What is the social impact model that is focused on creating employment for youth in small towns across India? Ms. Mythili Ramesh, Co-Founder and CEO, NextWealth Entrepreneurs Pvt Ltd., explains the workings of this model in a freewheeling discussion with Mr. C. Siva Kumar, Managing Partner, Prabha Associates and RV Industries.
We all have many myths about small towns which I would like to bust. I’ll dwell on three areas:
a) The story small towns
b) What we do at NextWealth; and
c) The social impact of what we are doing.
The BPM industry is the Business Process Management industry. All of us are familiar with the IT industry, which is basically into the technology part of it. BPM is all the back-office, call centre type of work that is being done. It provides 1.4 million jobs today; but the problem is, it is largely concentrated in the top 10 cities in the country, including Pune and Hyderabad. The industry has very high attrition of almost 40 to 50 percent per annum, which means that they are recycling their entire set of employees every two years. You can imagine the cost to train and re-train them. The loyalty factor is also less and there is always a war for talent between the top organisations.
Mapping Demand and Supply
On the other hand, we have more than 250 small towns in Tier 3, 4 and 5, and these are not necessarily the villages. Around 2009, we realised that more than 60% of the colleges in the country—engineering or general graduate colleges—are concentrated in these small towns. These graduates don’t have local job opportunities because they aspire to work in an IT or a BPM organisation. But in a town like Vellore or Salem, there are very limited job opportunities for them from the IT and BPM sector. So, many of them migrate to the city. Not all of them migrate because, typically, many families are from a very conservative background—even men are not allowed to migrate to the cities. It’s like going to the US for them. Women definitely are not allowed to move to the cities. About 30% of them move to the cities looking for a job. You will find in most large IT or BPM organisations like Wipro, Cognizant and Infosys, almost 30% of their employees are from small towns. During Covid, it was really tough, because all of them went back to their hometowns and now many of them don’t want to come back. 70% of the people who stay back don’t have local job opportunities. They take up something which is not commensurate with their education. They are all first generation graduates, which means they must have taken loans for their education and they need to pay that back. They need money to pay back the loan. As for those who migrated to the city, even if they earn 25 to 30K per month, they don’t save anything because the stay, boarding and all that is expensive. On one hand, you have the demand in the city and on the other, you have supply in small towns. In 2009-10, after I quit Wipro, I was looking to do something, which has a social impact. Four of us joined to create a model which will match this demand in the city and the supply in the small towns, so we can provide a social impact. We asked, “Can we have the work moving to where the people are, instead of people moving to where the work is? If work can be given 3000 miles away from the US to India, why not 300 miles away from the cities to the towns?” That’s how this whole thing started.
But to do that, we had to understand what small towns’ people are capable of. We went to Salem to understand the demographics of the people there. We realised that the people are generally very conservative, soft-spoken and very diffident. For them, to be able to survive in the city, in large organisations is tough. Their oral communication in English is poor but their written communication is somewhat good. Their employability in the IT, BPM organisations is much lower. But what we found was that they are highly trainable, because their aspiration and commitment to whatever they took up was very high. We estimated the talent pool and found that 0.5 million graduates are employable by the sector. They are capable of doing all BPM as well as IT type of work. The things that are tough for them are international voice and some complex IT /software development. The physical infrastructure is not bad. There is always power back up with UPS and generator. In fact, in the last 12 years we have been in Mallasamudram, which is 20 minutes’ drive from Salem, we probably had a down time of 15 minutes.
- Lower cost of living
- Less attrition
- Better operational stability
- Inclusive workforce
- Reversing the migration and crowding in the cities
The cost of living is much lower. To give you a perspective, if any of the large BPM organisations charge 15$ an hour for an invoice processing work, their cost will be in the range of $10 an hour. We do that work between 3 to 5 dollars an hour, still making money. We are not making a loss. Essentially, we are able to do the work at one-third the cost of work done in the US and probably 30% to 40% lower than what you would see in the cities. A large pool of trainable talent is available. Attrition, which is a big problem for this industry, is only 10 to 15 percent per annum in these locations and it gives operational stability. We have an inclusive workforce. Reversing the migration and crowding in the cities is a big benefit of it. Thanks to Covid situation, work from home is a reality and we can look at disruptive delivery models and reimagine the future of work, different from what it is today.
- Local conditions
- Cultural nuances
- Different local languages
- Lack of managerial talent
Large organisations find it very difficult to go and replicate in small towns. They are used to running 5,000 to 7,000 people centres. For them to run a centre of 500 to 1000 people is a big challenge.
Distributed Delivery Model
In NextWealth, we pioneered a distributed delivery model to bridge this demand-supply gap. Instead of setting up one large centre, we can set up many small centres in small towns and do the same work of a large centre. The distributed centres can have even 1500 or 2000 people. We can impact the local ecosystem only if we hire people from those small towns.
For instance, if we put up a centre in a town and bring in managers from Bangalore or Chennai, they will come back running because of the glamour of the cities. So we wanted to build managers from the grassroots in these locations. Therefore, we went with an entrepreneur-driven model.
Entrepreneurs: Our Chosen Few
We appoint entrepreneurs in these locations, who will run the delivery centres. They were carefully chosen because we wanted entrepreneurs who are emotionally connected with the place, understand the local language, can get the last mile connectivity done and want to create an impact. For example, the entrepreneur who runs our centre in Mallasamudram is from Salem. He did his engineering and then MBA in the US. He is a Six Sigma Black Belt and has worked for 15 years in the industry. He wanted to come back to Salem and give back to the town.
Similarly, the one who runs our Chittoor centre has done M.Tech and PhD. He has four or five patents to his name. His mother used to run a hospital in Chittoor. He wanted to go back and set up the centre there because he is emotionally connected with the place. In an entrepreneur model, if we build managers from the grassroots, from the local people available, twenty years from now, these centres will be like Bangalore or Pune of today. With such a model, a lot more work can be brought from the US or UK and all over the world, into these locations. This is the first-time that such a model has been set up.
What are the benefits for people in the chain? For the graduates, they get a job locally, and even if they earn 20K rupees, they save 90% of what they earn; there is clearly a value proposition. For the entrepreneur, because he is emotionally connected with the place, he is able to give back to the town. As a business proposition, he makes 10 to 15 percent margin. It also impacts everybody else. For a centre of 2,000 people, you must have that many buses, canteen, etc. A whole ecosystem works around it. For the customer, clearly there is affordability because they get 30% to 50% savings. For us at NextWealth, we have profit with social impact. Thus every factor or every person in the chain gets benefited in this model.
Between NGOs and Corporates
When we started, we wanted to be a social entrepreneurship firm. But how do we define social entrepreneurship? NGOs are typically for ‘do-good.’ They have a purpose. They want to do good; they have commitment to a cause and a social objective but they are highly dependent on grants. It is difficult to establish accountability and achieve scalability.
For corporations, it’s all about ‘do-well.’ They have profit objectives, commitment to the business and are self-sustaining. They are accountable to the stakeholders. NextWealth is about do-good as well as do-well. We combined the purpose part of NGOs with the profit part of corporations. When we have to choose between these two, we choose the social objectives, self-sustaining and high accountability. We are not dependent on grants. We are profitable and whatever profits we make, goes back into the sustenance of the business. Thus, this is a model of purpose with profit. It is a social venture, in a distributed delivery model.
When we started, we had two challenges. One is to find a good entrepreneur who can run this and second to get customers that will give us work. In getting entrepreneurs, we found absolutely no challenge. More than 150 entrepreneurs showed interest, wanting to set up in places like Patna, Lucknow and Nasik. We had to say no to a lot of them, though we ran an entrepreneurship workshop. Getting customers was a challenge because they thought, as we were operating from Salem, they can give us only data entry type of work. When we talked of social impact, they wanted to link the job to their CSR budget. They also thought that we would be very, very cheap, which was not the case.
We do very high-end work, with a quality equal to that of the major players, if not better. It is purely because of the aspiration and the commitment of the people here. The work that we do is AI/ML data services, digital CX and IT services. Some of the AI/ML data services are in very complex areas. Our customers were worried if we could meet international quality standards. We ourselves had that doubt initially. With our first customer, we were not meeting our targets, which was an accuracy level of 98%. We were only at 85%.
I went to see what our people were actually doing and when I sat with them, they were very happy with their 85% accuracy, which they equated with their college marks. Then I had to tell them why even 98% may not be good enough. We had to train them on the quality aspects and on why 98 or 99% is important. One thing we have always ensured is: quality first and business next. We have refused businesses where we thought we could not meet their quality norms.
70 Plus NPS
We do our customer satisfaction survey every year and we ask for the NPS: Net Promoter Score. The customer is asked one single question—if they will refer NextWealth to anybody else. It’s a universal score known worldwide and our score is 70 plus. That is one of the highest in the industry. The next highest is 36 or 37. It is because customers are highly satisfied with our work and our quality of delivery.
We do computer vision, image annotation and semantic segmentation. These days, driverless cars are becoming a big thing in the US and worldwide. How do these driverless cars go without hitting anything? They have an AI/ML algorithm inside the car which guides the car. But that AI/ML algorithm has to be trained so the car can differentiate between a billboard, a moving object, a road divider, etc. We train the car.
One of our customers has low-flying aircraft or drones which will take pictures of the railroad tracks in Texas and it will then suggest that a certain nut is loose or there could be a possibility of an accident. The aircraft constantly takes videos and the algorithm will just say that this area has a problem. As it covers thousands of miles, physical inspection is not possible. Those pictures come to us and we verify whether that algorithm has correctly pointed out the deficiencies. If not, we train the algorithm to report correctly. We also work in NLP (Natural Language Processing), Chat Bots, conversational AI and so on, which are all high end types.
Apart from that, we also do IT services. Now, the in-thing in IT is called ‘Low code, no code.’ It is very difficult to find data scientists who can do this sort of work. This is something that we do from these locations. Apart from that, we do low-end application development work as well.
We are in six centres at Salem, Vellore, Mysore, Hubli, Chittoor and Bhilai. We have more than 4,500 employees. We have done about 557 million plus transactions so far and have over 100 customers. We have all the certifications that are required—ISO, HIPAA compliance, PCI DSS compliance, etc.
We also want to ensure that we give jobs to more women than men. We have 56% women and 44% men. Men are able to impact the family a lot but women impact the family, society and the community. We have found that both men and women are able to fund their siblings’ as well as their own education and their siblings’ marriage, which is a big thing in these locations. They’ve been able to buy vehicles and get loans for buying appliances. The standard of living itself is going up.
Let me give a few examples of the work that we are doing from these small towns. We do identity verification. If you go to the US and go to an Airbnb residence or any other place, for ID verification, you give your passport and they take a picture of that. Then they take your picture on a camera. They have to compare both and see whether it is the same person or not. When they run on a platform, it comes to our centre in Mallasamudram and within 85 seconds, we have to come back and say whether it is the same person or not, with 99% accuracy, which is not easy. We have 1100 people doing that work, 24x7x365.
For a worldwide retail multinational corporation that has millions of catalogue items, their product managers have to take a decision on a daily basis on what pricing they have to put. The AI/ML algorithm compares their product pricing with that of competition and suggests a suitable price. Sometimes, a decision is also taken automatically by the algorithm. Wrong decision means leakage of revenue and loss of profits. We train that algorithm to do the correct match and make the correct decision. It is a lot of judgement work. We do invoice processing for a worldwide multinational IT organisation: catering to 75 suppliers, across 75 countries, in 10 different languages, including Spanish, French and so on. They have more than 700 types of tax codes that people have to remember. This is being done at 99.95 percent accuracy. There is no opportunity to do QC on this, as it goes straight through to the customer’s system. So we have to be very, very careful in doing this.
We work with an equity research company. We access and collate data from 35 different sites. Typically, banks use this platform to know about an organisation, its financials, whether there are any litigations and who the directors are.
These are all just examples of the type of work that people in small towns are capable of doing, if given an opportunity. They are highly trainable. They take a little more time to learn but once they have learned, they are absolutely bang on, in terms of the quality and the focus with which they do the work.
For the social impact of all of this, our metrics are quality of life, self-esteem, family and community. They have been able to buy jewellery. They get self-esteem or pride. Just wearing the badge and coming to office means a lot to them. They are a happier family, because they are able to support themselves, even during the hard times. Earlier, they would marry off daughters at the age of 21 as soon as they finish graduation. Now this age limit is going up to 23 to 25, which is better, as they are more mature and more knowledgeable rather than being fresh from college. They are able to choose their partner. Earlier they had no say in their partner.
In a few cases, they have been able to say no to dowry, which is a fundamental change that is coming up. The statistics of women who were thriving before NextWealth was 28%. Now it is 50% and 5 years hence, it will be 79%. We have many stories of social impact.
With 4500 people, we are just a small drop. We are trying to take this to the industry itself. We have put forth to Nasscom our vision to expand the revenue and the footprint of the BPM industry by redefining the future of work. Earlier the openness to move to small towns was not there in the industry. But after Covid, they have realised that 30% of their workforce work from small towns and that quality work is possible from small towns. The target that we have taken is that 10% of the revenues of industries must come from small towns. 10% of the employees (out of 1,40,000) must come from a distributed workforce from these locations. We want them to set up centres in 100 towns across India. Our approach to the industry is, ‘Why can’t we move from WFH to WFHT (Home Town)?’
For manufacturing, there is a manufacturing unit and various subcontractors function from in and around this factory, supplying various parts. Such a subcontracting concept has not been picked up by the service industry, especially the IT and BPM industry. We are trying to promote this concept by subcontracting the work to smaller towns and moving into a WFHT model.
Our focus is on providing services at world class quality, leveraging people in small towns, providing jobs to the people, impacting the local ecosystem, having a model that will help every person in the chain and one that will have operational stability and operational excellence. Through this model, the large organisation also doesn’t need to invest in capex, as they can partner with somebody. This is what we are trying to do with the industry itself. We strongly feel that the next decade is the decade of the small towns.