Big data and metrics turn a shopping trip into a data-mining bonanza as retailers market products towards customers. A retail purchase, coupled with some personal information such as an email address, a physical address or a look into your Web browsing habits, returns tons of information on how you shop.
Retailers realize that men and women experience a shopping trip differently, and as such, some stores learn to make the search for the perfect item a lesson in gender dynamics. Birchbox discovered a pattern of shopping behavior between men and women that altered the way the company approached how someone makes a retail purchase on its website. Women typically buy one of an item, such as a bar of soap, after testing several samples sent to them in the mail. Men, on the other hand, buy several of the same bar of soap upon viewing their choices and making a decision quickly. Instead of going through each sample one by one over time and then picking one, men usually saw what they liked and then made a larger purchase. Birchbox originally tagged those mass orders as fraudulent, but then the company realized that's simply how men make a retail purchase.
Other microcosmic case studies have shown the differences between genders in terms of shopping habits. For example, men typically see a shopping trip as an expedition to find specific things, then they get out of the store and move on to the next task. Women find shopping as a social experience as they walk around the mall in groups. If a woman shops alone, she may post a photo of possible purchases on social media websites to get her peers' opinions before buying an item.
An Interactions Marketing study shows that women browse more leisurely than men, and they are more susceptible to impulse buys. When women have a one-track mind to find one specific item, they may spend more time finding the correct retail purchase rather than settling for an alternative. Men, on the other hand, may quickly move to a similar item if they cannot find exactly what they look for within a certain time frame.
Online shopping behavior is also different between men and women. As many as 68 percent of women abandon a clothing purchase on a mobile device, versus just 51 percent of men. Women see shopping as a way to build up a cart and then cull their selections. Men usually read more product descriptions and then decide what to add to a cart. Combine these retail purchase habits with data metrics such as local shopping, previous purchases, local weather conditions and a smartphone's location, and retailers have a lot to go on when it comes to finding ways to get people to buy items. Both men and women love personalized shopping experiences that make a shopping trip more convenient.
As big data continues to pinpoint shopping habits of the typical consumer, customers may find more ways to find a retail purchase based on someone's gender. Predictive technology could make retailers even more savvy about the patterns of smart customers, which makes everyone a winner in the retail game.
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