We caught up with Suresh Shankar the Founder of Crayon Data. Suresh is serial entrepreneur with over 20 years of consulting and marketing experience, having fuelled growth for companies such as: Citibank, ABN AMRO, Standard Chartered, HP, Nokia, Motorola, Unilever, Pond’s, Pepsi and McDonald’s. Before founding Crayon, Suresh was the Founder and CEO of RedPill Solutions, which was acquired by IBM in 2009. Suresh then worked for IBM as their Director of Analytics until 2011, before founding Crayon. He earned his M.B.A from the Indian Institute of Management.

Can you tell me a little bit about yourself and what Crayon is doing?

My name is Suresh Shankar and I co-founded Crayon along with my business school classmate from thirty years ago, a guy called Srikant Sastri. Crayon is headquartered in Singapore where I live, and we started about eight months ago. What we’re doing in Crayon is bringing together what we call a choice engine, as opposed to a search engine.  The reason we’re trying to build this is because we think choice is a very big problem.  Today, we can all search for the information that we need and we can find that easily, but that doesn’t mean that it’s actually easier to make choices.  We have more and more choices and life is becoming more and more complex as a result of having more choice.

What we are trying to do in this platform, to solve this problem of choice, is to bring together the vast amount of data that lies inside and outside of enterprises, and join these disparate sources of data to paint a colorful picture.

We want to be able to predict your choices, whether that’s a choice you might like to make or by giving you four or five relevant choices. The challenge with this today is that this information lies in multiple places because it is so wide-ranging.  So, a bank might know certain things about you from looking at your past behaviour, but they wouldn’t know anything about your taste, for example.  But online review sites  will know something about your taste and even people like you.

What needs to happen, therefore, is that you really have to bring together all of this to say, at this point in time, this is the relevant choice for you. This is what Crayon is doing.

How does this work?

We started by building what we call the Massive Test Graph or the World Test.  Currently, we have about six hundred million test points, and growing larger every day, and we have about six billion connections.  These tests are around the world and in categories like, shopping, dining, music, movies, books, hotels, airlines, television shows, you name it.

We are able to find out in this test graph how any one particular entity or product has led to any other product, what the affinity is between them, and furthermore, not just within a category, but across categories.  Then we are able to tell customers, that by mixing what you know about an individual with the anonymous test graph, you are actually able to make a better prediction of four or five relevant choices for each individual.

That’s what we’re trying to do when we build our product, what we’re calling the choice engine.  We really think the choice engines are the future, because more and more we are overwhelmed by choices.  Look on Google and what do you find?  What do you do with a hundred thousand results?  Even if you never go beyond the first page, to find what you really want you have to go and look at each link, and you have to find what you want in there.  The real challenge we see now is understanding the query question and putting together a set of four pieces of information that you should look at.  So, that’s what we are trying to build.

How did you come up with this idea?

Tough question.  Partly, it’s a result of our experiences, both professionally and personally.  When I look at what enterprises want now – to know how to predict what a consumer wants – and I think about the amount of connectedness in data and the world as it exist today, we’re really much closer to addressing the problem from a technological standpoint.

The methods from a research standpoint haven’t changed, but what we now have is the ability to look at huge amounts of data to be able to answer the question: what would you want at this point of time?  In a sense it’s just an evolution of things that I did twenty-five years ago, when the standard procedure was to go and do a usage and attitude study of five thousand customers to understand what they want, and then build a product for that.  It’s just that now we’re doing it on a massive scale with real-time inputs.  In the old days, if you went to your grocery store the grocer would know exactly what you came to buy, because he knew that you bought something three days ago and are running out of that.  We’re really trying to do just that on a massive scale.

Part of it is also a professional thing in that I’ve been influenced by working at IBM on Watson, their big platform to make intelligent human decisions and understand natural language, through which I’ve realised that technologically the problem is solvable now.  I think it’s a variety of all these different influences that you are confronted with.  Mostly, it’s fun doing something that no one has ever done before.  And no one has done this, no one has solved the problem of a choice engine.

What is your business model?

So, it’s a SaaS model where you pay a subscription fee, for example, for every customer you pay a few cents a month.  Or it could also be a system where you pay per lead provided.  A variety of different models are available on the transactional level, charging a monthly fee for actually dipping into our choice engine and being able to deliver better choices to their customers.  That’s the model we’re working with.

Are you looking for anything in particular, like funding or special talent to hire?

Talent is always important.  For funding, there’s always more money, but we realized we’ll always need more and more.  But we’ve not had too much of a problem raising money, because I think investors and others can see that the space is big, we have a good team, and we’ve actually made progress with clients and the product that we’re building.  Talent is always a hard one, and what’s interesting in Germany is that I’ve been asked by people across the age spectrum what we do and how they can get involved.

What’s interesting is that in this country practically everybody seems to have a doctorate in statistics or machine learning or something algorithmic.  Perhaps we would be looking at trying to see how we can get more of the talent here.  Clearly it’s a very big economy, and I think we’re looking at big clients in Germany.  For me I would say I think from a German perspective it’s client and talent that would be interesting for us.


Crayon Data Pte Ltd. engages in the process of building a business and technology platform that democratizes the use of big data for the average business and consumer. It offers Simpler Choices Engine to help consumers and businesses make better-informed and smarter decisions to go from providing more choice to less choices; and One Drop Analytics to boost B2B sales, and marketing intelligence and demand generation by providing integrated insights on companies and markets. Crayon Data Pte Ltd. was founded in 2012 and is based in Singapore.


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