Marketing Customer Information File (MCIF)
Many financial organizations already have a custom customer view called a marketing customer information file. Other industries have these as well, called by different name such as master patient index in the healthcare space. These files are created through existing software or internal jobs to merge data from various sources into a master file or database. This file contains relevant information on each customer from all systems to enable a holistic view of your customers interactions with your organization. A sample MCIF data set would include information (one row per customer) such as:
- Customer ID
- Member Tenure
- Checking Account Indicator
- Saving Account Indicator
- ATM Card Indicator
- Number of Monthly Transactions by Type
- Auto Loan Indicator
- Home Loan Indicator
- Last Contact Date
- Number of Calls to the Contact Center
In addition, any other information that can be used for marketing, sales, or product development can be added to this file. Once collated and check for data quality this file can be used for a variety of business operational improvements.
Power BI for MCIF
MCIF’s can be accessed and visualized using products such as Microsoft’s Power BI. Leveraging the work that has already been done to create the marketing customer information file. Power BI enables your team to interact with the data to gain insights and see trends. In addition, you can create dashboards, kpi’s and reports that are accessible via powerbi.com. Doing this allows your team to access the data in the MCIF via the web, even when they are not in the office via a tablet or phone. The image below shows the impact of married customers on each graph. The darker portion represents the married customers compared to the total customers across various metrics such as Tenure in Months, Age (by 7 year groupings), & Product Usage.
Predictive Analytics and MCIF
Companies that have a MCIF can leverage the information to build predictive analytic models to increase revenue and decrease expense. Predictive models leverage historical information and various types of algorithms to predict the likelihood of an outcome. For instance, a bank or credit union could create models to predict the next best offer for each individual customer. Thus creating a customized marketing experience based on what an existing customer may actually use. Another use case for predictive analytics is creating customer segments to better understand your customer base. Creating segments allows for optimized marketing based on how customer groups behave. Demographic data can be modeled to create targeted postal codes to grow your customer base. Knowing which products are most likely to be needed in a postal code enables you to create marketing campaigns with higher rates of return.
If your organization does not have this type of file. Building one can be a great way to enable better insights into your customers. Start with the easy data that you can obtain, perhaps from a single system. Then as required add more data points to build a more robust view of your customers. Our team works with customers to build models in 2-3 months to start delivering insights and value to generate an ROI. When used together MCIF, Power BI and Predictive Analytics can accelerate your company’s goals.
Learn More – Investopedia – Customer Information File
Learn More – Power BI
Learn More – Predictive Analytics