Managing Metrics: 4 Insights to Maximize How You Use Your Data

This blog post comes from Ben DiMeo, Enact’s Senior Sales Consultant. He spends his time helping customers solve business problems by maximizing efficiencies, improving quality, and enhancing the customer experience.

Data driven decisions. We believe in collecting a wealth of data to make the most informed and relevant business decisions, but the processes put in place to achieve this are not always clear-cut and easy to follow. When considering how you use data and what processes you follow once you collect your metrics, it’s important to remember and use the tips we’ve outlined in this post.

Use your Metrics Correctly

When analyzing “process” metrics directly linked to the mortgage loan origination process, data points provide accurate measurements about how a process is performing AND a baseline to measure against when considering actions, outcomes, and how to achieve and sustain long-term improvements.

Below is a list of characteristics which exemplify how metrics are used correctly:

  • Are well-defined and aligned to help drive strategy and operating models
  • Measure process performance against defined goals
  • Provide a view of the employee performance (i.e., a calibration against peers)
  • Help differentiate and calibrate “person vs process” performance
  • Ensure that business activity is aligned with customer needs

You will be better aligned to achieve your long-term goals and solve business problems along the way if you include these basic tenets to help you navigate and decide where to look for data and what is most relevant.

Determine Which Metrics are Most Important

There are certain aspects to consider when accumulating data and trying to determine what is most relevant to the performance of your organization’s strategy and business model. The question should be: “What data points are most impactful to the business decisions we are making and the problems we are trying to solve?”

There are 2 important components to consider when it comes to establishing the right metrics. They should be meaningful and representative of the data you need to measure:

  1. Top-level indicators – Metrics directly associated with the strategic measurement of stated goals and objectives – think large scale, high level. For example:
    • Our goal is to increase purchase production in a given geographic area by X%
    • We want to increase the number of loan products we offer customers by introducing ARM products and government loans
  2. Key Process Indicators – Metrics directly associated with how your “process” and “staff” are performing. Three areas include:
    • In-line metrics – Critical data points in your process to assess how your process is performing (e.g., % of loans NOT APPROVED AFTER INITIAL UNDERWRITE)
    • Outcome metrics – Overall process performance indicators (e.g., % of loans closed within a designated cycle time)
    • Individual performance metrics – Critical items to assess individual performance (e.g., # of loans in pipeline per Processor, # of loans decisioned per day per UW, # of applications submitted per month per LO)

Always consider these attributes when it comes to data gathering and metric configuration to ensure that you are measuring performance in the right ways which align and support your business.

Ensure the Data Captures the Desired Information

It’s vital to ensure that the data gathered captures the desired information, and is both reliable and repeatable.

Here are 3 key components to follow to achieve this:


  • Establishing Operational Definitions – Maintaining a precise description of how to measure the performance on a critical metric. Everyone (regardless of role) should have the same understanding and interpretation of the operational definition
    • Purposeto remove ambiguity and identify what to measure, how to measure, and when to measure
    • Example Metric*: Processor Cycle Time
      • Bad Example: Defined as the average # of days a file sits in a Processor’s pipeline
      • Good example: Defined as the # of days from when a file is initially received into processing until the day that file is initially submitted into UW

*Note the distinction between the two examples and how much more precise the “good” example is in terms of how to measure and the information it yields.

  • Determining the type of data to be measured and how that affects outcome
    • Discrete data – data that can only take certain values (e.g., a loan is funded within our cycle time goal of 30 days or less – “yes” or “no”?)
    • Continuous data – data can take any value within a range (e.g., average # of conditions on an approved loan type – 1-20…)
  • Identifying a defect vs an accepted tolerance
    • Defect – anything that results in a customer dissatisfaction
    • Tolerance – establishing a measurement outcome deemed as “acceptable”
    • Example: Our goal is to approve all loans with a total of 5 conditions or less. Any loan approved with more than 8 conditions is considered a “defect” in our process quality. Approved loans that have up to 7 conditions fall within our “tolerance” levels and require no immediate action.

Knowing how to define a metric provides everyone with the same information on to how to collect and measure meaningful data. This also helps ensure stability and repeatability during the collection process thereby preventing errors which will skew your metrics.

Additionally, it’s important to understand the type of data you are using so that you can determine what a “defect” is compared to your organization’s “tolerance” levels. Both variables will help shape and deliver true process performance.

Improve your Reporting Processes

Collecting and analyzing data is not enough in terms of establishing and maintaining a comprehensive effort towards “continuous improvement” around process optimization. There must be a sequential process on how to utilize the metrics to support your organization’s strategies and goals.

Consider the following steps when it comes to post data analysis and collection:

  1. Define Content and Cadence of Reporting
    • What, where, when and how will data be gathered and displayed?
    • How often will reports be generated?
    • How often will reports be reviewed by senior management?
  2. Define Distribution and Accountability
    • Who will receive reports?
    • What is the express responsibility of each party receiving a copy of the report?
  3. Define Tolerance
    • For each defined metric, what is the defined tolerance (i.e., Cycle Time – Target cycle time is 35 days from application to funding – Anything over 40 days requires action!)
  4. Define Process for Response
    • Identify explicitly who will respond to issues reflected on the reports
    • Determine explicitly what steps they are to take and to whom they are to report on action
    • Define expectations for timing of resolution and updates

Reports do not have to be intimidating. Defining each step of the process can help you streamline reporting and improve efficiencies. Utilize these tips to help you drive decision making and impact.

Consistency and diligence are essential to get the results you want and the metrics you need to make data driven decisions that best suit your business needs. With optimization and an emphasis on strong processes, you can more effectively collect, analyze, and use the data you gather for your loan originations.

Want to Learn More?

Because going the extra mile for our customers is in our DNA, we provide a wealth of resources. If you want to learn more about specific topics or need some extra insight, you can always contact your Enact Sales Rep for more info. They’ll be happy to help you meet your business needs and point you in the right direction.


Source: Ben DiMeo, Enact’s Senior Sales Consultant.


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