Mutual funds go data-mining

How predictive analysis is making slow but definite inroads into the asset management sector

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There is a story about retail behemoth Target getting an angry call from a father, after his teenage daughter received a mailer intended for new mothers. The executive apologised profusely, and then called back in a few days to say how sorry he was, only to be told that the daughter was indeed pregnant.

A New York Times story explained that Target's customer tracking statistical model had analysed her purchasing pattern (such as the personal care products she bought) and identified her pregnancy even before her immediate family.

Now, Indian mutual funds (MFs) are beginning to analyse all the data they can get their hands on to better understand customer behaviour and improve sales'though it is still said to be at a nascent stage.

So far, it's more in the nature of identifying who to target with a tax-saving equity fund, based on whether they have invested in a diversified equity fund. But deeper data analysis amongst MFs does seem to be on the way.

"Hypothetically, a person's trading pattern can be used to identify his risk appetite. These and other social media information such as who he follows on Twitter can be used to put together a client profile, which could help in determining what kind of products are best sold to the person," said Mukund Mudras, co-founder and chief executive officer (CEO) at finance data analytics company Heckyl Technologies.

"We have been making use of predictive analysis….Data analysis has been picking up over the past two-three years. We use it as a resource for both distributors and customers. The former, we try to keep an eye on trends, in terms of whose assets are growing and whose aren't and what corrective steps we can help distributors take….Customer behaviour can help us identify which products can be pitched to a client. It can also help us identify, amongst existing customers, who can be a potential candidate for additional products," said A Balasubramanian, the CEO of Birla Sun Life Asset Management Company.

A CII-PwC report entitled 'Indian mutual fund industry: Challenging the status quo, setting the growth path' released in 2014, noted the increasing role big data can play in asset management. "Fund houses can…leverage advanced analytics and big data collected from structured transactional data from R&Ts and other sources, as well as unstructured data from social media and other platforms to extract meaningful information on investor or distributor behaviour and use this information for more effective targeting of their respective investor groups. Analytic techniques can be used to cross-sell or up-sell products and increase each investor's stake, among other applications," said the report.

A Deloitte Centre for Financial Services' '2015 Mutual Fund Outlook' report, which looked at global trends, mentioned the increasing use of data analytics. It said predictive analytics can be used to find which competitor products are facing stress, to find out when redemptions are likely and pick out opportunities to gather assets. Fund houses mine data to identify new target markets, which are yielding rich dividends so that they can allocate more resources there. "In India, it is still at a nascent stage but will likely play a bigger role in client conversion and cross-selling," said Jimmy Patel, chief executive of Quantum Asset Management Company.

One chief executive said there was a lot of catching up to do with e-commerce companies, until make use of complex algorithms to identify customer behaviour and execute sales. "Many fund houses still use distributor names when reaching out to the customers through online promotions like mailers. They want to avoid antagonising them. But this also means that they have to pay the distributor when the sale happens, even though the fund house has made all the efforts. Many are reluctant to take on the additional cost of data analytics when they have to pay the distributor anyway," said the person on condition of anonymity.

MFs' DATA FORAY:

• Predictive analysis finding gradual acceptance
• Data mining used to identify customers who can be pitched products
• Also used to better distributor practices
• Still lag behind e-commerce firms in identifying buying patterns, and acquiring customers
• Cost one of the factors holding back wider adoption