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If you have ever shopped on a major e-commerce website, such as Amazon, Walmart, or Victoria’s Secret, you may well have noticed a sidebar on your personalized account page that lists a series of items that are specially “Recommended for you.” It probably isn’t going too far out of the realm of possibility to suggest that you may even have made a few “impulse buys” based on seeing something that caught your eye among these handy recommendations.

What Exactly Is A Recommendation Engine, And What Does It Do?

A recommendation engine is an algorithm that generates “personalized” suggestions for customers based on their product viewing and buying habits. For instance, if you spend a lot of time on a gourmet coffee website viewing products that incorporate the flavor vanilla, the next time you visit that website, you’re very likely to see a whole host of vanilla coffee flavored items listed as “recommended for you.”

Of course, the presence and constant use of recommendation engines as a sort of ad hoc customer surveillance isn’t limited merely to e-commerce sites. Social media networks, such as LinkedIn, Facebook, and many others, also make use of recommendation engines in order to influence users to “like” certain pages.

Predicting A Customer’s Interest Means Anticipating A Future Sale

It’s all about collecting data on the various things that users are interested in, and then using this data to predict as accurately as possible which items and pages a social media user is likely to take an active interest in. Predicting this interest is the best means of anticipating as accurately as possible the items that they will be most likely to purchase in the near future.

How Exactly Does A Recommendation Engine Work?

A recommendation works by collecting data that you build up on a certain website or social media network, and then using this data to create a number of specialized “predictions” that are designed to anticipate what your next purchase might be. Although such a system is hardly foolproof, it has already been shown to be remarkably effective in performing its appointed task.

Because the program is fully automated, it can be used to narrow down the range of possible items that you may be interested in purchasing to a select group that reflects its best possible prediction concerning your past, present, and future buying habits. Likewise, a recommendation engine used on a major social media network, such as Facebook, can narrow down the range of “People You May Know” to anywhere from a few dozen to a few hundred out of millions of possible candidates.

What Sort Of Data Should You Use A Recommendation Engine To Collect?

Of course, a recommendation engine is worthless without its user first being able to define its goals and then set the parameters of its data collection within reasonable limits. As noted above, it’s important to collect data based on the actual products that a person purchases directly from your official online web store. As a secondary collection point, you should next include the various items that a customer may have viewed on your website before arriving at their ultimate choice.

The various products that a customer surveys on a website before making their choice and checking out can be an excellent clue to the sort of items that they are interested in purchasing at a future date. Of course, a recommendation engine can also make the occasional mistake at this point, as there are countless reasons why a particular item might catch a viewer’s eye without necessarily motivating them to consider it as a serious future purchase.

Recommendation Engines Can Also Be Used In The Health Care Industry

A new and potentially revolutionary use for recommendation engines is currently developing within the international health care industry. Many insurance sites are now making use of recommendation engines in order to collect data on people with regard to their currently existing health issues. Such data collection can be made use of in order to pinpoint current issues and predict the occurrence of future medical problems.

This data gives insurance companies and health care providers alike a valuable resource of information that can be used to predict not only the future health problems that a particular individual may face, but also what sort of coverage that person may require.

Further Information About The Use Of Recommendation Engines In Websites

There are a host of additional sources on the world wide web where you can locate further information concerning the use of recommendation engines to drive company profits. The website design professionals who fashioned Bobby Kotick’s page, among countless others, have incorporated many of these strategies in their work.

While recommendation engines have yet to be perfected to their absolute maximum potential, they have already more than proven their usefulness to a myriad of major corporations with global reach. If you have been considering adding such a device to your own website, the chances of increasing sales among your customer base by doing so are very positive.

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