How Data-Driven Marketing Mix Modelling Transformed a Bank's Home Loan Performance in a Competitive Market

Introduction: The Challenge of Growing Home Loan Applications in a Volatile Economy

In today's increasingly complex financial landscape, Australian banks face mounting pressure to acquire and retain home loan customers. For one leading financial institution, the key challenge was clear: increase home loan applications in an environment characterized by rising interest rates, intense competition, and economic uncertainty.

This bank partnered with More Than Data to implement a sophisticated marketing mix modelling (MMM) solution that would:

• Accurately measure the impact of marketing across all channels
  
• Optimize media spend in real-time
  
• Navigate fluctuating economic conditions
  
• Outperform competitors in customer acquisition

The results were transformative, delivering consistent quarter-on-quarter growth in home loan applications while providing unprecedented visibility into what truly drove performance.

The Complex Challenges in Today's Home Loan Market

Data Collection and Competitive Intelligence Gaps

The bank faced significant hurdles in gathering the right data to inform decisions:

• Competitor interest rates changed frequently and were difficult to track manually
  
• Offline media performance data (TV, radio, OOH) was fragmented across multiple sources
  
• Economic indicators needed to be incorporated but came from disparate systems

Without accurate, timely data, the bank was essentially "flying blind" when making marketing decisions.

Fierce Competition with Undifferentiated Offers

The Australian home loan market has become increasingly commoditized:

• All major banks offer similar interest rates and cashback incentives
  
• Customer switching incentives make loyalty difficult to maintain
  
• Digital disruptors have lowered barriers to entry

This created a situation where marketing effectiveness, not just product features, became the key differentiator.

Economic Headwinds Impacting Borrower Behaviour

Multiple macroeconomic factors were suppressing demand:

• Rising RBA cash rates increased mortgage stress
  
• Soaring property prices reduced affordability
  
• Economic uncertainty made borrowers more cautious
  
• Changing immigration patterns altered demand dynamics

Traditional marketing approaches couldn't account for these complex, interrelated factors.

The Attribution Challenge Across Marketing Channels

With marketing running across:

• Digital channels (social, search, display, online video)
  
• Traditional media (TV, radio, print)
  
• Partnerships (mortgage brokers, real estate agents)

The bank struggled to understand:

• Which channels were actually driving applications
  
• How different media worked together
  
• Where to allocate budget for maximum impact 
  

The Marketing Mix Modelling Solution

Automated Competitive Intelligence System

More Than Data implemented:

• Custom web scrapers to track competitor rates daily
  
• Automated data pipelines to ingest Nielsen media spend data
  
• Real-time dashboards showing competitive positioning

This gave the bank an unprecedented edge in understanding and responding to market movements.

Unified Marketing Measurement Framework

The solution:

• Integrated all media spend data (digital and offline)
  
• Connected marketing data with business outcomes (applications, approvals)
  
• Incorporated external factors (interest rates, housing data)

This created a single source of truth for marketing performance.

Advanced Attribution Modelling

The MMM approach:

• Quantified each channel's contribution to applications
  
• Identified optimal media mix across the customer journey
  
• Revealed synergistic effects between channels

This moved the bank beyond last-click attribution to true incremental impact measurement.

Economic Impact Modelling

The model incorporated:

• CoreLogic housing market data
  
• RBA interest rate changes
  
• Employment and wage growth figures
  
• Immigration statistics

Allowing the bank to:

• Adjust strategies based on economic conditions
  
• Anticipate demand fluctuations
  
• Allocate budget more effectively
  

Implementation and Results

Phase 1: Data Foundation (Weeks 1-4)

• Built automated data collection systems
  
• Established data validation processes
  
• Created unified marketing performance database

Phase 2: Model Development (Weeks 5-8)

• Developed base marketing mix model
  
• Incorporated economic variables
  
• Established attribution framework

Phase 3: Optimization (Ongoing)

• Monthly model refreshes with new data
  
• Continuous media mix recommendations
  
• Creative performance analysis

The Results: Consistent Growth Amid Challenges

Additional outcomes included:

• 12% improvement in marketing efficiency
  
• 23% reduction in cost per home loan application
  
• Industry award for "Most Preferred Home Loan Product in APAC"
  

Testimonials

Bank's Head of Marketing

"More Than Data's marketing mix modelling transformed how we approach home loan marketing. Their automated competitive intelligence alone saved us hundreds of hours previously spent on manual rate tracking. But the real value came from their ability to isolate the true impact of our marketing amidst all the economic noise. We've been able to grow applications consistently despite rising rates and increased competition - something none of our peers have achieved."

Media Agency Account Director

"As the agency managing this bank's media, we'd struggled for years to prove the value of brand-building investments like TV and OOH. More Than Data's modelling finally gave us the evidence we needed to show how ATL and BTL work together across the customer journey. Their work has fundamentally changed how we plan and buy media for this client - with measurable improvements in performance."

Key Lessons for Financial Services Marketers

• Competitive intelligence must be automated - Manual tracking can't keep pace in today's market
  
• Economic factors can't be ignored - They must be built into marketing measurement
  
• Full-funnel attribution is essential - Last-click models dangerously undervalue brand building
  
• Continuous optimization beats annual planning - The market moves too fast for static approaches
  
• Creative quality matters as much as media placement - Emotional resonance drives differentiation

Conclusion: The Future of Banking Marketing

This case study demonstrates how marketing mix modelling has evolved from a backward-looking reporting tool to a forward-looking decision engine. By combining:

• Automated data collection
  
• Advanced statistical modelling
  
• Economic intelligence
  
• Continuous optimization

The bank has created a sustainable competitive advantage in customer acquisition that will continue to pay dividends as market conditions evolve.

For any financial institution looking to grow in today's challenging environment, this approach provides a proven blueprint for success.