Striking the right balance in customer data
The relationship between a user and a fintech begins with data. Customers enter their details, and the extent of that data collection has the power to define a fintech’s relationship with that customer for the user’s entire lifecycle. Collecting poor or insufficient data can hamper fintechs, yet asking for too much in the first instance can be prohibitive as well.
Indeed, according to research from software company Fenergo, a third of financial institutions report losing customers due to a slow or inefficient onboarding process. In reality, it is likely to be higher – it is, after all, a statistic that many providers shy away from acknowledging. It’s a problem, Fenergo says, that costs the industry US$10bn a year in lost revenue.
Balancing personalisation and data collection
The question of how much data to collect is a constant balance between observing the fintech’s business requirements and respecting the customer experience. Asking for too many details at first signup puts customers off, as Alistair Dent, Chief Strategy Officer for data consultancy Profusion, explains: “You can either have an involved and detailed data collection that few people complete, or a simple and quick route to signup with more customers about whom you know less.
“Over the last 10 years, fintechs have tended to migrate to the latter end of this spectrum. The argument goes that if you sign up more customers, you can collect information as you go (progressive signup) or from usage data. You can still find products at all points on this spectrum, but more and more are at the ‘simple’ end.
“The downside is that it does allow for less personalisation, meaning it’s harder to encourage usage once customers have signed up, and sometimes the products seem less ‘smart’. What the best brands are doing (copied from the signups of tech firms like Spotify) is to give customers the choice. Make the data collection optional, but make it obvious at each stage how each piece of additional information helps make their experience better. Visibly adding or removing tabs, shifting buttons up or down the page, or even just a ‘profile-o-meter’ filling up will show users how the tool is learning more about them and will adapt to their usage.”
The quality of data is just as important as the quantity of data, Louise Potts, Head of Banking Customer Advisory Practice at SAS UK&I, tells FinTech Magazine: “The more good-quality data and information an organisation has on its customer, the better service it can offer. However, the quality of this data is hugely important, especially for those using artificial intelligence (AI) and analytics to inform decision making.
“For example, in Latin America, where many people have thin credit files, we are seeing organisations turning to alternative data sets – not just traditional credit information – to understand if a person is a candidate for credit. This might be data surrounding their regular monthly repayments, such as utility bills or a phone contract, rather than solely relying on a banking file.”
Jay Reilly, SVP EMEA at Precisely, concurs that fintechs tend to lack integrity of data rather than quantity: “To have insight into customer lifecycles, fintechs need to be able to quickly connect customer data across the organisation and understand the context in which customers are engaging. In most cases, financial services data isn’t lacking in quantity – it’s the integrity of that data that’s the biggest challenge.”
He believes that, as part of strong data governance, fintechs should ensure they have a data catalogue which precisely defines its ownership and ability to set data policies and specific standards, and implementing a data quality management framework.
The difference between fintechs and legacy organisations
In many cases, fintechs have an inherent headstart on legacy institutions. Many were founded on cloud technologies, and, with the exception of some early fintech pioneers, most will be free from the shackles of legacy technology.
The challenge for fintechs comes with the sheer number of data integrations that are necessary to offer services to customers. “Fintechs often need to integrate data from sources such as banks and data enrichment credit bureaus,” Reilly explains. “These companies use methods such as APIs, data lakes or data warehouses to consolidate this and manage the data more effectively.”
“A good customer data strategy should enable smarter and more informed decision making, as well as helping organisations to offer a better and more personalised level of service,” Potts continues.
“Many fintechs excel at this, having developed their processes and strategies around the idea of customer-centricity. With all information and data organised in a customer-centric way, rather than in single databases, fintechs can access a complete overview of their customers, the products they are using and the status of their accounts.
“Many of the traditional banks are dealing with more siloed data. For example, data is structured by service or product, such as mortgage, loan or current account. As financial services have evolved, and in a digital-first world, this sort of data ecosystem will hinder the level of service the organisation can offer.”
This improved level of data agility can help create better customer service, according to Potts. “One example of this is money-saving apps such as Snoop, where you can connect your accounts via a single app to view and manage your spending across different accounts and get money-saving suggestions. These apps allow customers to have a better understanding of their finances, which is something that many will find useful in the current economic environment.”
How can fintechs improve their data strategy?
What more can fintechs do to improve their data strategy, ensuring they are meeting the fine balance between collecting enough data to personalise services without putting off consumers at the onboarding stage?
“Data plays a crucial role within these businesses to enable them to optimise and scale,” Profusion’s Dent says. “Everything from marketing and customer service through to product development and HR can be informed by collecting and analysing company data. The insights gained can both help inform the strategic direction of a company and make existing processes much more efficient and effective. With many fintechs growing rapidly and operating in an incredibly competitive sector, the edge data analytics brings could be the difference between success and failure, especially in today’s uncertain economic times.”
Precisely’s Reilly continues: “Most fintechs have undergone significant digital transformations, aiming to improve efficiency and the customer experience. To succeed, it is imperative that they fuel business intelligence reporting with data that is accurate, consistent, and contextual – data with integrity. Although many fintechs recognise both the benefits of effective data management, and the risks that come with hosting a vast amount of data, they do not always have an effective strategy in place nor clear business goals.
“To drive actionable decisions and realise game-changing benefits from true business intelligence that can keep up with the business’ growth, fintechs need to invest in people, process and, certainly, technology that combines data integration, data quality and governance, location intelligence and data enrichment capabilities. This will enable them to establish a base of high-integrity data that they can genuinely trust to inform their business decisions. By doing so, they will also have the insights needed to ensure they are using customer data in a meaningful way and can identify new ways to enhance the customer experience.”
