Unlimited Possibilities: Relationship Marketing Using MCIF 2.0
In a 24/7 world where data about your customer or member is generated at an almost unimaginable rate, it is important to find solutions that provide a complete picture of your customers at the click of the mouse. Remember MCIF? Market Customer Information File (MCIF) is simply a database of all of your customer accounts and relationships. The concept has been around for several years, and the financial institutions that invested in the first round of MCIF systems find the information is only updated with account data periodically -- monthly or quarterly at best. These systems still provide key analysis at a certain level. For example, pre-defined queries can be run in the data for various purposes such as identifying loyal customers versus those who are likely to choose a product or service elsewhere. Yet, is this enough? Not in today s competitive market. Successful, growing financial institutions need a solution that will provide a view of the data based on the needs of their internal stakeholders. Five employees can have different requirements and need to look at the same data differently. The ideal MCIF solution would feature an automatic process for building models to leverage variable data. Such a feature would create efficiencies while providing an accurate analysis and better managing relationships with customers. Such a solution would be the next evolution of MCIF and it exists. IQR Consulting, in collaboration with OnApproach, has developed an advanced MCIF framework MCIF 2.0 - that will help financial institutions create a marketing approach that is more effective in creating targeted, meaningful relationships. It does this by mining data that takes into account information about channel preferences, interactions taking place in the branches, a member s use of a branch, their online transactions, and their demographics. Ultimately, MCIF 2.0 takes predictive modeling, executes customer profitability calculations to create optimization across products, marketing channels and promotions. As a result, MCIF 2.0 can help answer: ONAPPROACH OnApproach 2016
Which customers are the best candidates for cross-selling other services and products? Where are your customer s other financial relationships and how loyal are they to you and the other financial institutions. Who are the most profitable customers? Are we effectively onboarding new customers, and how do we build successful, long-term relationships with them? Why MCIF 2.0? How can MCIF 2.0 do this, when the original solutions couldn t? Let s take a closer look. MCIF 2.0 is the ultimate engagement tool and guide for both outbound campaigns and real-time interactions. It helps optimize the value of individual customers and the entire customer portfolio. 1. Which customers can successfully onboard to our other solutions and products? The existing MCIF systems can t answer this yet because the systems are not flexible enough to pull the type of data necessary to answer this question. To do this successfully, a learning model is required to store the behavior of the customers early in the relationship and then take that data to determine the inclination of your customers to onboard to a specific product or services by modeling the prior transaction and visit behavior. Many financial institutions are using MCIF 2.0 to create marketing strategies to onboard customers from traditional products to mobile banking. 2. Where are your customer s other financial relationships and where do their loyalties lie? MCIF 2.0 helps track changes in customer accounts. These changes may include inactivity, life stage changes, and decrease in usage on a real-time basis. The system is designed so you can
MCIF 2.0 has real -time data. This is a true competitive advantage as the credit unions have an instant access to customer information that allows them to generate real-time reports, helps them to be more effective in onboarding new customers and gives them better responses in their acquisition campaigns. set triggers to notify you when an account indicates a trend that could mean change. MCIF 2.0 also makes it possible to integrate social media channels into the system to determine their loyalty toward their institution and leverage it to your advantage. 3. Who are our most profitable customers? MCIF 2.0 makes it easy to identify the most loyal and profitable set of customers, as well as those who have switching tendencies and are currently with us because of a lucrative promotion. Also MCIF 2.0 has predictive capabilities which can be leveraged to drive active behavior modification tactics that increase the overall loyalty and the profitability of the customers. 4. Are we effectively onboarding new customers and how do we build successful, long-term relationships with them? MCIF 2.0 can set up an onboarding monitoring system with triggers linked to specified transactions and activities within the first 30 days of the relationship. MCIF 2.0 then helps identify and prioritize cross-selling opportunities based on the individual customer s behaviors, as well as the ability to easily recalibrate onboarding programs if necessary. Enhanced Reporting and Planning Capabilities In addition to giving solutions to the challenges listed above, MCIF 2.0 will generate more detailed reports than those currently churned out by existing systems. Existing MCIF systems are capable of listing what products the customers have, give access to historical data, and demographics. They lack the ability to analyze campaigns, lag in terms of data recency, and are limited in terms of conducting predictive analysis, doing campaign planning and campaign execution across multiple platforms. Unlike old MCIFs which were updated monthly/quarterly, MCIF 2.0 has real-time data. This is a true competitive advantage as the credit unions have an instant access to customer information that allows them to generate real-time reports, helps them to be more effective in ONAPPROACH OnApproach 2016
onboarding new customers and gives them better responses in their acquisition campaigns. The built-in machine learning modeling capability makes the mining of anonymous profile information possible for credit unions. MCIF 2.0 is also important in charting out future plans for financial institutions. It is easier to pin down objectives with greater focus while creating a step-by-step process to achieve them. For instance, your financial institution might be looking for an annual growth rate of 10-15 percent, or more. MCIF 2.0 can project scenarios with difference combination of growth increase and attrition reduction parameters. The financial institution can then pick the best solution to help balance growth, membership and risk parameters. MCIF 2.0 also helps in untangling the attribution dilemma. The increase in mobile banking in terms of customer acquisition and customer servicing is making attribution difficult. Comparing the efficacy of online/mobile channels with the traditional channels has always been a challenge. MCIF 2.0 makes the harvesting of anonymous profile information possible. The builtin machine learning modeling capability makes the mining of this huge data possible for credit unions. Because of these advanced data mining capabilities, the consumer s path to relationship with the financial institution can be better understood. MCIF 2.0 provides a real-time actionable strategy to target themembers and prospects. The real-time aspect of MCIF means the bank staff will receive in real-time segmented customer lists
based on the criterion they supply. This will support the launch of a personal banking strategy focused toward retaining and cultivating relationship with the customers. This increased level of customization will help increase loyalty among its most profitable customers. Managing Customer Attrition Attrition is very common at the start of the relationship and is a perennial challenge for financial institutions. For the banking industry the figures are relatively high. Based on a survey done at the 2013 BAI Payments Connect Conference by Financial Brand, the first year attrition rates range from 25% to 30% for financial institutions. This indicates customers are seeking solutions outside of the existing relationship. The reasons for attrition are mainly wrong offers, offering lower credits in the case of credit cards, and aggressive marketing from competitors developing the switching mentality. MCIF 2.0 can do a root cause analysis for every individual customer and address the underlying issue to stop them from leaving. For instance, if the credit limit offered to them is not sufficient, the institution can increase the credit limit. Using this framework, financial institutions can keep a firm tab on the customer life stages in order to help the financial institution stay ahead in the quest of making the right offers at the right time. Boosting Onboarding Success An effective customer onboarding process is key to an enriched and long term relationship with a customer. Using analytics and segmentation, MCIF enables financial institutions to onboard a new customer based on their early behaviors and ward off any tendencies to look for solutions from the competitors. Even a traditional MCIF systems can give substantial benefits. FO- RUM Credit Union developed a successful onboarding campaign for new members using the integrator MCIF system. In the 18 months since launching the onboarding program, FORUM Credit Union averaged a 37 percent response of new members accept- ONAPPROACH OnApproach 2016
MCIF enables financial institutions to onboard a new customer based on their early behaviors and ward off any tendencies to look for solutions from the competitors. ing one or more of those offers compared to a 21 percent crosssell rate among members who had been with the credit union for more than a year. The MCIF 2.0 competitive advantage is the ability to customize the message based on the data. Using this framework a credit union client was able to save almost 50% of their mailing cost by mailing to only the high and mid value customers while retaining the same response rate. Digital campaigns were used to reach out to the customer at the other end of the value scale. Creating Cross-Selling Opportunities Choosing the right product for cross-selling is critical to success and a lasting relationship. Financial institutions have relied on product propensity models for identifying the next best product for cross-selling to the existing customer. The existing MCIF systems support this approach by listing out product propensity based on the past transactions. MCIF 2.0 goes a step ahead by segmenting the customer pool into three different segments based on their cross-sell propensity. This framework was recently used by a credit union to identify its cross-selling targets based on the transaction data of the initial few days along with the demographic data. This segmentation approach allows a financial institution to be unique and sets up a cross-selling system to hyper-target customers. Conclusion The next generation MCIF is capable of automating a lot of the data manipulation and data flow. Like the first generation, it has pre-built models and easy-to-use interface that shortens the time it takes to develop predictive models. Ultimately, MCIF 2.0 essentially puts the institution in charge. The updated system can pull all transaction data from a dedicated data mart then apply different churn predictors including the number of checks specific customers have written, their total
deposits, and even withdrawal volume-to determine account usage patterns. This monitoring can produce a list of likely-tochurn candidates. MCIF 2.0 segments these customers based on their originating branch and send lists to bank managers allowing them to connect with these customers via a preferred channel. They can proactively intervene and re-establish the customer s relationship with the bank. Thus, it helps the financial institution be forward thinking, making cross selling more efficient, managing account attrition, and identifying the sweet spot in their business. MCIF 2.0 is the ultimate engagement tool and guide for both outbound campaigns and real-time interactions. It helps optimize the value of individual customers and the entire customer portfolio. It also reports on campaign specific results and manages complex list selections for multi-product and multistage marketing campaigns. Banks and credit unions that are quick to synergize their business operations with this new framework for approaching customers will be customer-centric in responding to existing and prospective customer needs and better able to offer customers the best experience well into the future. About the Authors: Karan Bhalla is managing director for IQR Consulting Inc. With 15 years of relevant industry experience, Karan is a graduate in Management Information Systems from Virginia Tech and holds an MBA in Finance from the University of Maryland. After spending 6 years at Capital One in the Credit Risk & Review function, Karan consulted at Fannie Mae, American Express and British Telecom. At British Telecom, he was responsible for developing a process to analyze results at a consolidated Global level. As managing director at IQR, Karan is responsible for enhancing the company s client delivery, as well as bringing a new perspective to current analytical solutions. Karan can be reached at karan.bhalla@ iqrconsulting.com Austin J. Wentzlaff is the Director of Business Development at OnApproach. He is responsible for developing marketing strategies, driving prospects to contract award, building and maintain- ONAPPROACH OnApproach 2016
ing high-level relationships with current and prospective clients and strategic partners, as well as elaborating business development plans that support business growth. Austin is an alumnus of Gustavus Adolphus College in Saint Peter, MN. He graduated from Gustavus Adolphus College with a B.A. in Financial Economics. Austin can be reached at Austin.wentzlaff@onapproach.net About IQR Consulting: IQR Consulting Inc. provides strategic solutions to difficult business challenges in a variety of industries; including Banking, Credit Unions, Retail, Insurance and others. With an eye towards profitability, our team provides accurate and actionable research and analytics to help our clients become analytically mature companies. We have expertise in analytics solutions, modeling and forecasting, marketing research, database design and support, automated tools, custom reporting and business strategy and consultation. Our process-based approach has helped companies anticipate market shifts and quickly respond to changing business environments. Most importantly, we help businesses identify potential market conditions, evaluate associated risks and opportunities and be more predictive. http://www.iqrdataanalytics.com/ About OnApproach: OnApproach is a Credit Union Service Organization (CUSO) that focuses on providing credit unions with the power to use data as a competitive advantage both independently and cooperatively. With OnApproach, credit unions can now harness the value of Big Data through integration and predictive analytics. This deeper understanding of data allows credit unions to discover vital trends in member behavior, resulting in improved financial performance, reduced risk, and enriched relationships with members. http:// www.onapproach.com/ Contact Information Austin Wentzlaff, Director of Business Development Austin.wentzlaff@onapproach.net (888) 523-6121 Ext. 110 www.onapproach.com