I have a fascination with big data and recently attended a one-day workshop IBM ran to showcase what they are doing in this space. My interest in big data stems from a long-standing interest in how data improves marketing decision-making.
Big data is not a replacement for behavioral data, such as user and usage data, or psychographic data, which includes information on attitudes, values, opinions and lifestyle. Big data is simply a term to describe large complex data sets, and data often collected from unstructured sources such as comments consumers leave on blogs (as opposed to answers given to more structured surveys).
Just over 70% of the US population uses a social media network (Jones 2013) and so it is hardly surprising that big data has become the latest “must have” data source as organizations look for fresh new consumer insights or better ways to measure behavior and psychographic variables.
Those who share my interest in marketing to women see big data as one way to gain insights into her world because women became the majority of web users in the US in 2000 (Learned 2013). Furthermore, Nielsen reports that women talk on the phone more than men, text more than men, use the social features of phones more than men and visit Internet community sites more than men (Frighetto, 2011). Women also outnumber men in the use of Facebook and Pinterest (Fehling, 2012).
But, like any data source, big data is not perfect. At the Academy of Marketing Conference in the UK early in July I was reminded of John Suler’s (2004) work on online disinhibition to describe someone who can be whoever they want to be, whenever they want to be, online. Suler gave six reasons why people will act differently online than when in person: 1. “You don’t know me.” Anonymity. I can adopt a username but it might not reveal much about me and I will only disclose what I want to disclose. In fact, I don’t have to own my own behavior at all; even if I am hostile I can evade responsibility for my comments and I might even convince myself that those online behaviors I demonstrate aren’t really me at all. 2. “You can’t see me.” Invisibility. I might be anonymous but even if you know who I am, you can’t see me when I post comments so I don’t need to worry about how I look or sound. Am I frowning as I write? Am I bored when I read your post? 3. “See you later.” Asynchronicity. Because there are delays in the way we communicate with each other online, I might lose my train of thought as we reply to each other over a day or two. Or I might walk away if either of us post a message that is “personal, emotional, or hostile.” 4. “It’s all in my head.” Solipsistic Introjection. Because I can’t hear you speak, I start to imagine how you sound or how you look. 5. “It’s just a game.” Dissociative Imagination. I see what I do online as a game. It’s not really me. 6. “Your rules don’t apply here.” Attenuated status and authority. I don’t know anything about you when we are communicating (for all I know, you might even be my boss), so you can’t intimidate me and I will speak my mind.
Of course big data has promise but like any data source big data has its own limitations. Online disinhibition is one such limitation. With big data, it is not always possible to determine, for example, the gender, age, and ethnicity of the person posting comments and so researchers can’t necessarily attribute sentiments to say women consumers. Worse, when conducting sentiment analysis it is usually not possible to determine whether the person making good and bad comments is offering an accurate representation of the context or of his or her feelings. But, as with any newer technique, our understanding of big data best practices will improve as we gain more experience using it.
Fehling, April (2012) “So Pinterest is a Woman’s World. Does that Matter?” Retrieved from http://www.npr.org/blogs/alltechconsidered/2012/02/22/147222619/so-pinterest-is-a-womans-world-does-that-matter
Frighetto, Jennifer (2011) “Women of Tomorrow: A Study of Women Around the World”, Nielsen.
Jones, Kelsely (2013) “The Growth of Social Media v 2.0 [Infographic]“, SearchEngine Journal, 15 November.
Suler, Suler (2005) “The Online Disinhibition Effect,” International Journal of Applied Psychoanalytic Studies, 2 (2), 184-188 and (2004), CyberPsychology & Behavior 7 (3): 321-326