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Israeli entrepreneur and inventor Dov Moran envisioned the creation of a mobile device that was a small, stand-alone, fully functional mobile phone that could be slipped into a variety of enclosures, or “jackets,” that would provide added functionality and better reflect the personalities of its users. As the development of the Modu phone began to take shape, Moran and his team decided that to ensure the success of the new phone's much anticipated launch, Modu would develop and market the accessory jackets itself. The question now was which of the eight jackets to develop and what factors should be considered in making that decision. The case is about how to estimate optimal product-line extensions after accounting for experience curve and cannibalization effects of products that share similar features, cost, and price. This will require quantitative analysis that estimates the effect of the experience curve and cannibalization on cost, revenues, and ultimately, profit. The issue is how to optimize profits by choosing an ideal set of products.

• Understand the importance of quantitative analysis in launching product-line extensions while taking into account demand and cost side interactions

• Combine both qualitative and quantitative data in choosing a targeted segment

• Reflect on the strategic and financial considerations in choosing a segment for a new technology product

• Evaluate the implications of experience curve and cannibalization when introducing product-line extensions and their impact on the decision under consideration

Understand the importance of quantitative analysis in launching product-line extensions while taking into account demand and cost side interactions

Combine both qualitative and quantitative data in choosing a targeted segment

Reflect on the strategic and financial considerations in choosing a segment for a new technology product

Evaluate the implications of experience curve and cannibalization when introducing product-line extensions and their impact on the decision under consideration

By 2009 Netflix had all but trounced its traditional bricks-and-mortar competitors in the video rental industry. Since its founding in the late 1990s, the company had changed the face of the industry and threatened the existence of such entrenched giants as Blockbuster, in large part because of its easy-to-understand subscription model, policy of no late fees, and use of analytics to leverage customer data to provide a superior customer experience and grow its e-commerce media platform. Netflix's investment in data collection, IT systems, and advanced analytics such as proprietary data mining techniques and algorithms for customer and product matching played a crucial role in both its strategy and success. However, the explosive growth of the digital media market presents a serious challenge for Netflix's business going forward. How will its analytics, customer data, and customer interaction models play a role in the future of the digital media space? Will it be able to stand up to competition from more seasoned players in the digital market, such as Amazon and Apple? What position must Netflix take in order to successfully compete in this digital arena?

To examine the benefits and risks of investment in analytical technology as a means for mining customer data for business insights. Students will develop a strategy position for Netflix's investment in technology and its digital media business. Students must also consider how new corporate partnerships and changes to the customer channel model will allow the company to prosper in the highly competitive digital space.