We’d all like to think we’re making our own shopping choices, but tech is playing a growing role.
At this time of the year, when many people shop more heavily than at any other point on the calendar, mobile commerce algorithms are playing an increasingly important and strategic role in helping us to place items in our cart before we check out.
Marketing strategies such as recommended and suggested items have become a normal part of shopping.
As natural as it may feel to see suggested products while we browse a store’s website or scroll through our social media feeds, these ads are having a bigger impact on our buying habits than most of us know or would like to think. With widespread sale events such as Black Friday increasingly move online, marketing to digital shoppers is having a direct sway on individual purchasing decisions.
The method is called algorithmic mobile commerce, and it involves the use of technology – often artificial intelligence – for tracking and analysis of shopper searches and purchases. Throughout this process, it uses the data to predict and suggest other products to purchase. As digital shopping and AI continue to expand, so is the use of this type of marketing.
Anyone using mobile commerce has had a similar item recommended following a product search.
Amazon is a key example of just this type of process, where a search for a product is conducted and while search results are offered based specifically on keywords entered by the user, additional sponsored and recommended posts for similar or associated items also show up in the same results list.
Often, such results can even redirect a consumer away from the items originally searched for, sending them to other brands, styles, or products with other features that fill a similar requirement.
The same can be said when browsing the web or shopping on other store sites or mobile commerce apps. The algorithms continually analyze online activities to inform and enhance the results of future searches – whether they are for information, products, or services – by anticipating that user’s needs based on data already collected.
