Homework answers / question archive / ESSAY QUESTION (1000 word required)? Identify an actual business that has one or more products as its primary revenue source

ESSAY QUESTION (1000 word required)? Identify an actual business that has one or more products as its primary revenue source

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ESSAY QUESTION (1000 word required)?

Identify an actual business that has one or more products as its primary revenue source. Describe a comprehensive end-to-end approach for managing the product lifecycle including strategy, financial concepts, product feature concepts, and product analytics in order to improve overall business performance.

The actual business would better be a famous company or product. For example, Instagram, Tik Tok, or some fashion brand. Also, the class slide is provided for supporting basic framework, like match the cash flow things or match the product lifecycle style

BANA 290 Product Lifecycle Class Session #3 Summary Richard W. Selby Adjunct Professor University of California, Irvine rselby@uci.edu 0 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Learning Objectives In this course students will learn: ? Foundational knowledge, skills, methods, tools, and resources for product lifecycle management and business analytics ? Understanding of ideas, strategies, and approaches for how leading companies use product lifecycle management and business analytics to generate inspiring ideas, create rich customer experiences, develop world-class products, and foster cultures of innovation ? Hands-on skills for defining, performing, and presenting product lifecycle management and business analytics for datadriven decision making and innovation 1 ? ???????????: ? ?????????????????????????????? ? ????????????????????????????????? ????????????????????????????????? ??? ? ????????????????????????????????? ????????? © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Overview Key Topics ? Revenue ? Products ? Lifecycle ? Cash flow analysis ? Return on investment ? Analytics 2 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Overview Example Product Lifecycles ? Classic ? Waterfall ? Phase gate ? Agile 3 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Overview Product Lifecycle Attributes: ? Breadth ? Depth ? Frequency ? Entry barrier ? Switching barrier ? Maturation ? Internal development phases and gates ? External development phases and gates 4 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Classic Approach Source: Theodore Levitt, “Exploit the Product Life Cycle”, Harvard Business Review, 1965. 5 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Incremental Software Builds Deliver Early Capabilities and Accelerate Integration and Test Figure 4.3-4. JIMO Incremental Software Builds We provide incremental software deliveries support integration and test activities and synchronize with JPL, Hamilton Sundstrand, and Naval Reactors to facilitate teaming, reduce risk, and enhance mission assurance. CY 2004 2005 2006 2007 2008 2009 2010 A B C ATP PMSR SM PDR SM CDR 11/04 1/05 6/08 8/10 Flight Computer Unit (FCU) Builds P FCU1 Prelim Exec and C&DH Software P FCU2 2011 2012 2013 D BUS I&T SM AI&T 8/12 8/13 JPL/NGC, Prelim. Hardware/Software Integration JPL/NGC, Final Hardware /Software Integration JPL, Mission Module Integration Final Exec and C&DH Software P FCU3 Science Computer Interface P FCU4 Power Controller Interface Reactor AACS (includes autonomous navigation) P FCU5 Thermal and Power Control P FCU6 Configuration and Fault Protection P FCU7 Science Computer Unit (SCU) Builds Note: Science Computer builds for common software only (no instrument software included) Prelim Exec and C&DH Software SCU1 SCU2 Final Exec and C&DH Software Data Server Unit (DSU) Builds DSU1 Prelim Exec and C&DH Software DSU2 Final Exec and C&DH Software P DSU3 Data Server Unique Software Ground Analysis Software (GAS) Computer Builds P Preliminary Ground Analysis Software GAS1 Final Ground Analysis Software GAS2 Legend: = 1 2 3 4 5 = 1 2 3 4 5 = 1 2 3 4 5 N Design Agent Performer of Activity N JPL P Prototype NGC Role/activity shared by Activity JPL and NGC Delivered to, Usage Power Controller NR, Reactor Integration NGC, AACS Validation on SMTB NGC, TCS/EPS Validation on SSTB NGC, Fault Protection S/W Validation on SSTB JPL, Prelim. Hardware/Software Integration JPL, Final Hardware/ Software Integration NGC, Prelim. Hardware/ Software Integration NGC, Final Hardware/ Software Integration NGC, HCR Integration on SMTB JPL, Prelim. Integration into Ground System JPL, Final Integration into Ground System N is defined as follows: 1 Requirements 2 Preliminary Design 3 Detailed Design 4 Code and Unit Test/Software Integration 5 Verification and Validation 04S01176-4-108f_154 6 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Synchronize-and-Stabilize Lifecycle Model Enables Frequent Incremental Integrations and Deliveries Phases Timeline Milestones Major Reviews Documents and Intermediate Activities Milestone 0 Vis ion statemen t Specification documen t Planning 3-12 months 6-10 weeks • Cod e and optimizations • Testin g and d ebu ggin g • Featu re s tab ilization Sched ule co mp lete Subproject I Project plan approval Subproject II 2-5 weeks • Integration • Testin g and d ebu ggin g 6-16 months Development Development 6-12 months 2-5 weeks • Buffer time Subproject III Development Subproject 2-4 month s (1/3 of all features) Specificatio n review Implementation plan Milesto ne II release Milesto ne III release Vis ual freeze Code complete Stabilization 3-8 months Project review Milesto ne I release Feature complete Optimizations Tes ting an d debug ging Optimizations Tes ting an d debug ging Internal tes ting Buffer time Beta testin g Buffer time Zero bug release Releas e to manufacturing (Ship date) 7 Proto types Des ign feasib ility stu dies Tes ting strateg y Sched ule Pos tmortem documen t © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle Cash Flow Analysis 8 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Cash Flow Analysis 9 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Cash Flow Analysis 10 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Cash Flow Analysis 11 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle Product Lifecycle Analytics 12 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Key Concepts Analytics Leadership Using Analytics Framework ? Managing analytics using an overall business analytics framework enables analytics leaders to successfully implement analytics-driven management and rapidly create value ? An overall business analytics framework is supported by several advanced data analytics techniques and tools to enable successful implementation of strategies and approaches for defining, performing, and leveraging analytics 13 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Analytics Methodology Framework Analytics Framework ? Overall business analytics methodology framework for successfully implementing analytics-driven management and rapidly creating value ? Four major methodology elements: ? Goal definition ? Data collection ? Data analysis and modeling ? Interpretation, action, and feedback 14 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Business Case Analysis Worksheet ? What are 2-3 products or services developed and/or sold by the company? ? What are 3-5 potential dependent variables that correspond to the company’s products or services that would be useful to analyze in order to create value at the company? ? What are 5-7 potential independent variables that correspond to the company’s products or services that would be useful to include in the analysis of the dependent variables? ? What are 2-3 potential actions that a business leader could take at the company to improve the performance of products, services, and/or the overall company based on analysis of the data? 15 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Overview Overview: ? In order to support the overall product lifecycle management framework, this course teaches students how to use several product lifecycle management and business analytics techniques and tools including hands-on skills for using these tools to implement strategies and approaches for defining, performing, and leveraging product lifecycle management. 16 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Overview Overview: ? Example product lifecycle management and business analytics techniques and tools include: ? product lifecycle models, ? market research, ? product specification and feature analysis, ? trade studies, ? design of experiments, ? automated and manual data collection, ? data management, graphics, and statistics, ? database management and joins, ? data visualization, ? parametric modeling, ? analysis of variance, ? clustering and unsupervised learning, ? classification and supervised learning, and ? neural networks. 17 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Key Concepts Analytics Example Leading Companies ? Apple ? Facebook ? Procter & Gamble 18 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Focus on Success in Data-Driven Leadership ? Class session focus: Analytics and strategy ? Case reading and analysis: Thomas H. Davenport, Marco Iansiti, and Alain Serels, “Managing with Analytics at Procter & Gamble”, Harvard Business School case, Article Product Number 9-613-045, April 3, 2013, 20 pages. ? Reading: Thomas H. Davenport, Leandro Dalle Mule, and John Lucker, “Know What Your Customers Want Before They Do”, Harvard Business Review, Article Product Number R1112E, December 1, 2011, 8 pages. 19 © Copyright 2012-2021. Richard W. Selby. All rights reserved. 1) Vision a. Real-time availability of information will lead to real-time decisions and actions by decision-makers and radically increase the pace at which we do business b. Make better, smarter, real-time business decisions c. Speed decision-making and ultimately improve time to market d. Ability to easily access information, quickly identify areas in need of attention, and rapidly assess potential responses e. Provide a new level of transparency to the organization 2) CEO Bob McDonald, Procter & Gamble: a. “We see business intelligence as a key way to drive innovation, fueled by productivity, in everything we do. To do this, we must move business intelligence from the periphery of operations to the center of how business gets done.” b. “What it does is, it allows you to flatten the organization … Everybody gets the same data at the same time” Summary Procter & Gamble case Discussion Points: Analytics and Strategy 20 Vision links analytics to business value © Copyright 2012-2021. Richard W. Selby. All rights reserved. 3) Business Sphere executive conference room a. Two 8-foot x 32-foot screens display visualizations of data and analyses b. Leaders can drill down into the data on-the-spot using color coded charts and displays, based on 200-terabyte data warehouse Summary Procter & Gamble case Discussion Points: Analytics and Strategy 21 Vision links analytics to business value Analytics enable real-time decisions and actions © Copyright 2012-2021. Richard W. Selby. All rights reserved. Procter & Gamble case Discussion Points: Analytics and Strategy 3) Business Sphere conference room (continued) 22 © Copyright 2012-2021. Richard W. Selby. All rights reserved. 4) Decision Cockpit a. Web-based customizable real-time dashboard that tracks the most relevant data and news for each individual employee b. 50,000 employees have access to Decision Cockpits Summary Procter & Gamble case Discussion Points: Analytics and Strategy 23 Vision links analytics to business value Analytics enable real-time decisions and actions © Copyright 2012-2021. Richard W. Selby. All rights reserved. Procter & Gamble case Discussion Points: Analytics and Strategy 4) Decision Cockpit (continued) 24 © Copyright 2012-2021. Richard W. Selby. All rights reserved. 5) Transform management focus from “what” to “why” and “how” a. On the Decision Cockpit, users are able to drill-down into the data to reveal not only “what” is happening, but also “why” it is happening and better understand the options available for “how” P&G can react b. Visualization of the data makes it easy to focus on the exceptions and realize business opportunities and where interventions are necessary c. Set alerts if data are outside of a specified range or if competitor product introduction becomes available d. To collect data from social media, P&G developed Consumer Pulse, which provides real-time sentiment analysis for web buzz of each P&G brand Summary Procter & Gamble case Discussion Points: Analytics and Strategy 25 Vision links analytics to business value Analytics enable real-time decisions and actions © Copyright 2012-2021. Richard W. Selby. All rights reserved. 6) Analytics implementation concepts and principles a. Single, company-wide database serves as the “one truth” for the entire company, using analytics as a centralized service b. Standardize the way data is visualized across the company c. Simply click on higher level data to drill down and view performance by brand, initiative, retailer, and individual store d. Statistical forecasts for the next 12 months e. Analysts and managers from one area can step into a role in another product or region 7) Systems and processes enabled and emphasized the use of up-to-date data and advanced analytics throughout the company a. Standardize, automate, and integrate systems and data so we can create a real-time operating and decision-making environment b. Network systems together globally and digitize from end to end c. Make data as accessible as possible, with standards for type and quality Summary Procter & Gamble case Discussion Points: Analytics and Strategy 26 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling © Copyright 2012-2021. Richard W. Selby. All rights reserved. 8) Forecasting and business sufficiency models a. Concept of business sufficiency is that we commit to a performance goal, and the key question is whether we have sufficiency to deliver that goal. It may be a supply chain goal, a production goal, or a sales volume goal. b. Business sufficiency models address key business domains for P&G, including product, customer, supply chain, distribution channels, human resources, and the “Top 50” global product-market combinations c. Models produce statistical forecasts for 6000 product-market combinations covering market size, P&G sales, and market share d. Areas that seem unlikely to meet objectives are highlighted e. Ranges alert managers to markets with higher uncertainty and variability, shifting the focus from large but stable markets to those that warrant increased attention f. Centralized forecasting team runs thousands of virtual simulations and provides 12-month forecasts including point forecasts and probabilistic ranges, with results available through the Decision Cockpits Summary Procter & Gamble case Discussion Points: Analytics and Strategy 27 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling © Copyright 2012-2021. Richard W. Selby. All rights reserved. 8) Forecasting and business sufficiency models (continued) g. Time series-based forecasting models produced the most accurate forecasts, but provided little insights as to why the outcome would occur h. Propensity models attempted to better understand the causes, or drivers, of the outcomes such as new product introductions, marketing campaigns, and competitor actions, so propensity models served as useful tools to measure the potential impact of management actions Summary Procter & Gamble case Discussion Points: Analytics and Strategy 28 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling © Copyright 2012-2021. Richard W. Selby. All rights reserved. 9) Analysts embedded in the business units and functions a. Use of all this information by business leaders requires facilitation, and the Information and Decision Solutions (IDS) analysts serve this role b. Analysts are dedicated to work alongside leaders and managers of a particular team on a daily basis c. Successful performance as an analyst requires a high level of business domain and technology understanding as well as communication and leadership skills to engage with senior executives d. “[Analysts] are embedded into the business to understand where technology can help. We are business people first, then technology experts. We invest in the business needs and solutions, not technology.” e. Managers are also expected to understand the nature of statistical forecasting and Monte Carlo simulation Summary Procter & Gamble case Discussion Points: Analytics and Strategy 29 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling © Copyright 2012-2021. Richard W. Selby. All rights reserved. 10) Roles at P&G engaged in analytics a. CEO and other executives b. Managers of brands c. Managers of all major business functions, such as supply chain, production, etc. d. Embedded analysts e. Modelers/forecasters f. Each employee 11) President of Global Business Services is also Chief Information Officer Summary Procter & Gamble case Discussion Points: Analytics and Strategy 30 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling © Copyright 2012-2021. Richard W. Selby. All rights reserved. 12) Test markets, experiments, and data sources a. Cedar Rapids, Iowa served as a test market for the P&G compacted powder laundry detergent products b. Usual data sources were scanner data from large retailers and Nielsen c. Incremental product rollout and testing enable P&G to conduct comparisons of laundry detergent sales at retailers that have and do not yet have the revised products Summary Procter & Gamble case Discussion Points: Analytics and Strategy 31 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 13) Impacts and benefits a. P&G had previously managed by a “preview culture” where any data could be previewed and digested by middle and business unit managers before business leaders saw it, and there was time to develop a plan to address any issues b. New approach meant that information could be viewed at the same time by everyone c. Within a couple of years, the company reduced the number of reporting levels from seven to five and eliminated over 15% of senior management positions d. Over eight-year period, Global Business Services costs decreased by $900 million while expanding the number of services offered and improving service levels e. Tools, talent, and culture, not just the data, are the true competitive advantages to driving business value Summary Procter & Gamble case Discussion Points: Analytics and Strategy 32 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 14) Procter & Gamble a. Founded in 1837 and became leading supplier of candles and soap including Ivory soap b. Launched first ever national advertising campaign that directly targeted consumers 15) Focused on innovation and branding to become the world’s largest consumer packaged goods company a. $80 billion annual sales in 2011 b. 180 countries c. 300 brands, including 25 brands that generate over $1 billion in annual sales Summary Procter & Gamble case Discussion Points: Analytics and Strategy 33 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 16) In 1999, P&G started reorganization called “Organization 2005” a. Realignment of the organization structure, work processes, and culture designed to accelerate growth by streamlining management decisionmaking, manufacturing, and other work processes to increase the Company’s ability to innovate and bring initiatives to global markets more quickly b. 129,000 employees reorganized into four global organizations: c. Global Business Units, based on product lines: Beauty; Grooming; Health Care; Fabric Care and Home Care; Baby Care and Family Care d. Market Development Organizations, based on geographies: North America; Western Europe; Asia; Latin America; Central & Eastern Europe, Middle East, and Africa e. Global Business Services, based on centralized services for scale, sharing, and standardization f. Corporate Functions, based on a lean executive staff Summary Procter & Gamble case Discussion Points: Analytics and Strategy 34 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 17) Global Business Services a. Provides back office services, including over 170 shared services and solutions b. Leverages external partners to deliver many services to gain efficiencies without sacrificing strategic capabilities c. Kept in-house the information technology (IT) capabilities for innovation, system design and architecture, and those that supported decisionmaking, and renamed these essential IT capabilities Information and Decision Solutions (IDS) Summary Procter & Gamble case Discussion Points: Analytics and Strategy 35 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 18) Targeting individuals with perfectly customized offers at the right moment across the right channel is marketing’s holy grail a. “Next best offers” (NBO) use increasingly granular data, from detailed demographics and psychographics to consumers’ clickstreams on the internet, to create highly customized offers that steer consumers to the “right” merchandise or services at the right moment, at the right price, and in the right channel 19) NBO is actually a “best offer”, with “next” suggesting forward looking, for new or ongoing customer engagements customized for: a. Seller’s goals (increase sales, build customer loyalty, etc) b. Consumer attributes and behaviors (demographics, shopping history, etc) c. Product or service characteristics (shoe style, type of mortgage, etc) d. Purchase context (in store, online, kiosk, etc) e. Examples: Promotions, offers, incentives, coupons, discounts, social media peer recommendations Summary Know What Your Customers Want Discussion Points: Analytics and Strategy 36 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 20) Adopt best practices methodology for using “next best offers” (NBO) a. Define objectives. Craft NBOs to achieve specific goals, such as attracting new customers or increasing sales, loyalty, or share of wallet. Be ready to modify your objectives to exploit changing circumstances. b. Gather data. Collect detailed data about customers (demographics and psychographics; purchase history; social, mobile, and location “SoMoLo” information), your offerings (product attributes, profitability, availability), and purchase context (customer’s contact channel, proximity, the time of day or week). c. Analyze and execute. Use statistical analysis, predictive modeling, and other tools to match customers and offers. Use business rules to guide what offers are made under what circumstances. Carefully match offers and channels. Make offers sparingly, time them deliberately, and monitor contact frequency. d. Learn and evolve. Think of every offer as a test. Incorporate data on customers’ responses in follow-on offers. Formulate rules of thumb for designing new offers that are based on performance of previous ones. Summary Know What Your Customers Want Discussion Points: Analytics and Strategy 37 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 21) Define objectives a. Overall sales b. New customers c. Loyalty (existing customers) d. Share of wallet (existing customers, items not usually purchased) Summary Know What Your Customers Want Discussion Points: Analytics and Strategy 38 Vision links analytics to business value Sales New customers Existing customers, items usually purchased Analytics enable real-time decisions and actions Existing customers, items not usually purchased Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 22) Examples: Customize offer based on customer data a. Microsoft sends email offers to try its Bing search engine. Offers are tailored to the recipient at the moment the email is opened, within 200 milliseconds, based on real-time information about him or her including location, age, gender, online activity both historical and immediately preceding, and most recent responses of other customers. These offers have lifted conversion rates by as much as 70%. b. UK-based retailer Tesco has a loyalty Clubcard program that targets shoppers who buy diapers for the first time by mailing them coupons not only for baby wipes and toys but also for beer. Analysis revealed that new fathers tend to buy more beer and spend less time at a pub. c. Tesco has “flash sales” that as much as triple the redemption value of certain Clubcard coupons to make its best offer even better for selected customers. A countdown mechanism shows how quickly time or products are running out to build tension and drive responses. d. Tesco achieved redemption rates ranging from 8% to 14%, which is far higher than the 1% or 2% seen elsewhere in the grocery industry. Summary Know What Your Customers Want Discussion Points: Analytics and Strategy 39 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 22) Examples: Customize offer based on customer data (continued) e. Amazon has numerous “people who bought this also bought that” offers f. CVS provides discounts on things a customer has bought previously g. Sam’s Club provides individually relevant offers for categories in which a customer has not yet purchased and rewards customer loyalty h. When Tesco wants to identify products that appeal to adventurous palates, it will focus on a “leading indicator” such as Thai green curry paste and then analyze the other purchases that these buyers make i. Walmart is working to predict shoppers’ Walmart.com online purchases on the basis of their social media interests Summary Know What Your Customers Want Discussion Points: Analytics and Strategy 40 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 23) Examples: Customize offer based on purchase context a. Foursquare makes customized offers according to how many times consumers have “checked in” their location at a certain retail store b. Apparel retailer H&M has partnered with the online game MyTown to gather customer location data. If potential customers are playing the game on a mobile device near an H&M store and check in, H&M rewards them with virtual clothing and points. If they then scan promoted products in the store, it enters them in a sweepstakes. Results show that of 700,000 customers who checked in online, 300,000 went into the store and scanned an item. c. Qdoba Mexican Grill, a quick-serve franchise, delivers coupons to customers’ smart phones to increase sales and smooth demand. Latenight campaigns near universities have seen a nearly 40% redemption rate versus a 16% average for Qdoba’s overall program. d. Airlines can hike prices on a Sunday evening, because more people search then than, say, midday during the week Summary Know What Your Customers Want Discussion Points: Analytics and Strategy 41 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 23) Examples: Customize offer based on purchase context (continued) e. A Chinese shoe retailer is testing offers that target primary buyers’ companions. When a woman walks into one of its stores with her husband, she is usually the primary buyer and the retailer’s offer is usually a relatively inexpensive item for the husband. Men who accompany their wives shopping but are not actively shopping themselves are more price sensitive than solo husbands who are searching for a specific product. f. Footlocker promotes only fashion-forward shoes through social media g. Nordstrom provides offers through sales associates in face-to-face customer interactions h. Starbucks uses at least 10 online channels to deliver targeted offers, gauge customer satisfaction and reaction, develop products, and enhance brand advocacy i. CVS ExtraCare offers are delivered not only through kiosks but also on register receipts, email, targeted circulars, and messages sent directly to customers’ mobile phones Summary Know What Your Customers Want Discussion Points: Analytics and Strategy 42 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 23) Examples: Customize offer based on purchase context (continued) j. T. Rowe Price provides call-center representatives with targeted offers, but it has concluded that if a representative delivers the offers in more than 50% of interactions, he or she probably is not tuning into customers’ needs Summary Know What Your Customers Want Discussion Points: Analytics and Strategy 43 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 24) Rules of thumb a. Previous purchases are often the single best guide to what a customer will buy next b. Click stream or recent online purchase data are often the most relevant in guiding an online offer strategy c. A carefully crafted offer is only as good as its delivery d. Channel through which the customer made contact is often the appropriate channel for delivering the offer e. A complex offer should not be delivered through a simple channel f. Companies often test offers through multiple channels to find the most successful ones g. Limit the overall contact frequency for a customer if analyses have shown that too much contact reduces response rates Summary Know What Your Customers Want Discussion Points: Analytics and Strategy 44 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. 25) Examples: Data sources a. Companies are beginning to craft offers based on where a customer is at any given moment, what his social media posts say about his interests, and even what his friends are buying or discussing online b. In the United States alone, mobile devices send about 600 billion geospatially tagged data feeds back to telecommunications providers every day. Analyses can compare a consumer’s movements with billions of data points on the movements and attributes of others and use this location history to estimate the consumer’s age, travel style, level of wealth, and next likely location c. Channel through which a customer is making contact with a business (face-to-face, voice, mobile device application, internet website, email, kiosk, point-of-sale checkout register), reason for contact, voice volume and pitch (indicating whether calm or upset), weather, time of day, day of week, and whether alone or accompanied d. Customer’s expected lifetime value Summary Know What Your Customers Want Discussion Points: Analytics and Strategy 45 Vision links analytics to business value Analytics enable real-time decisions and actions Analytics benefits from end-to-end data and modeling Accelerate experiments and impacts © Copyright 2012-2021. Richard W. Selby. All rights reserved. Key Strategic Idea ? Transform your business culture using analytics to enable real-time decision making, data-driven actions, and sustainable competitive advantage 46 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle Machine Learning 47 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Machine Learning 48 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Machine Learning 49 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Machine Learning 50 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Machine Learning 51 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Machine Learning 52 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Machine Learning 53 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Machine Learning 54 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Machine Learning 55 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers Classification matrix (also called confusion matrix) ? TP = True positive ? TN = True negative ? FP = False positive ? FN = False negative 56 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers Classification matrix (also called confusion matrix) ? TP = True positive ? TN = True negative ? FP = False positive ? FN = False negative 57 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers Predicted Values Classification matrix (also called confusion matrix) ? TP = True positive ? TN = True negative ? FP = False positive Actual Values ? FN = False negative 58 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers Classification matrix (also called confusion matrix) ? TP = True positive ? TN = True negative ? FP = False positive ? FN = False negative ? TPR = True positive rate = Sensitivity = Recall ? TNR = True negative rate = Specificity ? PPV = Positive predictive value = Precision ? Accuracy 59 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers 60 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers 61 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers 62 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers 63 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers 64 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers Neural network activation functions ? Reference: https://keras.io/activations/ ? softmax - Softmax activation function ? elu - Exponential linear unit ? selu - Scaled Exponential Linear Unit ? softplus - Softplus activation function ? softsign - Softsign activation function ? relu - Rectified Linear Unit ? tanh - Hyperbolic tangent activation function ? sigmoid - Sigmoid activation function ? hard_sigmoid - Hard sigmoid activation function ? exponential - Exponential (base e) activation function ? linear - Linear (i.e. identity) activation function 65 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers Neural network activation functions ? softplus - Softplus activation function ? relu - Rectified Linear Unit Rectified Linear Unit In the context of artificial neural networks, the rectifier is an activation function defined as the positive part of its argument: f ( x ) = max ( 0 , x ) where x is the input to a neuron 66 Source: https://en.wikipedia.org/wiki/Rectifier_(neural_networks) © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers Neural network computations ? Nodes serve as computation and knowledge representation “units” or building blocks 67 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers Neural network computations 68 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers Neural network computations 69 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers Neural network computations 70 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Supervised Learning Classifiers Datasets ? Training dataset = Dataset of examples used for learning, that is to fit the parameters (e.g., weights) of, for example, a classifier. ? Test dataset = Dataset that is independent of the training dataset, but that follows the same probability distribution as the training dataset. If a model fit to the training dataset also fits the test dataset well, minimal overfitting has taken place. A better fitting of the training dataset as opposed to the test dataset usually points to overfitting. 71 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Key Concepts Supervised learning classifiers ? Numeric variables ? Categorical variables ? Dependent variables ? Independent variables ? Population ? Sampling ? Random ? Prediction ? Learning ? Model definition ? Training data ? Test data 72 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Key Concepts Supervised learning classifiers ? Model evaluation ? Model application ? Feedback 73 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Key Concepts Supervised learning classifiers ? Classification matrix ? Sensitivity ? Specificity ? Accuracy 74 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Advanced Data Analytics: Key Concepts Supervised learning classifiers ? Neural networks ? Artificial neural networks ? Input layer, hidden layers, output layer ? Layers ? Nodes ? Weights ? Activation functions ? Model training epochs ? Standardization of variables: Range, standard deviation 75 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Python Python 76 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Python Python ? Python is an interpreted, high-level, general-purpose programming language ? Python is both very powerful and easy to use, and it is a very effective programming language for solving problems ? Python is open source software (free software) ? Python runs on Windows, Mac OS X, Linux/UNIX, etc. Jupyter notebook ? Jupyter notebook is an interactive shell interpreter that enables you to create and execute Python programs and related commands incrementally ? Jupyter notebook is open source software (free software) ? Jupyter notebook is a web application that runs in a browser 77 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Python Python resources ? https://www.python.org/ ? https://en.wikipedia.org/wiki/Python_(programming_languag e) ? https://www.w3schools.com/python/default.asp ? http://nbviewer.jupyter.org/github/phelps-sg/pythonbigdata/blob/master/src/main/ipynb/intro-python.ipynb ? Google “python tutorial” ? Note: We will be using Python 3 (not Python 2) Jupyter notebook resources ? http://jupyter.readthedocs.io/en/latest/install.html ? https://jupyter.org/try ? Google “jupyter notebook tutorial” 78 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle: Discussion Time Discussion Time 79 © Copyright 2012-2021. Richard W. Selby. All rights reserved. Product Lifecycle Attributes: Breadth Depth Frequency Entry barrier Switching barrier • Maturation Internal development phases and gates External development phases and gates