What is Hot on the Market and Trends. SDA Bocconi Quantitative Methods Competence Center

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1 What is Hot on the Market and Trends SDA Bocconi Quantitative Methods Competence Center

2 1. What is hot in the market 2. Focus: Big Data Application 3. Trends 4. Examples of Techniques for analyzing data 5. Short bio 2

3 1. What is hot in the market (1/2) 1 Forecasting Area How Examples Predictive analytics Big data Risk Analysis Risk Revenue Performance measurement Planning By predicting future demands level By determining the «causality» relationship between two or more time series or variables By predicting non linear behavior By scenario By managing risks Forecasting the demand of replacement parts at a municipal bus maintenance facility Discovering most likely patients responding to a given treatment Predicting churn rate for a TLC company Reduce traffic congestion (i.e. Dublin) Creating the most effective promotion for specific customers Detecting fraud.. 2 Optimization Prescriptive analytics Revenue Logistics Planning. By providing the best response or action, given the limited resources of the enterprise By making better decisions through achieving the best outcome and by addressing uncertainty in the data Optimizing Capital Investment decisions Stochastic Optimization Advance customer segmentation Pricing optimization Marketing mix optimization Social network Genetic Algorithms to improve, for example, the job scheduling in manufacturing. 3

4 1. What is hot in the market (2/3) 3 Simulation & Scenario Modeling Area How Examples Predictive analytics Financial valuation Risk Analysis Risk Revenue Performance measurement Scenario Decision Planning. By simulating future behavior and perfomance of relevant business variables By envisioning potential impact of choices before making decisions By determining the impact and influence between two or more relevant variables By simulating non linear behavior By anticipating the future under various scenarios By exploring and realizing contingency plans By analyzing results probability distribution Enabling executives to envision the impact of their decisions before making investments Moving from dashboards to cockpits for market decisions Valuations of different economic and competitive scenarios Build performance and risk metrics Reduce volatility about the results Reduce time to market Understanding impact of internal and external risks Planning with a reliable frame of hypotheses Define scenario priorities Build a project risk profile Support a business plan. 4

5 1. What is hot in the market (3/3) Area How Examples 4 Big data Analysis Predictive analytics Segmentation Risk Analysis Risk Revenue Data visualization Performance measurement Planning.. By processing and analyzing large amounts of varied data and data sources together to generate actionable business insights By magnifying and accelerating the business ability of understanding customers and fine-tune products By advanced visualization of multiple sources of information Applicable to many sectors: Health Care, Public Sector admin, Retail, Banking and Insurance Sensing demand by reducing forecast error (i.e. inventory) Creating the most effective promotion for specific segments of customers Daily adjustment of prices and promotions based on data feeds from online transactions, visits by consumers to own website and customer service interaction 5 Media/Social Network Data Mining Cluster Segmentation Market basket Predictive Social Media Social Network By mining data from social network in real time, companies database By determining segments most likely to respond to an offer Analyzing consumer postings about companies on socialmedia sites (such as Facebook or Twitter) to gauge the immediate impact of their marketing campaigns and to understand how consumer sentiment about the brand changes Online market basket to identify the purchase of behavior of customers Planning. 5

6 2. Focus: Big Data Application An example: The impact of Big Data Across the Insurance Value Chain 6

7 3. Trends The use of data as the next frontier for competion, innovation and productivity in many sectors and companies Request of employees with quantitative skills Revolution in measurement and adoption of nonfinancial metrics Cultural change about how to use data: many companies think they re using data (bar and pie charts and numbers in management presentations are often used). But, historically, that kind of data was used more to confirm and support decisions that had already been made, rather than to learn new things and to discover the right answer. The cultural change is for managers to be willing to say, You know, that s an interesting problem, an interesting question. Let s set up an experiment to discover the answer 1 View and use of data in the context of competing strategic priorities Need of determining/controlling uncertainty and volatility which have increased over the past five years due to the increase in economic instability Usage of data originating from digital and other technologies to apply new analytic, statistical and computational modeling techniques Business analytics for realize business advantages of increased innovation, improved productivity, enhanced customer experience and loyalty and lower costs 7

8 4. Examples of Techniques for analyzing data (1/2) Technique Aim Example Example of applications A/B testing A control group is compared with a variety of test groups in order to determine what treatments (i.e., changes) will improve a given objective variable, e.g., marketing response rate. Split testing or bucket testing Marketing response rate (ie. determining what copy text, layouts, images, or colors will improve conversion rates on an e-commerce Web site). Association rule learning A set of techniques for discovering interesting relationships, i.e., association rules, among variables in large databases. These techniques consist of a variety of algorithms to generate and test possible rules. Market basket Data Mining, Market basket, in which a retailer can determine which products are frequently bought. Classification A set of techniques to identify the categories in which new data points belong, based on a training set containing data points that have already been categorized. Prediction of segment-specific customer behavior (e.g., buying decisions, churn rate, consumption rate) where there is a clear hypothesis or objective outcome. Cluster Analysis A statistical method for classifying objects that splits a diverse group into smaller groups of similar objects, whose characteristics of similarity are not known in advance. Segmenting consumers into self-similar groups for targeted marketing. Data fusion and data integration A set of techniques that integrate and analyze data from multiple sources in order to develop insights in ways that are more efficient and potentially more accurate than if they were developed by analyzing a single source of data. Signal Processing To develop an integrated perspective on the performance of a complex distributed system such as an oil refinery. Data from social media, analyzed by natural language processing, can be combined with real-time sales data, in order to determine what effect a marketing campaign is having on customer sentiment and purchasing behavior. Data Mining Set of techniques to extract patterns from datasets by combining methods from statistics and machine learning with database management. Association rule learning, Cluster, classification, Regression Applications include mining customer data to determine segments most likely to respond to an offer, mining human resources data to identify characteristics of most successful employees. 8

9 4. Examples of Techniques for analyzing data (2/2) Technique Aim Example Example of applications Genetic algorithms Used for optimization that is inspired by the process of natural evolution or survival of the fittest. In this technique, potential solutions are encoded as chromosomes that can combine and mutate. These individual chromosomes are selected for survival within a modeled environment that determines the fitness or performance of each individual in the population. Algorithms are well-suited for solving nonlinear problems. improving job scheduling in manufacturing and optimizing the performance of an investment portfolio Machine learning Design and development of algorithms that allow computers to evolve behaviors based on empirical data Natural language processing (NPL) One application of NLP is using sentiment on social media to determine how prospective customers are reacting to a branding campaign Neural networks. Computational models, inspired by the structure and workings of biological neural networks (i.e., the cells and connections within a brain), that find patterns in data Pattern recognition and optimization (ie. identifying high-value customers that are at risk of leaving a particular company and identifying fraudulent insurance claims) Network A set of techniques used to characterize relationships among discrete nodes in a graph or a network Identifying key opinion leaders to target for marketing, and identifying bottlenecks in enterprise information flows Predictive Modelling A set of techniques in which a mathematical model is created or chosen to best predict the probability of an outcome Regression To estimate the churn rate Pattern recognition A set of machine learning techniques that assign some sort of output value (or label) to a given input value (or instance) according to a specific algorithm Classification Companies applying sentiment to analyze social media (e.g., blogs, microblogs, and social networks) to determine how different customer segments and stakeholders are reacting to their products and actions 9

10 5. Short bio Pelin Pekgün, Ronald P. Menich, Suresh Acharya, Phillip G. Finch, Frederic Deschamps, Kathleen Mallery, Jim Van Sistine, Kyle Christianson, and James Fuller ; Carlson Rezidor Hotel Group Maximizes Revenue Through Improved Demand and Price Optimization; Interfaces January/February :21-36; doi: /inte Karl G. Kempf, Feryal Erhun, Erik F. Hertzler, Timothy R. Rosenberg, and Chen Peng; Optimizing Capital Investment Decisions at Intel Corporation; Interfaces January/February :62-78; doi: /inte J. Paul Brooks; «The Court of Appeals of Virginia Uses Integer Programming and Cloud Computing to Schedule Sessions Interfaces» November/December : ; published online before print May 31, Report McKinsey Global Institute; «Big data: The next frontier for innovation, competition, and productivity»; May 2011 byjames Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, Angela Hung Byers Probability and Time Trade-Off. Baucells Alibés, Manel; Heukamp, Franz\ The Certainties of Uncertainty. Ariño Martín, Miguel Angel Using Scenarios to Plan for Tomorrow. Rosenberg, Mike Fico-analytic-cloud-to-enable-real-time-customer-engagement Big Data helps city of Dublin improve public bus transportation and reduce congestion The Business Analytics Revolution Prescriptive vs Predictive FICO awarded eight patents for credit scoring, fraud detection and predictive analytics offerings (PREDICTIVE) Predictive analytics in field service The secret to better credit-risk management: economically calibrated models New credit-risk models for the unbanked Ratings revisited: Textual for better risk management Four innovative ways Asian banks can create actionable insights from customer data How advanced analytics are redefining banking The big-data revolution in US health care: Accelerating value and innovation Competing through data: Three experts offer their game plans Seizing the potential of big data Clouds, big data, and smart assets: Ten tech-enabled business trends to watch 10