Chapter 1 Introduction to Business Analytics PDF

Chapter 1 Introduction to Business Analytics Lecturer: Nguyen Van Dung Ph.D. Slides are based on slides accompanied the book “Business Analytics: Methods, Models, and Decisions”, with improvement from the lecturer  Business Analytics (Business) Analytics is the us...

Chapter 1 Introduction to Business Analytics Lecturer: Nguyen Van Dung Ph.D. Slides are based on slides accompanied the book “Business Analytics: Methods, Models, and Decisions”, with improvement from the lecturer  Business Analytics (Business) Analytics is the use of:  data,  information technology,  statistical analysis,  quantitative methods, and  mathematical or computer-based models to help managers gain improved insight about their business operations and make better, fact- based decisions. Business analytics is “a process of transforming data into actions through analysis and insights in the context of organizational decision making and Examples of Applications  Pricing ◦ setting prices for consumer and industrial goods, government contracts, and maintenance contracts  Customer segmentation ◦ identifying and targeting key customer groups in retail, insurance, and credit card industries  Merchandising ◦ determining brands to buy, quantities, and allocations  Location ◦ finding the best location for bank branches and ATMs, or where to service industrial equipment  Social Media ◦ understand trends and customer perceptions; assist marketing managers and product designers Evolution of Business Analytics  Business intelligence (collection, management, analysis, and reporting of data)  Information Systems  Statistics  Operations research/Management science  Decision support systems  Perhaps the most influential development in the use of business analytics have been the personal computer and spreadsheet technology. A Visual Perspective of Business Analytics Impacts and Challenges  Benefits ◦ …reduced costs, better risk management, faster decisions, better productivity and enhanced bottom-line performance such as profitability and customer satisfaction.  Challenges ◦ …lack of understanding of how to use analytics, competing business priorities, insufficient analytical skills, difficulty in getting good data and sharing information, and not understanding the benefits versus perceived costs of analytics studies. Scope of Business Analytics  Descriptive analytics: the use of data to understand past and current business performance and make informed decisions  Predictive analytics: predict the future by examining historical data, detecting patterns or relationships in these data, and then extrapolating (ngoại suy, suy luận ra) these relationships forward in time.  Prescriptive analytics (Phân tích đề xuất): identify the best alternatives to minimize or maximize some objective Tools  Database queries and analysis  Dashboards to report key performance measures  Data visualization  Statistical methods  Spreadsheets and predictive models  Scenario and “what-if” analyses  Simulation  Forecasting  Data and text mining  Optimization  Social media, web, and text analytics Example 1.1: Retail Markdown Decisions  Most department stores clear seasonal inventory by reducing prices.  Key question: When to reduce the price and by how much to maximize revenue?  Potential applications of analytics:  Descriptive analytics: examine historical data for similar products (prices, units sold, advertising, …)  Predictive analytics: predict sales based on price  Prescriptive analytics: find the best sets of pricing and advertising to maximize sales revenue Software Support  IBM Cognos Express ◦ An integrated business intelligence and planning solution designed to meet the needs of midsize companies, provides reporting, analysis, dashboard, scorecard, planning, budgeting and forecasting capabilities.  SAS Analytics ◦ Predictive modeling and data mining, visualization, forecasting, optimization and model management, statistical analysis, text analytics, and more.  Tableau Software ◦ Simple drag and drop tools for visualizing data from spreadsheets and other databases. Data for Business Analytics  Data: numerical or textual facts and figures that are collected through some type of measurement process.  Information: result of analyzing data; that is, extracting meaning from data to support evaluation and decision making. Examples of Data Sources and Uses  Annual reports  Accounting audits  Financial profitability analysis  Economic trends  Marketing research  Operations management performance  Human resource measurements  Web behavior  page views, visitor’s country, time of view, length of time, origin and destination paths, products they searched for and viewed, products purchased, what reviews they read, and many others. Data Sets and Databases  Data set - a collection of data. ◦ Examples: Marketing survey responses, a table of historical stock prices, and a collection of measurements of dimensions of a manufactured item.  Database - a collection of related files containing records on people, places, or things. ◦ A database for an online retailer sells instructional fitness books and DVDs, for instance, might consist of a file for three entities: publishers from which goods are purchased, customer sales transactions. Example 1.2: A Sales Transaction Database File A database file is usually organized in a two- dimensional table, where the columns correspond to each individual element of data (called fields, or attributes), and the rows represent records of related data elements. Records Entities Fields or Attributes Big Data  Big data to refer to massive amounts of business data from a wide variety of sources, much of which is available in real time, and much of which is uncertain or unpredictable. IBM calls these characteristics volume, variety, velocity, and veracity.  “The effective use of big data has the potential to transform economies, delivering a new wave of productivity growth and consumer surplus. Using big data will become a key basis of competition for existing companies, and will create new competitors who are able to attract employees that have the critical skills for a big data world.” - McKinsey Global Institute, 2011 Metrics and Data Classification  Metric (đơn vị đo lường) - a unit of measurement that provides a way to objectively quantify performance.  Measurement (sự đo lường) - the act of obtaining data associated with a metric.  Measures (giá trị đo lường ra) - numerical values associated with a metric. Types of Metrics  Discrete (rời rạc) metric - one that is derived from counting something. ◦ For example, a delivery is either on time or not; an order is complete or incomplete; or an invoice can have one, two, three, or any number of errors. Some discrete metrics would be the proportion of on-time deliveries; the number of incomplete orders each day, and the number of errors per invoice.  Continuous metrics are based on a continuous scale of measurement. ◦ Any metrics involving dollars, length, time, volume, or weight, for example, are continuous.  Measurement Scales  Categorical (phân loại)/ (nominal) (định danh) data - sorted into categories according to specified characteristics.  Example: a firm’s customers might be classified by their geographical region (North America, South America, Europe, and Pacific)  Ordinal (thứ tự) data - can be ordered or ranked according to some relationship to one another.  Example: Rating a service as poor, average, good, very good, or excellent Measurement Scales  Ordinal data have no fixed units of measurement ==> cannot make meaningful numerical statements about differences between categories. We cannot say that the difference between excellent and very good is the same as between good and average. Measurement Scales  Interval (khoảng) data - ordinal but have constant differences between observations and have arbitrary zero points.  Examples are time and temperature. Time is relative to global location, and calendars have arbitrary starting dates.  Both the Fahrenheit and Celsius scales represent a specified measure of distance—degrees—but have no natural zero. Thus we cannot take meaningful ratios; for example, we cannot say that 50 degrees is twice as hot as 25 degrees Measurement Scales  Ratio (tỷ lệ) data - continuous and have a natural zero.  For example, the measure dollars has an absolute zero. Ratios of dollar figures are meaningful. For example, knowing that the Seattle region sold $12 million in March whereas the Tampa region sold $6 million means that Seattle sold twice as much as Tampa.  This classification is hierarchical in that each level includes all the information content of the one preceding it. Copyright © 2016 Pearson Education, Inc. publishing as Prentice Hall 1-22  al rv te In al Example 1.3: Classifying Data Elements rv te In tio Ra tio Ra tio Ra tio Ra al ric go te Ca al ric go te Ca al in rd O al ric go te Ca Data Reliability and Validity  Reliability (Ổn định/tin cậy) - data are accurate and consistent.  Validity (Chuẩn xác) - data correctly measures what it is supposed to measure.  Examples: ◦ A tire pressure gage that consistently reads several pounds of pressure below the true value is not reliable, although it is valid because it does measure tire pressure. Data Reliability and Validity  Examples: ◦ The number of calls to a customer service desk might be counted correctly each day (and thus is a reliable measure) but not valid if it is used to assess customer dissatisfaction, as many calls may be simple queries. Data Reliability and Validity  Examples: ◦ A survey question that asks a customer to rate the quality of the food in a restaurant may be neither reliable (because different customers may have conflicting perceptions) nor valid (if the intent is to measure customer satisfaction, as satisfaction generally includes other elements of service besides food). Models in Business Analytics  Model - an abstraction or representation of a real system, idea, or object.  Captures the most important features  Can be a written or verbal description, a visual representation, a mathematical formula, or a spreadsheet. Example 1.4: Three Forms of a Model The sales of a new product, such as a first-generation iPad or 3D television, often follow a common pattern. 1. Verbal description: The rate of sales starts small as early adopters begin to evaluate a new product and then begins to grow at an increasing rate over time as positive customer feedback spreads. Eventually, the market begins to become saturated (bão hòa) and the rate of sales begins to decrease. Example 1.4 (continued) 2. Visual model: A sketch of sales as an S-shaped curve over time Example 1.4 (continued) 3. Mathematical model: S = aebect where S is sales, t is time, e is the base of natural logarithms, and a, b and c are constants. Influence Diagrams  Influence diagram - a visual representation of a descriptive model that shows how the elements of the model influence, or relate to, others.  An influence diagram is a useful approach for conceptualizing the structure of a model and can assist in building a mathematical or spreadsheet model. Example 1.5: An Influence Diagram for Total Cost Basic Expanded Example 1.6: Building a Mathematical Model  total cost = fixed cost + variable cost (1.1)  variable cost = unit variable cost × quantity produced (1.2)  total cost = fixed cost + variable cost = fixed cost + unit variable cost × quantity produced (1.3) Mathematical model:  TC = Total Cost  F = Fixed cost  V = Variable unit cost  Q = Quantity produced  TC = F +VQ (1.4) Decision Models  Decision model - a logical or mathematical representation of a problem or business situation that can be used to understand, analyze, or facilitate making a decision.  Inputs: ◦ Data, which are assumed to be constant for purposes of the model. ◦ Uncontrollable variables, which are quantities that can change but cannot be directly controlled by the decision maker. ◦ Decision variables, which are controllable and can be selected at the discretion of the decision maker. Nature of Decision Models Example 1.7 A Break-Even Decision Model TC(manufacturing) = $50,000 + $125*Q TC(outsourcing) = $175*Q Breakeven Point: TC(manufacturing) = TC(outsourcing) $50,000 + $125 × Q = $175 × Q $50,000 = 50 × Q Q = 1,000  General Formula F + VQ = CQ Q = F/(C - V) (1.5) Example 1.8: A Sales-Promotion Decision Model  In the grocery industry, managers typically need to know how best to use pricing, coupons and advertising strategies to influence sales. Example 1.8: A Sales-Promotion Decision Model  Grocers often study the relationship of sales volume to these strategies by conducting controlled experiments to identify the relationship between them and sales volumes.  That is, they implement different combinations of pricing, coupons, and advertising, observe the sales that result, and use analytics to develop a predictive model of sales as a function of these decision strategies. Example Model Sales = 500 – 0.05(price) + 30(coupons) + 0.08(advertising) + 0.25(price)(advertising) If the price is $6.99, no coupons are offered, and no advertising is done (the experiment corresponding to week 1), the model estimates sales as Sales = 500 - 0.05 × $6.99 + 30 × 0 + 0.08 × 0 + 0.25 × $6.99 × 0 = 500 units Model Assumptions (Giả định)  Assumptions are made to ◦ simplify a model and make it more tractable; that is, able to be easily analyzed or solved. ◦ better characterize historical data or past observations.  The task of the modeler is to select or build an appropriate model that best represents the behavior of the real situation.  Example: economic theory tells us that demand for a product is negatively related to its price. Thus, as prices increase, demand falls, and vice versa (modeled by price elasticity — the ratio of the percentage change in demand to the percentage change in price). Example 1.9: A Linear Demand Prediction Model As price increases, demand falls. Example 1.10 A Nonlinear Demand Prediction Model Assumes price elasticity is constant (constant ratio of % change in demand to % change in price) Uncertainty and Risk  Uncertainty is imperfect knowledge of what will happen in the future.  Risk is associated with the consequences of what actually happens.  “To try to eliminate risk in business enterprise is futile (vô ích). Risk is inherent in the commitment of present resources to future expectations. Indeed, economic progress can be defined as the ability to take greater risks. The attempt to eliminate risks, even the attempt to minimize them, can only make them irrational and unbearable. It can only result in the greatest risk of all: rigidity.” – Peter Drucker Prescriptive Decision Models  Prescriptive (đề xuất) decision models help decision makers identify the best solution.  Optimization - finding values of decision variables that minimize (or maximize) something such as cost (or profit).  Objective function - the equation that minimizes (or maximizes) the quantity of interest.  Constraints - limitations or restrictions.  Optimal solution - values of the decision variables at the minimum (or maximum) point. Example 1.11: A Prescriptive Pricing Model  A firm wishes to determine the best pricing for one of its products in order to maximize revenue.  Analysts determined the following model: Sales = -2.9485(price) + 3240.9 Total revenue = (price)(sales) = price × (-2.9485 × price + 3240.9) = 22.9485 × price2 + 3240.9 × price  Identify the price that maximizes total revenue, subject to any constraints that might exist. Types of Prescriptive Models  Deterministic model (Mô hình tất định) – all model input information is known with certainty.  Stochastic model (Mô hình ngẫu nhiên) – some model input information is uncertain. ◦ For instance, suppose that customer demand is an important element of some model. We can make the assumption that the demand is known with certainty; say, 5,000 units per month (deterministic). On the other hand, suppose we have evidence to indicate that demand is uncertain, with an average value of 5,000 units per month, but which typically varies between 3,200 and 6,800 units (stochastic). Problem Solving With Analytics 1. Recognizing a problem 2. Defining the problem 3. Structuring the problem 4. Analyzing the problem 5. Interpreting results and making a decision 6. Implementing the solution Recognizing a Problem Problems exist when there is a gap between what is happening and what we think should be happening.  For example, costs are too high compared with competitors. Defining the Problem  Clearly defining the problem is not a trivial (tầm thường) task.  Complexity increases when the following occur: - large number of courses of action - the problem belongs to a group and not an individual - competing objectives - external groups are affected - problem owner and problem solver are not the same person - time limitations exist Structuring the Problem  Stating goals and objectives  Characterizing the possible decisions  Identifying any constraints or restrictions Analyzing the Problem  Analytics plays a major role.  Analysis involves some sort of experimentation or solution process, such as evaluating different scenarios, analyzing risks associated with various decision alternatives, finding a solution that meets certain goals, or determining an optimal solution. Interpreting Results and Making a Decision  Models cannot capture every detail of the real problem  Managers must understand the limitations of models and their underlying assumptions and often incorporate judgment into making a decision. Implementing the Solution  Translate the results of the model back to the real world.  Requires providing adequate resources, motivating employees, eliminating resistance to change, modifying organizational policies, and developing trust. Fun with Analytics www.puzzlOR.com  Maintained by an analytics manager at ARAMARK.  Each month a new puzzle is posted.  Many puzzles can be solved using techniques you will learn in this book.  The puzzles are fun challenges.  A good one to start with is SurvivOR (June 2010).  Have fun!