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How Finance Brands Can Leverage Discretionary Activity Data

Personalisation is key to the success of any marketing campaign from a financial institution

Photo by Mikhail Nilov from Pexels

By Carolyn Corda

Financial institutions have long used data to target customers with marketing messages. Credit card businesses, for example, have utilised spending data to identify customers most likely to be interested in a certain type of credit card.

However, while they used to lead the way when it came to customer targeting, financial institutions are now lagging behind other sectors due to advancements in data tracking, privacy management, and analytics. Brands in every sector are now able to segment and target potential customers with relevant and personalised marketing messages, leaving these former financial trailblazers behind. In fact, a report by IDC and Backbase found that 38% of traditional banks’ revenues could be at risk in the next five years. This is due to more than 35 neobanks and other new digital challengers arising across APAC following agile best practices, giving them an edge over traditional models when it comes to self-service capabilities, customer needs, permissioned-personalisation and more.

Personalisation is key to the success of any marketing campaign from a financial institution. Delivering the right message at the right time not only drives results but customer loyalty as well, proving the value of the brand on an ongoing basis. According to a report by SmarterHQ, 72% of customers – ranging from Gen Z and Millennials to Gen X and Baby Boomers – will only engage with marketing messages that are personalised and tailored to their interests. Another report by Adobe shows that companies leading the way in customer experience – where personalisation plays a key role – are more successful than their peers. Taken from a global sample, these companies are three times more likely to have exceeded their 2019 business goals.

If financial institutions want to reach and connect with customers, they must first be able to deploy data-driven strategies that leverage more than just a few data points to build a profile of an individual. For example, banks may be able to leverage internal data to find customers that are more likely to be interested in a certain account type. However, they can’t identify this with a huge amount of certainty, or understand the specific needs and desires of an individual. This is also not useful in attracting new customers. Only by understanding customer buying preferences can financial brands personalise marketing messages and even their services more effectively, securing high-value future customers with compelling marketing offers. 

Discretionary activity data encompassing both intent and spend is a key tool that can give true insight into an individual. Teasing out intent behaviour from actual spend differentiates between aspiration and actual commitment. For example, a consumer might search for expensive, gourmet restaurants or front-row seats at a blockbuster concert, but when it comes down to the purchase, they may opt for activities more in line with their budget. Getting a clearer view of the combinations of behaviour is also valuable. By knowing that customer X buys multiple train tickets and goes to fine dining restaurants away from their hometown, a marketer might get a different understanding, as compared to knowing that the person attending these concerts is the primary driver for these trips.

Discretionary activity spend propensities can unlock the most crucial customer insights. In the example above, it could provide clarity on whether VIP pre-sale access to concert tickets might make a more relevant offering to the person than offers on rail fares. 

Similarly, the hotels we choose and the attractions we go to paint a picture of an individual, and it is this individual profile that finance marketers need to unlock in order to truly serve existing customers and – crucially – attract new ones with compelling offers. For example, a bank is deciding between a promotional annual percentage rate (APR), or the option to join an experience-based rewards programme or one with discount offers from relevant retailers. A wise target for the discount option is a person who has shopped high-end luxury goods, but ended up opting for a more modest transaction. Meanwhile, someone who buys an expensive spa package may find the experience-based rewards programme more appealing. In what is a sticky market for attracting new consumers, personalised experience offers can help a finance brand get a new customer over the line. 

This extends to understanding the best targets for an air miles-based credit card, or realising that high-value customers may be looking for a more effective account type based on their current circumstances and interests. A person who books a family room will most likely be interested in different financial offers and promotions as compared to solo travellers. Families may be keen to redeem air miles in exchange for a family holiday or a group ticket to a theme park ride, while solo or business travellers might respond to no foreign exchange fees, free car rental insurance or airport lounge access. The case for knowing your customer goes on and on. It is only through leveraging verified customer identities, built from ethically sourced data points across a spectrum of discretionary spend, that this can be truly maximised. 

Financial institutions must draw on privacy-compliant customer behavioural data in order to develop more customer-centric and personalised marketing strategies that put the individual consumer at the centre of the operation. Only then will they be able to truly understand what a given customer wants and needs, boosting both the customer experience journey and marketing effectiveness.

Carolyn Corda is the Chief Marketing Officer, ADARA

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