As you would agree, “Data” has become the buzzword today for all businesses. But do we really understand what data means, how we can capture/store /manage/analyze data?
While evolved companies already have set-up teams that can collect data from internal and external sources in an efficient manner,  start-ups are struggling to hire the right talent at the right price to get the Analytics team kick-started.

We @ Econolytics, conducted a survey on 22 of the new age and upcoming Fintech startups in India to understand how they perceive Data Analytics and the challenges they face in using data to make informed decisions. Here is an attempt to summarize our findings.

Let’s first understand what makes up the Fintech landscape in India today. Fintech as a term simply implies the use of technology to make the financial system more efficient and functional. This could include various financial services that were previously provided through traditional routes and are now made available through technology at a faster pace and lower cost. Example: Payment gateways that make disbursements to merchants quicker such that cash flows can be managed better.

The Fintech industry specifically requires heavy churning of data and generating valuable insights. For instance, it becomes crucial to identify segments where the profit margins are higher: A P2P lending company acts as a marketplace for borrowers and lenders; Just like a traditional bank. However, this model works well if we can identify specific segments where the Lenders are able to finance borrowers at an interest rate that is higher than what they would get from traditional investment avenues but lower than what the borrowers would be paying the banks. Data will indicate where these segments lie.

 

                                             Figure 1

 

Therefore, you would think that all Fintech startups consider ‘Data Analytics’ as a ‘Need to have’.
Our survey results (Figure 1) show that 60% of the companies have actually a team size between 1-5 individuals with a Data Analytics profile. However, the surprising aspect of the result was that none of the companies had a team size of more than 10 individuals.

 

 

                         Figure 2

 

Now, one might argue that having an Analytics team in itself resonates it’s importance. It is imperative for a start-up to have a lean but efficient team. Therefore, the next question we posed to the group was the core function of the data analysts. Interestingly, 80% of the cohort responded saying that the core function of the analytics team was reporting on the trends and KPIs of the company. This is an interesting observation, given that the founders of many of these start-ups have a technical or quantitative background and therefore, are in a position to draw out intelligence from data themselves.

 

We also probed further to see whether these companies are keen to drive more focused analytics that require expertise from consultants or more tenured data experts. And most of them responded with a positive answer and immediately identified pain points that they knew analytics can solve.

 

Figure 3: Increasing User Activation

 

 

 

 

 

Figure 4: Identifying Customer segments

 

 

 

 

 

Figure 5: Cost Optimization techniques

 

 

 

 

 

 

 

 

In addition to the above, some companies also felt that they could really gain a competitive edge if they could build ‘Recommender systems’ that would leverage customer data to actually auto select the right products for the customer, removing human bias and increasing product efficiency. Example: A balanced financial portfolio that would be based on the customer’s demographics such as age, geographic location, risk appetite and short term/long term needs.

However, even with well defined problems to be solved, these companies did not hire any expert Analysts or engage with an Analytics vendor. Reasons highlighted below:

 

 

 

 

 

 

 

 

In view of this, we held a focused group discussion with 5 such randomly selected companies and found that such companies were open to engaging with data experts on a project basis, that involved a shorter time period of engagement and well defined objectives.

At the end, lets look at some of the ‘1st word’ that popped up when start-ups were asked to respond to ‘Data Analytics’.

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