Recommendation Engines are being used by many online retailers today to cross-sell and up-sell products and services to online consumers. Amazon.com, Newegg.com, Buy.com seem to use some sophisticated algorithms around the recommendation engines at the heart of their online selling portal. They are also an integral part of any choice optimization strategy.

Without even going to the technicalities of recommendation engines available, we have put together a few points which can help businesses can harness the power of recommendation engines:

• Get the Basics Right
  Understanding some salient features of your products or services can help your business classify and categorize what parameters are important to track and assign weights to and make recommendations to users on. If you are a seller of toys, the most important parameters (with varying weights) may be price, toy features (learning toys or motion-based toys etc.) and toy accessories (batteries, replacement parts etc.). If you sell high-end luxury goods, the most important parameters may not include price and may just center on niche features and custom options.

• Take Baby Steps
  Unless your business is high up on the maturity curve (a la Amazon), you need not start with algorithms that are very sophisticated. Take a baby step approach to developing algorithms. Broadly speaking, recommendation engines can be classified into 3 categories:
1. Content Based filtering engines – those that are based on the contents of an item and tries to pair user interests (if known from previous web visits) to ‘contents’ of an item (can range from books to luxury cars)
2. Collaborating filtering engines or Social filtering engines – those that try to match user profile and interests with other users with similar profile and interests. Takes into account user reviews of products and services.
3. Knowledge-based filtering engines – the most complex of the three, these try to leverage knowledge of user needs and a target product or service that may not be directly related. For example, an engine may try to recommend you a GPS unit based on your interest in books related to Roadtrips or North American geography.
   
   

Ample literature is available on these recommendation engines and other emerging architectures (Few links here). The choice of what recommendation engine is best for the business should be entirely based on the business goals and the products and services being offered.

• Make them Adaptive
  No matter what the nature of the business is, recommendation engines should be built around the belief that there is never a perfect algorithm for designing them. They should be constantly capturing and storing usage data and patterns and must continuously learn from user website visits, time spent on web-pages, third party websites visited and so on. Every time the same user visits your website, the recommendations that are presented will be a little more relevant than on the previous occasion. A sample starting point architecture can be found here.


• Keep it True
  Last but not the least, objectivity of recommendations is valued by the savvy consumer. An online platform for freely expressing opinions (on user experience or product performance) can feed into your recommendation engine to make it better with time. Amazon.com and Buy.com provide not only good platforms for customer feedback but also opinions on where else shoppers can get the same product they are looking at cheaper or costlier rates. The neutrality of the ratings and objectivity of the price positioning influences a customer positively and presents an image that these sellers are not trying to peddle their wares necessarily, but want you to make a good purchase decision as per your needs, preferably through their own stores but not necessarily.


Photo Credits: ieatcrayonz, scott92007, Jmb, losiek