To address the needs and expectations of new generations, financial institutions must harness customer data and leverage advanced analytics to provide personalized products, services, and messaging.
In wealth management, financial advisors could leverage next best action (NBA) capabilities to identify investments aligned to customer objectives. Similarly, retail banks might develop targeted direct marketing strategies for debt, insurance, and other products based on a customer’s profile, preferences, and transaction history (with opt-in from the client).
Insurance companies have the opportunity to cross and upsell clients with products that complement their coverage and personal profile. For instance, someone who already has house insurance, but just had their first child, may want to add life insurance to their policy. In short, personalization is not only desirable but necessary for Millennials and Gen Z, and any organization which is unwilling to adapt should expect to lose market share to more savvy competitors.
The research backs up this assertion that there is still a gap between customer expectations and services offered by institutions. A Salesforce survey on financial services providers and customers revealed that only a quarter of customers said that services match their expectations1.
Figure 1 summarizes the gap between services that customers expect and what they receive.
Figure 1: Customer-centricity expectations vs reality
Source: Salesforce
To close the gap, financial services firms need accurate and relevant customer data at scale. But while customer data is available in massive quantities from multiple sources, many institutions struggle to manage and organize it to support more personalized experiences and messaging.
The good news is that if institutions succeed in harnessing the ‘right data’ to personalization strategies they will see a revenue boost both from customer retention and the strength of their brand. In other words, by understanding what constitutes the ‘right data’, institutions can narrow their focus and achieve measurable results.
When developing a personalization strategy, it is important to note the distinction between macro-segments, that is a targeted product that appeals to a subset of the institution’s customer population, and micro-segments that are personalized products and services at an individual level.
Take a customer whose click history on an institution’s website history indicates an interest in investing, and whose personal data reveals that they are employed at an environmentally focused start-up. If the financial institution is using data-driven personalization, then it could serve the customer with relevant content with information about Environmental Social Governance (ESG)-conscious investment products and strategies.
The outcome can be compared to the recommendations of a personal financial advisor, but the use of analytics and automation, with accurate insights at scale, widens the availability of personalized services to a greater number of customers.
Measuring Maturity
To properly assess where an institution stands, we have identified three stages on the personalization journey.
Figure 2: The three stages of attaining mature and sustainable personalization capabilities
Source: Capco
As the chart shows, the ‘right data’ encompasses geographic and demographic data (Stage 1), psychographic and behavioral data (Stage 2), as well as preference-driven data geared towards solutions or relevant products that address the customer’s present or future needs (Stage 3).
Figure 3 – Example Scientific Persona
Returning to our earlier example of the environmentally conscious customer, the institution’s model might note their job, age, total financial portfolio, as well as click data from the website and external social media data. This leads to the conclusion that the client is receptive to an ESG-focused investment strategy.
Still Some Way to Go
Most US financial institutions are at Stage 1 and maintain basic customer profiles using demographic data. Some have reached Stage 2 incorporating segmentation based on observable markers including life events, profitability, and product holdings.
However, knowing the ‘who and what’ of a customer is insufficient. To progress to Stage 3, banks must further develop their data and behavioral science models so adding greater context to customer behavior.
This is where the latest advances in artificial intelligence and machine learning come into play. By combining customer data with artificial intelligence (AI) or machine learning (ML) models, banks will be in a position to fully understand customer motivation, shape personal objectives, and craft deeply personalized experiences.
Above all, a successful (Stage 3) institution will have understood and applied the following concepts:
Where an organization can apply these concepts, it will be far better positioned to grow and maintain sustainable relationships with customers across the entire customer lifecycle in a way that is agnostic of products and services.
The main barriers to personalization include obsolete data architectures, isolated initiatives that fail to drive organizational change, inability to leverage advanced analytics, and a failure to tackle ethical and regulatory data concerns. All of these can impede the ability to find and use the ‘right data’ for personalization.
Despite these challenges, a survey undertaken by fintech Blend revealed that 75% of financial institutions are planning to increase personalization across the customer lifecycle, while 28% say they are going to implement increased personalization at a substantial level.2
Figure 4: Financial institutions planning to increase personalization
Source: Blend
To overcome the aforementioned barriers, institutions should consider the deployment of three categories of data management and marketing tools.
To succeed, personalization strategies must also be applied without exception from the ‘macro’ perspective that determines customer segments, to the ‘micro’ perspective of individual customer interactions with a product or service on a specific channel. Institutions must also prepare for innovation in institutional operations and customer experience to take full advantage of AI and ML driven insights.
The good news is that customers are likely to grant permission to share their data in exchange for more personalized services. For example, a survey by Capco in 2023 of 1,000 US insurance policyholders revealed that almost nine out of 10 respondents (89%) say they are willing to share additional personal data with their insurers – an increase of 17% since Capco’s previous survey in 2021.3
The two leading motivations among the US respondents for data sharing are to secure a cheaper premium (46%) and to gain more personalized services (41%). Only one in ten respondents (11%) said they only want to share basic information with their insurers, with older adults particularly likely to say this (30%). Respondents would consider a variety of ways of sharing data, with a smart device in the home being the favored route in the insurance context.
Figure 5: Capco’s research into the likelihood of US Insurance Policyholders sharing additional personal data for increased personalization
Source: Capco
Below, we summarize the main challenges for financial institutions.
Legacy data infrastructure is holding back customer-centric collaboration architectures. The data architectures of many financial services institutions were built with organizational silos that prohibit analytical teams from gathering a 360-degree view of the customer and prevent more personalized customer relationships.
True personalization capabilities require a centralized approach. Modifications to the company’s operating model are necessary for long-term success. Teams including Marketing Insights, Compliance, and Oversight must collaborate to deliver targeted client marketing. Investments that lead to standalone department changes without improving core processes will fail to deliver expected returns. Personalization at-scale will require new capabilities that should be leveraged across all channels, products, and services. These capabilities include a single view of the customer, data analytics and behavioral science, automation, and cross-channel offerings.
Advanced analytics is key to a deep understanding of customer needs. Customer intelligence traditionally relies on customer data specific to products or channels. Today, institutions should adopt a disciplined and in-depth approach based on data analytics and behavioral science. Data scientists can build ML models that process profile and behavioral data while developing an understanding of contextual behaviors and preferences across multiple touchpoints. Institutions should also consider the deployment of software tools including customer data profiles, MarTech, and customer 360 to realize the full potential of their personalization strategies.
Data ethics and regulatory challenges need to be addressed. Companies must implement strong governance and compliance practices which ensure the responsible and ethical use of customer data while respecting customer preferences for privacy and data sharing. This includes privacy regulations, opt-in/opt-out options, and consent tracking. Specifically, firms must manage the influx of customer data from two sources: additional opt-in data where consent has been provided; and the centralization of data from different departments. For example, a large insurance company where customer policies are spread across multiple divisions. For banks to build and maintain trust with customers, they must be transparent, fair and safeguard customer interests when using customer data, especially when applying AI and ML capabilities. This approach also requires an internal partnership between Data Governance and Data Analytics teams to ensure that customer data passes through the proper governance channels before use.
Collaboration and culture
A standout personalization campaign requires more than just a mature Data Science practice. Marketing, Digital Experience and Innovation, Data Governance, Information Architecture, and Strategy functions all have a part to play to achieve Stage 3 personalization.
Figure 6: A holistic customer centricity approach
Source: Capco
Through the collection and organization of data, institutions can prepare their data science and analytics teams to accommodate persona strategies and develop customer predictions and classifications. These roles include:
By organizing teams in this way, institutions can acquire insights that are in-depth and scalable enough for the development of personalized products and services. Additional benefits of this approach include:
To sum up, key success factors include a strong commitment to data transparency through data privacy and security, access to accurate and relevant data, and a culture of continuous learning and improvement. In addition, decisions that relate to collecting and utilizing customer data cannot be made by a single department. Instead, institutions must foster collaboration between many different lines of business, operations, technology, legal, and compliance teams.
The approaches to personalization discussed in this article have enabled us to add value for our clients by improving customer experiences, reducing operational costs, and uncovering sales opportunities.
In one recent example, the client wanted to introduce a new bank to the market and needed help understanding their target customer. They engaged Capco to help differentiate themselves in a crowded marketplace and deliver a digital experience that better served unmet customer needs.
At the conclusion of this engagement, Capco delivered a data-driven customer segmentation strategy that leveraged the latest AI tools and advanced analytics to focus on high potential customers. Our team also created an insights-driven customer experience using an envisioned future state and breakthrough digital banking opportunities.
Value was unlocked value for the client in the following areas, enabling the client to establish a competitive advantage with its new offering and follow a roadmap towards full maturity:
Segmentation, customer equity and collaboration
Capco’s strategy put customer heterogeneity at the heart of the segmentation effort reflecting the real-world where each customer brings unique value to the company. Segmentation also enabled the organization to identify important trends that go into greater depth and detail than just acquisition and retention.
Furthermore, the digital experience created by Capco generated an abundance of strategic metrics focused on customer equity. The client can now gather data on all points of contact across the digital journey and obtain a holistic understanding of each user – and their value.
Finally, Capco’s framework ensured collaboration and clear communications between all stakeholders from day one. This meant that Capco could create KPIs and operational standards to measure success through all phases of planning, development, go-live, and maintenance.
