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Market trends and statistics prove that personalization in banking has acquired a strategic value. Over 70% of customers rate tailored offers as highly important for banks and other financial companies. Ironically, banking institutions remain the last bastion of personalization with only 14% of banks providing contextually relevant experiences. 

Current state of financial institutions seems confusing. Every day, banks generate a huge amount of customer data that can be turned into competitive advantage. Yet, it often remains unused for delivering unique offers to the existing users and attracting potential customers.

In our conversations with clients, we see that banking executives are nonetheless eager to improve customer satisfaction and increase the customer lifecycle with personalized customer experiences. Marketing, customer service, and customer experience teams realize that personalized banking is crucial to making indirect revenue. 

By building personalized relationships with clients, banks get additional financial worth such as up-and cross-selling of financial products, new clients through recommendations, inter-bank transfers, among others. All these complement direct revenue streams and are the result of brand affinity.

So what’s the problem? Why don’t banks use their customer data assets to the fullest? Let’s reveal the key problems and see how personalization can help cope with them.

Challenges on the way to personalization in financial services

A deep understanding of customer persona and consumer expectations and preferences is what leads to a bespoke experience in financial services. Consider that over 60% of customers want their bank to be as personalized as Amazon or a personal shopper. That’s the standard the banks should aim for. However, obtaining it isn’t easy as granular offerings are often hampered by common limitations present in the financial services industry.

Legacy software

According to Deloitte, outdated technologies are considered the main bottleneck on the road to deeper personalization.

Tech debt, 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 already available 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.

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 a whole infrastructure or connect legacy systems to the new infrastructure component.

Neglected customer needs

All too often, the banking industry focuses on products and services rather than customer needs. However, profound customer needs’ research is intrinsic to top-selling initiatives. Without good customer experience, it is impossible to sell effectively and grow your profitability.

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;
  • Well-defined interest rates.

Luckily, the aforementioned challenges can be eliminated. Tech companies solve these problems by helping banks put all their customer data in place, analyzing it, and creating customized offers at the right time and place.

Five secrets of acquiring and retaining bank customers through personalization

The good news is that personalization in banking is attainable. By implementing advanced tech tools and digital-savvy approaches, banking businesses 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 organizations 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.


Harness Artificial Intelligence, Machine Learning, and Deep Learning

Your data won’t speak unless you ask it. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) can uncover hidden relationships between data values and deliver a unique client perception. While all the three are equally helpful at discovering data patterns, Deep Learning is cited in most examples of personalization in banking.  

Being a branch of AI and ML, Deep Learning excels at aggregating a patchwork of customer data and generating actionable insights for tailored products. Moreover, DL models specialize in analyzing both structured and unstructured data. The latter accounts for around 80% of banking data and is impossible to analyze without special algorithms. 

Deep Learning algorithms can discern inexplicable patterns in data and predict future outcomes based on huge amounts of information. Manual analysis can never be on par with intelligent systems as traditional data analysis can only draw high-level conclusions through visual summaries and Excel tables with no deep insight into the problem or correlation. 

Deep Learning models can single-handedly analyze buying patterns, demographics, transaction volumes, and audio files to create targeted credit or savings offers that are low-risk for banks but high-value for customers. All these actionable outputs are based merely on available datasets. Without Deep Learning, finance companies will end up wasting years on manually building links between customer footprints.

Machine Learning as a whole can drive personalization for any client, be it high rollers or low-value customers. This way, intelligent algorithms can identify hidden and subtle spending tendencies and suggest a bespoke solution or contextualized customer experiences for all customers. 

Also, both ML and AI can amplify advanced data analytics models and provide banks and credit unions with competitive differentiation. For example, if some percentage of existing customers with X amount of annual income tend to spend money on traveling rather than on deposits, ML models will discover this link. It means that banks can serve tailored cashback offers on hotels and such to this group of clients.

Build lookalike audiences with ML

Since it’s impossible 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 super-targeted experiences 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 deliver personalization.

Companies from the financial services industry can avail themselves of third-party event data consolidation to hunt for new customers. These may include communication tools, social media data, and other third-party databases and applications. 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 create contextualized customer engagement opportunities in real-time. It means that when the client decides to choose new account offers when checking their balance online, 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 includes the real-time categorization of spending. When a client makes a purchase at a grocery shop or gets gas, the bank’s money monitoring tools notify the client of the spend type and budget portfolio, keeping the client aware of their spending pattern. This nice touch nurtures brand connection even with no real interaction with the client.

Be where your customers are

90% of customers expect consistent interactions across all channels. Therefore, omnichannel excellence isn’t an option, but a necessity. Digital-first finance companies should deliver uniform experience and service to clients across multiple 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.

cross-chanel personalization

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 your customers’ banking experience

Turning inactive clients into bank evangelists is an uphill struggle. However, personal experiences can beef up your sales and bring you closer to the customers. Tailored, meaningful, and timely messages help financial institutions build deeper relationships with customers with no additional risks or tedious effort. 

To enable personalization 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 initiates a data flywheel to get continuous customer insights.

Our consulting-led approach enables organizations to design a robust data strategy and build a set of new capabilities for managing the data-to-decisions value chain. Get in touch with our experts and we’ll conquer any data complexities you may have.

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Anna Vasilevskaya
Anna Vasilevskaya Account Executive

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