Predicting Consumer Behavior: The Power of Analytics, Human Understanding, and Design
In business, having a deep understanding of human behavior is a major advantage because all business is predicated on it. According to researchers at Northeastern University, human behavior is 93 percent predictable. This finding lends some credibility to the claim that — to a certain extent — customer behavior can be predicted, something marketers and organizations would love to be able to predict.
One of the simplest ways to predict customer behavior is through carefully designing your customer experiences. As Dan Ariely mentions, a simple phrasing difference in government forms across countries is correlated with significant differences in the number of people who sign up for organ donations. This illustration of the number of organ donation sign-ups in various countries is an example of predicting consumer behavior through customer experience design. The organ donation example also supports Dan Ariely’s assertion that as consumers, we often have our choices made for us without realizing it. Customer experience is a broad term that encompasses the design, delivery, and management of a customer’s interaction with a brand. When using customer experience to predict customer behavior, the design and delivery aspect of the interaction are the primary focus. These two aspects of the customer experience can greatly influence the behavior of a customer. As Dan Ariely explains in his talk, humans do not know their own preferences well. As a result, designing a customer experience that demonstrates both what the customer wants and what they don’t want can have a great impact on consumer behavior.
Another method of predicting consumer behavior is through the use of big data. Data collected by social media sites and apps can be incredibly useful for businesses who leverage it. Retail is one industry that especially benefits from big data because it relies on — and profits from — the data more than many other industries. For retail and a host of other industries, social media holds valuable consumer data that can be used for business purposes. Predictive models on customer behaviors can be built using the data from social media sites. Machine learning can also be implemented to understand and predict behavior based on the data available. This approach uses computer algorithms, data mining, and pattern recognition to better understand consumer behaviors and do so relatively quickly.
There are many opportunities for business leaders to use analytics and an understanding of human behavior to predict future behavior of customers. Of course, not every decision a consumer makes can be predicted, but that doesn’t mean prediction as a practice isn’t valuable. Machine learning, modeling, and designing experiences can all help businesses make more informed and effective decisions while understanding their customer on a deeper level. Predictive analytics offer a great amount of value to the firm that employs such tactics. The firms that employ predictive analytics will not only develop a deeper understanding of their customer, but they will be equipped to make better business decisions in general.
