Knoldus Blog Audio
Established financial institutions need to target the right people at the right time. But truly seizing the opportunity in this space will require hyper-personalization.
Most banking customers don’t think much about the industry unless they’ve reached a crossroads where they need a particular service. That means few people are actively looking to switch banks or are particularly susceptible to traditional marketing tactics.
If banking customers only think about switching providers at certain times in their lives, it’s vitally important for financial institutions to leverage data for a holistic view of potential clients.
That way banks can avoid wasting time and energy marketing to people at the wrong time. They can target the right individuals when it counts. And they can approach potential customers with a personal touch.
The low level of customer churn creates a challenge in finding new customers.
Banks that have successfully drawn new customers have spent mightily to do so. For example, in the UK, three established, non-fintech institutions (Nationwide, HSBC and NatWest) actually expanded their list of active accounts in 2019. Each of these institutions offered £100 to £200 incentives to buy customer attention.
In 2019, the UK retail banking industry spent £233 million on conventional, above-the-line advertising: TV, radio, billboards, online ads, etc. Rather than this blunt force approach, there must be more efficient ways.
Banks would be able to offer compelling enough reasons for potential customers to ditch their current providers and open new accounts – if they know a bit about what those people need.
The current customer acquisition model doesn’t work, despite regulatory intervention, and new entrants switching volumes are declining.
It’s important to use modern technology to offer the right products at the right time.
Banks used to develop campaigns around segmentation models that were marketed toward specific groups. They would try to drive conversion through the funnel regardless of where an individual prospect was in life – without any knowledge of whether their circumstances indicated they were ready for a new product.
Artificial intelligence (AI) and machine learning (ML) enable a new degree of hyper-personalization that can produce predictive models that actually “reverse the funnel.”
Rather than starting with raising awareness and ending with selling a product or service, banks can target people in need of specific products at the appropriate time and make the sale. With success and quality, brand awareness will grow.
This requires much stronger marketing and acquisition data. Enterprise-quality data – that which is the basis for large resource allocation decisions – is managed, governed, cared for, updated, and defined with high-grade technology and data lineages. By contrast, most marketing and customer experience data is of far lower quality.
New versus old customer segmentation techniques
A new take on an old truth
It is a well-known truth that customers don’t think about banking products. A more positive way to look at this is that people do think about banking when it most affects their lives. Indeed, many banks are even organized to reflect these life stages. For example, most banks have dedicated teams for students or retirees.
Nevertheless, most banks are not unlocking the value in their first and third-party data to effectively identify these moments. They are not targeting customers proactively when the opportunity to serve them new products arises based on their current or impending circumstances.
Life stage view of customer product holdings and opportunities
The way to capture new customers is to offer them products at the right time; this means knowing when their circumstances present opportunities for new products.
A Life Stage View of a Customer Segments
For such an approach to work, financial institutions need a granular understanding of individual customer needs. A simple demographic comparison quickly reveals how a single variable can drastically change customer product holdings. These differences only increase as you focus on the circumstances of an individual person rather than a segment of customers.
Using data to improve the experience
Hyper-personalization can also improve customer advocacy and minimize the amount of people who drop out during the onboarding process. This personalization happens at three levels (with increasing complexity):
- Dynamic creative – Supplying specific marketing content that’s most likely to resonate with that individual customer.
- Product personalization – Changing the financial product features and pricing to reflect the circumstances of that customer.
- Flexible design – Creating a personal experience through highly modular and loosely coupled design. Ultimately, banks should apply computational strategies to the design process. By integrating customer data and pattern analysis, banks can change the customer interface continuously throughout the journey.
Personalized recommendations have now leveled up. Staying ahead does not only mean putting the right product in front of people but making it happen at a precise point in time when it will make the maximum impact. Explore more how real-time analytics can enhance the Customer Experience.
What’s at stake and what it takes
In the inert British market, banks could be forgiven for not tackling their acquisition challenge proactively by providing a compelling reason to use their products. However, other markets show that this could lead to significant disruption if just one player figures out how to drive customer acquisition through data.
Zhong An, an online insurance company in China, is one such business to achieve phenomenal success. It offers targeted micro-insurance policies at the point of transactions (e.g. shipment insurance made through online marketplaces like Alibaba). In just five years, Zhong An went from a startup to a business with 432,000 customers that underwrite 5.1 billion annual policies.
To fend off this type of disruption, banks need to act now. That means unlocking the value of their first-party data and enriching it with third-party data to enable predictive models that identify those life stages which create the need for a financial product. These interventions need to be delivered in the channel where the customer is (be it a banking app, social media, or web). All of this will need to be enabled by a fast-paced, test-and-learn approach to experience design.
How to get started
The most effective way to build and scale leading customer-acquisition capabilities is through in-market experiments. This ensures that a regular customer feedback loop can validate the approach taken while the core capabilities are developed.
This will require a set of iterative cycles to incrementally develop the capability, starting with value and ultimately taking the new acquisition capability into production.
Example cycles to create the new capability
Old vs new segmentation for customer segmentation
Globally, banks have failed to acquire new customers. But that doesn’t need to be the case. With smart hyper-personalization strategies, powered by robust data, they can finally change this long-time trend. Eventually, and not in the too distant future, they will need to.