Showing posts with label analytics. Show all posts
Showing posts with label analytics. Show all posts

Wednesday, August 28, 2013

It's Time for Banks & Credit Unions to Embrace Change

As I travel across the country, visiting financial institutions in the midst of their annual planning cycle, it is like a trip down memory lane. While the technology and distribution channels have changed, banks and credit unions are still faced with the many of the same strategic challenges we talked about 20 years ago.

As a long time banker and friend, Michael Bencic said, "Improving the customer experience, embracing change, deriving value from data, building strategic partnerships, leveraging technology, ensuring privacy and security, cutting costs and generating fees is like deja vu all over again."


I agree. While the details behind these goals have changed, why have the overarching themes stayed the same? Is it because the planning process usually begins with broad financial requirements and many involved in the process simple dust off last year's plan and hit the restart button? Or is it because, despite a lot of talk around embracing change, the industry (and the regulators) frown upon the potential risk associated with innovation and doing things differently?

In a new report just published by KPMG entitled, Reshaping Banking in a Dynamic Business and Regulatory Climate, the author emphasizes the importance of getting out of 'survival mode' and embracing change, creating new strategies, crafting new infrastructures and focusing on the customer. While there is no denying the importance of each of these issues, this report is not much different than similar reports I read in the 1990's. The primary difference is that the risk of ignoring these issues has far greater implications.

Dusting off last year's planning document and making small alterations is not enough. It will take more than simply finding ways to 'do more with less', cost-cutting and operational improvement. According to Brian Stephens, national leader of KPMG's banking and capital markets practice and author of the report, "There must be acceptance among the entire leadership team that the rapid, unpredictable, and profound change we are witnessing is structural -- not cyclical." He continues, "The debate in not about the need for change, but what changes should be made."

As in the past, the issues that must be addressed are many. The difference is that today, while the issues may look similar to the past, the issues are more interconnected than ever before and the environment where these changes need to be made is evolving at breakneck speed.

The KPMG report provides a perspective into the following critical areas as banks and credit unions plan for 2014 and beyond:

  • Culture of embracing change – In today's environment, change is constant, so banks must be nimble and innovative. "Banking leaders must choose to adapt and evolve, or risk irrelevance," says KPMG. "In the future, when banks look back on this time of change, an organization's resilience will not be measured by how much adversity it endured throughout the financial crisis and this period of recovery; rather, it will be measured by how well it adapted to it." The challenge is a tradition of rigid internal resistance to change and a consequent inability to execute. The change in culture must come from the top, starting with the board and senior leadership. And it must me more than just words.
     
  • Focus on customers, not products – To increase revenue, banks must determine the appropriate customers to target and how best to package the products and services for which they are willing to pay. The challenge, related to the first issue above, is that banks have a legacy of talking to the masses and giving services away for free. Without better segmentation and an understanding of what customers will pay for, the impression of any revenue initiative will be negative. Alternatively, bundling services such as mobile bill pay, alerts, ID protection, payment services, etc. using a customer-centric perspective can results in a win-win.
     
  • Deriving value from data – Banks and credit unions that can extract more value from all available data sources to develop a better understanding of customer needs can serve customers more effectively and profitably, while developing a competitive advantage and staving off threats posed by new market entrants. The challenge is that all internal product-centric data silos (retail deposit, credit card, small business, mortgage, commercial, etc.) must be integrated to provide a single customer view. Once data is integrated, the customer insights need to be leveraged for better product development, new cross-sell and revenue opportunities and reduced risk.
     
  • M&A/Alliances – Despite many predictions around increased M&A activity in the past that have not come to fruition, the environment today is prime for consolidation due desires for geographic expansion, product enhancement and cost reduction. The immediate issue is that organizations need to strategically evaluate whether they are a buyer, a seller, or neither, while also examining the possibility of developing alliances where strategic fit warrants.
     
  • Technology – At a time when costs are being cut, the appetite for investment in technology is usually tainted by the memories of previous IT upgrades that never met expectations. Nonetheless, the ability to effectively support the integration of new delivery channels and a customer-centric view leaves most banks no choice but to upgrade aging infrastructure. "The promise of harnessing technology advances can help banks streamline operations to reduce operating costs, connect future and existing customers across a multitude of new and emerging channels, tap new revenue streams, enhance customer loyalty, and build better defenses against cybercrime and denial-of-service attacks," says KPMG. In the end, ignoring or putting off the inevitable is a risky strategy, especially with the risk of noncompliance, losing market share or not being able to support an ever more important mobile strategy.
     
  • Cybersecurity – The increasing scope, frequency, and sophistication of cyberattacks on banks means institutions need to be better prepared to address a risk with implications that both enormous and unknown. With the public's trust in banks finally recovering from the impact of the financial crisis, this trust can be shattered if life savings (or even access to funds) are at risk. In addition, there are some who believe that we are at the tipping point in the acceptance of mobile banking (and mobile payments) without greater ID protection and mobile security in place. 2014 will be a year when most of these issues need to be addressed (if not sooner).
     
  • Capital & Compliance – Banks will continue to need to prepare for stress testing, while also monitoring various capital adequacy and liquidity requirements and associated staffing and compliance costs. For many banks, the issue of capital adequacy may be secondary to the ongoing costs and internal 'friction' that is associated with the added staffing associated with meeting regulations
     
  • Accounting for Credit Losses – Banks will need to understand revisions to accounting for credit losses on financial assets and other rules. These changes could not only have a significant impact on an institution's reported earnings, but also on its capital ratios due to the need to carry larger loan loss reserves.

While the list of issues may not be new to any banker who has been in the business more than 6 months or more than 20 years, the risk of not proactively addressing these issues has never been greater. So, if you are in the midst of planning for 2014, make sure your team is just not listing these in a SWOT analysis without building strategies to address the risks and opportunities. If you are 'done' with the formal strategic planning process, it may make sense to review the strategies and tactics planned for 2014 to make sure some version of 'status quo' is not your plan.


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Sunday, June 9, 2013

Banking Industry Taking Small Steps With Big Data

As was discussed in the first of my series on Big Data in Banking, the financial services industry has a vast reservoir of data on their customers, but is in the infancy stage of utilizing this data for financial or competitive gain. 


A new study published by the IBM Institute for Business Value confirms that while the majority of financial firms believe data can create a competitive advantage, the scope of data used and the analytic capabilities lag behind other industries.


Second in a series on Big Data and Banking


In a study published by the IBM Institute for Business Value in conjunction with the Said Business School at the University of Oxford entitled, "Analytics: The Real World Use of Big Data in Financial Services," it was found that 71 percent of banking and financial firms globally believe that the use of insight and analytics creates a competitive advantage, compared with 63 percent of cross-industry respondents. This compares with only 36% reporting this advantage in 2010, representing a 97 percent increase in just two years.


Pragmatic Customer-Centric Strategy


Not surprisingly, the IBM research found that most 'big data' strategies being implemented by the financial services industry begin by initially identifying business requirements, then leveraging existing infrastructure, data sources and analytic capabilities before incrementally expanding sources of data, technology and analytic capabilities. This 'slow to go' progression is actually on par with the global cross-industry counterparts reviewed.



It should be noted that the progress with almost any data initiative in the financial services industry is directly correlated to the size of organization due to the investment required and current infrastructure of the organization. I was reminded of this very important distinction by James Robert Lay, credit union industry thought leader and CEO of PTP New Media in a Twitter response to my big data post last week.



Despite the size of organization surveyed, and reinforced by the Celent research reviewed in my previous post, customer-centric objectives dominate the focus of most data activities in the banking industry. In fact, 55 percent of active data efforts revolved around customer outcomes in the IBM study.

As mentioned in my previous post on big data and banking, focusing on the customer is increasingly important as channels for transacting and communicating continue to increase, developing new segments of customers based on the ways(s) they want to perform transactions and hear from their bank and credit union. Through this customer-centric focus, the customer experience should improve as financial institutions can better anticipate customer needs in a multichannel environment.

Second in importance for financial organization use of data was for fraud and risk mitigation and achieving regulatory and compliance objectives (23%). This focus was significantly higher than the cross-industry sample in the study.


The study also found that, while the majority of institutions surveyed said they had much of the infrastructure in place to manage the increasing flow of data (87 percent), only slightly more than half reported that their data was integrated across silos. This continues to be a challenge as customer expect their financial organization to understand their entire relationship when working with their bank or credit union. This challenge is obviously exacerbated with smaller organizations who may not even have a CRM system in place.

Focus on Internal Data Opportunities


Despite industry and solution provider hype, most early big data initiatives are focusing on analyzing the tremendous amount of untapped opportunity that still resides within most financial institutions. More than 4 out of 5 financial organizations surveyed in the IBM study are analyzing transaction and log data that has been collected for years, yet not analyzed due to system constraints.

Where banks and credit unions lag their cross-industry peers is in using more varied data that requires more sophisticated (and expensive) technology. For instance, while call centers are still very important to financial institutions, only 21% of larger banks analyze this data (compared with 38% on non-financial organizations). Financial institutions also significantly lag their cross-industry counterparts in evaluating social data (27 percent for banks compared to 43 percent for non-banks).



Analytic Capabilities Lag Non-Bank Counterparts


Consistent with my review of recent Celent research in the post entitled, "Customer Analytics is Key To Growth In Banking", data mining of structured internal data such as basic inquiries, predictive modeling, etc. is on par with other industries. There is a significant drop off in capabilities, however, when financial institutions are asked about the ability to analyze unstructured data such as voice and social streams.

While the investment in these types of analysis should lag the basic capabilities described earlier (analyzing internal, structured sources), the growth and power of advanced analytics that includes unstructured data needs to be tested by banks to determine monetization opportunities (ROI).


Go Forward Recommendations


Advancing technology in combination with vastly expanded data sources are combining to provide the foundation for tremendous advancements in the application of big data insights within the financial services industry. Despite this potential, however, even the most advanced organizations are following a very structured path of integrating data analytics and insights within the organization.

In writing and speaking on the subject of big data for more than two years globally, I have found that much of the hype surrounding 'big data' has significantly preceded the proven financial benefits of using all of the data available to banks and credit unions. Unfortunately, many organizations still believe they are required to play 'catch up' to the minority of organizations that have the resources and talent to conduct an expansive test and learn process around unstructured data.

Without regard to resource availability, here are some foundational common sense steps that the IBM study, Celent research, other studies in the financial services industry (and myself) believe should be taken before expanding capabilities around big data.

      • Begin with initiatives that will have a proven financial impact of increased revenues and/or decreased costs (increased sales, lower cost delivery, enhanced service, reduced risk)
      • Build a blueprint that aligns business needs with IT capabilities (and resource requirements)
      • Engage all impacted parties (executive level buy-in is required)
      • Start with internal data sources (logical, cost effective and with great upside potential)
      • Apply a test and learn process for all initiatives with measurement applied against preset objectives
Big data provides the potential for big opportunities for banks and credit unions. But the definition and application of 'big data' should begin with small steps applied against internal data that is readily available. As successes are achieved, the financial and operational benefits and learnings can be applied towards more ambitious projects that are deemed to be financially viable.






Additional Resources


Analytics: The Real World Use of Big Data in Financial Services - IBM Institute for Business Value (May 2013)

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Thursday, June 6, 2013

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.




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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