes:
– Assessing the problem
– Collecting and verifying information – Identifying alternatives
– Anticipating consequences of decisions
– Making the choice using sound and logical judgment based on available information
– Informing others of decision and rationale – Evaluating decisions
12.0 DSS - Introduction
• Decision problem
• What kind of decisions are there?
12.0 Decisions
options
(alternatives)
goals
• FIND the option that best satisfies the goals
• RANK options according to the goals
• ANALYSE, JUSTIFY, EXPLAIN, …, the decision
• Types of decisions
– Easy (routine, everyday) vs. difficult (complex)
– One-time vs. recurring – One-stage vs. sequential
– Single objective vs. multiple objectives – Operational, tactical, strategic
– …
• DSS address complex decisions
12.0 Decisions
• Characteristics of complex decisions
– Novelty
• There was no prior similar decision
– Unclearness
• Incomplete knowledge about the problem
– Uncertainty
• Outside events that cannot be controlled
– Multiple objectives (possibly conflicting)
• Maximize economic benefits vs. minimize environmental costs
– Important consequences of the decision – Limited resources
12.0 Complex Decisions
12.0 Decision-Making
• Decision making can be difficult for people.
Can we help decision makers make better decisions?
– Decision Support: Provides methods and tools for supporting people in making complex decisions.
How?
12.0 Decision Support
12.0 Decision Support
• Decision support systems (DSS)…
– are interactive, computer-based information systems – developed for improving the decision-making
process
• Characteristics
– DSS incorporate both data and models
– They support rather the replace managerial judgment
– Their objective is to improve the quality and
effectiveness rather then efficiency of decisions
12.0 DSS - Introduction
• Types of DSS
– Data-driven, emphasizes access to and manipulation of data e.g., time-series
– Document-driven, manages, retrieves and
manipulates unstructured information stored in electronic formats
– Knowledge-driven, provides problem solving expertise stored as rules or procedures
– Model-driven, make use of statistical or financial models and simulations
12.0 DSS - Introduction
• Technologies DSS rely on
– Data mining
– Data warehousing and OLAP – Traditional approaches
12.0 DSS - Introduction
• Data Mining
– Association rule mining
– Sequence patterns and time series – Regression analysis
– Classification – Clustering
12.0 DSS - Introduction
• Data Warehousing
– As support for OLAP
– Online Analytical Processing (OLAP)
• Traditional approaches
– Common mathematical modeling e.g., what-if-analysis – Non-rigorous modeling
– Rule-based systems (RBS)
12.0 DSS - Introduction
• DSS capabilities should offer…
– support for problem-solving phases
• Gather intelligence, identify and design the options, make the choice, implement it, monitor for feedback
– support for different decision frequencies
• Ad hoc DSS: decisions that come up once in every 5 years (e.g., where should a company open a new distribution
center?)
• Institutional DSS: decisions that repeat (e.g., what should the company invest in?)
12.0 DSS - Introduction
– support for different problem structures
• Highly structured problems: known facts and relationships
• Semi-structured problems: facts unknown or ambiguous, relations vague
– E.g., which person to promote/hire for a position?
– support for various decision-making levels
• Operational level
– Daily decisions
• Tactical level
– Planning and control
• Strategic level
12.0 DSS Capabilities
• DSS architecture
12.0 DSS - Introduction
GUI
Analytical engine
Model Management Database
Management
• The database management subsystem
– Purpose:
• Handles personal and unofficial data so that users can
experiment with alternative
solutions based on their own
judgment -
- sandbox like
• Tracks data use within the DSS
12.0 DSS Architecture
• The model management subsystem (MMS)
– Strategic models: non routine mergers, impact analysis, capital budgeting
– Tactical Models:
sales promotion planning
– Operational Models:
routine-day-to-day
production scheduling, inventory control,
quality control
12.0 DSS Architecture
• Major functions of the model manager
– Creates models either from scratch or from existing models
– Allows users to manipulate models so that they can conduct experiments and sensitive analysis e.g.,
what-if or goal seeking analysis
– Manages and maintains the model base e.g.,
• Store, access, run, update, link, catalog and query
12.0 MMS
• The analytical engine or knowledge based subsystem
– Component of more advanced DSS
– Provides expertise in solving complex
unstructured and semi-structured problems
• Expertise is provided for example by an expert system
– Analytical engines are usually based on OLAP, data mining, or expert systems
12.0 DSS Architecture
• The user interface
– Interactive, dialogue oriented – Intuitive, graphical, symbolic – Intelligent, context aware – Customizable
• For the non-technical user, the user interface is the system
12.0 DSS Architecture
• Applications of DSS
– Marketing Models
– Supply Chain Management
12.0 DSS - Introduction
• Marketing decision processes are characterized by a high level of complexity
– Simultaneous presence of multiple objectives
– Countless alternative actions resulting from the combination of the major choice options
• Massive sales transactions data are available making DSS a important tool for reaching marketing intelligence
12.1 Marketing Models
• Marketing intelligence comprises 2 prominent topics
– Relational marketing (RM)
– Sales force management (SFM)
12.1 Marketing Models
• Relational marketing as DSS application
– Designed to create, maintain, and enhance strong relationships with customers
– Application of predictive models to support relational marketing strategies
– E.g.:
• An insurance company wishes to select the most promising market segment to target for a new type of policy
• A mobile phone provider wishes to identify those
customers with the highest probability of churning
12.1 Marketing Models
• Why is RM important?
– It costs five times as much to attract a new
customer as it does to keep a current one satisfied
• Advertising doesn’t come cheap at all!
– It is claimed that a 5% improvement in customer retention can cause an increase in profitability of between 25-85% depending on the industry
– Likewise, it is easier to deliver additional products and services to an existing customer than to a first-time buyer
12.1 Relational Marketing
• RM strategies revolve around the following choices
12.1 Relational Marketing
Relational marketing
Sales processes Distribution
channels Products
Services
Segments
Prices Promotion
channels
• How do we implement RM?
– E.g., using pattern recognition and machine learning models on a company’s DW
• Derive different segmentations of the customers which are then used to
design and target marke- ting actions
12.1 Relational Marketing
• Cycle of RM analysis, phases:
1. Exploration of the data available for each customer
2. Identify market segments by using inductive learning models
3. Knowledge of customer profiles is then used to design marketing actions
4. The designed actions are
translated into promotional campaigns
which generate in turn new information for
subsequent analyses
12.1 Relational Marketing
Collect information on
customers
Plan actions based
Identify segments and needs Perform optimized
and targeted actions
• General statistics show…
– The average business never hears from 96% of its unhappy customers
• 91% never come back
• Dissatisfied customers may tell 9-10 people about their experience
– Every positive experience is told to 4-5 people
– For every complaint received the average business in fact has 26 customers with a similar concern
12.1 Customer Relations
– Of the customers who register a complaint, as many as 70% will do business again with your organization, if the complaint is resolved effectively
• This figure goes up to 95% if the complaint has been resolved quickly
– 40% of complaints are the result from customer mistakes or incorrect expectations
– A complaint that is handled efficiently is actually better than no complaint at all
• Customers who complain and get satisfactory results are 8% more loyal than the others
12.1 Customer Relations
• Important part of RM is customer relationship management (CRM)
• CRM
– The software tools which allow tracking and
analysis of each customer's purchases, preferences, activities, tastes, likes, dislikes, and complaints
– Enterprise vendors/products
• Oracle/Siebel, Salesforce.com, Amdocs, Microsoft Dynamics
– Open source tools
12.1 Customer Relations
• E.g., XRMS
– Contact
information screen
12.1 Customer Relations
• Aspects of CRM systems
– Operational – Collaborative – Analytical
12.1 Customer Relations
• Operational CRM
– Provides support to "front office" business processes, including sales, marketing and service
– Each interaction with a customer is generally added to a customer's contact history, and staff can
retrieve information on customers from the database when necessary
– Main benefits is that customers can interact with different people in a company over time without having to describe the history of their interaction each time
12.1 CRM
• Collaborative CRM
– Covers aspects of a company's dealings with customers
that are handled by various departments within a company
• E.g., sales, technical support and marketing
– Staff members from different departments can share information collected when interacting with customers
• E.g., feedback received by customer support agents can provide other staff members with information on the services and
features requested by customers
– Goal of collaborative CRM is to use information collected by all departments to improve the quality of services
12.1 CRM
• Analytical CRM
– Analyzes customer data for a variety of purposes:
• Design and execution of targeted marketing campaigns to optimize marketing effectiveness
• Design and execution of specific customer campaigns, including customer acquisition, cross-selling, up-selling, retention
• Analysis of customer behavior to aid product and service decision making e.g., pricing, new product development
• Management decisions, e.g. financial forecasting and customer profitability analysis
• Prediction of the probability of customer defection (churn)
• Acquisition? Cross-selling? Up-selling? Retention?
Churn? Let’s see the lifetime of a customer
12.1 CRM
• Lifetime of a customer
– Lost proposal
• Before becoming a customer, an individual may receive repeated proposals from the enterprise to win him/her as a customer
– Acquisition
• The individual
becomes customer
12.1 Relational Marketing
– Cross/up-selling:
getting more business from current customers by selling them additional or complementary
services
– Retention:
the continuous attempt to satisfy and keep current customers actively involved in conducting business
• Highly satisfied customers are
– Less price sensitive
– More likely to talk favorably about you – More likely to refer you to others
12.1 Lifetime of a customer
– Churn (defection):
the percentage of customers who leave a business in one year
– Interruption:
customers leaving a business. Possible reasons are that they:
• Die
• Move away
• Leave for competitive reasons
• Are dissatisfied
12.1 Lifetime of a customer
– Dissatisfied?
• United Airlines Brakes Guitars
12.1 Lifetime of a customer
• Sales force management (SFM)
– Management of the whole set of people and roles that are involved with different tasks and
responsibilities in the sales process
• Why SFM?
– It plays a critical role in:
• The profitability of an enterprise
• The implementation of the relational marketing strategy
12.1 Marketing Models
• Designing the sales network and planning agents activities involve complex decision making tasks
– Remaining activities are operational and sales force automation (SFA) software can be used
• SFM decision-making process can be grouped in 3 components each interacting with each other
– Design – Planning
– Assessment
12.1 Sales force management
Sales force management
Planning Design
• Design
– During start-up phase or during restructuring – Includes 3 types of decisions
• Organizational structure
• Sizing
• Sales territories
12.1 Sales force management
– Organizational structure
• May take different forms corresponding to hierarchical agglomerations of agents by group, products, brand or geographical area
• In order to determine the organizational structure it is
necessary to analyze the complexity of customers products and sales activities
– Decide whether and to what extent the agents should be specialized
12.1 Design
– Sizing
• Decide the number of agents that should operate in the selected structure
• Depends on several factors
– Number of customers, prospects, sales area coverage, estimated time for each call, the agents traveling time, etc.
• Conflicting goals
– Reduction in costs due to decreasing sales force size is often followed by a reduction in sales and revenues
12.1 Design
– Sales territories
• Deciding on assigning territories to agents
• Depends on factors such as
– The sales potential of the geographical areas – The time required to travel from an area to
another
– The availability time of each agent
• Purpose of assignment is to determine a balanced situation between sales opportunities in each territory to avoid
disparities among agents
12.1 Design
• Planning
– Decision-making process involving the assignment of sales resources structured and sized during design phase, to market entities
• E.g., sales resources
– Work time, budget
• E.g., market entities
– Products
– Market segments
– Distribution channels
12.1 Sales force management
• Assessment
– Measure the effectiveness and efficiency of the individuals in order to decide incentives and
remuneration schemes
• Define adequate evaluation criteria that take into
account the personal contribution of each agent having removed effects due to area or product characteristics
12.1 Sales force management
• Sales Force Automation software
– Most CRM tools include SFA functionality – Enterprise vendors/products
• Oracle/Siebel, SAP, Salesforce.com, Microsoft Dynamics, Netsuite
– Open source tools
• XRMS, SugarCRM, Vtiger
12.1 Sales force management
• For producing industries, another field of business operation is of great importance:
– Supply chain management (SCM)
• A supply chain summarizes the logistic and
production processes of a single enterprise as well as a network of companies
– Covers the flow of materials and products from the raw material down to the end product at the customer
• Contains acquisition of raw materials, production, transportation, storage,
12.2 Supply Chain Management
• Within a single company, internal supply chain can be modeled and optimized
– Contain aspects of material purchase, production and distribution
12.2 Supply Chain Management
Internal Supply Chain
Purchasing Production Distribution
Suppliers Customers
• However, global supply chains may form
complex networks of various material flows and costs
12.2 Supply Chain Management
Main Plant European Plant
Asian Plant Asian Suppliers
US Assembly
Asian Market European Market Recycling 1
Asian Assembly European Assembly
Kit Supplier European Suppliers
US Suppliers
• Supply chain management is about managing
and optimizing those complex supply networks
– Eliminating excess inventory
– Improve on-time delivery performance – Maximize the value of procurement – Minimize transport costs
– Minimize storage costs – Etc.
12.2 Supply Chain Management
• Steps of SCM
– Plan (strategic portion of SCM)
• Strategy for managing all the resources that go towards meeting customer demand
• Developing a set of metrics to monitor the performance of the supply chain so that it is efficient, costs less and delivers high quality
– Source
• Choose suppliers to deliver the goods and services
• Develop a set of pricing, delivery and payment processes with suppliers
• Create metrics for monitoring and improving the relationships
• Put together processes for managing goods and services inventory, including receiving and verifying shipments, transferring them to the manufacturing facilities and authorizing supplier payments
12.2 Supply Chain Management
– Make (manufacturing step)
• Schedule the activities necessary for production, testing, packaging and preparation for delivery
• Most metric-intensive portion of the supply - measure quality levels, production output and worker productivity
– Deliver (the logistics part)
• Coordinate the receipt of orders, develop a network of warehouses, pick carriers to get products to customers and set up an invoicing system to receive payments
– Return
• Receive and manage defective or excess products Recycle used products
12.2 Supply Chain Management
12.2 Supply Chain Management
• For solving these tasks, SCM has to span across most other enterprise management areas
– Thus, software
solutions are usually very diverse and customized
– Highly dependent on data from
all branches of business
Supply Chain Management
Supply Chain Strategy
Supply Chain Planning
Supply Chain Enterprise Applications
Asset Procurement
Product Lifecycle Management
Logistics
• The traditional approach for optimizing supply chains was severely hampered by the unavailability of
necessary data
– Thus, usually only future demand was forecast as good as possible, using statistical trending and “best fit”
techniques – Trend Analysis and Trend Channels
• Only high level data necessary (aggregated values from OLAP cubes)
– e.g. by weekly data by product category and customer group
• For dealing with unpredictability, security margins are added
• Based on the estimates, the supply chain could be optimized
– Capacity Planning
– Bill of Material problems
12.2 Supply Chain Management
• However, due to improved data warehouse strategies, more dynamic and fine-grained optimizations are possible
– Forecasting at much finer-granularity (DW allows for drilling into the data)
• e.g. calculate the best inventory level per article for each store
• So called model stock
12.2 Supply Chain Management
– Allows for new optimization techniques
• Simulation
• Stochastic models
– Include wider verity of metrics
• Stackability constraints
• Load and unloading rules
• Palletizing logic
• Warehouse efficiency
• “Shipping air” minimization
12.2 Supply Chain Management
• Decision Support Systems
– Decision Making Process – Decision Support
– Typical Applications
• Marketing Models
– Relational Marketing – Sales force management
• Supply Chain Management
Summary
Next week
• We have seen the theory, how about the praxis?
– Next week: practical problems in DW – Guest: Toma Buchinsky, CEO Adastra,
Germany.
– Adastra Corporation specialized in DW-based solutions and
Business Intelligence services.
The End
• I hope you enjoyed the lecture and learned at least some interesting stuff…
– Next semester’s master courses:
Multimedia Databases, Information Retrieval, Relational Databases 2
