CDP use cases: Retail Banking

Retail banks have to balance providing customers with the kind of connected experiences they have come to expect from every service with regulatory standards, making it critical to use a customer data layer able to collect and connect customer data to their infrastructure securely.

To find the right CDP, marketers need to consider their current data maturity and how it can be used as well as what their future goals are and what data will be needed to achieve those goals. Data maturity levels can be broken down across four maturity levels, from least to most mature: Foundational, Insight and Experimentation, Omnichannel Engagement, and Continuous Optimization. This blog will take you through use cases at each level to help you determine which CDP features are relevant to your situation and find the CDP that will help you meet your current and future business needs.

Objective:

Establishing essential data processes and deploying standard marketing technologies

Organization focus:

Centralizing of clean customer data, connection to marketing, BI, and analytics tools.

Modern marketers want to innovate as quickly as their businesses and customers, but struggle to get the engineering resources needed to implement new tools. A well-instrumented CDP can democratize data access around a single source of truth, delivering and maintaining clean, complete data feeds to different business stakeholders’ systems of choice via pre-built connectors and/or API, without depending on engineering. A CDP should also be able to create, update, and send audiences to marketing and advertising platforms, without manual list pulls, enabling unparalleled speed and agility.

Use case: Regulation-adherent insights based on data

Retail banking has historically been slower to adopt new technologies because of the sensitive nature of the data and its surrounding regulations. Sharing customer data across the organization is complicated not only because it is siloed across channels and devices, but also because there are regulations as to what data can be accessed by different parts of the organization.

A CDP can collect and unify customer data to drive insights into product strategy and feature development, and, as importantly, track and manage privacy consent. Using a CDP allows retail banks to share the data each team needs and nothing more, allowing growth and optimization while upholding customers’ privacy.

The majority of active internet users still interact via browsers, making it the most important digital channel for consumers. As a result, web analytics are essential for marketing organizations; however, the shift towards mobile and connected device engagement has shown brands that they also need to be able to collect and analyze data from every touch point across the entire journey to understand how interactions influence customers. This can be tough because legacy systems are not built with mobile in mind, but a CDP can help; using mobile-specific SDKs to collect data from apps then delivering it to web analytics platforms creates a complete view of the customer’s journey and enables further analysis and attribution.

With a complete view of the customer journey, marketers don’t have to rely on “last click” attribution. Instead, they can test and integrate new tools to attribute weight to each interaction using a CDP. Marketers can use data from their as input to these tools to test different algorithms and interfaces without instrumenting each attribution tool individually. A CDP can also track the long-term performance of customers acquired through advertising by associating campaign membership with full lifecycle events and attributes with acquisition source to inform strategic resource allocation decisions or direct systems that programmatically calculate bids to reflect the value of each opportunity.

Use case: Collect and activate customer data securely

Retail banking often encounters difficulties launching new products, tools, and systems because of the adherence to legacy systems and regulatory standards surrounding customers’ financial data. At the same time, modern customers expect their banking experience to be comparably personalized and streamlined to that of other service industries. That means that banks need to be able to implement new tools that allow them to use customer data effectively, but many data management systems are out of the question because of their inability to meet regulatory standards. For example, tag managers and DMPs don’t fit the bill because they are unable to send encrypted data securely and are unable to store personally identifying information (PII), respectively. Fortunately, CDPs are able to collect data from every bank touchpoint, including digital properties like the bank’s site or mobile app or ATMs, as well as physical touchpoints like in-store visits securely so banks can use this data to personalize and optimize future engagements while upholding customers’ privacy.  

Brands want to create a superior experience for their users, which means they need a roadmap that delivers the best product to their customers consistently. For apps, that means minimizing reliance on third-party code that requires additional instrumentation and maintenance that may burden the user experience and divert engineering time.

By serving as a centralized data hub, a customer data platform is able to capture first, second, and third-party data through a single endpoint, then share it with multiple systems without placing additional tech strain on the app. This centralized data layer ensures the end-user remains unaffected as additional tools are introduced or updated, or as data schemas are changed. Minimizing dependency on third-party code allows product and engineering to avoid unforeseen SDK implementation and maintenance projects from marketing and other business stakeholders, so they can focus on building the best, most differentiated product.

Use case: Stay up to date with customers' devices

Customers access retail banking apps from all sorts of devices and the teams behind the apps need to ensure that each one functions perfectly. When devices with new capabilities roll out or new operating system updates roll out, engineering and product teams have to be prepared with a version update for those affected. With the rollout of the iPhone X, for example, banking apps needed to prepare to develop a way for customers to sign in using facial recognition because there is no home button available for fingerprint scanning. This sign on process not only allows customers to sign in to use the app, it also shows customers that the bank is keeping up to date with emerging technology and employing it to increase customers’ security.

To prepare for an iOS update, instead of having to making updates to individual services’ SDKs to ensure all tools are able to perform correctly and receive this new type of data, using a CDP to connect their app to services means they only have one SDK to test and update. When there are changes to the types of data available, like the addition of facial recognition data, a CDP can ensure that the 80 nodal points used to map a customer’s face can be mapped correctly as a unit to their customer profile and can be analyzed to confirm additional demographic data, like gender or age, for use in targeted marketing.

Objective:

Creating structured methodology for running test-and-learn processes and creating a culture of data-driven decision making. This may include leveraging non-marketing data.

Organization focus:

Measure ROI and customer lifetime value (CLV) impact of new marketing and customer experience initiatives.

Brands want to understand the mobile customer journey holistically and use this knowledge to prioritize future roadmap items based on customer needs, and demonstrate the business benefits of their product recommendations. Using a CDP enables them to combine mobile product, marketing, and purchase events through a single combined data set so that they can understand bottlenecks, identify key areas of improvement, and make better roadmap decisions.

Use case: Build the features your customers want

Going to the bank can be a tiresome chore for customers with limited amounts of time to run errands, especially if they only have time to run errands during the weekend when banks have shortened hours. Instead of relegating customers to spending their lunch hours in line at the banks, retail banks can collect customer feedback to learn what part of the banking experience could be improved. In this case, wait times and open hours may be a pain point that the team can address by leveraging their mobile app. By using a CDP, retail banks not only gather customer feedback, they also make it easy to create customer-centric roadmaps based on this feedback and help connect customer data to the best-in-class stack necessary to create an excellent customer experience. A retail bank, for example, might introduce features that address simple banking tasks like depositing checks to their apps, thereby allowing customers to avoid a trip to the bank altogether and allow customers with more complex banking needs to have shorter wait times.

Brands want to be able to not only know what customers are doing, but how they can improve customers’ experience while they’re doing it. Improvement can only come from experimenting with new product features, content, and workflows and using a CDP allows you to do experiment with different parts of your business more easily and quickly. A CDP reduces the data wrangling required for each experiment, reducing the cost of set up failure by making it easy to revert, and making it easy to create experiment segments and holdout groups on which to experiment. Experiment variants can be created based on customer attributes and behaviors across systems, including entry channel, initial product purchased, content consumed, current sales funnel stage, etc. Variant behavior is then gathered from source systems and third-party enhancements for analysis. Whether a brand is running experiments on purpose-built software or by hand, a CDP makes experimentation easier and more scalable.

Use case: Test differentiating features

Online banking apps that allow peer-to-peer payments are always looking for ways to improve their customers' experiences and differentiate themselves from the competition. Using a CDP, banking apps can gather customer engagement data and feed it into machine-learning models to gain insight into how they can improve their app . One banking app's modeling exercise uncovered that their social feed could be altered to significantly impact customer experience; the resulting product modification resulted in a 30% increase in engagement. 

Objective:

Optimize organization-wide initiatives using customer data and supporting marketing campaigns

Organization focus:

Optimizing marketing, digital advertising, and product-led retention/growth leveraging customer behavior, testing, and targeting across channels and touch points.

Modern marketers want to personalize messages by segment or persona to improve experiences and outcomes, but many legacy systems don’t collect and store the right customer data, at the right level, in the right way, at the right time. A CDP enables marketers to collect information about customer preferences and profile information to determine what information, content, or offers are most likely to appeal to them. By using rule-based segmentation, customers are automatically placed into audience segments comprised of customers with similar profiles which can be used to power marketing campaigns across channels, including search.

Use case: Find more customers

Peer-to-peer payment apps often have to get creative with their marketing. Because payment apps are relatively new, these brands have to be very specific when targeting customers if they don’t want to waste ad spend by serving a customer that does not see the need or feel comfortable providing access to the financial information necessary to use the app. By analyzing their customer base, payment apps have found that an important demographic is made up of young professionals that frequently have a need to transfer money to their friends or family. With this knowledge, payment apps can use customer attributes like age, location, and income level to determine how likely a young professional is likely to have roommates, which would warrant in a larger number of payments per customers and offer an opportunity to gain secondary customers. Creating this segment can be done within their CDP and can be used to guide which channels and language are used in marketing campaigns to increase conversions.

Brands need to know who their audience is to market efficiently. That means being able to convert a customer from unknown to known, and tying their unknown data back  can identify customers who have not yet registered and trigger messaging campaigns to encourage registration to help take customers from unknown to known. This same messaging feature enables a CDP to trigger any type of messaging to customers based on app/web behavior or other non-email behavior loaded into a CDP. Identifying known and unknown product users allows for distinct messaging based on the brand’s end-goal for the customer.

Use case: Increase registration rates

Retail banks can use their apps to provide customers with an easy way to activate credit card discounts, make payments, deposit checks, view statements, and check on their account status. Customers using a banking app can sign up for payment due date reminders, fraud alerts, deposit confirmations, and partner offers. For example, a customer may upload a check by taking a picture of it with their banking app then receive a message he next day when the money becomes available in their account. Making regular banking errands convenient for customers by offering these services via app help retain customers while offers encourage customers to make additional purchases. Financial data security is critical to the function of a bank’s app and so bank customers looking to start using the app can be prompted to enter the appropriate information to verify their identity.

Brands know that engaging existing and identifying additional customers that fit your ideal customer profile (ICP) is a solid growth strategy, but putting this into practice can prove difficult. Using a CDP enables you to select and deliver targeted messages to different cohorts of customers based on value-based scores by passing numeric attributes and customer IDs to paid media platforms, like Facebook. You can also create lookalike audiences using the value-based scores to find more customers like your current highest value customers with higher granularity than is available in the media systems’ limited data store.

Use case: Re-engage high value customers with exclusive credit offers

Retail banking relies heavily on customers desire to get more bang for their buck when it comes to their credit cards. Applying for a new credit card feels high-stakes for many customers because it involves pulls to their credit score, so the credit card offer needs to be of high value for that customers to be worth the “risk.” Essentially, the better and more tailored the rewards are, the more likely a customer will be willing to apply for a new credit card.

Retail banks can use their customer data platform to analyze the pool of customers that have applied and were approved for a new credit card to find what perks pushed them towards applying then use this data to identify similar credit card customers. For example, a credit card that partners with a major airline and offers additional miles per dollar spent on travel and restaurants is ideal for customers that travel for business frequently and already have loyalty membership with that airline. On the other hand, a credit card that offers additional points per dollar spent on groceries and fuel may be most enticing for a suburban customer with a long commute to and from work. By analyzing the customer data of both customers currently using one of these credit cards, banks can determine which other customers in their database fit these parameters and offer the one that is most likely to appeal to the customers’ needs.

Similarly, banks can securely identify customers in good standing with certain income thresholds to time credit line increases and credit card upgrades to keep these customers engaged with the bank’s credit offerings.  

Just as you can target exactly who sees an ad, a CDP can create and sync suppression lists to paid media platforms to ensure campaign dollars are not spent targeting the wrong people. Using custom rules set by marketers, the CDP can move individual customers in and out of suppression lists as their attributes and actions qualify or disqualify them from receiving certain ad content.

Use case: Suppress ads to customers that have completed competing offers

Objective:

Engaging and improving customer experience in real time

Organization focus:

• Incorporating algorithms for continuous optimization, managing channel-neutral customer preferences, and omnichannel attribution.
• Leveraging consistent data, technology, and processes across all channels to develop contextual customer engagement strategies that drive corporate objectives.
• Calibrating marketing technology capabilities for continuous adjustment based on customer needs.

At the highest stage of maturity, organizations need to focus on creating better experiences for their customers on an ongoing basis. Using a CDP allows companies to make adjustments based on data and improves how brands track attribution. A CDP’s view of product, marketing, and service interactions—and customer purchases—provides the data needed to inform bottom-up multi-touch attribution models that measure the impact of touch points on business results at the user level. Many businesses consider these models to be more reliable than top-down mix models that rely on statistical methods to discern contributions but struggle to assemble the granular data needed to make them work.

A CDP may ingest user scores from predictive models, whether they are homegrown or machine-generated, to provide the app or site CMS with real-time support for recommendations. These recommendations can include first product, cross-sell, and upsell. Because the CDP was also used to inform the models, these recommendations are based on data captured across every channel, not just the one the user happens to be visiting.

Use case: Improve partner offers

Retail banks often partner with retailer and service providers to drive sales and revenue for both by offering a discount or immediate rebate on credit card purchases over a certain dollar amount at the partner business. By tracking which offers a customer activates and which they redeem, retail banks can provide recommendations to additional services and retailers that may fit the customer’s tastes and needs. For example, a credit card customer that activated a $20 rebate offer when they spent $75 or more in a month on a dog walking and sitting service likely owns a dog that stays home while the customer is at work or stays home while the customer travels; with this information tied to their customer profile, the bank is able to offer a deal the next month on doggy daycare. For banks, providing this kind of recommendation is a win-win-win: the customer is happy because they get services they want for a lower price, the partner gains a new customer, and the bank increases credit card revenue and improves their partnership with the service provider.

Brands need to reach customers not only when their messaging is relevant, but also where it’s relevant. A CDP can ingest location information from web and apps, append it with signal from location data services, apply rules to uncover opportunities, and then trigger relevant messages. This extends far beyond conventional push notification systems and helps customers receive messaging relevant to their geographic context.

Use case: Use location to make offers and expedite services

Retail banking has long relied on customers visiting the brick and mortar bank and the ATM to market towards customers. In the digital age, location-based marketing still plays a big part in driving banks’ business revenue. Deploying location beacons for proximity-based marketing can make a big difference in the way customers interact with physical bank storefronts. ATMs can be used as a location beacon point to push notifications about various bank offerings and products, advertise offers from partner retailers, and even create offers based on the frequency of visits to the ATM. In-store, banks can use beacon services to provide customers with informational materials and expedite filling out authorization and application forms while customers wait to be seen by a teller.

A CDP can assemble a complete set of interactions between the company and each customer to create maps of customer touch points over time, with separate maps built for different segments, products, tasks, or locations. With this information, a CDP can identify the most productive paths and find the points where customers are falling out of the process.

Use case: Lower bar to usage

Peer-to-peer payment apps can use a CDP to help improve app experience to improve customer conversions by securely storing payment information and analyzing customer behavior. For example, an payment app brand may find that while linking bank account data to a customer’s profile to fund payments to others may be the most financially secure way to conduct business, customers may be hesitant to provide access to their checking and savings accounts to a third-party. By analyzing usage and connecting it to customer feedback across touchpoints, the payment app may decide to introduce alternative methods of payment by adding a credit card option or integrating with PayPal. The increased purchase protection afforded by credit cards and by PayPal can make customers more confident using the payment app and lead to more payments overall.

Legacy loyalty programs focused on serving promotions to repeat customers rather than driving loyalty. The next-generation of loyalty programs uses web/app usage data, in-store purchases, loyalty status, points balances, redemption, and in-store inventory to make the optimal offer for each customer. A CDP makes these data points available to systems that use predictive modeling and optimization to find the best offers while balancing customer goals, business goals, and business constraints, and can help these systems deliver relevant messages across channels.

Use case: Multi-tasking rewards programs

Retail banking relies on customer’s loyalty to maintain cash flow, but as customers come to expect more out of their interactions with brands across the board, retail banking brands need to be able to provide a higher tier of experience. Using location, profile, and purchase data, banking brands can partner with other service providers to create multi-tasking rewards programs that give customers more bang for their buck. For example, partnering with airline or hotel brands allows banks to offer additional points per dollar spent with these partners, which is sure to please customers that travel frequently for pleasure or business. Taking into account a customer’s specific needs when creating these additional loyalty benefits makes customers feel cared for and makes them more likely to continue using their banking provider in the long term. Other loyalty programs featuring increased reward earning power for things like fuel purchases or providing early access to ticketing for events in customers’ area can also go a long way in increasing customer loyalty, especially when redemption can be done on the go via the app. Using a CDP helps banks activate, track, and maintain these programs easily by connecting all pertinent data and improving loyalty offer targeting.

A fully-enabled CDP can ingest customer transactions on web, apps, call center, retail kiosks, and other channels in real time, as they happen. Using Identity resolution features, the customer can be identified and the information about the engagement can be used to inform channel systems of the customer’s specific preferences and history to guide current and future interactions.

Use case: Advanced fraud detection

As secure as online and credit card transactions are for modern customers, once in a while this financial information can get into the wrong hands. Dealing with charge disputes and attempting to track down how the information was accessed can be a huge headache for customers, but retail banks can help remedy this situation by using a CDP to collect and unify personal attribute and event data and feed it into machine learning models to determine the likelihood of this being a fraudulent purchase. Detecting fraud as it happens and alerting the customer before they look at their credit card statements helps banks address this problem before it goes further. Banks can also offer customers the option to freeze their credit card as a feature in their app and connect this data to their customer profile to provide additional security. Similarly banks can also track event data that would typically trigger fraud incorrectly by using location services in the app if a customer is traveling or by offering an easy app feature that alerts the bank that the customer will be traveling. That way, customers know their accounts are secure while also avoiding the hassle of accidental freezes while customers are on vacation. When it comes to finances, this level of proactive customer service reassures customers of their bank’s commitment to protecting their privacy and financial information.

Retail banks have access to unprecedented amounts of customer data thanks to customers' engagement across devices and channels. This data represents an opportunity that travel brands would be remiss to not jump on—the chance to understand how different facets of engagements influence customers' journeys towards conversion and act on those learnings. But marketers are no longer dealing with a finite number of systems; rather, marketers are dealing with legacy systems unable to keep up with the ever-increasing number of SaaS applications that house and action data. Companies need a CDP that can not only help them overcome the customer data silos and unify their data from across their stack, they also need a CDP that is able to take their insight, orchestration, and activation to the next level by providing a way to create and maintain persistent customer profiles, execute experiments, improve targeting, and power acquisition and retention.

Finding the right CDP requires that marketers consider their current and future data goals and what kind of data they need to achieve them. Using defined use cases as the basis of their search for a CDP will ensure that marketers choose and implement the right customer data platform for their business' needs, making it a safe investment.

This guide has provided some of the most common use cases for banking companies at different data maturity stages, but there are still many more advanced applications for companies looking to improve their marketing and analytics ROI. mParticle is not only able to meet all of the use cases described in this guide, it is capable of becoming the customer data hub and agility layer that brands need to succeed in the digital era thanks to its ability to:

• Natively collect data from all sources
• Cleanse and transform customer data
• Resolve customer identities
• Create and maintain persistent customer profiles
• Enrich customer profiles with data from first-, second-, and third-party tools
• Support consent and privacy management
• Segment audiences on the fly
• Orchestrate data to the marketing and BI tools needed
• Do it all in real time

As the customer journey continues to fragment, finding the right CDP will only become more important. If you’d like to learn how mParticle can help you unify your customer data, boost engagement, and increase advertising and marketing ROI, get in touch!