30th Jan 2017
In uncertain times, how do you provide a reliable, stable and effective service to 130,000 policyholders? For Pension Insurance Corporation (PIC), the answer is through data-based modelling. Such modelling is commonplace at financial services firms, but PIC pushes the data further, going beyond insurance risk to help predict future customer service requirements too.
The person behind this strategy is Matt Gore, chief administration officer at PIC, which insures final salary company pension schemes. His successful approach to the company’s operations is built on a sophisticated combination of data collection, cleansing and analytics processes.
Of course, nobody knows the future – especially with Brexit looming large – but through data-driven risk modelling and predictive analytics, Matt is attempting to answer some key questions and enable PIC to handle possible future scenarios.
“We just don’t know how Brexit will shake out,” acknowledges Matt, who’s been managing operations at leading pension providers since 1989. “A lot of questions still need answering. Hard or soft Brexit? What will the new trade agreements look like? All this and Article 50 hasn’t even been triggered yet, so we have modelled different outcomes for inflation and interest rates to prepare as well as possible.”
When it comes to understanding customers’ needs, a little data can go a long way, reckons Matt. “We’re a data business. We try to model lifespan averages, demographics and other factors that affect pension and risks,” he says. “Even your postcode has a strong correlation with your life expectancy. That’s how we make sure we have enough assets to pay our policyholders. But we can also use data to anticipate their service preferences.”
Analysing customer preferences and call handling data, and cross-referencing the findings with the demographic information more usually used for insurance modelling, has enabled Matt to make predictions that improve PIC’s service in the day to day.
It’s an important process, as PIC customers join not in ones and twos, but potentially in thousands at a time, as corporate pension scheme trustees transfer liability for an entire fund. By analysing the demographic make-up of each new cohort, Matt is able to find lookalike customers among PIC’s existing policyholders, and use this to model likely service preferences and behaviour.
The advances Matt has made in analysing customer information rely on accurate, complete data – no mean feat when some records might be more than half a century old. To keep things manageable, he takes a structured approach.
“Better-quality data leads to better service quality,” he says. “When we take on a new pension scheme, we go through a 12-month data clean-up programme.
This helps us deliver better service. It’s also cost-effective, saving time and resources through automation.”
Despite PIC’s extensive work with data, truly predicting the future with any certainty is impossible. Matt believes this is what puts such an emphasis upon a conversation-led, human experience. “For all the data you have, ultimately, it’s all about that human connection and experience,’ he says. “You can never rule out the unexpected.”
And, although data underpins PIC’s risk models and has given the company an edge when it comes to customer service and building trust, it’s only by matching cold, hard numbers with the personal, human element that customer service begins to flourish.
Matt explains: “Analysing data only gets you so far. It’s important to talk to customers and check their thoughts, feelings and responses. It’s only by confirming your insights with real people that you truly get the most value from your data.”
Click here to read the original article from Customer Focus magazine.