Breaking the Complexity of the Product Recommendations
The Sweetspot that no one talks about.
The Real Issue With the Current Sytems
With the rise of e-commerce in this era, a new frontier has opened up. It’s called product recommendations. It’s a no brainers, you recommend the right product to the right customers at the right time and your sales just bump up. As much as it sounds sweet and like a cookie-cutter in reality it’s the complete opposite of that.
Product Recommendation is one of the most misdelivered concepts in the practicality of the Data Science industry. There are basically two ways in which the whole industry does that.
- They do it in very basic terms with no tangible return in terms of accuracy. In other words more half of the time, the recommendations are not correct and they don’t sell.
- Others with a bigger team and budget do it very well with astounding results and accuracy, meaning the recommendations are actually accurate and it actually does bump up the sales.
The Good, The Bad, and The Ugly
Now let me introduce you to the concept of the good the bad and the ugly sides of these two methods.
The accuracy and reliability of the second one is the good side. The recommendations from this method are accurate and they do make customers buy more and eventually help the sales figure.
The bare bone structure and unreliability of the first method is the bad side. Half the times it won't even recommend anything relevant.
Now let’s come to the ugly one. Just a fun fact; This is my favorite. The efforts and time needed for the second one is the ugly side, perhaps the ugliest side. Just for the reference, it’s the equivalent of the way the valve’s half-life team develops. It will come out eventually at some point but no one knows how long it will take. Spoiler alert half-life 4 is confirmed, fingers crossed.
Not the Good nor the Bad Just the Ugly
The industry standard process is quite frankly a little bit of a long road. Don't get me wrong they do work but takes a humongous amount of time. The processes are as follows:
- Collect the data then clean the data.
- Do some of the feature engineerings to get the most out of the data.
- Train a bunch of machine learning models to figure out which one is slightly better than the other ones.
- Validate the models to adjust and tune the selected model.
- Test the model to justify the real-world implementation of the model.
There might be only 5 bullet points, but looks can be very deceiving. Specifically the second one. That’s the one that will eat up your weeks of work time for its lunchtime and let’s not even talk about the third one. This third one is the proof that third time is not always the charm. In a nutshell, to come up with a recommendation system of this scale can take up to months and not to mention the budgetary factors. Not every e-commerce company has that amount of budget and manpower to pull off such a task.
Say Hello To My Little Friend Called: Save your time.
Well, the name of my little friend is actually Enhencer. It’s a product recommendation tool that tries to be: the good the better and the beautiful. The good is the fact that it's very fast, the better that its super easy, and lastly it's very intuitive dashboard making it all but beautiful. It tries to solve the issue of the complexity and the budgetary that exist today. It has some primary features and which translates to; save your time, save your time and say goodbye to complexity.
Well, the real features are:
- It handles feature engineering automatically.
- It trains a bunch of machine learning models automatically.
- It compares and chooses the best one automatically.
- It validates the models automatically.
The whole process can be summed as: Enhancers require the users to upload the Sales, Customers, and Products data. After all the data are in the platforms Enhencer literally handles all the feature engineering, model training, and all on its own automatically. It uses a machine-learning algorithm behind the curtain to achieve this. This also means the users don’t have to know any data Science knowledge let alone write a single line of code.
In the end, Enhencer provides the Product Recommendations for each customer and each product category in a dashboard like this. The dashboard is just a representation of model outputs and translated into daily life dashboards that anyone can understand without having any prior data Science and technical knowledge. You can either download the lists in static file format or implement the system to your live e-commerce site with API. This makes the implementation real easy.
The biggest conservative thoughts you might be having right now; Is it accurate?
Since most of the things are done automatically by a machine learning platform, one can definitely raise such a question. Enhencer generated Recommendations are very precise accurate when it comes to real-world results and generating actual sales. The biggest reason is Enhencer takes customers' past purchasing behaviors and website visit data into account simultaneously before recommending any product. One other thing Enhencer takes into account that others fail is it takes customers' budget and purchasing power into account.
What this means is; If a customer wants to buy a phone and searches for some on the website it’s not enough to just recommend some phones. If the customer is on a budget and if you recommend an expensive phone to them just because it’s the most popular one right now then the recommendation is very likely to go to waste. Enhencer tackles this by combining both past purchase behavior thus considering purchasing power and website visits together to recommend the relevant product from the product category to maximize the possibility of a new sale.
A lot of large scales e-commerce sites have started using it on their system. You can find the whole list on their website https://enhencer.com/
Enhencer is just one step in the right way. It's super accessible and easy to use. It brings down the time required for such a process from weeks/months to just a few mere minutes. This means smaller e-commerce to larger ones all can obtain relevant and accurate preductions very fast and most importantly very easily.