Two shoppers visit your website: one has visited before and one has not. Which one is more likely to convert?
Absent other information, the one that has visited before is typically more likely to convert. He or she is more likely to be familiar with your brand, has probably been cookied, may have been retargeted, and may have received a promotional or recommendation email from you.
However, with additional information the answer can quickly change. If we know the new visitor is looking to quickly buy a gift during their lunch break and the returning visitor is there to find cleaning instruction for the product they already purchased, suddenly the new visitor is much more likely to convert. But how do we possibly know this if we can’t read the shopper’s mind?
We observe the shopper’s digital body language.
Digital body language, the deliberate and passive actions we take while shopping, can reveal even more about our intentions as our physical body language does in a bricks and mortar store. This is because during an online shopping session we can analyze hundreds of thousands of data points using machine learning to determine what it means. Although store associates are amazing, this is impossible for humans to do.
Retailers typically focus on using high level data to target shoppers based on deliberate actions. If the shopper viewed a baking sheet, we target them with baking sheet. If the shopper opened a savings email, we target them with more savings emails. While this is low hanging fruit to take advantage of, it misses a much larger opportunity. We must use behavioral shopper data to analyze digital body language and truly dig into why the shopper is on the website, what their goal is, whether they are on a path to achieve that goal and purchase and, if not, why they may be hesitating.
Are they looking at the baking sheet because they just moved into their first apartment, because it’s time for an upgrade or are they trying to find a gift for that hard-to-buy-for-relative? Are they hesitating to pull the trigger because they’re not sure if it’s a good deal, because they’re not sure if this is the best baking sheet to purchase or because they’re not sure if it can be easily returned?
The next level of retail personalization incorporates the hundreds of micro actions a person takes every second while shopping online, down to the smallest mouse movements. This leads to hundreds of thousands of data points during a typical shopping session. The only way to analyze this data in a useful way is apply machine learning technology. Much in the same way our brains analyze the physical body language of shoppers in our stores, machine learning can analyze digital body language online but with the capacity to analyze exponentially more information—exponentially quicker.
A well-trained machine learning technology can detect why a shopper is on the website, what they’re trying to accomplish, when they are or are not going to purchase and the reason they are hesitating to complete a purchase. The top retailers in the world are using this technology today to gain an advantage.
-Jeff Lawrence, Founder and CEO of Granify