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The Reason Why Retail Industries Love Recommendation Engines

Remember seeing the words, ‘you might also like’ while you are shopping on-line? Or the ‘people you may know’ on LinkedIn or Google Plus? How many times have we actually stumbled upon these words and ended up clicking them, just to find out a little something different?

When Big Data is mentioned, ‘benchmarking’ is often mentioned in the same breath. As amazing and accurate as benchmarking with data is, it is not the end all of big data. There is something more to Big Data- that gives it so much more value. It is called a “Recommendation Engine” in industry lingo.

Recommendation is that one step ahead of benchmarking or comparing data. Because, comparing data against your competitors is great from a ‘getting insights’ perspective. The next logical, and often-times undeniably important step, is using those insights productively. A recommendation engine is what turning insights into actions is all about.


It reduces Big Data to small data. A recommendation system suggests a few data points out of a large pool of data. Recommendations narrow what could become a complex decision to just a few recommendations. Big Data allowed us to do recommendations on a new scale that we did not see before.

-Lutz Finger (Read More…)


Recommendation Engines are traditionally perfectly complaint with the retail and media industries. And you dear retailer, are in luck, as we keep saying all the time. Apparel retail happens to be a subject that recommendation engines are great for.

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At IntelligenceNODE, we are associated with quite a few etailers who have their work cut out for them. In terms of planning and executing perfect marketing strategies, placing the right product in the right area at the right time, and generally understanding the needs of evolving ‘prosumers,’ etailers NEED to think different.

A challenge it seems to many. Etailers have to field a lot of flak as well as applause from consumers, media and the industry. A big sale day and undeniably, someone somewhere will talk about ill preparation, or servers crashing, or discounts being fake, products being high priced, et al.

To stay true to ones’ calibre, to please employees and customers alike, and to get positive attention from the media amidst all of this, is akin to wearing a crown of thorns on your head and smiling through the pain. We do understand. We see the struggle and the effort.

Which is why, at IntelligenceNODE, we are always finding new ways for you to stay ahead of your competition. As an etailer, you know your customer quite well. By now, you have established the connection with your buyer quite well, especially online.

The insights you get can literally be a gold mine, if used correctly. All you need to do is recognize the various data points that you can work on.

Our experience with retail, especially apparel retail, gives us a unique perspective into the brilliant usage of data. Here we talk about recommendation engines- and just pushing your limits a little. We believe recommendation engines are like the web’s filters. They take interest and availability into perspective and provide the end user with options that usually suit their (end user’s) tastes.

There are a couple ways to use this recommendation tool successfully…

One way to do it is popularly known as ‘Content Based Filtering.’ Simply put, this filtering recommends items based on a comparison between the content of the items and a user profile. Technically speaking, this method relies on ‘keywords.’


Pandora’s Music Genome Project comes to mind here. For the uninitiated, instead of making musical choices based on the song choices of other users with similar interests, Pandora recommends by matching up the user’s artist and song likes with other songs that are similar.


Another method is what is called ‘Collaborative Filtering.’ According to Wikipedia- In general, collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc. Applications of collaborative filtering typically involve very large data sets.


The basic idea behind Collaborative Filtering is providing recommendations or predictions based on the opinions of other like-minded users. A generalization drawn from similar patterns based on the ‘trends’ we like to call it.

We think though, that what method to use or how to recommend products, is subject to many factors, for apparel etailers-which we like to call attributes. Some of the most versatile attributes are price, color, size, pattern, design, etc.

Let us demonstrate by speaking about ‘price.’ When a particular product is seen as trending online- selling quite well, your recommendation engine picks it up; and informs you that tweaking the price will yield double the profit, visibility and media mentions. You decide to heed this advice, edit the price of your product, et voilà!

This we think is a very interesting way to make sure that you, the retailer, stay on top of the innovation ladder. At IntelligenceNODE, we construct, innovate and initiate processes that will eventually reflect in your sales, ROI, etc. But what we really value and we know you do too, is adaptation, and our collaboration!