It seems like just yesterday that business analysts were wondering whether this newfangled eCommerce fad would catch on.
- Would anxiety over credit card theft deter customers from trusting online retailers?
- Would people feel confident in buying stuff sight unseen or would they instead stick to going to brick-and-mortar stores to paw and sniff the merchandise?
Fast forward a few years and we now find Web retail giant Amazon, fueled by our insatiable urge to buy stuff on the Internet, with 2013 sales of more than $17 billion U.S. and a market capitalization of $153 billion.
We choose to buy from sites such as Amazon because of convenience, pricing, breadth of offerings, product reviews, and a simple and pleasant shopping experience.
One of the things these sites excel at is the ability to recommend products based on our browsing or purchasing history. Search for, say, a bicycle helmet and Amazon will tell you that “Customers Who Bought This Item Also Bought” cycling shorts, cycling shoes, cycling gloves, and every other cycling-related item including chamois cream (don’t ask).
Amazon’s recommendation engine encourages us to increase the number of items added to our shopping cart. Rather than feeling like these items were forced upon us, we are instead grateful to the site for making shopping so easy. Gone are the crowded parking lots, endurance of inclement weather, and eternal waits in checkout lines, replaced with anticipation for delivery which may soon come within minutes via a flying drone.
With millions of customers, Amazon has the big data to support a powerful recommendation engine. But really, it isn’t rocket science to suggest to someone shopping for a kitchen knife that they may also want to purchase a cutting board and maybe some adhesive bandages for potential sliced fingers.
Adopting an Amazon-like recommendation system in learning and development doesn’t require big data and teams of programmers. This can be done within any learning management system (LMS) that contains two simple features:
- The ability to have course-specific communication templates
- The ability to link directly to one or more courses
Here’s a typical course completion e-mail:
Here’s a variation that contains a couple of recommendations:
Adding recommendations to your communications with learners can provide measurable benefits:
- Increased enrolment, course completion, and certifications obtained
- Better learner engagement through a more pleasant experience
- For commercial learning content providers, increased sales
Successful online retailers such as Amazon would never let you buy a pen without also suggesting you take a look at notebooks. Consider using the same simple logic in your learning initiatives.