In banking, “hyper-personalization” represents not just a strategy for developing better overall customer experience, but as a way to monetize that customer experience more immediately.
Unlike other industries, such as hospitality or retail, a great experience in banking or finance does not as easily translate to earning more business. For example, just because a customer might feel their bank has a best-in-class mobile user experience, that does not mean they will go with that same bank for their next mortgage or even recommend others to do so. A seamless and resonant customer experience may earn you the right, but banks and credit unions must capitalize on that affection into monetizable moments.
Those banks and credit unions who can steadily mature their hyper-personalization capabilities stand to not only win hearts and minds, but also win new customers, deposits and overall share of wallet. BCG studies have shown that personalization in banking can lower rates of user churn and increase sales, leading to annual revenue uplifts of 10%.
It’s Easier Said Than Done
Advancing and ultimately monetizing the customer experience will be a function of how well data, technology and people can be threaded together to increase relevancy with customers at any given moment in their lives. Key capabilities are required in order to advance along what we call the “Hyper-Personalization Maturity Path”:
The reality is that banks and credit unions face several significant hurdles in their advancement to even demonstrate (let alone scale) a true hyper-personalized customer experience. A report titled “Maximizing Digital Engagement,” co-produced by Infosys Finacle and Qorus, revealed that while 71% of banks are running personalized campaigns and communications, less than 50% are leveraging crucial capabilities such as enterprise customer data management, proactive advice, data-driven micro-segmentation, and AI and machine learning recommendations.
Overcoming the Obstacles to Hyper-Personalization
Financial institutions, large and small, face five major common challenges on this very topic in which there is an opportunity to reassess their approach and think differently:
1. Think in terms of user stories.
In a recent CDO Agenda 2024 report lead by Tom Davenport and sponsored by AWS, cited that when (Chief Data Officers) were asked what they find the greatest challenges to realizing the full potential of Generative AI, “finding the right use cases” was ranked second. However, banks and credit unions need to elevate their thinking beyond functional use cases to a higher altitude of desired experience.
A desired experience can be written as a ‘user story’, which is a brief narrative around a need that has an outcome. For example, “I want to accurately risk score segments not traditionally served by us to expand our customer base” or “I want to sequence the right advice, resource and offerings to our customers to make them feel we have their backs”.
Taking an experience-led view allows banks and credit unions to deeply understand customer needs and behaviors, prioritizing the development of solutions that directly address specific pain points and create tangible business value.
2. Focus on the 10-15% of data that adds 80% of the value.
In the same CDO Agenda 2024 report nearly half (46%) of CDOs pointed to data quality as the top challenge to succeeding with Generative AI. In fact, a 2023 Accenture “Banking Consumer Study” highlights that only 53% of respondents reported that their organization can realistically achieve a unified view of their customers through accurate and accessible data.
Sure, your data looks more like a swamp than a lake, but guess what? So does everyone else’s. The truth is that what most might consider to be the most data sophisticated organizations don’t have it all architected perfectly either – they have the same messes you do – but they choose to focus on the most critical data assets that that are foundational to driving the user stories mentioned above.
In this respect, organizations are maturing their data management and governance capabilities through the lens of business value versus trying to fix everything in a bottoms-up fashion that takes years upon years to address, if ever.
3. Transform data into a signals marketplace.
A flexible data infrastructure enables the rapid collection, integration, and analysis of diverse data sources, facilitating the creation of real-time, personalized customer experiences. The concept of a signals marketplace is particularly relevant here. By centralizing and standardizing data into a marketplace of signals (different than a DataMart), organizations can orchestrate these signals to deliver highly tailored offerings. As highlighted in McKinsey’s article “What is personalization?”, this approach empowers businesses to move beyond simple customer segmentation and create truly personalized experiences.
This opens the aperture for the organization to use AI (beyond ChatGPT) to enhance its personalization offering. With trusted data, and flexible technology, AI can rapidly analyze and uncover hidden patterns and generate near real-time insights.
4. Think big, start small and scale fast.
Rapid prototyping and testing are key to succeed in the hyper-personalization space. An incremental approach often involves gradual changes, which can be slow and inefficient. By thinking big, organizations set a North Star that can truly deliver a differentiated experience to their customers. This allows them to determine the path and investment needed, as well as the go-to-market strategies they should adopt to quickly validate the effectiveness of their personalization strategies.
Once validated, successful initiatives can be scaled across the broader customer base, driving significant business impact. As BCG emphasizes in their report, Think Big, Start Small, Scale Fast, this approach enables organizations to maximize the value of their data and technology investments while minimizing risks.
Want to learn more about banking personalization?
- ITMs Are Not Part of Your Multichannel Strategy. They Are Your Multichannel Strategy.
- Relationship Fragmentation is Killing Loyalty: How Should Banks Respond?
- When Does Personalization Become ‘Surveillance’? Chase and Mastercard Are Swept Up in FTC Probe
5. Establish a common understanding of AI.
Although a customer-centric culture that prioritizes innovation and experimentation is essential for hyper-personalization, our experience has revealed driving organizational alignment across senior leadership (and Boards) is a formidable cultural barrier. At the root, much of this is strikingly foundational.
The varied level of understanding of AI and emerging technologies amongst leadership teams hurts strategic alignment and thereby dampens pace of change and underestimates what it will really take. Rik Reppe, Head of Cortico-X’s Innovation Practice, laments in Cortico-Xplorations: Reclaiming the Strategic Agenda in the AI Era that “the AI knowledge gap between young, emerging talent and current executives is widening, with the youngsters knowing considerably more about AI than executives”.
Establishing a baseline of AI fluency is essential to drive successful customer hyper-personalization initiatives. This fluency equips leaders with the knowledge to understand, advocate and make more informed-decisions across data maturity, technology investments, resource allocation and experimentation. Ultimately, when senior leaders are fluent in AI and emerging technologies, they can not only champion exceptional, personalized experiences, but also position organizational capabilities needed to deliver.
Anson Vuong leads the financial services practice at Cortico-X. Anand Balasubramanian leads Cortico-X’s Data Practice.