Marketing Data Leaders Say 'Goodbye' To Long Data Cycles And 'Hello' To Customer-Centric Targeting

Traditional marketing measurement hides the truth. Teams rely on averages and six-month cycles to guide decisions, missing what actually works in the moment. As a result, leading data teams are shifting toward real-time, cohort-level analysis that captures how different segments respond. In banking, that means moving beyond asking whether a consumer needs a checking account and instead evaluating the full financial context to decide what to offer next. But tracking hundreds of data points and waiting months for a lifetime value read still leaves money on the table.

We spoke with Derek Caramella, Marketing Data Analysis Manager at Citizens. Overseeing a team of six data analysts, Caramella rips out sluggish attribution models and replaces them with causal measurement frameworks, such as matched-market designs, geo-based testing, and A/B experimentation, to determine exactly where media dollars should go. For Caramella, modernizing a strategy means moving beyond industry averages and diagnosing how specific cohorts respond at different points in their relationship with the bank. "What has gotten you here today won't get you there tomorrow. You have to build on those frameworks and evolve," Caramella says.

The Blunt Instrument Problem

A diagnostic approach exposes how standard metrics mask top-tier buyers. Caramella points out that the Average Treatment Effect is a blunt instrument. An 8 percent average engagement lift might look acceptable, but it can obscure a hidden 20 percent spike within a specific cohort and negligible traction elsewhere. Reaching those high-value groups requires matching products to life stages rather than leading with generic offers. "If you own a home and you're in your mid-30s, just coming at you with a savings certificate of deposit probably isn't the best thing for you where you're trying to grow your family, or you're trying to make a renovation on a house," Caramella says. "Can we position a home equity line of credit for you so that you can grow, that you can add an expansion, and you can buy that pool in your backyard for your family to enjoy in July?"

Trimming Hundreds of Variables Down to Twenty

Many organizations build this kind of layered personalization by compiling data in a centralized feature store. Caramella explains that the database might contain 300 characteristics on prospects and customers, making it difficult to see which attributes actually predict outcomes. To cut through that noise, Caramella uses CATE to isolate performance peaks, leaning on machine learning tools like decision trees and random forests to rank feature importance, narrowing hundreds of variables down to a manageable 20 or 30. "I wouldn't go the traditional way of a logistic regression, but actually go a little bit more into the machine learning toolkit and start with some level of decision trees, which has a feature importance ranking built right into the approach immediately," he says.

Multi-Armed Bandits and the AI Throughput Boom

Testing those high-value features in market calls for multi-armed bandits. Instead of running standard A/B tests, Caramella uses A/B/n experiments. Because AI can generate massive creative throughput, marketers can now test dozens of variants simultaneously, reading signals from early exploration periods and instantly shifting budgets toward what resonates.

But generating 30 ad variants in seconds creates a new logistical puzzle. Deploying dozens of AI-generated assets simultaneously requires strict tracking. The sheer volume of variants creates operational bottlenecks in tagging and launching multiple ad sets across channels such as direct mail and email, and Caramella is clear that it's up to the team to understand both personalization and the value of unique ads. "Differentiating among your creatives is critical. If we have 20 different options and option one and option 14 could be literally considered the same thing, it would be a miss there from a data perspective."

Additionally, the deployment layer carries its own tax: the complexity of launching creative effectively on a given platform. "It's one thing to be able to make a picture and put copy on the page," Caramella says. "It's another to figure out how to launch that ad set into the market. Depending on the platform, that could be very challenging."

Compressing the Lifetime Value Feedback Loop

Caramella makes this deployment layer manageable by requiring creative teams to apply clear intent tags mapped to specific life events, such as having a baby or moving to a new neighborhood. That structure lets data scientists align creative versions with features in the store to make sure communications actually resonate. On the measurement side, he understands the value of short-term metrics and churn, pushing teams to compress their feedback cycles. 

By building an analytical layer that produces leading indicators of lifetime value, teams can optimize for net return much sooner. For Caramella, the most powerful results come from blending this faster profitability signal with modern testing frameworks. "Now the real orchestration of all of this, where it starts to come together, is when you multiply that incremental response that you gain from your test by the leading indicator performance of lifetime value. You're not waiting around for a lifetime assessment post-acquisition. You're able to get a faster result quicker, pivot, and market accordingly."

Sorting Customers From Sunk Costs to Persuadables

Shortening that feedback loop helps teams spot segments that look attractive initially but drain budgets over time. Caramella uses an RFM taxonomy to segment customers into practical groups based on engagement and value. That framework helps identify "sure things," "persuadables," "sleeping dogs," and "sunk costs," dictating exactly where to concentrate spending. Sunk costs, he explains, are "essentially people that are either going to game you. They're going to take your promotion and engage with you for a short period, and then your lifetime value is very limited. Or they're just never going to engage with you."

The persuadables are where the real budget belongs. Caramella describes persuadables as prospects that "fall shy of being a sure thing" but can become an organization's "sure things" with some investment. "With personalization, you start figuring out that your current messaging is not resonating with these people. So you have two options. You can either start to optimize your messaging, or you abandon those people altogether."

Evolving the Org Alongside the Math

As customer journeys span more channels, many marketing leaders are finding that standing out requires rethinking how messages are targeted and measured. For Caramella, building beyond traditional attribution means outreach delivers durable value. It also requires the entire organization to adapt to new frameworks as traditional methods fade. "Not only does the data talent need to get scaled up, but our marketer base, our stakeholders that it's getting delivered to, also needs to be receptive to those more advanced analytical techniques so that we can both move forward together in this AI revolution."