PlayCez : Local Recommendation Search Engine
Whenever you have to go out for eating, shopping or hanging out; how do you decide where to go?
Well … people generally ask someone in their social circle, but everybody has a limited social circle and asking someone is not always a feasible option. So they go on Internet to search and end up on a review site, which are rarely useful as most of reviews are from strangers and fake reviews.
Considering this problem of finding best places to eat, shop and hang-out nearby in your locality, PlayCez was created, which is a recommendation engine for local discovery. PlayCez provides you personalized suggestions in an experiential manner.
In mid 2010 Ashwani was back from his US project while working with Deloitte and was trying to figure out something in Location Based Services (LBS) predicting it to be mass adoption in near future. Anirudh was working with Google from few months after his two prior start-ups, he was thinking about starting some services for local businesses.
The two met through a common friend in Deloitte and started to figure out the idea. The next few weeks were spent in identifying the opportunity and brainstorming over ideas. “Barista Kondapur was our adda in this process.” Ashwani recalls.
“So we came-up with idea of PlayCez, formalized it into a business plan and started working. None of us were rock star programmer at that time but we wanted to make something incredible, so we started learning the basic blocks of technology required.
Meanwhile we took part in Asia’s largest business plan competition: Eureka, reached till the top 10 teams there; also pitched in the VC pitching team event at IITB (Vulture’s Nest) as the youngest team, without even a business card. People really liked our idea, which inspired us to prototype it.”
“When we started on prototypes, we wanted the product to be the best in every aspect. We decided to use best and latest technologies available. We hosted PlayCez at Amazon’s cloud computing platform for back-end from day 1.
We first made a Facebook application and did all our experiments and learning with that. Later we presented the first-cut at IIM Bangalore at their business plan competition: The Next Big Idea. A partner of well known VC firm in India liked our idea so much there, that they offered us funding.”
“There is nothing like getting a funding offer even before you have started making the real product. But when we understood the Term-Sheet, we realized that early stage VC investment comes with a lot of compromises (the largest one – valuation). More than that we realized that the funds we were going to raise will be over in 8-10 months, and we will end-up with very less marketing budget after the product development. It will also make our Series-B funding (yes, the Term-Sheet was Series-A!) almost impossible. So we decided to walk away from the deal.
We also realized that whatever we made till then was not a real product, and it won’t be possible to make something wonderful without dedicated work on it, so we decided to quit our jobs.”
“So we started bootstrapping. It was not easy but the learning was enormous. As we focused on product, the outcome was awesome.
“We are done with 80% of our product development and will be announcing some more stuff in coming weeks. We are also in talks for funding as sales & marketing will require a lot of money; but the good part is we need to put very little money on product, as it is almost completed.
We are planning to start our sales operations from next months. We have worked out some great algorithms to get meaningful social media sentiments on Internet for a local business. We will be providing these services at free of cost to these businesses. These services will provide businesses with analytics and consumer insight based on their social media reputation. For instance what kinds of people (location, demographic etc) are liking or disliking their businesses. It will be a freemium model and some premium services will be paid.
Currently the service is available for Delhi (NCR), Mumbai and Bangalore. We will be launching it for Hyderabad, Pune, Kolkata and Chennai in coming weeks.”
“We are constantly working on improving our algorithms to give best suggestions. We would love to hear back from you. Mail us at firstname.lastname@example.org or submit anonymous feedback with feedback tool at footer of our site.”
As told by Ashwani Gaur to iitstories
Follow Ashwani Gaur @ashwanigaur
Follow Anirudh Maitra @anirudhm