Introduction to Business Analytics and Modeling PDF

Introduction to Business Analytics and Modeling ITIS 1P97: Data Analysis and Business Modelling Instructor: Leila Tahmooresnejad Copyright © 2020 Pearson Education, Inc. What is Data and Data Analysis? What is Data? Data is the collection of raw, unstructured facts or...

Introduction to Business Analytics and Modeling ITIS 1P97: Data Analysis and Business Modelling Instructor: Leila Tahmooresnejad Copyright © 2020 Pearson Education, Inc. What is Data and Data Analysis? What is Data? Data is the collection of raw, unstructured facts or statistics that can be processed for analysis. Examples: Data: Your monthly expenses—how much you spend on groceries, entertainment, and bills. Data analysis: Comparing monthly expenses over several months to see where you can cut costs. 2 Example: Data and Data Analysis Example : Imagine you are running a lemonade stand. Data could be the number of cups sold, the weather conditions, or customer feedback. Data analysis: could mean figuring out which days have the highest sales and if sunny weather correlates with more sales. 3 Why Data Analysis Matters? Enables better decision-making Helps identify trends and patterns Provides a factual basis for problem- solving 4 COURSE DESCRIPTION Data analysis and business modeling, the process of transforming input data into useful information This data processing and business modeling enhance the managers' ability to use data effectively to understand customers and markets better and improve their products and services. A basic understanding of the value of data and information and hands-on experience in data analysis data management quantitative business modelling 5 Topics Definitions and concepts The role of analytics in today’s business environment The types of analytics Descriptive, predictive, and prescriptive analytics. The relevance and use of data in business. Models and modeling Deterministic and stochastic models. Descriptive, predictive, and prescriptive models. The problem-solving process 6 Business Analytics The process of looking at and assessing the wealth of data a company already has at its disposal and using it to make data-driven decisions. The process of collating, sorting, processing, and studying business data, and using models and methodologies to transform data into business insights. 7 Data, Application, and Processes PROCESSING INSIGHT (Modeling) Information DATA Technology Statistics Computer Science Psychology ….. 8  9 Examples of Applications (1 of 3) Pricing – setting prices for consumer and industrial goods, government contracts, and maintenance contracts – Example: an airline company that uses data analysis to determine ticket prices based on factors like demand, season, and competitor pricing Customer segmentation – identifying and targeting key customer groups in retail, insurance, and credit card industries – Example: An insurance company might analyze data to identify high-risk and low-risk customer groups. Tailored policies can then be offered to each group, maximizing profitability and customer satisfaction. 9  10 Examples of Applications (2 of 3) Merchandising – determining brands to buy, quantities, and allocations – Example: A retail store might analyze past sales data to determine which brands of jeans sell the most and during which seasons, helping them make smarter inventory decisions. Location – finding the best location for bank branches and A T M s, or where to service industrial equipment – Example: A coffee shop chain might use data analysis to find locations with high foot traffic but low competition, increasing the likelihood of business success. 10  11 Examples of Applications (3 of 3) Staffing – ensuring appropriate staffing levels and capabilities, and hiring the right people – Example: seasonal trends to determine how many cashiers are needed during different shifts or seasons. Health care – scheduling operating rooms to improve utilization, improving patient flow and waiting times, purchasing supplies, and predicting health risk factors – Example : Health care providers might analyze patient data to identify common bottlenecks in the patient flow, such as long waiting times for specific tests, and take corrective actions. 11 The Benefits of Business Analytics Reduce cost or keep companies on budget Example 1: A manufacturing company uses analytics to reducing warehousing costs. Example 2: Cutting down on personnel costs Better decision making Example: A fast-food chain uses real-time analytics to adapt its menu and pricing in different locations based on immediate customer feedback. The ability to measure accomplishments against goals. Example: An IT firm uses analytics to monitor employee productivity Building efficiency. Example: A travel agency uses customer analytics to personalize offers, thereby increasing customer satisfaction and profitability. 12 The Challenges of Business Analytics Lack of understanding of how to use analytics Example: A small business invests in an analytics tool but fails to train its staff on how to use it effectively. Competing business priorities Example: A healthcare provider delays implementing predictive analytics for patient care due to budget allocation towards immediate issues like equipment purchase. Insufficient analytical skills Example: A marketing team has access to extensive data but lacks the skills to analyze it for campaign optimization. 13 The Challenges of Business Analytics Difficulty in getting good data and sharing information Example: A retail business struggles with inconsistent data collection from different branches, leading to inaccurate analysis. Not understanding the benefits versus perceived costs Example: An educational institution hesitates to adopt analytics for student performance tracking, fearing the costs outweigh the benefits. 14 A Visual Perspective of Business Analytics Analytic Foundations and Modern Business Analytic 15 Types of Business Analytics I Type TECHNIQUES & TOOLS Descriptive analytics: the Statistical measures such use of data to understand as means and standard past and current business deviations, probability performance and make distributions informed decisions What happened? What is happening? Sales Analysis What happened?: Analyzing last year's sales data to understand which products were most popular. What is happening?: Monitoring real-time sales data to observe current trends. 16  Types of Business Analytics II Type TECHNIQUES & TOOLS Predictive analytics: predict Decision analysis and the future by examining decision trees historical data, detecting Regression models patterns or relationships in Forecasting these data, and then Simulation extrapolating these relationships forward in time. What will happen? Why will it happen? Real Estate Pricing What will happen?: Estimating future property prices. Why will it happen?: Based on variables like location, amenities, and economic indicators. 17 Types of Business Analytics III Type TECHNIQUES & TOOLS Prescriptive analytics: Linear programming identify the best alternatives to minimize or maximize some objective What should I do? Why should I do? Employee Scheduling What should I do?: Assign employees to shifts in a way that meets demand while minimizing labor costs. Why should I do it?: To balance staffing needs against budget constraints 18 Example: 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 in retail: Descriptive analytics: examine historical data for similar products (price, units sold, advertising, …) Predictive analytics: predict sales based on price Prescriptive analytics: find the best sets of pricing and advertising to maximize sales revenue 19 Data for Business Analytics Data: numerical or textual facts and figures that are collected through some type of measurement process. Cross-sectional: Data collected at a single point in time, often used to compare different groups or categories. Example: Customer Satisfaction Survey, customer feedback on products or services collected during a single month. Time series: Data collected over multiple time periods, often used to track changes, identify trends, or forecast future values Example: Monthly Sales Data, revenue generated each month for the last two years. Information: result of analyzing data; that is, extracting meaning from data to support evaluation and decision making. 20 Big Data Big data refers to massive amounts of business data (volume) in different types, forms, and granularity (variety), generated at a rapid speed (real time) (velocity), and much of which is uncertain or unpredictable (veracity). Volume: Massive amounts of data. Netflix accumulating multiple terabytes of user interaction and streaming data every hour to personalize recommendations and improve user experience. Variety: Different types of data. A healthcare system collecting structured data (medical records) and unstructured data (doctor's notes). Velocity: The speed at which new data is generated. Social media platforms where hundreds of thousands of updates are posted per minute. Veracity: The uncertainty of data. User-generated content on review sites, which can be both valuable and misleading. 21  22 Data Reliability and Validity Reliability - data are accurate and consistent. Validity - data correctly measures what it is supposed to measure. 22  23 Example : Data Reliability and Validity The number of calls to a customer service desk: To assess customer dissatisfaction: Is a reliable measure? Is it valid? 23  24 Example : Data Reliability and Validity 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. 24 Models in Business Analytics Model - an abstraction or representation of a real system, idea, or object. Captures the most important features A written or verbal description, a visual representation, a mathematical formula, or a spreadsheet. A stochastic or probabilistic model presents data and predicts outcomes that account for certain levels of unpredictability or randomness. A deterministic model gives you the same exact results every time for a particular set of inputs 25 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. 26 Example: Running a Lemonade Stand Data (Constant for the model) Cost of making one glass of lemonade, assumed to be constant for a given period (e.g., $0.50 per glass). Uncontrollable Variables (Not directly controlled) Weather conditions, like unexpected rain, which can affect customer turnout. Decision Variables (Controllable) Whether to offer any promotions like "Buy One, Get One Free." 27  Example : An Outsourcing Decision Model (1 of 2) Example: An outsourcing decision model Production cost: $125/unit plus fixed cost of $50,000 Outsourcing cost: $175/unit TC (manufacturing) = $50,000 + $125 x Q TC (outsourcing) = $175 x Q Breakeven Point: TC (manufacturing) = TC (outsourcing) $50,000 + $125 x Q = $175 x Q $50,000 = 50 x Q Q = 1,000 If Q < 1,000, outsourcing is cheaper. 28 Example : An Outsourcing Decision Model (2 of 2) Which option is better if Q = production volume is 1500? Descriptive models explain behavior and allow users to evaluate potential decisions by asking “what-if?” questions. 29 Example: A sales-promotion decision model Predictive models are developed by analyzing historical data and assuming that the past is representative of the future. Example: A sales-promotion decision model In the grocery industry, managers typically need to know how best to use pricing, coupons, and advertising strategies to influence sales. 30  Example Model: A sales-promotion Price Coupon Advertising Store1 Sales Store 2 Sales Store 3 Sales Week Model: ($) (0,1) ($) (Units) (Units) (Units) Total Sales = 1105.55 + 56.18 x 1 6.99 0 0 501 510 481 Price + 123.88 x Coupon + 5.25 x 2 6.99 0 150 772 748 775 Advertising 3 6.99 1 0 554 528 506 4 6.99 1 150 838 785 834 If the price is $6.99, no coupons 5 6.49 0 0 521 519 500 are offered, and no advertising is 6 6.49 0 150 723 790 723 done (the experiment 7 6.49 1 0 510 556 520 corresponding to week 1), the 8 6.49 1 150 818 773 800 model estimates sales as 9 7.59 0 0 479 491 486 Total Sales = 1105.55 + 56.18 x 10 7.59 0 150 825 822 757 6.99 + 123.88 x 0 + 5.25 x 0 = 11 7.59 1 0 533 513 540 1,498.25 units 12 7.59 1 150 839 791 832 13 5.49 0 0 484 480 508 Total Sales = 501 + 510 + 481 = 14 5.49 0 150 686 683 708 1,492 units 15 5.49 1 0 543 531 530 16 5.49 1 150 767 743 779 31  Example : A Prescriptive Pricing Model Prescriptive models help decision makers identify the best solution to a decision problem. 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 x Price + 3,240.9 Total Revenue = Price x Sales = Price x (-2.9485 x Price + 3,240.9) = -2.9485 x Price2 + 3,240.9 x Price Identify the price that maximizes total revenue. 32 Problem Solving With Analytics Recognizing a problem Defining the Problem Structuring the Problem Developing a Solution/ Analyzing the Problem Interpretation Implementation Note: Iterative and incremental process depending on size and complexity of the problem 33  34 Recognizing a Problem Problems exist when there is a gap between what is happening and what we think should be happening. Example : The customer service team notices an increasing number of subscription cancellations over the past three months. 34  35 Defining the Problem Clearly defining the problem is not a trivial 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 Example : The customer retention rate has dropped by 15% over the last quarter, which is negatively impacting recurring revenue and overall business growth. 35  36 Structuring the Problem Stating goals and objectives Characterizing the possible decisions Identifying any constraints or restrictions Example : Objective: To improve the customer retention rate by at least 10% over the next quarter. Constraints: Limited budget for customer retention initiatives, existing contractual obligations. 36  37 Analyzing the Problem 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. Example: Conduct customer exit surveys to identify reasons for cancellations. Analyze customer usage data to identify patterns or trends among those who canceled. Examine customer service logs to spot any recurrent issues. 37  38 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. Example: 60% of customers cited poor customer service as the reason for cancellation. 30% mentioned a lack of advanced features. Decision: Invest in customer service training and fast-track the development of advanced features. 38  39 Implementing the Solution Requires providing adequate resources, motivating employees, eliminating resistance to change, modifying organizational policies, and developing trust. Example: Roll out an intensive two-week customer service training program. Prioritize the development of the most requested advanced features and aim for release in the next product update. 39  40 Conclusion Data: numerical or textual facts and figures that are collected through some type of measurement process Business Analytics : The process of looking at and assessing the wealth of data a company already has at its disposal and using it to make data-driven decisions. Types of Analytics: An overview of descriptive, predictive, and prescriptive analytics and their applications. Decision Models: How to formulate and analyze decision models to make informed choices. Problem-Solving Framework: A systematic approach to recognizing, defining, structuring, and solving business problems. 40