A one-dimensional approach to managing risk could lead companies to lose out on potential opportunity instead of optimising for profit
A blanket approach to risk management can lead to missed opportunities.
“In a bid to cut costs, companies often turn away business from large groups of customers that they perceive to be high-risk, or accept risks they really can’t afford to.
“But doing this without analysing the data in detail means they are often losing potentially valuable customers and revenue,” says Ben O’Brien of risk consultancy Jaywing.
The art lies in balancing risk with opportunity – and Jaywing has significant experience of both. The company was founded in 1999 by expert statisticians to harness data analysis across the dual disciplines of risk management and markeing.
The company describes itself as a risk consultancy with analysis at its heart, and its expertise lies in analysing data, both to reduce risk and optimise profits. Specialist services include consultancy and delivery of analysis and modelling, data products and systems.
Its clients include Lloyds Banking Group, RBS, M&S Money, Swinton and Carole Nash.
“After the credit crunch, it’s tempting for companies to be overly conservative and adopt a general rather than customer-specific risk management strategy,” says O’Brien. As an example he cites the banks’ approach to lending to small and medium-sized businesses (SMEs).
“Technology and mathematical modelling techniques can compute multiple dimensions that differentiate SMEs by risk and opportunity. The results can reveal a picture of the kind of SME that makes a good risk, enabling lenders to reopen their doors to these customers.”
He also cites examples of retail banking strategy being aided by detailed analysis.
Jaywing was enlisted by a leading retail bank to help it decide which of its credit card customers should be offered debt transfers from other cards – and what kind of deals to offer.
“We analysed three years of data to identify which customers had accepted previous balance transfer offers and which had not, and the revenue generated from each group. We also segmented the data by characteristics such as age and marital status to reveal which were most likely to accept a transfer offer and which were most likely to generate a bad debt.”
Beyond the financial sector, data analysis helped a leading insurance broker to decide its pricing strategy. Come renewal time, inertia means some customers are not price sensitive. Others, however, will shop around for as little as a £5 saving.
“We analysed historical data to model the value of different customers and ascertain the level of discount to offer them, while retaining profit margins,” says O’Brien.
By using these sophisticated analytical and mathematical modelling techniques, both bank and insurer made large savings at the same time as reducing risk.
The same techniques can be used by companies across all sectors. Jaywing’s data analysis and marketing expertise is designed to complement an organisation’s key competencies rather than replace them, a philosophy O’Brien sums up as:
“You know your business, we know your data”.
“Companies which do not analyse data in sufficient detail may succeed in reducing risk but they may also lose potential customers and revenue,” say O’Brien.
“Is that a risk worth taking?”
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