It was generally acknowledged that analytics are opening up vast new ways of understanding how people are consuming media, in ways that sometimes are counter-intuitive. But, in spite of that, computers can only produce good data when humans ask good questions, the experts said. Finding the balance between man and machine is not easy, however.
Foster Provost, Professor and NEC Faculty Fellow at the NYU Stern School of Business, spoke of the need for causal inference-the reasoning process in which we come to a conclusion that something is, or is likely to be, the cause of something else-to properly interpret data on social commerce.
Marketers assume for example, that if people "like" them and tell their friends about a product via social networks, they are influencing them to purchase the product. However that doesn't take into account homophily, or the tendency of people to cluster with others who share their own values and interests. There are also confounding factors to take into account that may influence people to take the same action at the same time; for example, if it's raining, many people pull out an umbrella.
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