Data-Driven Account Decisions

Moneyball isn’t just for baseball - it’s the future of banking. As Michael Lewis said: “To the extent you can eliminate beliefs and biases and replace them with data, you gain a clear advantage.” That’s exactly what banks are missing: Most executives still rely on gut feelings and broad campaigns. But when we run the numbers, the truth is clear: ~94–96% of deposits are controlled by a fraction of customers. Yet most banks market to every customer the same. The result? Wasted spend, missed growth, and best customers slipping away to competitors. When I was leading marketing at regional banks, I realized: the answers were in the data. And the key was to focus on answering the right questions. In baseball, teams once judged players by batting stance or “look.” Today, every decision is made on on-base percentage. Bank marketing is stuck in the first era - tracking clicks instead of balances. That’s why we built a data-driven framework that has generated $25B+ in measurable balances across nearly 350 banks and credit unions. It comes down to 5 steps: 1) Assess the real opportunities At one $8B bank, households with ≥$250k on deposit averaged $458k in balances. Taken together, this group represented 43.8% of all deposits - yet they received the same generic "spray and pray" message as everyone else. 2) Plan with precision We identified 10,246 households without checking that had high propensity to open. That’s not a demographic. That’s a list of names. 3) Execute omnichannel campaigns Use Connected TV to reach precise first-party audiences, not entire markets. Use email and digital based on observed behavior, not ZIP codes. 4) Measure what matters Not impressions. Actual accounts opened. In one campaign, responder households grew +$36,000 on average while non-responders shrunk. 5) Refine relentlessly (especially pricing) In controlled tests, a 3.75% CD attracted 96% new money, while a top-of-market rate attracted only 38%. That difference saved about 64 bps in cost of funds while creating stickier, multi-product relationships. In a separate engagement, one bank’s actual COF came in 87 bps better than projected after executing the framework. The results speak for themselves: • Deposit growth case: Opportunity assessment sized $264M at a 134 bps contribution spread. Execution delivered nearly 2× that in balances, with COF ~87 bps better than projected. The CFO turned into marketing’s biggest advocate. • New-household case: After years of decline, +19% new-to-bank in year one at just $271 per funded household (not per lead). The lesson: small, systematic, data-driven improvements compound into outsized growth. Just like Moneyball, it’s not about swinging for home runs - it’s about stacking singles that add up to wins. At Infusion Marketing, we put our own money at risk. No balance-sheet growth, no fee. Curious what Moneyball would reveal inside your customer base? DM me and we’ll show you exactly where the ROI is - by name, not guesswork.

It’s time to stop thinking like it’s 2005. Correlation may flatter your GTM story, but only causation proves impact. More than 80% of companies missed their sales forecast in at least one quarter over the last two years (Gong, 2024). In H1 2024, 49% of companies missed their revenue goals (GTM Partners Benchmark Report, 2024). At the same time, executives keep putting faith in attribution models that only tell a sliver of the story. 𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺: too often, data is interpreted in ways that confirm existing assumptions rather than test them. Harvard Business Review found that sales leaders are frequently blindsided by overinflated forecasts driven by “all-too-human behavior” (Harvard Business Review, 2019). GTM Partners research shows that poor data quality can cost companies up to 25% of annual revenue, yet 60% don’t even measure these costs. That’s value leakage every CFO cares about. It’s time to fix this. Here are 5 ways to make GTM decisions actually data-driven: 1. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗻𝘂𝗹𝗹 𝗵𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗶𝘀: Harvard Business Review notes that “consistently accurate sales forecasts are rare because many companies fail to align their sales and marketing departments.” Assume your campaign 𝘸𝘰𝘯’𝘵 work—then try to prove yourself wrong.     2. 𝗥𝘂𝗻 𝗽𝗿𝗼𝗽𝗲𝗿 𝗶𝗻𝗰𝗿𝗲𝗺𝗲𝗻𝘁𝗮𝗹𝗶𝘁𝘆 𝘁𝗲𝘀𝘁𝘀: Compare your marketing results to a control group to see the actual lift your efforts create. MIT Sloan warns that confirmation bias leads us to “interpret ambiguous facts in light of preexisting attitudes.” Stop crediting natural growth to your LinkedIn ads.     3. 𝗕𝘂𝗶𝗹𝗱 𝗿𝗲𝗱 𝘁𝗲𝗮𝗺𝘀 𝗳𝗼𝗿 𝗺𝗮𝗷𝗼𝗿 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀: MIT Sloan recommends bringing together “different perspectives on the same issue” because organizational biases cloud interpretation. Create space for contrarians—the risks of blind spots are too expensive to ignore.     4. 𝗧𝗿𝗮𝗰𝗸 𝗹𝗲𝗮𝗱𝗶𝗻𝗴 𝙖𝙣𝙙 𝗹𝗮𝗴𝗴𝗶𝗻𝗴 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀: Research shows the average B2B buyer has ~31 touchpoints with a brand before deciding (Dreamdata, 2024). Your last-touch attribution is missing most of the story.     5. 𝗣𝗿𝗲-𝗿𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗺𝗲𝗻𝘁𝘀: Record in advance your testing methodology and success criteria. This prevents “analysis after the fact” bias and ensures accountability when results don’t fit expectations. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲: If your data never challenges you, it’s not science; it’s storytelling. The companies that break through are the ones willing to let the data argue back. What’s the most obvious confirmation bias you’ve seen in GTM? #GTM #MarketingLeadership #causalinference  

𝐅𝐨𝐫 𝐲𝐞𝐚𝐫𝐬, 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐫𝐚𝐧 𝐨𝐧 𝐡𝐢𝐧𝐝𝐬𝐢𝐠𝐡𝐭. Dashboards told us what already happened—open rates, MQLs, churn numbers. By the time we saw the problem, it was too late. 𝐋𝐞𝐚𝐝𝐬? 𝐃𝐞𝐚𝐝. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫𝐬? 𝐆𝐨𝐧𝐞. 𝐁𝐮𝐝𝐠𝐞𝐭? 𝐁𝐮𝐫𝐧𝐞𝐝. But AI and predictive analytics are flipping the game. 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐫𝐞𝐚𝐜𝐭𝐢𝐯𝐞 𝐚𝐧𝐲𝐦𝐨𝐫𝐞. 𝐈𝐭’𝐬 𝐩𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞. 🔹 𝐋𝐞𝐚𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 Traditional lead scoring is broken. A whitepaper download? That’s not intent—it’s noise. When we actually analyzed behavioral data using platforms like HubSpot, we found that multiple pricing page visits and engagement with onboarding content predicted conversions 3x better than generic lead scores. 𝐖𝐢𝐭𝐡 𝐦𝐮𝐥𝐭𝐢-𝐭𝐨𝐮𝐜𝐡 𝐚𝐭𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧 𝐦𝐨𝐝𝐞𝐥𝐬 and 𝐛𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐜𝐨𝐡𝐨𝐫𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 ✔ Leads with 𝐫𝐞𝐩𝐞𝐚𝐭 𝐯𝐢𝐬𝐢𝐭𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐩𝐫𝐢𝐜𝐢𝐧𝐠 𝐩𝐚𝐠𝐞 had a 𝟑𝐱 𝐡𝐢𝐠𝐡𝐞𝐫 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐜𝐨𝐧𝐯𝐞𝐫𝐬𝐢𝐨𝐧 ✔ Prospects engaging with 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐯𝐞 𝐝𝐞𝐦𝐨𝐬 moved through the funnel 𝟒𝟐% 𝐟𝐚𝐬𝐭𝐞𝐫 ✔ Combining 𝐢𝐧𝐭𝐞𝐧𝐭 𝐬𝐢𝐠𝐧𝐚𝐥𝐬 𝐰𝐢𝐭𝐡 𝐟𝐢𝐫𝐦𝐨𝐠𝐫𝐚𝐩𝐡𝐢𝐜𝐬 increased lead quality 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐢𝐧𝐟𝐥𝐚𝐭𝐢𝐧𝐠 𝐚𝐜𝐪𝐮𝐢𝐬𝐢𝐭𝐢𝐨𝐧 𝐜𝐨𝐬𝐭𝐬 We stopped chasing the wrong leads. And our pipeline? Tighter than ever. 🔹 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐑𝐞𝐭𝐞𝐧𝐭𝐢𝐨𝐧 A churn report tells you what you lost. But by then, it’s a post-mortem. Advanced platforms flag disengagement before it happens. A simple tweak—triggering check-ins for inactive accounts—cut churn by 15% in six months. A simple intervention—𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐢𝐧𝐠 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐫𝐞-𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 when customers showed 𝟑+ 𝐝𝐢𝐬𝐞𝐧𝐠𝐚𝐠𝐞𝐦𝐞𝐧𝐭 𝐭𝐫𝐢𝐠𝐠𝐞𝐫𝐬—led to a 𝟏𝟓% 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐜𝐡𝐮𝐫𝐧 𝐢𝐧 𝐬𝐢𝐱 𝐦𝐨𝐧𝐭𝐡𝐬. 🔹 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐅𝐢𝐭 Guessing what users want is a waste of time. Predictive analytics showed us which features had a 𝟒𝟎% 𝐥𝐢𝐤𝐞𝐥𝐢𝐡𝐨𝐨𝐝 𝐨𝐟 𝐚𝐝𝐨𝐩𝐭𝐢𝐨𝐧 before launch. The result? No wasted dev cycles, no misfires—just 𝐝𝐚𝐭𝐚-𝐛𝐚𝐜𝐤𝐞𝐝 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬. If you’re still relying on past data to drive strategy, 𝐲𝐨𝐮’𝐫𝐞 𝐩𝐥𝐚𝐲𝐢𝐧𝐠 𝐲𝐞𝐬𝐭𝐞𝐫𝐝𝐚𝐲’𝐬 𝐠𝐚𝐦𝐞. 𝐌𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠 𝐢𝐬𝐧’𝐭 𝐚𝐛𝐨𝐮𝐭 𝐥𝐨𝐨𝐤𝐢𝐧𝐠 𝐛𝐚𝐜𝐤. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐤𝐧𝐨𝐰𝐢𝐧𝐠 𝐰𝐡𝐚𝐭’𝐬 𝐧𝐞𝐱𝐭. #PredictiveAnalytics #MarketingStrategy #DataDriven #Growth

I’m just going to say it… Everyone is chasing “AI collectors.” That’s not the next wave. That’s table stakes. The real innovation is a data-first CRM built around the consumer, the account, and the market conditions around them. Not just name, balance, and phone number. A system that tracks at the account level: – Consent and revocations – Best channel to use: call, text, email, mail, RVM – Channel history and response patterns – Consumer lifecycle stage – Payment behavior – Settlement sensitivity – Litigation risk – State-by-state compliance rules – Local economic pressure – Gas prices – Housing trends – Employment conditions – Inflation stress – Real-time engagement analytics Because collectability is not static. A consumer does not exist in a spreadsheet. They exist inside a financial environment. If gas prices rise, rent jumps, local jobs tighten, or housing equity shifts, liquidation behavior changes. So why are so many agencies still working accounts like the only data that matters is balance, age, and last payment date? The system should know: Who to contact. When to contact them. Which channel to use. What offer makes sense. What risk exists. And whether outreach should happen at all. That is the difference between a CRM and a decision engine. AI collectors will become common. Dialers will get commoditized. The real moat will be the intelligence layer: Consent lineage. Channel-level strategy. Consumer lifecycle data. Macroeconomic pressure signals. Behavioral analytics. Account-level decisioning. The future of collections is not a ai dialer. It is a data-first CRM that understands the consumer, the account, the economy around them, and the best path to resolution. - Don of Debt

📌 The Secret to Smarter Data-Driven Decisions (and How to Apply It Today) Have you ever wondered why your data dashboards sometimes fail to provide actionable insights? The issue often lies deeper than the surface-level metrics—it’s in the root cause of the problem. Root Cause Analysis (RCA) is the backbone of effective decision-making in data-driven environments. When analyzing data, trends, and anomalies are just the starting points. 👉 To truly leverage data for impactful decisions, you need to ask the right questions and dig deeper into the ‘why.’ 1️⃣ Identify the Problem Begin by clearly defining the issue. For example: “Why is the churn rate spiking in Q4?” 2️⃣ Analyze Contributing Factors Leverage your BI tools to identify patterns or triggers. ⤷ Are there regional variations? ⤷ Does the spike correlate with pricing changes or product updates? 3️⃣ Drill Down with the 5 Whys RCA often uses the 5 Why Analysis technique to keep peeling back layers until you reach the true root cause. For instance: ⤷ Why are customers leaving? Higher subscription costs. ⤷ Why are costs higher? A price adjustment in Q4. ⤷ Why was the price adjusted? To offset operational costs. And so on, until the underlying issue is revealed. 4️⃣ Validate the Findings Use historical data, A/B testing, or predictive models to confirm whether the identified cause aligns with observed outcomes. 5️⃣ Develop Solutions With the root cause identified, propose data-backed solutions. For instance: “Offering region-specific discounts reduced churn by 15% last year—let’s apply a similar strategy.” 🤔 So why is RCA crucial for decision-making? Without RCA, you risk treating symptoms instead of solving the actual problem. This leads to wasted resources and decision fatigue. RCA will help you: ✅ Make data-driven decisions confidently. ✅ Prevent recurring issues by addressing them at the source. ✅ Improve data reliability and trust in dashboards. 👉 When was the last time you conducted a Root Cause Analysis? Share your thoughts in the comments! #DataAnalytics #BusinessIntelligence #DecisionMaking

Good data powers good decisions. Here’s why data is the backbone of ABM. ABM hinges on targeting the right accounts with the right message at the right time. But without solid data, even the best strategies fall flat. Here’s how to make your data work for you: Segment strategically. Not all accounts are equal. use data to group them based on engagement, intent, and potential value. There are 1000 ways you can segment your data for your program. I recommend the list be built on more permanent characteristics (like industries, revenue ranges, employee count, and technologies). Then prioritize based on intent and engagement. Invest in enrichment. It's the worst when you perfectly curated a program to engage an account, only to find out the people you've been targeted are no longer at the company. Today, data is becoming outdated at a rate of 30% year over year. So, if you always want to stay up to date, invest in enrichment. Then, you're targeting will always be as accurate as possible. Focus on progression data. It takes time for an account to close. Which is why measuring movement is crucial. Metrics like engagement velocity and pipeline movement tell a richer story than just closed won or closed lost. So make sure you're telling the full story as you build your program. How are you using data to drive better ABM outcomes in Q1? Let’s learn from each other.

Agreeing on 2025 GRR and NRR Targets? Here’s What You Should Prioritize: When it comes to setting Gross Revenue Retention (GRR) and Net Revenue Retention (NRR) targets for 2025, alignment across the C-suite is critical—CCOs, CEOs, and CFOs need to be on the same page. This isn't just about reaching ambitious goals; it's about committing to realistic, data-driven targets that the entire organization can rally behind and achieve. As a Chief Customer Officer, your ability to be grounded in the data is CRITITCAL. Here's how to make sure the numbers you sign up to deliver next year are both achievable and impactful: 1. Know Every Data Point Inside Out GRR and NRR are influenced by many factors—renewals, expansion, churn, product adoption, and customer satisfaction. Understand the nuances of your data. What drives churn? Which segments are generating the highest upsell opportunities? How does customer onboarding affect long-term retention? The better you understand these data points and their relationships, the more accurate your forecasting will be. 2. Debunk the “Gut Feel” Approach While intuition has its place, it should never overshadow data-driven decisions. Concrete metrics and cause-and-effect ratios will not only help you identify opportunities but also create buy-in across leadership. For example: If customer segment A contributes to 25% of expansion revenue, can that be scaled in the next fiscal year? Do retention efforts for high-risk customers significantly offset renewal rates? When you show that your targets are grounded in facts, you'll have the support of your CFO (and their green light on initiatives). 3. Focus on Cause and Effect Retention targets are interwoven with operational strategies—what actions today drive outcomes tomorrow? If we invest in more customer success managers (CSMs), how does that impact NRR within 6-12 months? Test your assumptions in Q4. Model scenarios and stress-test them with your data analysts. 4. Speak the Language of Finance Finally, clear communication between the CCO, CFO, and CEO is essential. Avoid vague terms and translate strategies into impact metrics that resonate across leadership—for example, articulate how investment in automation could reduce churn percentage while scaling GRR. Buy that course Jay Nathan and Jeff Breunsbach created if you need to uplevel. Trust me. Remember: Ruthlessly understanding and leveraging your data isn't just about hitting a number—it's about building a sustainable, predictable revenue engine. Let's set the bar where it needs to be. How are you aligning with your leadership on targets? Drop your experiences in the comments below—I'd love to hear your insights!

When it comes to making business decisions — don’t guess. Make decisions based on data. 🤝   We recently scaled a client we scaled $0 to $10,000/month in ad spend. A few months in, they came to us and said: “We aren’t generating more net profit — and we’re seeing more returns. We think it’s because of the ads.” So, we looked at the numbers. Their highest net months were all while ads were running — ranging from $2,000 to $40,000 higher than their previous peaks without ads. Ads are an expense — yes. But they’re also the engine driving the return. Too many business owners see ads on a credit card statement and assume cutting spend will “save” them money. But often, it’s their biggest cost because it’s their biggest profit driver. Now, about those returns… The business was processing more returns, which they thought was due to the places ads were appearing... But when we ran the numbers: - 2024 return rate: 3.08% - 2025 return rate: 2.84% The reason they felt like they were getting more returns is because when you're generating more volume, more returns naturally follow. However, if you applied the 2025 return rate to their 2024 sales, they actually would’ve had $3,000 fewer returns. Overall, it's a case study on not making emotional business decisions. And the need to make data-driven decisions. Your intuition can tell you what to look at — but the data should decide what to do. Feelings lose money. Data compounds it. 🚀 #Amazon #ecommerce #digitalmarketing #digitaladvertising #PPC

Every decision in a paid media account starts with data. Data creates a story → A story creates a narrative → A narrative drives conclusions → Conclusions drive actions. If the data is wrong, everything downstream is wrong. This sounds obvious, right? Here is the part that isn't. → Most accounts are making structural decisions based on platform reporting or attribution data as though it were business truth. It isn't. Attribution is directional. Platform reporting is incentivised to show wins. Neither of them can tell you whether the business is actually healthier this month than last. We've worked with eight-figure accounts, nine-figure accounts, brands that have been professionally managed for years by experienced people. The same structural mistake is still there. Not because the teams were bad. Because they were looking through the wrong lens. Wrong lens. Wrong story. Wrong conclusions. Wrong actions. And here is what makes this hard to catch. If you've been running on the wrong metrics long enough, the account looks consistent. The patterns make internal sense. The team agrees on what's happening. Everyone is aligned around a picture that doesn't match the bank account. The most expensive mistakes in paid media are the ones that look fine until they don't. → Business health first. → Platform health second. That's the only order that doesn't eventually produce a problem nobody can explain.

I recently had a conversation with a CFO friend who admitted something many decision-makers quietly acknowledge: model calculations are often just used to confirm decisions that have already been made. It’s like using data as a shield to feel more confident about doing what you were going to do anyway. This quote by Cassie Kozyrkov captures it perfectly: “Decision-makers end up using data to feel better about doing what they were going to do anyway.” But is that all we can achieve with data? Absolutely not. What’s missing is a framework—a structured way to ensure that decisions are not just justified retroactively, but built comprehensively, from start to finish: 🖼️ Frame the Problem: Identify the core decisions and set clear boundaries. Without defining what we’re really deciding, it's easy to end up in a fog. 🔀 Explore your Options: Go beyond the obvious. The best opportunities often lie outside the narrow field of conventional thinking. 🎯 Define Clear Objectives: You can't get what you want if you don't know what it is. Making your goals measurable is crucial. ✳️ Pinpoint Drivers & Uncertainties: Identify the key factors that will drive your outcomes. Know what you know—and just as importantly—what you don’t know. ⚙️ Model It: Bring everything together. A model isn't just for validating—it’s a dynamic tool for exploring trade-offs, assessing risks, and truly understanding the possible outcomes. Don't just use data to confirm the past. Use a structured approach to shape the future. #decisionmaking #decisionquality #decisionscience #decisionanalysis #riskanalysis #financialmodeling #financialmodelling #datadriven #problemsolving #decisionsupport #management #ceo #cfo #decisionframework Cassie Kozyrkov Ben Cattaneo