ecommerce Recommendation Engine Advantages

Are you one of them planning to build your own ecommerce recommendation engine to provide consumers with personalized product and service recommendations? How much the process is tedious and difficult and what does it takes to do it in the right manner? We had a word with some of the leading retailers who realizes the worth of up selling and cross selling of the products and finally decided to commence on a Do-It-Yourself path. May be it is a sort of biased thing for some of you, but I believe that product recommendations is one of the arenas in which you don’t want to re-invent and re-introduce the whole process. Well, in few cases, do-it-yourself approach does not make sense at all and there are reasons behind it. The foremost reason is the developmental cost that incurs in building an entire system that actually works, and the other one is the learning curve needed in optimizing it. So how hard is it to build a recommendation engine? Online retailers and vendors who finalize to develop their own recommendation engines are not aware of the different components that need to be in line. Few of them are mentioned below in brief.

·         The recommendation engine needs to keep a track of each and every activity of visitors and shoppers performing on the website including brands, categories and viewed products, analyze and determine search keywords they use, items added to the wish list and to the shopping cart and bought, geo-location information, source of the traffic visitors and viewers and more.

·         Along with supporting multiple types of recommendations, the system should be built-in a way that it should be able to showcase the right one and more than one at a single web page entirely based on the fact where the user is that time, might be he/she is at the purchase funnel means if your system has less than ten algorithms than it is extremely naïve for sure.

·         Finding likeliness between items and users is a simple process. The difficult thing is to figure out which correspondences should be taken and which ones to be ignored.

·         The system should be developed as per standard testing and reporting capabilities so that it could be demonstrated and optimized according to its value. This point is quite critical to ascertain as few online merchants and vendors in real measure the impact of their native systems.

·         The system should have a user-friendly and appealing interface that lets merchandisers and retailers to handle and control the outputs based on varied variables of the recommendation engine.

 Another major reason is the experience it takes and demands to optimize such recommendation engine system. There are several things that will evaluate the strike an engine will render on the business and if you are novice and a new comer building this system then there are less chances that you are going to invest time to grab more knowledge and learn them. Moreover, you would be happy to start with a naïve system and stay with v1 in the long run. You are not going to take the initiative to test different widget designs and placements on a web page. The decision to purchase or develop a recommendation engine system should be completely ROI based. You have to consider the kick it will give to your business along with the development costs. Developing a naïve system could be cheap and more likely going to deliver poor results but this is also true that without supporting them you will never learn and come to know about it.

Few of the leading giants like Wipro, Infosys, IB Technology, TCS, IBM and more can assist you to create and suggest tips to build recommendation engines for your business as well as reap favorable gains from them.