Tracking Customers’ Spending Patterns Can Drive Better Personalization

By Shawn Conahan, Chief Revenue Officer at Wildfire Systems

Published on July 14th, 2026 in Customer Experience

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Banking customers have been trained by the likes of TikTok and Netflix to expect individualized experiences that make it feel like a company “knows” them, and doesn’t just make inferences based on their broad demographic cohort.

Yet most banks and credit unions base their marketing on these segments, sending batch offers calibrated to the institution’s campaign calendar, instead of what’s really relevant in the customer’s life.

Key strategic issue: The data to do better exists. So does the customer appetite for it. What’s missing is the architecture to connect them.

Loyalty programs and personalized engagement are the elements through which banks can transition from being considered a utility that simply helps customers move money into a partner that knows them and provides more value.

Need to Know:

Only 42% of a bank’s customer engagement is driven by what’s actually happening in a customer’s financial life, while the rest is driven by pre-planned campaigns, according to the 2026 Personetics Global Banker Survey.
74% of consumers want more personalized banking, and 66% are comfortable with their bank using data to deliver it, according to a 2024 survey by Q2.
Trigger-based marketing tied to actual customer behavior delivers a 553% higher return on marketing investment than traditional batch campaigns.

Banks’ Internal Architecture Hobbles Personalization

Most institutions know they have a personalization problem.

In its study Personetics reported that when banking executives were asked what prevents them from deriving actionable intelligence from customer transaction data, 56% cited data silos between business lines. In addition, 55% cited the inability to build a unified customer profile. Significantly, only 11% pointed to a shortage of AI or machine learning capability.

Key insight: The bottleneck is structural.

The issue is that most banks are currently built around products, not customers. Credit lives in one system, deposits in another, lending in yet another. Loyalty programs, when they exist at all, are typically locked to individual product lines. The result: A customer with three products at their bank gets treated identically to one with just one. Unfortunately, that shows the customer that the institution doesn’t really know them at all.

Why it matters: A segment-of-one experience should be the baseline expectation for any institution competing against fintechs and digital platforms that are unburdened by legacy infrastructure that keeps customer data trapped and unusable.

Some steps banks and credit unions should take to unify their various data sources:

Audit data infrastructure to identify where customer-level signals are siloed by product line.
Map the gap between behavioral data that’s theoretically available and what’s actually being activated in real time.
Prioritize: Identify two or three high-value customer journeys where unified signals would meaningfully change the offer delivered.
Build a cross-functional working group spanning product, data and marketing, not just one of the three.

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Payments: The Intelligence Layer Many Banks Are Missing

Banks and credit unions could learn from the retail industry, where a few personalization leaders have figured out that the richest customer intelligence source is in what someone actually purchases.

Brands like Ulta Beauty, Macy’s and Sephora have built personalization engines on this granular purchase data. Ulta achieved a 95% repurchase rate. Sephora drives 80% of its transactions through its loyalty program.

How to mine insights: Such results come from systems that know what customers are researching, comparing and buying in real time, and use that data to deliver relevant offers and personalized messaging at just the right time.

Banking institutions can access the same intelligence layer, without crossing the fine line between helpful and intrusive, through a consent-based value exchange.

For example, when a customer who has been actively comparing refrigerators accepts a cashback offer surfaced by their bank, the bank earns something it can’t buy: verified purchase intent, captured in the moment, with the customer’s full knowledge and willing participation.

Key insight: The most powerful first-party signal an institution can earn is the one a customer is willing to share. Embedding value-adding shopping experiences into digital banking creates a natural, consent-driven mechanism to collect the insights that enable personalization.

As banks and credit unions look to draw more individual insights, they should focus on these strategies:

Explore partnerships that can surface relevant merchant offers inside the institution’s digital experience without disrupting its core functionality.
Prioritize consent-driven data capture.
Design the value exchange explicitly: Customers should understand what they’re sharing and what they receive in return.

How the Engine Generates Personalized Offers

The goal is a genuine intelligence ecosystem which learns from each customer’s behaviors over time to build a robust profile that enables more relevant offers. A customer could interact with their institution’s app or web portal and encounter relevant merchant offers there. They engage with those offers: by viewing a merchant, activating an offer, completing a purchase, or even all three.

The institution is able to capture that individual’s behavioral signals on those offers. Over time, those signals build a living customer profile that includes actionable insights such as merchants they like, spend categories, types of offers they respond to, apparent life stage, and products under active consideration.

Here’s a concrete example: A customer makes a series of purchases in two weeks across home improvement retailers, furniture stores and décor brands. That pattern reveals a customer insight which may indicate a home project or a move is under way. An institution with the right intelligence layer can respond accordingly with personalized offers.

The customer might see timely offers for related categories like appliances or home services. Plus, the financial institution can surface their own relevant products (such as a home equity line of credit or a credit limit increase) at the moment the customer is most likely to need them.

Alternatively, this may be an opportunity to remind the customer which card or account benefit delivers the best rewards for those purchases.

This could even be a chance to offer to help them budget for the project.

Key tactical shift: The experience moves from generic to genuinely useful. The institution becomes a helpful companion that responds to what the customer is actively trying to do.

How Tracking Transcends the Status Quo

The Personetics survey puts a finer point on the perils of generic messaging. First, financial institutions convert only 53% of digital engagement into measurable business outcomes, on average. Second, the two most-cited causes of failed conversion are offers that aren’t aligned with customers’ financial needs (33%) and offers that lack personalization (28%).

Why it matters: This model functions as an intelligence architecture that gets smarter with every interaction. This compounds value for the bank or credit union, for merchant partners, and for customers who now see their provider as a financial ally rather than a pure utility.

Key elements:

A unified customer data layer that ingests signals across product lines, not just a single account type.
An AI engine capable of updating profiles in real time and sequencing offers by predicted relevance.
Commerce or merchant network integrations that generate the behavioral signals the personalization layer needs to learn about individuals.
Measurement frameworks that connect personalization investment to retention, cross-sell and engagement.

What Financial Institutions Stand to Gain

Personetics found that the average financial institution takes 12 weeks to move a new personalized offer from concept to live launch. But in a setting where customer situations can shift every week, that offer can already be outdated when it actually arrives.

Banks and credit unions that reward the total customer relationship across all products, not just individual account activity, can see measurable lifts in customer retention, a meaningful advantage in a market where consumers have near-unlimited alternatives.

For consumers, the institution stops being a “paycheck motel” and instead is seen as a useful financial companion. For merchants, bank-embedded commerce delivers them access to premium, high-intent audiences in a high-trust environment, with closed-loop attribution tied to actual transactions.

Key insight: Banks hold the raw materials for personalization, transaction history, product relationships and trusted digital touchpoints. The gap is connecting those inputs to an intelligence layer that learns and responds.

Best practices to shift towards more personalized customer messaging:

Treat your bank’s mobile app as a marketing channel, not just a servicing interface.
Set personalization benchmarks tied to retention and cross-sell conversion, not just engagement rates.
Align product, marketing and data teams around a shared definition of the customer relationship that spans all product lines.
Evaluate technology and commerce partners on the quality of behavioral signal they generate, not just the size of their merchant networks or the richness of individual merchant offers.

Bottom line: Customers will no longer tolerate generic, segment-based marketing. The expectation of relevant, timely messages and offers has never been higher. Ultimately, the path forward for financial institutions is connecting the data they already hold to an intelligence layer that responds to customers’ actual lives and making personalization at scale the foundation of a more durable, more valuable relationship.