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Prabakar Murugaiah found TechFetch in 2008. Praba was a technology person with knowledge in data mining and in data processing. When he was an IT consultant  he came across abundant no of job openings, where tons of calls from the HR team and consultants will  come in but 90% of the jobs will not be the right fit. This is a huge waste of time both for the candidate as well as for the employer in trying to match the jobs and the right candidates. He personally also faced problems while trying to find people for his jobs using the job portals because most of the search engines were keywords based. There was a huge in-efficiency in matching the  jobs and candidates and people were wasting so much of time in trying to bridge

He sought out to save several million hours to bridge this gap of unstructured data matching. He began thinking about a technology to search the entire job description and match the keywords with all possible parameters  rather than the keywords only. He wanted to automate the process to fetch rather than only to search and wanted to make the experience of searching for jobs and candidates to be fun filled like it should be like a trained puppy that will go get only the ball that you throw rather than bringing in what it likes.

As an entrepreneur, he was trying to solve a massive problem that the industry was facing and it was easy for him to find his customers. He took a conscious decision and did not look at how much money he can make, how much he can grow etc but to solve the industry problem . Praba says that for him money was a by-product at that time. He knew that what he was building is complex than writing a software product where one can write it once and can monetize in multiple customer locations by copying the software and installing as there is only one side of the problem.  In this case, it is a two-fold problem, – two sided marketplace. If you unfold one side, you will still not be able to see what is underneath, you need to unfold both at the same time. He needed a critical mass of the two sides and he decided that he did not want to get the paid subscriptions and for 18 months, they were blowing the money to build a great product and Prabakar says that the product that they had in their early days is only 20% of what they have today as an evolved product. They decided to build this for the smaller companies who had an appetite to try something new rather than going after large enterprises and building a product for them where the resistance to try something new will be very high. They identified the early adoptors and gave them  freebies and took their feedback build the product block by block and got lot of them trying out the product free of cost.

Prabakar recollects the day when he put a throttle in the few services to restrict some of features and asked the customers to pay, around 95 % of the free customers dropped out and then they could retain only around 5% of the customers. They analysed the buyer persona of the 5% customers who stayed back and began the segmenting based on these people and began targeted marketing. They explored different ways that they could market, and they effectively analysed the behaviour of customers using the system and dropping out by using an in-house technology  and began attracting real customers.

They found out that the final automated solution saved three hours a day for the recruiters in companies.  With this value proposition, they did e-mail marketing, web marketing, cold calls and they built an internal CRM that started suggesting which prospect is responding to what and they understood the early adoptors. They did their research on these companies and extrapolated the niche segments that are more likely to buy that the others and began articulating the solutions to the customers and began getting customers

Now came the challenge of solving the other side of the marketplace – Candidates. He researched enough to find out the behaviour of candidates and stuck the market with a great value proposition. They might have been the first company which within 30 seconds of the candidate uploading their resume, sends an alert of what jobs matches their resumes and skillsets with a lot of algorithm matches that the candidates were thrilled and began spreading the word that the candidate ecosystem began filling up so fast. TechFetch initially was sending a lot of alerts as emails and they debated internally if they should send lots of emails a day or send once a day only. After a bit of thought they they took the bold decision of sending as many mails as the fits because they wanted to give the candidate a quick option to check out multiple matches which was their value proposition. They figured out that 90% of the candidates liked it while 10% did not like and they gave those candidates a frequency limit of how many times they want the jobs.

They solved the problem of getting a job within 24 hours  as they know the pain point connected the employees and the employers within a few minutes. Prabakar says that this took 9 months for his team to build the feature – the core engine. They do around 400000 real time matches a day.  Since 95% of the candidates looking out for the job are currently employed in IT industry and timing is important for candidates for finishing projects and taking the next one and they also are so stressed at work with tight deadlines and the engine made it easy for them with using one click to delete or to apply. Some may have 2 week notice or they know the projects will end in one week and that helps them to find jobs faster

Prabakars advice to early startups – 90% of the things that you plan will not work and hence be ready for massive changes. There will be perception differences between you and the customer on your product and your value proposition and both of you may not meet eye to eye on the benefit being a Pain Killer  or a Vitamin Pill. Customer acquisition will be difficult and time consuming if that happens. Money is a by-product , if you solve a pain for 100000 people, you will make million dollars, and you can make billions if you solve it for millions  of people and hence focus on solving a bigger problem and money will automatically come behind the solution.

TechFetch (http://www.techfetch.com/), now has 3 Mn unique visitors out of the total population of the 5Mn IT people in the US visiting the site yearly. More than 300000 custom jobs are posted live and the site delivers about 7 Million responses. They have around 50  employees globally. They are fully bootstrapped. They took a little bit of angel money in the beginning.  Silicon India featured them. Their offshore team is in a Tier 3 city – Tirunelveli in TamilNadu, Prabakar is proud that if a founder takes interest in building opportunities for employees in Tier 3 cities in addition to building a global product,  they can build an awesome team and the entrepreneur journey will be a crown on one’s head.

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