Not just bought but how much enjoyed

The driver of recommendation engines is not just whether the item was bought, but how much the item was enjoyed. For instance, one can watch Transformers 2, but not enjoy it much. To put it another way, most Americans will say they are against burning the American flag. The question, however, is how far out of their way will they go to stop the flag from burning. They most likely will cross the street to put it out. Will they go to the next town? Will they go across the country? That level is the driver of a recommendation engine.

Customer Want - Not Human Categories

A recommendation engine shows the customer's wants and not a human category. Humans are imperfect in defining a category. For instance, romantic comedy may be a category to market the film. It does not categorize the elements that the viewer will enjoy.

The elements that define a customer's likes are difficult to determine. It could be the resolution of the story, the amount of humor, the type of humor, the style of story telling, the amount of action, the level of dialog.

Instead of displaying the most popular rap albums for the year, the store through a recommendation engine can recommend the rap albums that the customer most likely will purchase. More interestingly, the store can display non-rap albums that a standard search wouldn’t reveal without predictive analytics.

Companies Leveraging Predictive Analytics

The list of companies who are finding the benefits of predictive analytics is growing. The benefits of improved profits and better customer experience make for sound business.

Capital One

Google

IBM

Pandora

Netflix

Apple iTunes genius

NKD

Kobo Books

Indigo Books

Fishpond

Lovefilm

YouTube

Tvister.de

Prefence predicting - long-tail of curve

Recommendation engines deliver items that more closely meets customer’s wants. Traditionally, web pages display “other users like.” This is similar to offering the movies Transformers and The Hangover. These items are the popular items and lie in the fat of the bell curve. They are a small percentage. This ignores the lesser known items in the long-tail that will be enjoyed by the customer. Netflix delivers movies that are lesser known but that receive high ratings by the user. It uncovers the unknown. Otherwise, only a handful of popular movies would be watched and the customer would end his/her subscription.

Online stores increase the popularity of popular items by offering "other users like" to the buyer. A user becomes aware of the item and is more likely to purchase the item thereby making the item more popular. The effect is a self-perpetuating popularity list. New items that have not been purchased will not make the "other users like" list regardless of matching a user's preferences.

For the online store, items that meet a customer's wants are displayed. Given the limited space available for user browsing, hitting a customer's wants is more valuable.

Google leverages Predictive Analytics

Predictive Analytics

Google has applied the methods successfully across its enterprise. Netflix keeps users' queues packed through it. Pandora is reinventing the radio with it.

Before Google, internet search was sorting through 1,000 items. Google simplified internet search to the 3 most likely to match a user's need. Google beat Dogpile and Yahoo by applying predictive analytics.

Recommendation engines will improve online stores by turning searching into finding and in return improve the company's profit.

Don't Need to be 100% Right Just Predictably Right

Predictive analytics are a prediction of a customer’s actions. They are an improvement over present tools.

Similarly, ERM and CRM systems do not create 100% correct forecasts. They are an improvement over the tools prior to these systems. They improve the decision and thereby improve profits.

A recommendation engine creates a more accurate prediction and leads to better decisions. The engine increases the chances of targeted communication and matching customer’s needs over a non-predictive based system. “Predictably right” lowers costs of communication and improves revenue by more closely matching the customer’s actions.

Predict Customer Actions

The better a system can determine the customer’s actions, the better the company can target the customer. A recommendation engine can predict the customer’s actions and increase profits.

For example, a company can lower its costs of customer acquisition by knowing which customers will become repeat customers through the offer of a coupon. The system can determine the 60% of customers who will become repeat customers. The targeting of this set rather than all customers lowers the costs and improves revenue on the 40% of customers who are inclined to become repeat customers without a coupon offer.