Hyper-Personalisation in Banking: The Paradox of Customisation

The Double-Edged Sword of Personalisation

As the digital age reshapes the banking landscape, the drive for efficiency has often come at a steep cost: the erosion of personal touch. In a world where customers can switch services with a swipe, the stakes for banks have never been higher. An Accenture survey starkly highlights this reality—customers are increasingly inclined to explore new players for additional services, leaving their primary banks in the dust¹. This growing disengagement begs the question: How can banks turn the tide?

At first glance, the answer seems obvious: personalization. The logic is simple—more tailored the solution, happier the customer, right? But scratch beneath the surface of this promising strategy, and the picture becomes murkier.

Banks have certainly attempted to push the boundaries of customization, but the results have been inconsistent at best. The path to true personalization is fraught with complexities and risks that often overshadow the potential rewards. So, how much customization is enough? Is hyper-personalization the holy grail, or just another buzzword?

Personalisation vs. Hyper-Personalisation

The distinction between personalisation and hyper-personalisation lies primarily in the intensity and depth of customisation. While these differences can be contextual and subjective, they generally boil down to the following:

• Personalisation: involves tailoring products, services, or communications based on broad customer data, such as demographics, purchase history, income, or general preferences. Personalised offerings would follow a set menu for a customer to choose from. For instance, a young professional who frequently travels might receive or select a tailored offer for a travel rewards credit card, highlighting benefits like airline miles and hotel discounts. This approach personalises the offer based on broad customer segments and general preferences.

• Hyper-personalisation: goes much deeper, using real-time data, advanced analytics, and AI to create highly individualized experiences. This approach considers not just demographic data, but also real-time behavior, contextual factors, and predictive analytics. A key differentiator is timing. For instance, if a customer typically earns cashback on retail purchases with their credit card but is planning a vacation, as indicated by recent searches for flights or hotels, the bank could instantly trigger an offer to temporarily adjust the card’s rewards structure to offer double points on travel-related expenses for a limited period and a lower foreign transaction fee or customized travel insurance options tailored to the customer’s upcoming trip.

In contrast to traditional personalisation, which offers generic rewards based on broad spending categories, this hyper-personalised approach dynamically adjusts to the customer’s immediate needs and preferences, creating a highly relevant and timely offer.

In short, while personalisation tends to offer menu-like customisations based on broad segmented preferences derived from past choices or data, hyper-personalisation operates on the principle of the ‘Segment of One’, offering highly individualized assessments and offers at the point of purchase based on past behavior, real-time context, and predicted future needs.

Why Hyper-Personalise?

But why should a bank or fintech even bother with this ‘Segment of One’ approach when broader segmentation strategies have worked well so far? There are three primary reasons that make the case for hyper-personalisation:

1. Rising Customer Expectations: Customer expectations are not just evolving—they’re accelerating. As more niche players and fintechs enter the market, the pressure on traditional banks to keep up is intensifying. According to a Deloitte report, less than a third of customers feel that they receive personalized service from their banks². I guess this figure is likely an underestimation; in reality, the dissatisfaction may be even more widespread. Modern banking customers are no longer content with generic, off-the-shelf solutions—they demand personalized, relevant, and timely experiences that meet their individual needs and preferences.

2. Enhanced Risk Management: Hyper-personalisation enables a more granular approach to risk assessment by analysing individual behaviors, rather than relying on broad generalizations typical of traditional segmentation. For instance, two customers who share the same demographics, similar salaries, same organization, and with comparable debt-to-income ratios—may still have different risk profiles. A hyper-personalised approach uncovers these subtle differences, which are often obscured by generalisations in traditional risk assessment models.

3. Improved Efficiency: Although it may seem counterintuitive, hyper-personalisation can actually enhance operational efficiency. Here’s how:

-Automation: Process automation is a foundational requirement for hyper-personalisation, necessitating a tech architecture that supports automated customer interaction, decision-making, and service delivery. This reduces the need for manual intervention, thereby improving efficiency and gains of real-time insights.

-Data Sanity and Efficiency: Hyper-personalisation demands data sanity at the source to support pre-filling of forms or parsing in real time. The design inherently implies better data sourcing, reduced data redundancy, or bloat.

-Better Customer Retention and Cross-Selling: It leads to better product fitment, crisp customer engagements, and targeted communication, resulting in higher customer satisfaction and retention. Cross-selling can be more targeted, with only the products that customers truly need, thereby reducing customer dissonance and improving conversion rates. A study indicates that banks can expect a 20% increase in revenue from primary customers by taking steps to build more meaningful personal relationships³.

The Challenges of Hyper-Personalisation

Hyper-personalisation not only requires suitability assessment but also organisational readiness at all levels. The key is to do it right for the right customers with the right practices, with the right tools, at the right cost. A 2023 Forrester survey highlights several obstacles to achieving personalisation objectives, including limited customer understanding, outdated legacy systems, fraud risk, and regulatory challenges⁴.

• Technological Hurdles: Hyper-personalisation is neither easy nor inexpensive. It demands advanced data infrastructure and real-time analytics. Notwithstanding how casually we talk about data analytics and AI, at the moment of truth it may mean upending the entire tech architecture. According to a McKinsey survey, only 28% of banks have the capacity to integrate data for AI initiatives, and even fewer (14%) have established governance frameworks to manage AI risks⁵. In India, larger banks struggle with outdated systems, limiting their ability to fully implement personalisation initiatives. The rapid pace of technological obsolescence only exacerbates these challenges, potentially making hyper-personalisation efforts prohibitively expensive or even unfeasible.

• Data Privacy and Customer Experience Issues: Increased customisation requires more information. Going beyond the well-established data sources like bureaus, salary slips, and bank statements to more invasive data such as social media analysis and financial goals raises significant privacy concerns. Customer sensitivity to share additional information varies for different data points^2,6 and hence the banks cannot assume that customers will be ready to provide all the required data for the model. The increasing regulatory scrutiny surrounding data privacy further complicates this. Then there is the issue of UX. With each additional item in the application form, the user experience takes a hit, leading to mid-journey dropouts.

• Over-Personalisation Pitfalls: Determining when to shift gears to hyper-personalised offering is often easier in hindsight. In real time, the enthusiasm may lead to overkill. Not every product requires hyper-personalisation. Banks have tried and faltered. The key is to clearly understand the drivers of customer decisions and solve it for scale. As an analogy, it is crucial to realise that not to offer a recipe of the dish when all that the customer needs is an à la carte menu!

In essence, Banks need to carefully evaluate the costs of personalisation, mitigate concerns, and justify the potential benefits in terms of customer loyalty and value. The next question, therefore, is how to proceed.

Strategic Deliberation: To Do or Not to Do?

There are no easy answers. However, as a first step, two simple questions can help bring strategic clarity:

• Is This for Us? For banks with legacy systems characterized by siloed data, batch processing, and disparate data sources, the journey to hyper-personalisation may mean starting from scratch. While banks with existing cloud-based infrastructure, Customer Data Platforms (CDPs), real-time analytics, and AI-driven personalisation engines are in a far better position to implement it effectively. If the bank is far away from this starting line, it may be wise to prioritize revamping tech architecture before thinking about hyper-personalisation. Even when some building blocks are in place, focusing on areas where moderate tech investments can leverage the existing setup is often a smarter course of action.

• Who Is It For? The key lies in identifying which products, services, and customer segments the bank currently has a competitive edge. Focusing on such customer segments is a better starting point compared to using hyper-personalisation as a tool to service peripheral customer segments. This approach not only helps manage costs effectively but also enhances the customer experience by delivering meaningful, targeted personalization.

However, a key point to remember is, even when strategic choices are made, execution remains the key to successful hyper-personalisation initiative.

But Is There Really a Paradox?

With each new entrant in the financial services arena, the market continues to get sub-segmented. The influx of fintech companies has only hastened the process. So, the question for banks is not about ‘whether’ but ‘when’.

Hyper-personalisation presents a powerful tool for banks to reclaim their competitive edge and reconnect with their customers. Considering banks have both, a large customer base and high amount of data per customer, the case of hyper-personalisation at scale is well justified. Customisation in banking isn’t new. The challenge is doing it at the ‘Segment of One’ and at scale.

It will require careful planning, the right technology, and a strategic focus on areas where it can truly add value. Those banks that successfully navigate the complexities will be well-positioned to lead in the future of banking, offering tailored, customer-centric experiences that set them apart in an increasingly crowded marketplace.

References

1. accenture.com/us-en/industries/banking?c=acn_glb_pressrelease_10910716&n=mrl_0319
2. deloitte.com/content/dam/Deloitte/uk/Documents/financial-services/deloitte-uk-hp-the-future-of-retail-banking.pdf
3. accenture.com/_acnmedia/PDF-140/Accenture-Global-Banking-Consumer-Study.pdf
4. Forrester report: Unlocking Hyper-Personalization At Hyper-Scale
5. mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/getting-personal-how-banks-can-win-with-consumers
6. capco.com/Intelligence/Capco-Intelligence/Insights%20for%20Investments%20to%20Modernize%20Digital%20Banking