Acquire and Retain: How To Nail Personalization In Banking and Build Customer Loyalty

When personalization feels baked into every scroll and click on platforms like Netflix or Amazon, customers no longer see tailored treatment as a premium perk. What once felt like a VIP touch is now simply the baseline across all areas of life, banking included.

Interestingly enough, the demand for personalized banking cuts across generations. 74% of respondents, including Gen Z, Millennials, Gen X, and Baby Boomers, want banking services that feel made-just-for-them.

Ironically, banking institutions remain the last bastion of personalization with 94% of financial institutions acknowledging their inability to provide the kind of hyper-personalization customers desire.

Every day, banks generate a huge amount of customer data that can be turned into competitive advantages through delivering unique offers. Yet, it often remains untapped.

So what’s the problem? Why don’t banks use their abundant data assets to the fullest? Let’s uncover the main challenges of personalization in banking and how to overcome them to attract and retain more clients.

What is personalization in banking?

Banking personalization is the use of data collected from customer financial transactions, behaviors, preferences, customer feedback, and life events to identify needs, predict intents, segment dynamically, and deliver timely, relevant products, offers, and advice across channels.

For example, if the bank detects a new parent, it might recommend education savings plans or family insurance. Frequent online shoppers can see cashback rewards and real-time fraud alerts. A customer saving for a home might get personalized mortgage options tailored to their budget and timeline.

Benefits of financial services personalization

Nearly 92% of banking executives are ramping up investments in personalization efforts, betting it’s the key to reach customer satisfaction from the very first interaction through to long-term loyalty.

• More effective customer acquisition

Offering the right product at the right time improves engagement and conversions, all while reducing customer acquisition costs (CAC).

For example, a Brazilian bank’s dynamic, personalized online banking menu drove a 56% surge in monthly loan applications and a 30% higher conversion rate. Similarly, the U.S. Bank’s tailored marketing campaigns, powered by a real-time customer data platform, resulted in a 127% increase in annual booked accounts. Moreover, as stated by Mastercard, leveraging available data in a right way to deliver personalized customer experiences from the very first page view helps banks optimize acquisition ROI and reduce CAC.

• Higher customer retention

Today, 72% of banking customers prefer to stay with banks that anticipate their needs even before they’re articulated. When every interaction feels genuinely relevant and helpful, account holders remain loyal to such a level of care. Conversely, even the slightest hiccup in the way a bank develops its sales and marketing communication with a customer can drive churn.

We helped one of our clients, a prominent Czech bank, enrich their new conversational AI chatbot with a personalized virtual financial advisor. Pattern recognition under its hood allows the bot to anticipate customer needs and spot keywords, such as “pay”, “send”, “transfer”, etc. to give users faster access to relevant financial products.

By analyzing customer data, the chatbot provides tailored insights, proactively helping users reach their financial goals. A trial pre-subscription run of an AI-powered solution exceeded expectations, helping increase 30-day user retention by 7%. Read the full case study >>

• Revenue growth

By building personalized relationships with clients, banks get an additional revenue stream through up- and cross-selling their financial products, thus increasing customer lifetime value. In fact, banks excelling at personalization generate 40% more revenue from marketing activities compared to their peers, who rely on generic campaigns and broad segmentation. 

Challenges on the way to a personalized banking experience

A deep understanding of customer persona and customer expectations across all touchpoints of the customer journey is what leads to a seamless and personalized experience in financial services. As more and more customers want their bank to be as personalized as Amazon, financial institutions need to step up and deliver.

However, obtaining it isn’t easy as granular offerings are often hampered by common limitations present in the banking sector.

Legacy software

According to Deloitte, outdated technologies are considered the main bottleneck on the road to deeper personalization. Tech debt, the absence of real-time advanced analytics, and inflexible customer databases leave customers’ behavior unmotivated to finance organizations. As a result, companies lack strong cross-channel offerings, revenue growth, and, most importantly, a holistic vision of their customers.

Moreover, the lack of consistent data analytics stops banks from leveraging customer data as a competitive advantage. This means that banking institutions are unable to compete with tech-savvy banks by default, thus losing profit and potential regulars.

Organizational silos

Siloed data and isolated departments also hobble the successful adoption of a customer-first mindset and big data analytics. Silo mentality is detrimental to both internal and external policies since it limits data flows to a specific branch or employee. As a result, no uniform data governance approach is possible, making personalization and advanced analytics unviable at all stages of customer journey.

Typically, organizational silos refer to incompatible tech systems that cannot programmatically interact with each other. As a result, data is fixed in one department and segregated from other parts of the system architecture. Therefore, before implementing a new setup, companies can either update their whole infrastructure or connect legacy systems to the new infrastructure component.

Neglected customer needs

All too often, the banking industry focuses on products and financial solutions rather than customer needs. However, profound customer needs research is intrinsic to top-selling initiatives in digital banking. Without good customer experience, it is impossible to sell effectively and achieve business growth.

A well-shaped customer vision lays the ground for:

• Competitive customer service
• Smart digital banking
• Relevant fees on banking accounts
• Convenient branch locations
• In-demand types of services
• Positive brand image
• Stronger customer relationships
• Well-defined interest rates
• Branch sales productivity

Luckily, the challenges above can be eliminated. Tech companies solve these problems by helping banks create and implement robust personalization strategies by putting all their existing data in place, analyzing it and offering personalized solutions at the right time and place.

Five secrets of acquiring and retaining customers through personalization in digital banking

The good news is that personalization in banking is attainable. By implementing advanced tech tools and digital-savvy approaches, financial institutions can tap into the hearts and minds of their customers and deliver initiatives polished to a tee. Here’s your secret sauce that will help you reel in clients and drive more value.

Establish a single source of truth

Some financial businesses have their customer data siloed across departments, which makes it isolated from the rest of the organization. As a result, the customer journey and personas are incomplete if created at all.

Clean, relevant, and accessible data is key to discerning the stimuli, preferences, and financial behavior of your customers. To create a single view of the client, financial services companies should unify and activate the miscellany of the operational data at hand. 

However, data unification and activation require the elimination of organizational silos and system modernization. Data lakes and warehouses contribute to delivering a 360° customer view and promote interoperability and immutability of data. Within them, data is drawn from multiple locations across departments, with all input being analyzed by specific criteria. 

Once the analysis results are ready for use, custom or platform-based Business Intelligence tools visualize the insights and prepare new reports so that businesses can monitor and compare crucial metrics and KPIs. For example, a loan department can source specific transaction data from a huge data repository to amplify loan decision-making at any time.

Moreover, comprehensive data governance policies will maximize the use of big data and align data collection and classification across organizational boundaries. Data governance also connects the data points in a cohesive whole and standardizes them across warehouses, lakes, cloud storage, and databases.

To better understand a customer, banking leaders also enrich their data collection through external APIs. This increases access to additional customer insights premised in enterprise and accounting systems as well as partner and public datasets such as PSD2 account information.

Tap into generative AI capabilities

Follow the lead of forward-thinking banks doubling down on generative AI across multiple use cases to freshen up customer experience, cut out tedious tasks, and stay competitive with digital-first challengers.

• Personalized experiences on a frontline

Old-school, rule-based banking chatbots are frustratingly limited and rarely solve problems on the first try. The architecture of these solutions fundamentally prevents personalization. In contrast, virtual assistants with robust NLP under the hood are a whole different ball game when it comes to providing customers with effective, individualized care. Rather than just repeating scripted answers, they understand context, detect intent, and anticipate needs. Beyond hyper-personalized customer service, your banking app can also be boosted with features like:

1. Instant account updates (“Show me last month’s dining expenses”)
2. Proactive push notifications (“You’re nearing your budget limit. Want to adjust?”)
3. Personalized financial advice (“Based on your spending, you could save $200/month by refinancing.”) 

• Personalized marketing content at scale

When you let deep neural networks and machine learning algorithms process structured and unstructured data, say, customers’ transaction history, social media activity, or demographic info, you dig into the very fabric of personalized messaging across your marketing collateral.

Not only does the quality level up, but the quantity does too, thanks to LLMs’ powerful natural language generation capabilities. Accenture shared how a retail bank they worked with managed to produce 30x more high-converting marketing content with no delay in turnaround.

• Risk assessment

Traditionally, evaluating a borrower’s creditworthiness involved manual reviews, gut instincts, and incomplete data. Mistakes were costly, either in the form of bad loans or missed opportunities.

Generative AI changes that. By analyzing vast datasets including spending patterns, market trends, and even subtle behavioral signals, machine learning models can predict risk with startling accuracy and generate further informed guidance in a couple of seconds. That way, banks approve loans faster, with fewer defaults, while customers benefit from fairer, more tailored terms.

Build lookalike audiences with ML

Since it’s really hard to yield tailored experiences for each client, financial institutions often implement look-alike models. This classification technique helps identify customer groups that share similar segment-specific data, be it spending habits or age ranges. 

By analyzing a wide array of metrics, ML-based look-alike models produce evolving customer profiles. Accurate segmentation, in turn, allows banks to predict the clients who are most likely to respond to particular financial services. In simple terms, finance companies get a smart opportunity index that allows them to create accurate marketing strategies and build a personalized banking experience that drive true value to clients.

Integrate life-event data

Customer profiling can never be too deep. Therefore, any bit of valuable information contributes to more awareness about customers’ behavior. On this line, event data, which describes actions performed by a client, can yield measurable or otherwise analyzable insights. As a result, finance firms can immediately react to new customer interactions and offer personalized services.

Companies from the financial services industry can leverage data from third-party events to hunt for new customers. These may include communication tools, social media data, and other third party financial apps. To enable automated processes and real-time data tracking, finance institutions must have this data integrated with in-house tools.

However, as third-party data sharing practices are tightening, integration approaches are subject to a wide range of regulatory acts that include GDPR, Dodd-Frank, MiFID II, and others.

Alternatively, banks can collect and integrate in-house event data to retain loyalty. On-site financial infrastructure with event-based architecture and event streaming are already awash in data coming from corporate sources. That being so, by sharing events across the company, finance businesses have an event data set ready for analysis. If we combine historical data with real-time insights, this further adds predictive capability to event streams.

Moreover, event data on its own can help create contextualized customer engagement opportunities in real-time. It means that when the client decides to choose new banking products when checking their account balances, for example, and leaves the application form unfilled, the system will notify the bank of the lost opportunity. This, in turn, allows banks to re-engage the client right away.

Another example of well-done event data management in digital banking includes real-time spending categorization. When a client makes a purchase at a grocery shop or gets gas, the bank’s money monitoring tools notify the client of the spending type and budget portfolio, keeping the client aware of their spending pattern. This nice touch on a customer’s financial well being nurtures brand connection even with no real interaction with the client.

Be where your customers are

70% of banking customers expect consistent interactions across all digital channels. Therefore, omnichannel excellence isn’t just one of the buzzword industry trends, but a necessity. Digital-first finance companies should deliver uniform experience and service to clients across multiple digital channels simultaneously. This, in turn, intertwines all client touchpoints and allows organizations to target the user with bespoke offerings based on previous customer interactions with the company’s platforms.

For example, customers can be served with granular ads on social media or ad-friendly websites after browsing information on a certain bank credit card or loan offers. Also, interrupted application processes can be remediated with personalized mobile notifications if a client has a banking app on their smartphone.

Major banks are already following the omnichannel principle. For instance, U.S. Bank has created a unified customer information database to ensure an omnichannel experience for users across 3,000+ branches in 25 states.

To ease the strain on the marketing department, banks can resort to marketing automation. The latter takes over multifunctional marketing efforts and facilitates sending personalized offers across the channels, whether it’s a mortgage loan or a retirement plan. Businesses that leverage marketing automation tend to land +451% of qualified leads.

From a tech standpoint, marketing automated tools lean on cross-channel data, feeding on email, website, app, and other interactions. The software then streams segmentation and targeting processes to group the right audiences and calibrate messaging to each customer automatically based on their profile. Being a competitive asset, marketing automation reaches customers on a personalized level, no matter the audience size.

Reimagine customer experience through personalized banking services

Banks of all kinds – traditional financial institutions and digital challengers alike – are realizing big gains by treating customers as individuals. By putting customer data to work (safely and ethically) to generate personalized financial advice, alerts and offers, banks can measurably raise retention rates, increase cross-selling/up-selling, and establish stronger account holder relationships.

To enable personalized banking initiatives, financial institutions need to establish an updated data infrastructure that allows for real-time analysis, exhaustive data collection, and intelligent capabilities. A concise data governance strategy will glue all components of your setup and initiate a data flywheel to get continuous valuable insights.