Ecommerce Websites: Recommendation Engines

Are you one of them planning to create your own ecommerce website recommendation engine to provide consumers with personalized product and service recommendations? How tedious and difficult is the process and what does it take to do it the right way? We spoke to some of the leading retailers who realize the value of up-selling and cross-selling products and ultimately decided to start down the do-it-yourself path. It might be a bit biased for some of you, but I think product recommendations are one of the areas where you don’t want to reinvent and reintroduce the whole process. Well, in some cases, the do-it-yourself approach doesn’t make any sense and there are reasons behind it. The main reason is the development cost involved in building a complete system that actually works, and the other is the learning curve required to optimize it. So how hard is it to build a recommendation engine? Online retailers and vendors finishing development of their own recommendation engines are unaware of the different components that need to be online. Some of them are mentioned below in brief.

• The recommendation engine must track each and every activity of visitors and shoppers on the website, including the brands, categories and products viewed, analyze and determine the search keywords they use, the items added to the wish list and shopping cart and purchased. , geographic location information, origin of visitor and viewer traffic, and more.
• In addition to supporting multiple types of recommendations, the system must be integrated in such a way that it can display the correct one and more than one on a single web page based entirely on where the user is at the moment. he/she is in the purchase funnel means if your system has less than ten algorithms then you are extremely naive for sure.
• Finding the similarity between items and users is a simple process. The hard part is figuring out which correspondences to take and which to ignore.
• The system should be built against standard testing and reporting capabilities so that it can be demonstrated and optimized for value. This point is quite critical to determine how few online merchants and sellers actually measure the impact of their native systems.
• The system should have an attractive and user-friendly interface that allows merchants and retailers to manage and control the results based on various variables of the recommendation engine.

Another important reason is the expertise that is needed and demanded to optimize such a recommendation engine system. There are several things that will assess the impact that an engine will have on the business and if you are a newbie and a newcomer to building this system, then there is less chance that you will invest time to gain more knowledge and learn it. Also, I’d be happy to start with a naive system and stick with v1 for the long haul. It will not take the initiative to test different widget layouts and placements on a web page. The decision to buy or build a recommendation engine system should be based entirely on ROI. You need to consider the boost it will give your business along with the development costs.