Mortgage Marketing Campaign

Objective

Partnering with a new mortgage lending client, our goal was to establish a new sales pipeline for their newest mortgage lending branch. This sales pipeline focused on trigger lead data solicitation through direct mail.

Mortgage Reporting Dashboard used for optimization of campaigns and decision making. Please note: Client data is fake to preserve confidentiality but functionality is representative of the live dashboard.

Process

Data Warehousing Infrastructure

Our team built an Access database for housing client prospect data. This data was paired with data from client’s CRM and QR scan reporting to build the data model for client reporting dashboard.

Establish Key Performance Indicators

We met with the client to establish the KPI’s that they used to evaluate performance. There were two primary KPI’s we focused on:

1) Response Rate: Represented by the number of calls and QR scans

2) Conversion: Represented by the early indicators of Number of Applications and Cost Per Application, as well as the final funding indicators of Cost Per Funded Loan / BPs

Segmentation

To allow for further optimization of the campaigns we focused on splitting the data for each campaign at a state level, which allowed us to adjust our data targeting strategy and creative to work towards improving our KPI metrics.

Optimization

After we identified our low-performing segments we used A/B testing to improve the performance. Our primary focus was decreasing the Cost Per Response/Application and Cost per Funded Loan. There are a few approaches we focused on to achieve this goal: decreasing cost, increasing response, increasing the conversion likelihood. We ran two A/B tests that leveraged the first two objectives.

Standard Mail vs First Class Mail

Due to the timeliness of trigger marketing we had initiated the campaign on First Class mail, which has faster delivery times. With that speed, though, is additional cost. So by switching to Standard (Marketing Mail) mail class we were able to eliminate some of that cost. We ran an A/B test on a small segment of the data to avoid doing any significant damage. While the cost of Standard mail was lower, the negative effect it had on response did not justify the switch.

Color Letter vs Black & White Letter

We also ran an A/B test between a color letter and a black and white letter option. The color mailing had a higher production cost, but we found for some of the campaigns having the color letter was justified by the Cost per Response. For those where the cost wasn’t justified, we were able to continue running the black and white piece and save some of the production cost.

Criteria Optimization

To increase the conversion likelihood we ran an analysis on the characteristics of converted leads for each campaign, broken out by the state sub-segment. From this we were able to compile the ideal criteria to target and leverage micro-segmentation within our data purchasing platform to apply this criteria.

Results

After developing an initial sales pipeline to support the new mortgage branch we were able to use insights from our Power BI Dashboard reporting to optimize the campaigns over a two-month span utilizing A/B testing and data micro-segmentation, resulting in a 70% increase in response and 78% increase in conversions.