Are you one of them planning to build 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 talked to some of the leading retailers who realize the value of upselling and cross-selling products and ultimately decided to start down the DIY path. It may be a bit biased for some of you, but I think product recommendations is one area where you don’t want to reinvent and re-introduce 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 difficult is it to build a recommendation engine? Online retailers and vendors who are finalizing the development of their own recommendation engines are not aware 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 that visitors and buyers perform on the website, including the brands, categories and products viewed, analyze and determine the search keywords they use, items added to the wish list and shopping cart and purchased. , geographic location information, source of visitor and viewer traffic, and more.
- In addition to supporting multiple types of recommendations, the system must be integrated in a way that it can display the correct one and more than one on a single web page based entirely on the fact of where the user is at that moment. he/she is in the buying funnel means if his/her system has less than ten algorithms then he/she is extremely naive for sure.
- Finding the similarity between items and users is a simple process. The hard part is figuring out which mappings to take and which to ignore.
- The system must be built to standard testing and reporting capabilities so that it can be demonstrated and optimized for its value. This point is quite critical to determine how few online merchants and sellers really 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 monitor results based on various recommendations engine variables.
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 bring to the business and if you are a novice and a newcomer to building this system then there is less chance that you will invest time to gain more knowledge and learn them. Also, I’d be happy to start with a naive system and stick with v1 for the long haul. You will not take the initiative to try out different widget layouts and placements on a web page. The decision to buy or develop 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.