What's in Your Wallet?

In the past, bank marketers have relied on models based on demographic, geographic, psychographic and purchase variables to better understand their customers and prospects. Some financial institutions even use attitudinal, lifestyle or customer value segmentation to improve the targeting of their marketing communications.

As consumers are provided more and more options as to how to transact business and make payments, however, a better way to segment may be achieved by using advanced behavioral segmentation based on payment decisions. In other words, when consumers open their checkbook, reach for their wallet, turn on their computer, or use their phone, what payment option they choose may help bank marketers improve targeted engagement, channel and relationship expansion communication.

Payments behavioral segmentation may also be the best indicator of future financial services purchases since it can gauge changes in consumer purchasing, saving and investment patterns and enable Payments to effectively join Product, Pricing, Place and Promotion as the fifth P of marketing for bankers.

There are definitely challenges posed by payments behavioral analysis, however, since it introduces an element of time into the analysis that is different from other types of modeling. As opposed to using a single point in time like marketers can do for age, income, geography or even attitudes and lifetime value, behavioral segmentation requires analysis over a period of time with the length of time impacting the nature of the segmentation. Different conclusions can be made when looking and long vs. short-term trends. This is especially true during a time of significant economic change like we have today, where people's buying, saving, borrowing and payment behavior may be in transition.

Another challenge is presented by the number of channels and insight capture options available within the payments landscape, since consumers can pay using checks, debit, credit, ACH and even P2P or P2B using mobile devices in person, online or through the mail. As a result, it may be easier to capture ranges of transactions (high/medium/low) or develop a segment grids measuring ranges of transactions based on method and channel. As a starting point, marketers could potentially track tendencies using just one component of the payments continuum like measuring just point of sale transactions over time.

A final level of complexity is added when you consider whether how the consumer decides between funding today's purchases out of current income, wealth or borrowed funds. This adds the element of financial management into the picture.

Using all or singular components of payments data, bank marketers can build attitudinal segments that answer the questions "what payment instrument does a customer usually choose" and "why does a consumer choose a particular instrument (or channel)". While no easy task, it is one that can reap significant rewards. This is because the foundation of most financial management decisions revolve around the consumer's choice of payment method. Their attitude around safety and security, borrowing and saving, electronic or traditional all provide insights not available with traditional segmentation and open the window to the customer's potential level of engagement and potential value for your bank.

While certainly not a flawless segmentation process since the environment is constantly changing and is far from frictionless, payments behavioral segmentation could provide a level of insight not found in with other modeling processes and could assist in proactively addressing customer needs, improving customer lifetime value and enhancing the customer experience.

I would love to hear from banks that may be employing some form of payments behavioral modeling to drive marketing communications beyond the selling of payments products. Are other behaviorally modeling techniques working?

Checking Changes Make Onboarding and Cross-Selling More Important

Over the past several weeks, many of the larger banks across the country have announced significant changes to their checking account continuum, including elimination of traditional Free Checking, discontinuation of rewards programs, ceasing reimbursement of foreign ATM fees, as well as potential fees and transaction limits on debit cards.

While each of these strategies are intended to reduce costs or generate revenue in response to Reg E and the Durbin Amendment, these changes could also present a challenge to banks as they seek to increase engagement and gain share of wallet. This is because debit card use and rewards program enrollment were two of the more important account engagement criteria and basis for a broader relationship growth.

According to an economic analysis on the effects of the Durbin interchange amendment presented to the Federal Reserve Board on February 22, between $33.4-$38.6 billion of debit card interchange will be lost during the first two years the new rules are in effect. This reduces the revenue on a personal checking account by $56-$64 and by $79-$92 on a small business checking account according to the study. These impacts make it more important than ever to optimize onboarding and cross-sell efforts for retail and small business customers thereby reducing costly attrition, improving engagement and providing a stronger foundation for ongoing relationship expansion.

Here are several of the steps financial institutions should consider as they begin to implement changes to their deposit accounts and debit products.

• Double Down on Onboarding Initiatives: While most banks currently have an onboarding process for new retail customers, many have yet to build an onboarding process for small businesses. In addition, many programs only reach out to the customer once or twice and don't leverage a robust mix of communication channels. The impact of recent legislation makes the opportunity cost of attrition more expensive than ever. Banks need to increase the number of 'touches' a customer receives by email, phone and direct mail with the message centered on maximizing the benefits of using the account the customer just opened. When the account becomes active, then begin to expand the relationship.
• Don't Walk Away From Debit: While the economics of the debit card have definitely changed, the use of this payment vehicle remains better than many of the alternatives and provides the consumer with constant brand reinforcement each time they open their wallet. David Stewart from McKinsey & Company wrote in a recent BAI Banking Strategies article entitled, "Keeping Debit in Focus Post-Durbin" that debit cards remain an important component of the anchor DDA. As a result, getting new customers to activate and use their debit card as part of the onboarding process should continue to be a primary objective.
• Expand The Definition of Engagement: In the past, most banks focused on debit card utilization, enrollment in online banking (with bill pay) and the sign up for direct deposit in their onboarding messaging. While you don't want to cover too much in the onboarding communication, there are some households you may want to encourage to apply for a credit card and/or activate an autosave transfer as part of welcome process.
• Encourage Channel Migration: Another way to stem attrition, potentially reduce cost and build share of wallet is to increase alternative payments channel use. As part of the onboarding process, some of my clients are building messages around the use of mobile banking early in the relationship lifecycle. This makes sense based on recent trend research done by Javelin Strategy and the potential for offline customer mobile adoption found in research done by Fiserv. While there may only be minimal channel shift from a payments perspective initially, there could be significant savings if call center inquiries are reduced.
• Focus on Share of Wallet Early: While I totally agree with Ron Shevlin in his Marketing Tea Party blogs (Honeymooning and Why Engagement Matters) that a new customer must be courted and engaged before they can be cross-sold, customers define the pace of this trust building as opposed to the bank. This level of engagement/trust is usually found by looking at transaction volumes and whether engagement services are active. Once actively engaged, the customer should be offered additional services that may improve their overall banking experience. This is where product propensity models and behavioral segmentation can be effective.
• Leverage the New Account Desk: Many of my clients have found that the new account desk can  be an effective cross-selling environment for the customer, especially if credit services such as credit cards, personal or small business lines of credit and even equity credit are pre-approved at the point of sale. The point of sale is also the best place to discuss the correct account to open in the first place and the benefits of engagement services and rewards alternatives.

The effective communication of your checking account changes to existing customers has been discussed in my recent blog (Minimizing the Impact of 'Unintended Consequences'). It is just as important to communicate well with new customers at the new account desk in the days, weeks and months immediately following the new account opening. Without an aggressive communication process, leveraging multiple channels and customized to the customer's stage in the engagement process, the investment in acquiring the customer will be lost or the value of the relationship will not be optimized.

How are you going to ramp up your new customer communications to maximize your marketing ROI? Are you considering new ways of onboarding your customer in the first 30, 60 or 90 days? Have you found a way to leverage any social media in your onboarding process? I would love to hear your ideas.

Customer Analytics Is Key To Growth In Banking

Understanding customers is the foundation to a sustainable competitive advantage in banking. Therefore, financial marketers can no longer wait to embrace the power of advanced analytics to gain insights and evaluate opportunities that will improve cross-selling, up-selling and enhance share of wallet.

Financial marketers also need to extract more value from internal and external data sources, guiding product development, customer communication, innovation and growth.

First in a Series on Big Data and Banking

In a recent report from Celent entitled, "Customer Analytics in Banking: Why Here, Why Now?", senior analyst, Bob Meara writes that now is the time for banks and credit unions to leverage the advances in processing, memory, database design and analytic methods to improve performance and reduce costs. While the Celent analyst notes that some institutions are already on the path of using advanced analytics for decisioning and optimization, other organizations have only limited experience (this correlates with several other studies).

The following are the primary reasons why banks need to step up their customer analytics game:

• The New Normal: The banking industry is expected to remain revenue challenged for the foreseeable future as a result of low interest rates, moderate fee revenue, onerous regulation and a less than robust economy. As a result, it will be more important than ever for banks and credit unions to focus on all possible strategies to reduce costs and increase revenues. Some of these strategies, enabled by customer analytics include:
• Improved targeting of customer segments
• Moving from a product focus to a customer focus
• Better management (and measurement) of sales leads across channels
• Inclusion of custom customer incentives/rewards to influence behavior
   
• The Imperative for Customer Centricity: With customer delivery and communication channels expanding, and more customers interacting with their financial provider using online and mobile channels, always-on, real-time sales and service become imperative. Analytics can respond to the migration to digital channels by:
• Improving branch efficiency and effectiveness
• Integrating sales and service tools within a new digital environment
• Helping to drive high value, high touch traffic back to branches
• Technology Advancement: Customer analytic applications are no longer the sole domain of highly skilled specialists. Today's solutions can be accessed and used by marketers and other business users to answer complex inquiries. Improvements include:
• Collapsing of product silos and ability to process increased data sources
• Increased number of specialized vendor solutions and expanded talent
• Cloud-based solutions

For readers interested in an excellent understanding of big data, data analytics, predictive modeling options, and the data analytics process, I suggest purchasing the Celent report here.

Customer Analytic Applications

As the Celent study makes clear, there is no shortage of analytic applications for banks and credit unions. While some are more general in nature, some are highly specific outsourced solutions, supporting a buy vs. build decision. Obviously, with a focus on containing costs, the ability to utilize outsourced solutions is good news.

"Key retail banking priorities - specifically, using self-service channels to drive branch foot traffic, improving branch channel efficiency and effectiveness, and learning how to sell and service through digital channels - all require customer analytics," says Meara from Celent. "The good news is that there has never been such a variety of specialized customer analytics solutions."

According to the Celent report, there are six key well-established business drivers for predictive analytics in financial services. Each of these are important as a bank or credit union builds an analytic strategy for the future.

Source: IBM and Celent

Customer Insight

Of special interest to most financial marketers is the ability to gain a better insight on current customers. While demographics and current product ownership are at the foundation of customer insight, behavioral and attitudinal insights are gaining in importance as channel selection and product use become more differentiated. Sentiment analysis and social media analysis are two additional examples. 

Another predictive analytic model is the FICO score. Scoring models such as FICO analyze consumers’ credit history, loan or credit applications, and other data to assess whether the consumer will make their payments on time in the future.

Business Strategy

The foundation of traditional banking business intelligence (BI), customer analytics are often used for product and channel development as well as economic forecasting, business improvements, risk analysis, and financial modeling.

Customer Experience Management

According to the Celent study, the key to using customer analytics for customer experience management (CEM) is about delivering personalized, contextual interactions that will assist customers with their daily financial needs. In addition, if done correctly, customer analytics in the context of CEM enables the real-time delivery of product or service offerings at the right time. It can also allow for highly sophisticated relationship pricing never before available.

Risk Management

One of the more common uses of 'big data' today is in the area of risk and fraud management. Data mining today has expanded well beyond internal purchase and balance insights to include transaction patterns and even social media interactions that can provide a leading indicator to potential losses or fraud.

This type of integration of structured and unstructured data can also be leveraged for traditional risk management uses such as for pricing decisions. 

Channel Execution

BI tools have helped banks understand channel effectiveness for some time. More recently, analytics capabilities have boosted the usefulness of these tools. Capabilities include providing comprehensive views of channel performance based on both customer behavior and transaction mix. Solutions help banks understand channel profitability and customer satisfaction and tailor retail operating models to improve retail delivery.

As more banks and credit unions work harder at migrating customers to digital channels, analysis of engagement and shifts in channel use become important indicators of satisfaction and re-pricing opportunities.

Marketing

Another traditional use of customer analytics is the ability to increase the effectiveness and efficiency of sales and marketing in financial services. The ability to derive the likelihood of purchase based on available information about individual customers has ushered in a seismic shift in marketing from product centricity to customer centricity. 

Rather than offering products and services based on what the financial institution would like to sell (campaigns), banks and credit unions are now able to make unique, timely, and relevant offers based on available customer insight. Doing this form of analysis across multiple channels allows financial marketers to significantly improve the efficiency of marketing spending and the close rate of sales leads.

For each of the applications shown above, the power is not just in the analytics themselves, but in the ability to do so in real time. With more challenges than ever in banking, analytics is at the center of it all as tweeted by the author of the report recently.

Banks have lots to do: grow sales, reduce costs, add digital engagement. Customer #analytics is central to it all: celent.com/reports/custom…

— Bob Meara (@Mearaman) May 28, 2013

Implementing a Successful Data Analytics Process

The Celent research emphasizes that while there are a growing array of use cases for data analytics, the process is definitely not a 'one and done' proposition. The move from a product/campaign based approach to a customer centric approach is huge and involves many moving parts.

Successful implementations always involve a series of steps and a test and learn process as shown below with a different amount of time and effort applied to each step based on the specific project being undertaken.

According to Bob Meara from Celent, "Most organizations (banks included) get good at specific analytics use cases and broaden their use once parts of the organization gain confidence and prove the business case. Only then is the approach used more broadly and extensively."  He recommended that  banks:

• Start small. Invest a little and wear out the application. See what it can do.
• Experiment – early and often. This requires a willingness to fail (in small and low-risk ways).
• Embrace analytics as a journey, not a destination. Keep learning and keep looking for ways to apply analytics for fun and profit.

In response to a question from me around whether banks should 'boil an ocean' in their analytics endeavors, Meara stated, "Of course, banks should walk before they run. By that, I mean banks should fully leverage in-house transactional data before investing heavily in external sources of information and insight."

He adds, "Social data is particularly compelling, but runs a big risk of being unrepresentative. SAS, for example, does a great job integrating social media data with internal data to arrive at more well informed models and more highly predictive outcomes. Either way, start with the treasure trove of data already onboard."

The reality is that, in the digital banking model of the future, data is a financial institution's most important asset. Banks and credit unions that are able to combine their internal and external data sources to create value will find themselves well placed to thrive in what some have called 'Banking 3.0'.

Those who are unable or unwilling do so at their own peril.

For readers interested in a thorough data analytics vendor analysis and a number of excellent financial institution customer analytics case studies from around the world, I suggest purchasing the Celent report here.

Additional Resources

Customer Analytics in Retail Banking: Why Here, Why Now? - Celent (May 2013) 

Time To Grow Up: Perspectives on Customer Insight and Analytics in Retail Banking - KPMG (2012)

Tap Into The True Value of Analytics - Infosys (2010)

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