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Tavaga Update – Taking the Robo beyond investing

Here is an excerpt from the update we emailed to our investors recently. Thought it would be useful to share with fellow entrepreneurs 🙂 

Dear Friends

The last update was focused on the product and what we were doing to build a simple, friction-less and jargon free investing app that talks to customers about their financial goals and not markets. This update is focused on the Tavaga Service. However let’s get the popular question out of the way first.




The absolute return on our 5 portfolios representing different risk levels over the last 4 months (May-August) are as follows.

The markets overall kept rising steadily with no major domestic impediments.  

However, there was one event that gives you a glimpse of our portfolios preparedness to face unexpected shocks. The surprise Brexit vote caused a temporary panic and fear across global markets and India was no exception to it. 

On the day of the Brexit results, Nifty was down 2.2%However, our portfolios showed a strong performance buoyed by gold and Nasdaq ETF (rising due to fall in the rupee). The intra day performance of the portfolios on the Brexit day in the table below.

We never got contacted by customers on days the market went up 2% or more, but a few of our customers did ping us on the Brexit day. This validates our diversified portfolio approach born out of the belief that retail customers are more concerned about losses than profits. The performance of our portfolios on the Brexit day assured our customers that our investment philosophy works in practice as well.



I have recently written about the developments in the ETF market here. There has been a 10x increase in volumes over the last 2-3 years. There are no signs of this momentum slowing down.

The most significant development however is that one more wicket of the MF mafia has fallen. This time that of their captain – the AMCs. There is unprecedented activity in the ETF space among the AMCs. There have already been 5 new ETF launches in the last month or two and another 20-25 ETF are set to hit the market in the next 5-6 months. AMCs are also finally willing to dedicate some marketing budget to promote their ETFs. Digital (Robo) will be their preferred channel as it is in the West and that makes Tavaga an important partner to all of them. We are working closely with a few of these AMCs trying to ensure that the ETFs our customers need are paid attention to. 


The feedback from our early customers has been very good. We have not started marketing the app yet, but still have managed to get over 2,500 downloads. We hit out TG perfectly with the product design and communication. 95% of the downloaders fall under our core TG of 20-35 year old urban Indians. On the product front our focus has been on getting the iOS app out as soon as possible. Similar to our Android launch, the basic iOS app will be released in the next 3-4 weeks, with a full version coming in the next 6-8 weeks. 

We have also been able to validate our belief that it is only the Indian domestic investors (VCs, Angels etc.) who believe that marketing ETFs and not Mutual Funds could be a disadvantage. Over 70% of the customers had very few or no questions about ETF Vs MFs. Of the remaining 30%, over half of them were fully convinced about ETFs after talking to us, reading our blog and researching by themselves. We are willing to wait for the remaining 15% to learn through experience that ETFs are better. If the Brexit fallout fears had come true, we could have easily handled it through a rebalance that MF sellers just simply cannot. One serious crash in the market will make these remaining 15% skeptics our most loyal customers 🙂 

The one lesson everyone learnt from the e-commerce boom is that Indians are highly price sensitive and price trumps channel and effort. A hefty discount at a Big Bazaar still makes Mr. Biyani a lot more money than a Grofers or Big Basket makes without discounts. Right now retail investors are not aware that they are paying 2.5% for a product(Mutual Fund) that can be substituted with something that will cost 0.5% (no, direct MFs still cost about 1.7%). Once they learn this, there is no way they continue to pay 2.5%. The focus of the marketing budgets mentioned in the previous section are very likely to focus on making sure people know this. 


Most of our focus over the last couple of months has been on building a service that matches our app experience. To bolster the service team, we have recently hired Abhinav Sinha to work on marketing and the on-boarding cycle. Abhinav is a 2012 IIM Bangalore grad and has spent the last 4 years with the Aditya Birla group. He has experience across retail and financial services. He also won the Young Achiever award across the Aditya Birla group in 2015.  

There are 7 users of our product, one external (customer) and 6 internal (on-boarding, front office, middle office, back office, marketing and product management). Each of these divisions also have multiple roles and users. At full capacity we will have about 15 roles in the company, although multiple roles are being managed by each person right now. The decision to keep the company lean means we are first going to automate as much as can be done and hire people only where required (please go through the on-boarding case study below to understand how we are implementing the lean model). 

We have already automated most of the work involved in on-boarding, front office and middle office over the past 2 months. The only human intervention involved in these functions today are things where our partners (broker, bank etc.) require such human intervention. We are working with our partners to also automate things at their end to remove these roadblocks.  

We have 6 major releases lined up for the next 8 weeks that will deliver solutions to automate back office and provide all data required (analytics, database data, campaign efficiency, customer funnel management etc.) for marketing through a single interface. This should help us ensure that every rupee we spend on marketing is well targeted and leads to conversions at the lowest price. 

Do reach out to me if you have any further questions.


On-boarding case study

Our idea of a lean on-boarding model is to ensure minimum friction and human intervention while providing a completely DIY solution with analytics driven communication to further the process. However, the current market brokerage on-boarding model is fraught with inefficiency.

The typical on-boarding of a brokerage customer involves the following steps:

  • Getting about 45 signatures from the customer on a 40–45 page form
  • Either getting the customer or someone in the office to fill out 130 data fields on the form
  • The customer needs to have a copy of a photograph, PAN, address proof and a bank document (cheque or statement)
  • Getting the signature of the customer and any other joint holder of the account on a NACH form
  • Check existing KYC details of the customer
  • Match KYC details with the evidence provided by the customer
  • Either validate or update existing KYC
  • In case of a customer with no KYC, register a new KYC with the KRA
  • Validate phone by getting the customer to answer a KYC verification call
  • Validate email by getting the customer to reply to a email
  • Forward documents to broker, depository participant, the exchange and the bank all of whom need to validate data and documents to setup brokerage, depository and payments for customers.

The complexity of this process, large amount of data and documents and large scale human intervention have resulted in a highly inefficient system. The average rejection rates across brokers for this system is about 30%. A typical brokerage house would have about 7–8 different people involved in the process. Including all personnel costs, the cost of on-boarding for the average brokerage firm about 1,000–1,500 rupees.

FYI, rejection implies failure to approve an account. All of these accounts do get opened eventually after the errors are fixed. The average cost of a fixing rejection is about Rs. 300–400 and a delay of about 4–5 days.

Some examples of the reasons for rejection we have seen in the first month of operations are:

  • Names on PAN (we have had a rejection for a person without father’s name on the PAN, while application for one other customer with the same issue went through).
  • No PIN code on the address proof (large number of people from small towns have only district names and no PIN codes).
  • No signature on cancelled cheque (a cheque with no signature was accepted in 3 cases and rejected in 2).
  • NACH signature rejection (signature on NACH didn’t match bank records)
  • Date of birth in existing KYC records was off by over 15 years
  • One of the validating parties refusing to accept that it is perfectly normal for someone to have the same middle name as his/her father

All rejections can be broadly classified into 2 categories — current human intervention and past human intervention. We have identified about 30 potential reasons for rejections and reducing those to 0 is a big challenge.

Our first focus was to remove human intervention from current process and reduce customer’s time requirement. We reduced the form to 18 pages and 20 signatures. We then set-up systems to automate form filling to ensure 120 of the 130 fields (10 fields relate to nominations) are pre-filled when the customer receives the form. These details are filled verbatim from the proofs the customer provides.

This helped with:

  • Reducing the chance of rejection as all data matches proofs exactly
  • Identifying mistakes from past human intervention like missing PIN or incorrect names in past KYC
  • Customer getting to see and point out any mistakes before signing the forms
  • 80% reduction in number of check points for the document collector to validate
  • About 40% reduction in probability of rejection
  • 35% savings on reverse logistics cost due to increased acceptance rates

Download one of our pre-filled forms and compare it to Zerodha’s, the brokerage believed to have the best tech, to judge the quality of our tech. While they do have a better looking cover page (which makes their file size 10x the size of ours), the forms we provide are designed for grey-scale printing, easier to read, identify locations to sign and have data in the right places.

The second step is to arrange for reverse logistics to accelerate the on-boarding process. We have tied up with a reverse logistics provider to pick-up documents across the big cities. The first set of pick-ups are scheduled to happen this week.

This helped with:

  • Reducing the average on-boarding time from about 12–15 business days to about 3–4 business days
  • Triple conversions (only a third of the people we email forms to courier it back right now, due to various reasons)
  • Further 10% reduction in rejections
  • Making a really good first impression 🙂

The third step was to build a custom CRM tailored to our long term needs. We are building out our own CRM on our Google for Work account (no not google spreadsheets). We have integrated our systems with Google analytics, Localytics analytics, Tavaga analytics, KYC agency API and our database on AWS. The system is built on Google apps, written in Google script and gives us full control of data and the flexibility to customise it to the specific needs of each of the 15 roles within our firm. It gives us Google server level security (Tavaga CRM gets hacked only if Google servers get hacked), while we can on-board or off-board an employee in less than 5 minutes. This implies that a cheap hardware locked Chromebook is all most of our employees will ever need. The laptop can be remotely disabled by the admin instantly.

This helped with:

  • Single account (Google) for all access
  • Keeping data very secure
  • Granular access control
  • Access to customer goals, app activity and all communication history (notifications, emails, phone conversations etc.) to the on-boarding team
  • Improving customer targeting and prioritisation based on data
  • 20% reduction in rejections
  • 40% improvement in turnaround time on customer queries
  • 80% increase in service capacity

The fourth step is to move all validations to before the form generation stage to ensure 0 rejections. This involves reworking our on app on-boarding flow to ensure all data is validated by the customer first and then by the back office before a form is generated. The product and on-boarding teams are working on implementing these solutions and we expect to roll this out in the next 4–5 weeks.

Once this step is implemented, our average all in on-boarding cost will be about Rs. 250 and we will have the capacity to on-board about 50 customers a day per employee handling on-boarding. For the same capacity, a typical broker currently has about 10 employees and spends about Rs. 1,000 — Rs. 1,500 including all personnel costs.

We can be reached @Tavaga_Invest on Twitter. 



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