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Operational Mortgage Sales Data Analytics Use Case
How to increase mortgage sales growth rate through operational data analytics and business intelligence
Mortgage sales performance is critical for the overall performance of the mortgage business.
So, even if you're slightly related to the mortgage industry, you're likely looking for how technology can help you improve sales performance.
However, it's often hard to see how new, shiny technology can actually achieve this.
Data analytics and business intelligence tech are no exceptions.
You've probably come across promises like:
Or that "Data is at the heart of everything an enterprise aspires to do."
But how can data analytics actually help improve mortgage sales performance and generate revenue?
So, to answer this question, I set out to write the post you're reading now.
In this post, I'll share my analysis of how data analytics technology can have a tangible impact on mortgage sales performance.
If you have anything else to add, please feel free to reach out, and I'll update the post.
Understanding the impact of technology isn’t as straightforward as we might hope.
The reason is that ROI doesn’t come from technology.
ROI comes from the process that you applied technology to.
In other words, the process and its impact on overall business performance define the returns you get from technology.
So, to get to the bottom of how technology can help close more loans, we need to look at:
But here’s the tricky part about understanding the ROI of data analytics.
Data analytics isn’t a technology.
At least not in the way we commonly think about technology.
To the surprise of many, data analytics isn’t just a dashboard or a piece of software; it’s a process.
As with most processes, you can improve them through technology.
In our case, data analytics tech covers ELT Pipelines, OLAP Cubes, and all the other fancy terms.
So, to understand the impact of operational data analytics tech on mortgage sales, we’ll look at:
In the context of mortgage sales, operational analytics is a process.
Analytics is the process of gathering, organizing, and analyzing data to get the information needed to decide what to do next.
You can think of analytics as finding answers to questions to make informed decisions.
Operational mortgage sales data analytics is the application of analytics to the mortgage sales development process.
It means processing data about mortgage sales operations to decide what to do next to improve mortgage sales performance.
The data analytics process isn’t attached to any specific software or tool.
If you’re trying to improve operational mortgage sales performance, you are already engaged in the analytics process. The scale might differ from when you leverage software, but you still do it.
The purpose of data analytics is to make better decisions about what to do next.
The purpose of operational mortgage sales data analytics is to make better decisions about what to do next to improve mortgage sales performance.
The better the answers you get from analytics, the more accurate your decisions will be.
And the more accurate the decision, the more actions you take result in improvement of mortgage sales performance.
The input of data analytics is an open question you need to answer to decide what to do next.
The input of operational mortgage sales data analytics is a question you need to answer to decide what to do next to improve sales performance.
Generally, operational mortgage sales analytics questions fall into one of four buckets:
Operational mortgage sales data analytics usually follows a four-step process outlined below.
We all engage in it while trying to improve performance on a different scale.
In some cases, the four steps below happen without leaving a single person's head, while in others, the whole department works on them.
1. Operational mortgage sales data collection
Collection involves extracting data about sales activity. It usually can be found in CRM, LOS, email, team members' heads, etc.
2. Operational mortgage sales data processing
Processing involves removing errors and irrelevant data, formatting, and combining data from different sources into a unified dataset.
3. Operational mortgage sales data analysis
Analysis means applying analytic techniques to make sense of the data and find an answer. One example is measuring a metric.
4. Operational mortgage sales data analysis presentation
Presentation means communicating the answer in a way that makes sense for the decision-maker. It can be a chart, report, or plain text.
The subject of the data analytics process is data.
The subject of operational mortgage sales data analytics is data about mortgage sales operations.
In other words, it involves data about the activity or behavior of everyone involved in the mortgage sales process and the results of their behavior/activity.
If you want to learn more about operational data, you can find a more in-depth overview here.
The output of data analytics is an answered question.
The output of operational mortgage sales data analytics is the answer to the question needed to decide what to do next to improve mortgage sales performance.
Now that we have defined operational mortgage sales analytics, let's examine how it impacts mortgage sales performance.
As with most processes or skills, analytics is not binary but a continuum.
It's not a question of whether you have it or not but how well-developed it is.
To understand how a process impacts business performance, we need to take a step back and see where it fits into the operational hierarchy. Then, we can look at how this process's performance impacts the performance of functions both upstream and downstream.
In our case, operational analytics is part of the sales development function. To see how it impacts mortgage sales performance, we'll look at:
Operational mortgage sales analytics is a part of the mortgage sales development function.
Mortgage sales development involves the efforts to improve the performance of the mortgage sales function (volume, quality, efficiency, etc.).
Here are examples of mortgage sales development operations:
Analytics is a part of the observe-decide-act cycle, where we observe current performance, decide what to do next to improve it, and then actually do it.
Examples of decisions within mortgage sales development operations include:
The quality of analytics determines the quality of your sales development decisions.
And the quality of these decisions determines the impact of your development work.
Better analytics means that decisions made are more likely to move towards the desired function performance.
Thus, the difference between sales development functions with better and worse analytics lies in efficiency.
The better the analytics, the fewer actions you need to take to achieve the change.
The worse the analytics, the more actions you must take to achieve the change.
A better-developed sales development analytics function results in faster performance improvement through making better decisions.
The primary goal of sales development is to improve sales function performance.
Thus, the performance of the sales development function impacts the rate at which mortgage sales performance grows.
The better the mortgage sales development performance, the higher the sales growth rate.
The worse the mortgage sales development performance, the lower the sales growth rate.
The previous section showed how well-developed operational analytics impacts mortgage sales performance.
However, a well-developed operational analytics function doesn't come out of the box.
As the sales organization grows, the performance of analytics declines.
The reason is that the larger the sales organization, the more volume of data it produces, and the harder it becomes to perform the analytics.
And the harder it is to do the analytics:
So, let's explore how technology can increase analytics performance, even when the data volume is beyond what a single human can analyze.
Operational data analytics requires data.
However, manual data entry is resource-consuming and highly prone to human error.
The result is limited data about sales activity that sales leaders can analyze, and the available data is often spread across CRM, LOS, Email, and Social Media.
Here, we can use Data Lakes, ELT tools, and APIs to automate the capture of raw operational data.
Raw operational data in each system (CRM, LOS, etc.) has a different format.
So, raw data requires cleaning and transformation to be ready for analysis.
On a bigger scale, this becomes unmanageable for a human to do.
We can use data modeling and transformation tools like DBT to automate cleaning, integration, and normalization.
Once operational data is ready for analysis, you need to do an actual analysis.
However, the larger the dataset, the more challenging it becomes for humans to perform meaningful analysis.
Here, we can use OLAP tools to define and automate metrics measurement and ML models to do predictive analytics.
Out of the box, most automated analytics techs require technical skills to do analysis.
But most end users of data analysis aren't technical.
And this becomes a bottleneck.
The harder it is to ask a question, the fewer your team will ask before making a decision.
To address this problem, you can use self-service data visualization and exploration tools. These tools democratize access to the analysis's results and increase the number of questions people ask before making a decision.
The latest development in data analytics is the conversational user interface built on top of large language models (LLMs) that further reduces the barrier and lets users ask questions in plain English.
Even with self-exploration tools, it takes some effort to get an answer.
You at least need to compose the question.
With one-off questions, this isn’t a problem.
However, when multiple questions must be answered regularly (e.g., performance metrics), it becomes a problem.
You get inconsistency in answers due to variance in the questions asked.
To solve these problems, you can automate reporting by creating dashboards and reports with predefined questions.
This approach will ensure consistency of the results and reduce unnecessary friction.
I hope this post gave you insight into how you can use operational mortgage sales analytics to improve sales performance.
If you’d like to stay on top of the latest mortgage tech and how it can be applied to mortgage operations, consider joining our mortgage technology newsletter.
I’m a software consultant with background in software engineering. Currently, I run a mortgage software consulting and development company that builds custom tools and automation solutions for mortgage lenders.
