AI@CBS

At the time of its launch, Contently was a digital marketplace, where small businesses, agencies, and corporations could search for writers. But recent financial results showed that Contently earned substantially more profit from its Software-as-a-Service (SaaS) business than from its writers' commissions. This case — which includes background on the evolution of content marketing, information on the SaaS industry, as well as data on Contently's financial performance — asks students to evaluate Contently's best path to profitable growth.

In late 2013, Bloomberg LP, the financial information powerhouse, was challenged by a competing messaging service owned by a consortium of banks, many of whom were Bloomberg's customers. This development came when growth was slowing and news was emerging that Bloomberg's business practices may have compromised the privacy of its customers. Did these events serve to open fissures in the company's ability to maintain industry dominance? This case asks students to consider Bloomberg's business model and the industry's competitive landscape to assess the company's market position.

As firms increasingly seek out consumers' ideas in various domains, they will encounter individuals with different levels of domain-specific knowledge. While both low- and high-knowledge consumers may be willing to share their ideas benevolently, the performance of the former is likely to be hindered by their lack of relevant knowledge in the problem domain. It is also well established that, despite their abundant knowledge, high-knowledge consumers may not perform in accordance with their full potential (due to factors such as shallow processing and inattention).

We present a method that dynamically designs elicitation questions for estimating preferences, focusing on the parameters of cumulative prospect theory and time discounting models. Typically these parameters are elicited by presenting decision makers with a series of choices between alternatives, gambles or delayed payments. The method dynamically (i.e., adaptively) designs such choices to optimize the information provided by each choice, while leveraging the distribution of the parameters across decision makers (heterogeneity) and capturing response error.

It is often claimed that we live in an information age. This has many implications for decision makers in general and business leaders in particular. For most businesses nowadays, increasingly, it is superior information that is at the origin of competitive advantage. The prime challenge in this new environment does not start with data analysis. Rather, the most important issue for the firm is to understand its information needs: what information, if it were available, would make a real difference for value creation and competitive advantage?

It is apparent that severe weather should hamper the productivity of work that occurs outside. But what is the effect of extreme rain, snow, heat and wind on work that occurs indoors, such as the production of automobiles? Using weekly production data from 64 automobile plants in the United States over a ten-year period, we find that adverse weather conditions lead to a significant reduction in production. For example, a week with six or more days of heat exceeding 90F reduces production in that week by 8% on average.

In organizations where agents face cognitive costs, communication patterns should reflect the relative value of their members to the organization. We propose to measure the impact factor of an agent by applying the Invariant Method — also known as Google's PageRank algorithm — to electronic communication data. To explore the validity of this measure, we analyze email exchanges among the top executives of a large retail company. We construct their individual impact factors based only on email patterns and we compare them to standard economic measures of organizational importance.

Web 2.0 provides gathering places for internet users in blogs, forums, and chat rooms. These gathering places leave footprints in the form of colossal amounts of data regarding consumers' thoughts, beliefs, experiences, and even interactions. In this paper, we propose an approach for firms to explore online user-generated content and "listen" to what customers write about their and the competitors' products. Our objective is to convert the user-generated content to market structures and competitive landscape insights.