Marketers Insight to Using Recommendation Engines

As marketers and promoters look for ways to implement rising amounts and value of personalization technology to enhance and make better customer experiences and raise sales lift, one of the most famous and vastly reckoned technologies especially in the retail and e-commerce marketplace is use of online product big datarecommendation engines. Such defined tools assist to render customized and tailored recommendation suggestions and solutions that enrich the online buying and selling journey by delivering proper and apt up selling and cross selling opportunities as integral part of user experience and acquaintance. Though with the availability of so many solutions in the marketplace at present it could be tedious to actually know what to look for in solutions that could help to surpass the needs of the brand as well as raise sales revenue. Most of the recommendation engines make use of collaborative as well as behavioral filtering approach to make sure that the relevant recommendations are timely based and precise or not, based on where the customers lie in the buyer’s online shopping experience. By using a perfect combination of such defined approaches aptly and properly, it assures that the recommendations are provided at the right time and in the right manner and most important of all in the right context. Just for an example, once the recommendations and considerations have been given to the item purchase and it has been shipped then it is vital and mandate for the recommendation engine to do not keep suggesting pushing alternatives and substitutes affecting the risk associated and disrupting a sale. This is the major reason that the technology should provide the capability and potency for the dealers and vendors to both affect the recommendation strategy as well as acknowledge how the outcomes are going to be derived so that one can actually stay in the control of buyer and seller journey.

Revenue contribution is for sure going to be one of the most important and key measures of success for recommendation engines but is good to go beyond this assuring the solutions meeting and fulfilling the wider objectives of the business. The other few key considerations could be like mentioned in below.

1.       Driving revenue and profit – Margin data could be a great way to tune up your recommendation engine assuring that you are influencing bottom line as well as optimizing profit opportunity for your business.

2.       Recommendations prediction – Always think and analyze about what impression you want from the visitors to go away and what they say actually about you people as retailers.

3.       Recommendations driving lift and the major focus – The foremost thing every marketer used to ask and query about are whether the recommendations are really making a big difference or not. It is another arena where business rules can assist in up selling of your products and leading incremental revenue.

Online product recommendation engines can for sure provide a good boost in your website revenue and when merged with the insight and knowledge of the marketing firm along with the user experience and sales distribution goes hand in hand. IB Technology , Cognizant, Oracle and Polaris are the few reckoned names offering such services all over the world. And make sure to choose your recommendation solution wisely that has the capability to optimize the opportunity from every customer engagement.