University of Massachusetts Amherst
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MIT Campus, Cambridge, Massachusetts
As the world continues to digitize and grow in complexity, virtually every enterprise will need to have a great digital business model, one that creates value by engaging customers and helping employees work smarter.
This program is designed to guide senior executives as they attempt to leverage opportunities and overcome challenges associated with competing in the digital marketplace. Based on extensive MIT research, it provides insights into how firms can achieve competitive advantage by providing unique digital content, an exceptional customer experience, and superior digitized platforms.
At MIT Sloan, we have created frameworks to help enterprises define and build powerful digital business models that will facilitate their ability to compete in the global digital economy and thrive in emergent ecosystems. Revitalizing Your Digital Business Model will help senior managers address the following issues:
- What is the source of competitive advantage for your digital business model?
- How can you manage business complexity in the global digital economy?
- How do you create digitized platforms that enable new and evolving digital opportunities?
- How can you simplify your customer experiences without creating burdensome organizational complexity?
- How can you create new information offerings that generate bottom-line value for the firm?
At the conclusion of this program, executives will be better prepared to address the following issues:
- What digital capabilities do you most need to focus on?
- What information offerings have the most potential for data monetization, and how can they most effectively generate value?
- How can you derive value from business complexity while keeping that complexity manageable?
- What metrics can you use to track whether you are delivering customer satisfaction on a daily basis?
- How will you gain competitive advantage in the digital economy?
--Jeanne Ross, Director, MIT Center for Information Systems Research (CISR)
MIT general map location link
Prof. Steven Boxer
Prof. Songi Han
New digital technologies have fundamentally reshaped marketing theory and practice the last decade alone. Technology has changed the modes of communication through which firms engage with consumers. Moore's law has made the storage and analysis of consumer data scalable, creating opportunities for fine-grained behavioral analytics. New monitoring tools have fostered precise and personalized customer relationship management practices. The rise of mobile phones and tablets has enabled location based messaging and reciprocal communication. The ubiquity of video content has promulgated rich, native advertising programs. The global emergence of social networking has enabled networked based predictive modeling and new forms of targeting and referral strategies based on the preferences of consumers' peers. And finally, new social media have brought all of this onto the public stage, with word-of-mouth conversations driving brand awareness and brand loyalty, and user-generated content on review and ratings sites making or breaking demand for products or services.
This two-day course provides a detailed, applied perspective on the theory and practice of digital marketing and social media analytics in the 21st century. We will cover concepts such as the difference between earned and paid media, predictive modeling for ad targeting and customer relationship management, measuring and managing product virality, viral product design, native advertising, and engaging the multichannel experience. Throughout the course we will specifically stress the theory and practice of randomized experimentation, AB testing and the importance of causal inference for marketing strategy.
Topics covered in this course include:
- Search marketing
- Social network marketing
- Social media analytics
- User generated content management and marketing
- Mobile advertising and commerce
- CRM strategy in the age of big data and digital advertising
- Earned vs paid media
- Predictive modeling for ad targeting
- Viral product design
- The multichannel experience
- Randomized experimentation
- A/B testing
Upon completion of this course you should have a fundamental understanding of:
- The digital advertising ecosystem and attribution and pricing models for digital advertising
- The fundamentals of web and app analytics and KPIs for web traffic and commerce
- Search engine marketing, search engine advertising, ad auctions, and strategies for optimizing search engine advertising
- Social network marketing and social network targeting
- Predictive analytics using social network data, peer-to-peer marketing and personalized social advertising
- Targeting and segmentation, specifically demographic targeting and segmentation, behavioral targeting and segmentation, social targeting and segmentation
- Social listening?analysis of user generated content, reviews, ratings and their effects on consumer demand
- Mobile commerce and analytics
MIT Campus, Cambridge, Massachusetts
In 2013, fourteen of the top 30 global brands by market capitalization were platform-oriented companies ? companies that created and now dominate arenas in which buyers, sellers, and a variety of third parties are connected in real time. In today?s networked age, the cloud, social media, and mobile devices are fueling this platform competition, and more and more companies want in. However, many companies do not succeed in becoming platform leaders because their technology and/or business strategies fall short.
While many platform strategies are well known (e.g. Apple?s iTunes), there are other less-heralded platforms that are exploring new ways to create and capture value. These include: dynamic pricing, usage fees, highly targeted product and service offerings, inbound marketing, and network effects.
Key questions the faculty explores include:
- Is a customer segment with the highest ?willingness to pay? the most valuable segment?
- When is tying a customer to a platform (sometimes called ?lock in?) counter-productive?
- Which pricing formats seem to boost revenues but actually slow platform adoption?
- How can companies get in front of the common evolution patterns of platforms?
- When should leaders be wary of ?platform envy??
Through case studies and Q&A, experienced managers will emerge with insights for refreshing their company?s strategic approach and participating profitably in the multi-sided marketplaces of the future.
By the end of this two-day course, participants should be able to:
- Identify examples of traditional and non-traditional forms of platforms
- Describe the common evolution patterns of multisided platforms, including same-side vs. cross-side network effects
- Identify customer and user groups whose affiliation with the platform is most valuable
- Decide whether to try to ?tie? customers to a platform or not ? the value of open vs. proprietary networks
- Design strategies to undermine an established platform or to defend against such attacks
- Describe the principles of platform pricing and how to inform the design of an effective pricing format
- Recognize the concrete implications of trade-offs in platform design, governance, and staging
- Decide whether a given value proposition is best developed as a stand-alone platform, or as a complement embedded into another platform?s ecosystem?or whether to pivot away from platform strategies all together.
Estimates of road speeds have become commonplace and central to route planning, but few systems in production provide information about the reliability of the prediction. Probabilistic forecasts of travel time capture reliability, and can be used for risk-averse routing in mapping services or as a component of fleet vehicle decision-support systems. We propose approaches for probabilistic prediction of travel time on large-scale road networks, and develop systems for use in both mapping services and ambulance fleet management. Our estimates are based on location data from vehicles traveling in the road network; for mapping services this is obtained from mobile phones, while for ambulance fleets it is obtained from automatic vehicle location devices. Our approaches are based on maximum a posteriori estimation in a class of mixture models. These approaches capture weekly cycles in congestion levels, give informed predictions for parts of the road network with little data, and scale efficiently in the size of the road network. We demonstrate greatly improved accuracy relative to a system used in Bing Maps, and show the impact of our methods for improving ambulance fleet management decisions.
Dawn Woodard leads data science for the Dynamic Pricing team at Uber. Dynamic Pricing creates the pricing systems for Uber, such as surge pricing and next-generation pricing technologies. The team includes specialists in operations research, economics, statistics, and machine learning. Dr. Woodard received her PhD in statistics from Duke University. She was then a faculty member in the School of ORIE at Cornell, where she received tenure. After a sabbatical at Microsoft Research she transitioned to a role at Uber, building and leading their Marketplace Optimization Data Science organization. This is now one of the premier data science teams at Uber and creates Uber’s marketplace-related technologies, such as dispatch, pricing, and incentives, across all of Uber’s products, such as UberX, UberPOOL, and UberEats.