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Decoding consumer demand signals to know which products are HOT or NOT

Savino Lobo, Assistant Vice President, Ugam,


Assortment Intelligence, Assortment decisionsSome of you may know that Mark Zuckerberg after (or during!) an afternoon drinking session, set up a website which was basically a “hot or not” game for Harvard students. He hacked Harvard’s student profile pages and used the images to populate his website – called ‘Facemash’. And apparently, his little ‘hot or not’ drunken experiment became a big hit (aka Facebook).

In my previous blog, I shared how today’s consumers are leaving a huge digital trail that can be tapped to get a range of insights from what they want, why they want something, to even what they would be willing to pay.

Have you spent time online trying to decode consumer demand signals? If you have, you’ve probably realized that you’re dealing with a multitude of democratic data… there are reviews and ratings, search volume, traffic volume, social signals (FB Likes/Shares, Pinterest, Tweets) etc. After you get the data, you need to be able to make sense of it all in order to figure out which products are hot/trending.

At Ugam, we have a big data platform that enables data acquisition, synthesis and analysis of this big and unstructured democratic data to be able to infer what’s hot.

Take the bar furniture category I’ve referred to over the past few posts ; we narrowed down to a hot products list (see top 10 below) after acquiring, normalizing, synthesizing and analyzing several consumer demand signals using proprietary analytics models.

Assortment Intelligence, Assortment decisions

The funky Restoration Hardware product I wrote about last time comes out on top because of strong social signals and high search volumes. Other Top 10 products also had strong review trends and social signals indicating consumer interest.

Now all you need to do is compare your assortment against the ‘hot’ list to understand how compelling your assortment is. The question is … can you do this on your own in a comprehensive, systematic way?

We can help.

Get a free executive preview to see how.



The Author:
Savino Lobo is an AVP at Ugam and is part of its retail analytics solutions team and oversees the assortment intelligence solution. He is passionate about leveraging democratic data to help retailers make better assortment decisions. Savino has over 12 years of experience developing research and analytics solutions. Outside of work, Savino enjoys traveling and trying cuisines from around the world besides spending time with his twin 3 year old boys.



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