Can We Solely Rely On Big Data To Make Marketing Decisions?
As modern marketers, we hear about Big Data all the time, so what exactly is Big Data? There are too many definitions on the Internet, but I prefer Dr. Han’s explanation, “(Almost) everything we do nowadays is leaving digital footprints and tracers (i.e., data) which can be used for analysis.” Companies will use those footprints and tracers to accomplish various things, such as product development, predictive maintenance, customer experience, and the list goes on and on. It seems like Big Data is super powerful. However, as a student with a liberal arts degree at undergraduate, I am always wondering, if Big Data is that powerful, can we solely rely on Big Data to make decisions, and what is the role of humans in this process?
After reading the example of Nokia and Netflix, let’s see if you can conclude with a similar answer to me.
In Tricia Wang’s Ted speech, she talked about her experience with Nokia, a company that missed opportunities because of overtrusting Big Data. In 2009, she started a research position with Nokia, and back then, Nokia was the dominant cellphone company in the world. Wang was helping them research how low-income communities use technology in China.
To obtain deeper insights, Wang put herself into low-income people’s lives, such as working as street vendors, selling dumplings to construction workers, and hanging out with Chinese youth to understand their habits in terms of phone use.
With the enormous qualitative data she obtained, she realized that even the poorest in China would want a smartphone, even if it means they would do almost anything to get it. Wang confidently reported that to Nokia; however, Nokia wasn’t convinced by Wang because it was not Big Data. Nokia thought the sample was too small, only 100 people, and they got millions of data that showed no indicators that anyone would want to buy a smartphone.
And, we know what happened to Nokia; it faded out of people’s sight. According to what Wang stated, the model and algorithm Nokia designed were to optimize an existing business model, but what she discovered in China is a new emergent human dynamic that hasn’t happened yet, so Nokia’s Big Data didn’t indicate this trend.
Another example is Netflix, but with a happy ending. Thanks to their great algorithm, Netflix can tell customers’ preferences. However, they also need to know some contextual data that Big Data could not offer, such as “What do users do when watching?” or “How do they behave?”. As a result, Netflix invited Grant McCracken, a well-known anthropologist, to help.
McCracken lived with Netflix users worldwide, understanding consumers’ psychology and observing their viewing patterns. One of the great things McCracken discovered was that users love to watch episode after episode and not even feel bad about it. It was a brand new insight for Netflix, so they went back to the data team and validated this finding. That is how Netflix began to release the whole season at once, the famous binge-watching.
Aren’t these two stories self-explanatory? What Wang and McCracken did was collect Thick Data. In the book The Moment of Clarity, Madsbjerg and Rasmussen demonstrated that Thick Data helps companies improve in predicting the most unpredictable of sciences, human behavior. Successful companies use it to understand the emotional, even visceral context in which people encounter their product or service, and they can adapt when circumstances change.
Compared to Big Data, which can be quantified, Thick Data is qualitative data such as observations, feelings, and reactions that provide insights into consumers’ everyday emotions.
Therefore, here comes my answer.
We cannot deny that Big Data has its advantages, but we cannot solely rely on it. Big Data concludes insights at scale, and Thick Data discovers the deeper insights within customers’ hearts. They bring different kinds of insights that complement each other.
Therefore, integrating Big Data with Thick Data can bring a better result.
