While banking executives debate IT budgets and regulatory compliance, a quiet revolution is reshaping the competitive landscape. The neobanking market is expected to grow at a CAGR of 35.8% during 2023-2030, and these digital disruptors aren't just stealing customers. They're redefining what customers expect from pricing transparency and value.
Chime grew from 1.5 million customers in February 2018 to 40 million users by March 2024, a 2,567% increase in six years. These aren't just numbers; they're a direct challenge to traditional pricing approaches that treat customers as statistical averages rather than behavioral profiles.
The evidence of traditional banking's pricing crisis is everywhere you look. Customer acquisition costs have skyrocketed by 60% over the last five years, while customer retention rates remain stagnant at about 75%. Meanwhile, deposit growth has essentially flatlined at 0.4% in 2024, forcing banks into destructive pricing wars that erode margins without building sustainable competitive advantages.
Here's the uncomfortable truth: To make strategic pricing adjustments, most banks are conducting A/B tests like it's still 1995. The standard 80/20 champion-challenger methodology — splitting populations by statistical convenience rather than behavioral intelligence — is not just outdated; it's potentially harmful to profitability.
Pricing is a hypothesis, not a divine revelation. Yet most banking executives treat their rate sheets like scripture, making minor adjustments based on competitor matching rather than understanding the fundamental question: Which customers will actually respond to your pricing and how should margins be allocated to maximize their lifetime value?
The traditional approach assumes that statistical significance equals business success. It doesn't. Statistical significance tells you that a difference exists; it doesn't tell you whether that difference matters to the customers who drive your profitability.
The solution requires more than just recognizing the problem. It equally demands a fundamental shift in how banks approach pricing execution. Banks need tools that take pricing from a place of rigidity to fluidity through a test and learn approach that answers the critical question: What is the optimal price for my product in light of my strategic goals and constraints?
Consider the transformation at a major Canadian bank struggling with mortgage renewal retention. Despite running textbook 80/20 champion-challenger tests and promoting successful strategies, they were bleeding market share during the critical renewal window when 60% of Canadian mortgages are set to renew by 2026, with many borrowers facing 15-20% payment increases.
The breakthrough came when they abandoned population-level testing for behavioral cohort segmentation. Instead of treating all renewing customers identically, they created distinct test groups:
The results were transformative: improved margins and significantly higher retention rates. They stopped subsidizing the wrong customers and started investing margins where they generated measurable returns.
A similar revolution occurred at a major US financial institution preparing for a CD maturity bubble. Rather than deploying blanket promotional rates — the traditional margin-destroying response —they implemented relationship-based cohort testing that eliminated expensive competitor rate matching and promotional extensions while maintaining deposit levels with the customers they most wanted to keep.
To technology teams viewing advanced price testing capabilities as an expensive IT investment, the real cost isn't the investment — it's the systemic misallocation of technology dollars that's crushing ROI across the industry.
Banks consistently fall into two expensive traps: either over-investing in massive transformation projects for cores or pricing execution systems that deliver minimal payback, or burning budgets on complex analytics consulting engagements that generate brilliant insights no one can actually implement.
According to Deloitte's 2025 Banking Industry Outlook, US banks with more than $10 billion in assets saw growth in total non-interest expenses outpace net revenue growth, with the three largest US banks raising their full-year expense targets by $4 billion in 2024 alone. Much of this represents precisely this pattern of expensive technology initiatives that don't translate to business results.
The trick is to invest in platform solutions that create a seamless feedback loop between pricing intelligence and pricing execution. Banks need technology that can simultaneously identify which customers to test, execute those tests in market, measure the results and automatically feed learnings back into the next iteration of testing.
When considering these type of platform investments, it is critical to ensure that they align with a few common core components of pricing intelligence.
Stop testing prices on random population splits. Start testing on behavioral cohorts that predict actual purchasing decisions. The Canadian bank's insight about call center inquiries predicting rate sensitivity should be replicated across every customer touchpoint. Digital engagement patterns, product usage intensity and renewal history all signal pricing responsiveness better than demographic data.
The fundamental question isn't "What rate should we charge?" but rather "Which customers justify margin investment for retention and which customers generate the highest returns from acquisition pricing?" Banks can use client profitability hurdles, such as return on investment, rather than just revenue to make deal-pricing decisions.
Market conditions change faster than traditional testing cycles. According to McKinsey's State of Retail Banking report, interest margins are likely to account for the bulk of margin contraction facing retail banks (roughly 70 percent through 2026). Banks need testing frameworks that adapt to rate environment shifts, competitive moves and customer behavior changes in real-time.
Banking executives face a binary choice: evolve pricing methodologies to match customer behavioral intelligence or watch market share and margins erode to competitors who understand that pricing is a strategic tool, not an administrative function.
The banks that recognize pricing as iterative hypothesis testing where customer behavioral cohorts replace statistical convenience samples will build sustainable competitive advantages. Those that continue treating pricing as a rate sheet exercise will find themselves explaining to boards why their deposits, margins and market share continue declining despite "competitive" rates.
The science of price testing isn't optional anymore. It's survival. Your customers are already voting with their wallets. The question is whether you'll listen to the data or wait for the quarterly earnings call to deliver the bad news.
Ready to transform your bank's pricing strategy with behavioral segmentation and dynamic testing?
Written by: Vishal Ramesh, Vice President of Product Management at Nomis Solutions