Data Insights Drive Personalization

In order to win and retain more customers in the digital economy, financial services providers—including traditional banks and FinTech startups—need to consistently improve the customer experience by analyzing the data they collect.

Being data-driven

By continuing their focus on being data-driven, financial services providers can access, collect, and ingest existing data into a centralized system to drive decision-making; train a model to accurately interpret masses of data; and produce an outcome that will provide customers with a personalized banking experience. Insights can help to gain an accurate and deep understanding of the data.

For example, a customer consistently spends $200 on groceries each month. One month he or she shops for groceries in a different city, paying a higher amount than usual. Most banks use this anomaly to send an email with an alert to guard against potential fraud. What was once a one-off alert has now transformed into real-time pattern analysis, including location of purchase, as well as the suggestion of local supermarkets where the consumer may save money. Thus financial services providers can not only give customers an easier banking experience but also manage their finances more efficiently, so they have more disposable income or savings.

Yet banks aren’t going much further, and we are simply stunned. Customers are demanding more personalized experiences in every facet of their lives—from entertainment, to travel, to healthcare—the big question is, what’s holding financial services back?

Today’s technology is so advanced, financial services players now have the ability to create value-added experiences for customers at virtually every stage of his or her lifecycle. We can now detect past changes or predict future shifts in the customer’s behavioral patterns. For example, lenders can recognize when a customer is planning to purchase a home or a new car, and automatically offer them affordable options or insights into the local property market according to the customer’s budget. What’s more useful is actually predicting what a customer will spend, and how much they will spend—and make sure they don’t overspend.

Financial services companies can also take action on location and mobile activity data. For example, an avid cyclist who often takes a particular cycling route to work can have his or her data tracked in real-time. After analyzing cycle activity, frequency, and location, the customer’s financial services provider can send targeted third-party offers from sports shops the customer passes on their journey.

Customers no longer tolerate generic advertisements, rather they are seeking personalized and useful advice, and dare we say ‘guidance’ on what to buy next. When coordinated carefully, consumers and their financial services providers may enter into a trusted, mutually rewarding relationship. Financial services providers should send offers based on past and present behaviors that help predict current and future activity. This data-driven personalization gives customers highly valuable information and experiences, encouraging them to spend more time with their provider.

Financial services providers can structure their big data architecture to enable a more connected experience, which further delivers insights to keep customers engaged, loyal, and productively using financial instruments.

So why isn’t the financial services model based more on data insight?

Today’s traditional banking system is fragmented because legacy systems lock valuable data and insights away in repositories that are inaccessible for those who need it most (i.e. the customers and retail bank employees). Gaining access to this information hived away in legacy systems is not easy because traditional banks are governed by compliance and regulatory rules, and business complexity is driven by product lines, customer segments, and many transaction volumes.

In some instances, the customer must weave through data siloed in multiple departments within a bank, including mortgage providers, customer services, card providers, and more. This lack of communication makes it hard to get a complete picture of banking over time, and a lack of understanding can make long-term banking difficult for customers.

So how can banks enable customers to make sound financial decisions, purchase and use a financial service provider’s preferred instruments (products + services), and continuously keep them engaged and interested?

Provide personalized banking

According to a recent BCG report, a retail bank with $100B in assets can generate up to $50M in daily revenues by personalizing banking pricing and product offers. The same report mentions that 68 percent of respondents of a recent survey deepened ties with an existing bank as a result of the bank’s data-driven personalized approach. There is no doubt: this is the future of banking.

Data-driven personalization is not a new concept—financial services providers have been using regressive-based statistical analysis for years to get better forecasts of demand on banking products and services. Financial services providers need to apply this modelling differently to data, tying it back to the shift in how customers prefer to bank and what they value.

Traditional banks are gradually putting more emphasis on using data-driven insights as they have historically feared falling behind and missing out on customers to FinTechs. Faced with dramatic shifts in technology and consumer preferences, traditional banks are collaborating with FinTechs to stay competitive. Customers have a much better handle on immediate financial health with FinTechs versus traditional banks—FinTechs provide customers real-time reporting on all banking expenses, whereas the banks typically have a couple of days of lag time with pending transactions.

Monzo, a UK financial service provider, offers customers a vault where money is credited to a savings account by automatically rounding up debit card spends to the nearest pound. The extra pennies missed don't feel like much to the customer, but they build up quickly, adding to significant savings.

Integrating data taken from the customer’s journey with new technologies, such as artificial intelligence (AI) and machine learning (ML) turns it into “smart data,” or data that can be used in meaningful ways for decision-making. The bottleneck is about managing, integrating, analyzing, and interpreting it.

AI and ML intelligently can even analyze data to personalize banking and provide instant and accurate decisions about mortgage or insurance applications, predicting outcomes, and improving ways to save the customer more money (or give them more money as pointed out earlier).

Applying ML and AI to a customer’s data can help banks forecast the possibility that a customer relationship may be at risk if they’ve had a bad experience. By understanding the internal and external drivers that are at work in the relationship, banks can interact with customers in a more strategic way and provide relevant products and services to keep them continuously engaged.

For example, by recognizing patterns derived from various mechanisms (i.e. feedback forms, log in times, and face-to-face interactions, etc.), AI can help to inform a bank that a customer may leave as a result of a bad experience or the lack of specific product offering. ML can then be applied to develop a proactive intervention strategy to counteract or neutralize the negative sentiment.

Nevertheless, does this mean that banks should provide personalized campaigns to target customers? Absolutely—it’s important that personalized campaigns use the right data. ML can profile people to make more relevant ads but it’s all down to the individual’s data.

Conclusion

The future of banking relies on applying data ingestion to a centralized system. This can be achieved by the utilization of data-driven insights, and leveraging them for better customer services, decision making, and giving the customer more money.