The Product Recommendations that Everyone Deserves

The Cool Kids vs The Not So Cool Kids

Almost every online shops have a product recommendation system that is either a very hit or a very miss when it comes to relevancy. It’s like the high school romance gossips; everyone says they did it but we all know only the cool ones actually did it. In reality, these cool kids are Amazon, eBay, Aliexpress, and maybe a few more of such gigantic size. What of the others then?

Big companies have huge R&D and big data science teams that can pull off some astounding results when it comes to recommending the correct and relevant products to the customers. All the other mid-sized companies tend to put it up for third party solutions (Projects); whereas smaller companies just have a barebone version of product recommendation system that is not even slightly relevant when recommending the right products to the right customers.

The barebone systems are mainly based on the visiting data of the website that fails to take into account the users'/customers' past behaviors and past records. Additionally, the process gets far too complicated along the way for smaller companies to handle.


The way e-commerce is expanding, it would be an understatement to say its the new big thing. The worldwide pandemic has also played a big role in making people get used to online shopping even more than anytime or any event before. This opens up a world of opportunities.

One Tool to Recommend Them All

The product recommendation tool that I am going to explain is called Enhencer. Long story short its a machine learning platform that is specifically designed for obtaining product recommendations with as minimal effort as possible.

In terms of accessibility, Enhencer is one of a kind. How so?

You have to upload Sales, Customers, Products, and visitors data to the platform. There are a few surprising things that Enhencer does. First, it takes care of the Data Pre-Processing and Features Engineering on its own. Secondly, it trains many machine learning models on the data and chooses the best one and creates a dashboard for the users. Believe it or not, that's it, you don't have to do anything else, just upload the data and relax.

If the users don't know what sort of data to upload they have some demo data that you can download and take some reference while uploading the data.

Old Tricks but Modern Magician

This is what the outputs look like after you upload the data to the platform. Enhencer provides a likelihood for each customer to buy a product for each category of products.

Unlike traditional methods, it does one very specific thing differently. Enhencer divides the customers into several segments based on their past behaviors and budget constraints. Yes, you heard me right; budget constraints. The reason is; It’s not enough to predict a person is interested in buying a new phone. If you recommend an expensive phone to a customer with a lower budget then the recommendations are very likely to go to waste. Therefore, by using this sort of segmentation Enhencer’s recommendation algorithm decides which product is recommended for a given category and segment.


The list you see in the picture is just for one category and one segment, in other words just the tip of the iceberg. It shows the likelihood of the purchase of that category product for each customer based on their past behavior as well as their website visit data. It does this for each and every product category and each and every customer.

You can download these lists of customers and their likelihood of purchase in excel format. Additionally, you can implement the system directly in your e-commerce platform by using the API.

The Cherry on the Cake

It's not a perfect system of course but what it does it does absolutely perfectly and that is the accessibility. That's the reason why its an all start tool.;

  1. It tells you what data you will need to achieve robust product recommendations
  2. It takes care of Data Pre-Processings & Feature Engineering
  3. It trains the machine learning models on its own
  4. It creates a super fun and easy dashboard from the results
  5. It requires no coding despite being a machine learning platform as a backbone
  6. It only requires the users to upload the data, That's it.

That how easily smaller and mid-sized companies can obtain product recommendations without any unnecessary hassle and at the same time be good at it. We all know how important and how big of a difference a good recommendation system can create in the longer run. Therefore try it out for free today.

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