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What’s up everyone, today we have the pleasure of sitting down with Nadia Davis, VP Marketing at CaliberMind.

Summary: Nadia learned early that attribution keeps you in business, proving to executives why the budget, the team, and the work matter. Seeing “attribution is dead” posts, she built her Attribution Periodic Table to show data modeling, measurement rules, and cross-team alignment as one connected system. In B2B, where budgets are treated like investment portfolios, she uses multi-touch attribution to connect brand and demand to revenue in CFO terms. For her, it’s an analytics tool, not a scoreboard, shaped by sequences like her govtech playbook where event conversations plus on-demand webinars moved deals forward. Chain-based and Markov models help her cut noise, drop vanity metrics, and ground decisions in logged, meaningful touches, all anchored in strong marketing operations that make multi-touch attribution something teams actually trust.

In this Episode…

• The Periodic Table of Marketing Attribution Elements
• Why Marketing Teams Face Higher ROI Pressure Than Other Departments
• Making Sense of MTA’s Mixed Track Record
• Treating Multi-Touch Attribution as an Analytical Tool
• Exploring Chain Based Attribution Models for B2B Marketers
• How to Decide When Attribution Data Is Good Enough to Guide Strategy
• Why Marketing Operations Defines Multi Touch Attribution Success

About Nadia

Nadia Davis is the VP of Marketing at CaliberMind, where she leads demand generation, ABM, and marketing operations. She is known for building teams from scratch, overhauling martech stacks, and creating data-driven programs that sales teams can act on immediately. With over 15 years in B2B marketing, she has worked across SaaS, IT automation, healthcare tech, and data platforms, consistently delivering measurable growth by aligning marketing execution with revenue goals.

Her career includes senior roles at PayIt, Stonebranch, LexisNexis Risk Solutions, Informa, and ND Medica Inc., as well as nearly a decade as an ABM and digital strategy consultant. She has led global campaigns, designed persona-driven targeting, run high-profile industry events, and built marketing programs that continue to deliver pipeline well beyond launch. A former Girls in Tech board member, Nadia combines hands-on technical expertise with the leadership skills to grow both teams and results.

The Periodic Table of Marketing Attribution Elements

Nadia has worked in revenue marketing long enough to know attribution is a survival tool. In every demand generation and performance role, she carried it like part of her standard kit. It was how she justified headcount, protected budgets, and kept the lights on in her department. Attribution helped her prove progress in a language executives understood.

When she took over marketing at CaliberMind, she noticed the volume of “attribution is dead” posts climbing in her feed. The pattern felt familiar. Marketing tactics often get declared obsolete the moment they fail for someone, then replaced with whatever is trending. From her perspective, most of those posts came from SMB marketers moving on after a bad run. Meanwhile, enterprise teams were applying attribution with discipline, pairing it with strong data modeling, and getting measurable results. They simply were not talking about it publicly.

That split in sentiment drove her to dig deeper. She wanted to measure the gap between what people were saying and what they were actually doing. The outcome was the State of 2025 Attribution report, anchored by her Revenue Marketing Periodic Table. Nadia built it to show attribution as part of an integrated framework, not a lone tactic. She broke it down into interconnected components:

• Data modeling that improves accuracy and removes noise
• Measurement frameworks that define terms and keep reporting consistent
• Cross-functional alignment that ensures teams interpret the data the same way

“So many things may seem completely disconnected, yet they all come together within a bigger ecosystem.”

The iceberg metaphor stuck with her. Most marketers focus on the visible metrics, but the real forces driving success are below the surface. Choosing the periodic table format brought this idea into focus. It showed each element as part of a larger system, each with its own role and complexity. Nadia even remembered struggling with chemistry in school, to the point where she once cheated on a test because she could not memorize the valency of certain elements. That frustration helped her appreciate the value of a clear visual framework when dealing with something complicated. The periodic table worked because it grouped related elements, revealed their relationships, and made the whole system easier to navigate.

Key takeaway: Build attribution like a connected ecosystem. Pair it with precise data modeling, clear measurement frameworks, and strong cross-team alignment so every metric connects to a broader strategy. Map your system like a periodic table, where each element has a defined purpose and a place in the structure, that way you can spot gaps, diagnose problems faster, and prove impact without relying on surface-level numbers.

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Why Marketing Teams Face Higher ROI Pressure Than Other Departments

Marketing leaders manage one of the most lopsided jobs in business. One half of the work runs on instinct, creativity, and the psychology of memory. The other half is rooted in measurement, analytics, and financial accountability. Nadia points out that most marketers do not come from a statistics-heavy background, yet they are expected to operate as if they did. The pressure is not just to build campaigns that inspire but to show how those campaigns directly affect the bottom line.

In B2B, the stakes climb even higher. Sales cycles can drag for months or even years, and the money behind your budget often comes from venture capital or private equity. Those investors see marketing spend as growth capital, not operational overhead. That means they expect a return. Nadia compares it to giving a retirement manager your savings. You would not leave them unchecked. You would want to see exactly how those dollars are working and why certain investments are made.

Other departments do not face the same revenue-tied scrutiny. Finance manages operating budgets. Sales has smaller discretionary pools for travel and entertainment. HR spends what it takes to keep the team functioning. None of those groups is routinely asked to tie their activities to closed-won revenue. Marketing is, because its budget is treated as a bet on future growth, not a cost of maintaining the business.

The challenge is translating marketing results into terms that matter to the C-suite. Nadia frames it clearly:

“You are here because you got money to spend that we invested with you, and we want to have the responsible output from how this money is performing.”

But that translation is rarely straightforward. Engagement, recall, and psychological impact are powerful, yet they do not speak the same language as pipeline targets and closed deals. In SaaS and tech, that disconnect is shrinking fast as investor pressure mounts. Marketing leaders who can quantify the financial impact of creative work are the ones who keep their budgets, and their seat at the table.

Some people struggle with making decisions without near-perfect certainty, relying on data to eliminate all doubt. Others use data to validate their instincts and move forward despite ambiguity. Those who succeed in can make decisions with incomplete information, accepting calculated risk instead of waiting months or years to gather every possible data point.

Some departments, like product, are universally seen as critical to a company’s success, while (sadly) marketing is often misunderstood or undervalued. Marketing’s depth and complexity take years to master, yet many assume it’s simple, rooted in outdated “Mad Men” perceptions. Constantly trying to prove its worth can lead to friction, but shifting the mindset toward enabling and empowering other teams builds trust. When others see marketing as a driver of their success, questions about headcount and value arise far less often.

Key takeaway: Treat your marketing budget like an investment portfolio. Build a measurement system that connects brand and demand activities to revenue outcomes, even if the attribution is not perfect. Learn the financial metrics your CFO uses to judge performance, and frame your results in that language. That way you can defend spend in terms leadership values most and position your team as an engine for growth, not just a cost center.

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Making Sense of MTA’s Mixed Track Record

Attribution connects marketing spend to revenue. Multi touch attribution is only one way to do that. The method you choose matters less than how you resource, interpret, and communicate it. Nadia describes how MTA became a bundled feature inside nearly every marketing platform. Like a free lipstick added to your purchase, it felt like a bonus, but the color rarely matched the reality of your business. Google Ads, HubSpot, Marketo, and Salesforce all pushed their own version, telling marketers they could run attribution without the expertise or infrastructure to make it credible.

In smaller SaaS companies, that promise often collapsed. One person is running ads, tweaking automation rules, handling demand gen, and trying to build a model between meetings. Data lives in different systems, resources are stretched, and nobody is translating business questions into data requirements. The output becomes unconvincing. Numbers from the attribution tool fail to match the sales pipeline, confidence erodes, and the method gets discarded.

“We have to be mindful about things that are free with purchase. Just because it’s bundled in doesn’t mean it’s built to answer the questions you actually have.”

Enterprise teams work differently. Almost 8 in 10 still use MTA, often paired with other models. They dedicate analysts and data scientists to setup and maintenance. They configure models for specific go-to-market realities. They use different frameworks for different questions, which keeps them from forcing one tool to do everything. The process is methodical, resourced, and grounded in the understanding that no model is flawless, but the right setup makes it valuable.

Teams that make attribution work treat data stewardship as non-negotiable. That means knowing what the data says, where it is incomplete, and communicating those limits before decisions are made. It means showing sales and leadership exactly how the model was built and why it produces the numbers it does. When everyone understands the framework and its boundaries, attribution becomes a tool for smarter decisions instead of a source of mistrust.

Key takeaway: When MTA was bundled into tools like Google Ads and HubSpot, it was oversimplified and often misused, leading many to abandon it. Big enterprises still use MTA, often running multiple models with support from data scientists and advanced tools. Its success depends on strong data stewardship: knowing what the data means, recognizing its limits, and setting clear expectations, or the framework will fail.

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Treating Multi-Touch Attribution as an Analytical Tool

In the GOVTECH market, winning a deal often means surviving a sales cycle that can drag on for two years and stretch across a dozen decision stages. Nadia dealt with constant data decay, elected officials cycling in and out, and databases bloated with mismatched or obsolete records. The only way to make sense of it was to swap attribution models based on the goal. When the objective was market expansion, she turned to First Touch to identify which channels were pulling in new contacts from the right segment.

One market stood out: small counties in the southeast. The playbook there was strict. First, connect with the right people at an in-person event. Second, get them to watch an on-demand webinar. Live attendance was rare because showing up publicly could be seen as revealing buying interest too early. On-demand viewing let them keep a layer of anonymity while still engaging deeply enough to trigger a meaningful follow-up.

“If you have the conversation at the event and they watch on demand, you’re in. The BDR calls, and they already feel like they know you.”

The sequence only worked when both touches happened. Spending more on events without the webinar step wasted budget. Pouring money into webinars without the in-person anchor did the same. Nadia made a clear distinction between reporting and analytics here. Reporting is the snapshot that says a channel performed well. Analytics connects touchpoints in context, showing why specific sequences convert and why they matter for a specific market.

Her updates to executives focused on scenarios, not dashboards. She showed which sequences worked for which segment and backed them with evidence. That kept the conversation on budget allocation grounded in real behavior, not guesswork. It also positioned marketing as a strategic operator capable of navigating one of the most complex sales environments out there.

Key takeaway: Multi-touch attribution works best as an analytics tool for uncovering nuanced, scenario-specific insights that explain why certain channel combinations succeed, enabling executives to see and trust the story behind the numbers rather than just a topline marketing-sourced revenue figure.

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Exploring Chain Based Attribution Models for B2B Marketers

Chain based attribution models built on Markov chains give marketers a sharper tool for separating meaningful activity from channel clutter. Nadia likes them because they work with real buyer journey patterns instead of forcing every touch to split credit evenly. In account-based marketing, where buying groups often move slowly and in unison, this type of modeling can reveal which touches consistently push accounts forward. That way you can cut spend on the channels that only create the illusion of momentum.

Whether this actually answers the question of incrementality is another matter. Nadia does not make the case that these models can tell you, with certainty, whether a campaign truly caused a conversion. Instead, she shifts the focus to the practical upside: cutting noise, spotting patterns, and making better directional bets when perfect measurement is off the table.

The method works best when combined with a ruthless audit of what is in your data. Nadia has seen email opens and clicks dominate attribution reports despite having no correlation to purchase intent. In one case, she suppressed entire categories of email activity after realizing they were coming from people casually scrolling through their inbox during downtime. She suggests asking a blunt question about every channel: “If this disappeared tomorrow, would deals still close?” If the honest answer is yes, it should have little or no place in your revenue reporting.

Dark social will always limit what you see. Podcast ads, recommendations in private Slack channels, and word-of-mouth at executive dinners will never fully appear in your model. Nadia believes that marketers should not wait for full visibility before making budget calls. Directional patterns supported by anecdotal knowledge and sales feedback are enough to act on.

“You still have to do the work. No model can save you if a sales rep will not log who they had dinner with.”

That line came from an event attribution project where Nadia tried to connect attendance to pipeline without dedicated attribution software. She pulled data from Salesforce and Marketo, even ran it through ChatGPT looking for correlations, and still found nothing conclusive. The bigger issue was human behavior. If a dinner guest’s name never gets entered into Salesforce, the opportunity does not exist in the system. No automation or probabilistic model can solve for that.

Key takeaway: Treat chain based attribution as a filter for separating signal from noise, not as a verdict on incrementality. Use it to surface touches that repeatedly move deals forward, and cross-check those findings with qualitative context from sales and customer conversations. Act on patterns without waiting for perfect coverage, and build a culture where key touchpoints are actually recorded. The model’s value depends less on its algorithm and more on the discipline of the humans feeding it.

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Why Customizing Markov Chain Attribution Improves Accuracy

Order in a Markov chain attribution model shapes how the model interprets the sequence of a buyer’s journey. Nadia says the most valuable models are the ones built to reflect how a company actually sells. At CaliberMind, her team works with clients to map the sales process step-by-step, identify the touchpoints that genuinely move deals forward, and configure the model so it can be updated as new patterns emerge. The goal is to keep it relevant over time, not leave it locked in its original setup.

“You explain your process, we understand what matters, and then you set it up in a way where you will change it based on what you see coming in.”

She recalls a conversation with Michelle Garner, a senior data scientist at Microsoft, who shared that their marketing teams use an advanced Markov chain multi-touch attribution model to guide decisions. Even with hundreds of analysts and machine learning experts, Microsoft still treats the model as something that needs to be tailored to their own go-to-market motion. Off-the-shelf versions often miss the nuances that make each sales motion different.

When the comparison to sales pipeline probabilities came up, the conversation took an interesting turn. Pipeline percentages, after all, are rarely static. Reps often adjust them on the fly, sometimes after a burst of encouraging emails, other times based on a gut sense of momentum. In some cases, teams even automate the changes. The point is that sales teams treat their pipeline as a living, adjustable system, while marketing attribution models often sit untouched once implemented. That difference in ongoing care might explain why pipelines, for all their subjectivity, remain central to decision-making, while many attribution frameworks never get a chance to mature.

When a Markov chain model is treated with that same level of attention, it becomes far more than a reporting tool. Mapping the real sales process, configuring the model to match it, reviewing the results regularly, and adjusting touchpoints or sequencing rules based on what actually drives deals keeps it aligned with reality. Over time, it turns into a trusted guide for where to invest and where to pull back.

Key takeaway: Start by mapping your actual sales process in detail, then identify the touchpoints that consistently move deals forward. Configure your Markov chain attribution model to reflect that sequence. Set a recurring quarterly review to adjust touchpoint definitions, sequence rules, and weights based on what the latest data shows. Use behavioral signals (like response times or engagement spikes) to fine-tune the model, just as sales teams adjust pipeline probabilities. This ongoing calibration keeps your attribution model accurate and ensures it directly informs budget and campaign decisions.

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How to Decide When Attribution Data Is Good Enough to Guide Strategy

Attribution data is rarely pristine. Nadia warns that numbers pulled straight from individual ad platforms are built on distortion. Each one reports inflated performance to justify its cost. Metrics are duplicated, tracking is siloed, and the connection to actual revenue systems like CRMs or ERPs is missing. This leaves marketers piecing together incomplete stories from mismatched reports, often relying on spreadsheets and manual checks to make sense of it all.

The organizations that move beyond this chaos invest in connecting every data source to a shared operational core. CDPs, data warehouses, and tools like CaliberMind become the backbone for unifying and cleaning disparate inputs. These systems work only when they are backed by strong data stewardship and leadership. Even with the best setup, Nadia says there will always be blind spots, such as the performance of an offline billboard or a conversation at an industry dinner. The question is whether that gap would materially change the next decision.

“You can collect more data than you realize, but people focus on what they cannot collect and let it stop them from moving forward.”

In high-consideration enterprise sales, the trackable moments are far more plentiful than many teams acknowledge. Large deals often begin with events, webinars, and niche community discussions. These are measurable if marketing and sales commit to logging them. Attribution quality erodes when sales data is excluded entirely. Many tools ignore sales touches, which fuels turf wars over whose contributions matter. Marketing cites ad impressions while sales points to meetings, and the argument overshadows the work.

Nadia sees the solution in building attribution as a joint, continuous narrative. Include every meaningful touch, from the first marketing impression to the final sales meeting. Document the calls, the event attendance, and the relationship-building moments. That way both sides can see their role in the outcome and trust the same numbers. When that trust exists, “good enough” stops being an excuse and starts functioning as a dependable guide for strategy.

Key takeaway: Good enough attribution means data that is integrated with revenue systems, includes sales activity, and focuses on touchpoints that actually inform decisions. Connect the sources, document the important interactions, and maintain a shared view of the buyer journey with sales. If missing information would not change your decision, treat it as noise and move forward with confidence.

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Why Marketing Operations Defines Multi Touch Attribution Success

Attribution debates often start in the wrong place. Leaders argue about ownership while ignoring whether the infrastructure even exists to make the model work. Nadia focuses on the operating system behind go-to-market execution. Funnel stages, channel definitions, campaign naming rules, and shared language across sales and marketing shape the quality of every attribution output. If these are vague, the data will be unreliable before the model even runs.

“The success of any ABM or demand gen tactic starts and stops with marketing operations,” Nadia says. “If you don’t have that excellence in how you bring things together, everything else is secondary.”

She has seen how strong marketing operations turns ABM programs and demand gen from theory into measurable results. Weak ops makes attribution a stack of questionable numbers. The fix starts with defining funnels so they are obvious to everyone involved. That means:

• Setting precise rules for how leads move through the lead funnel.
• Mapping the account funnel in detail.
• Clarifying what happens when leads become accounts or overlap in the system.

Misalignment at the executive level can derail this before it starts. Nadia recalls a study from the Association of National Advertisers where 120 CMOs and VPs of Marketing named 98 different top metrics. Thirty CEOs in the same exercise named only three. The CEOs were angry about the disconnect, and for good reason. Without agreement on what matters, attribution becomes a political exercise instead of a decision-making tool.

Ownership is a skill problem, not a title problem. A marketing analytics team is often best equipped to run models and interpret results. If that team does not exist, a marketing or revenue ops leader with strong data instincts can handle it. The real requirement is someone who can manage uncertainty, work directly with raw data, and translate findings into a narrative leadership can act on.

Key takeaway: Treat multi touch attribution as the last step, not the first. Define funnels in precise terms, align with executives on the metrics that matter, and give ownership to the person with the sharpest data instincts, regardless of their title. That way you can create attribution models that deliver clear, trusted results instead of confusion.

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Why Time Management Drives Career Fulfillment

Nadia treats her calendar like high-value property. Every commitment has to earn its place. Training runs, marketing strategy reviews, family dinners, and even piano practice are all competing for the same space. If an item cannot prove its worth, it gets cut. She describes herself as obsessive about scheduling, and that obsession is what keeps her from ending the day wondering where the time went.

A good day for her is easy to recognize. It is the one where she can look back and see that each hour went to something that mattered. Discipline starts before sunrise, sometimes at 4:30 a.m., and it continues until she shuts it down at night. Rest is built into the system so the cycle can repeat without burning out. She is not chasing a vague notion of balance, she is engineering it with deliberate actions and structured priorities.

Her schedule is just as much about subtraction as addition. She refuses to give time to activities that drain her energy. When a recent job candidate clearly was not a match for a role, she ended the process early and explained her reasoning. They may not have loved hearing it, but they valued her honesty. To Nadia, this is not bluntness for its own sake; it is respect for both parties’ time.

“Time is the most valuable thing you have. Who you give it to and how you spend it is what makes you happy.”

She knows her direct style stands out in a region where conversations are often softened. She still prefers it, because it clears the path for work and relationships that matter. Each no is space reclaimed for something that will make the day feel worth living.

Key takeaway: Treat your calendar as premium space and fill it with work and relationships that deserve the investment. Schedule intentionally, cut what drains energy, and protect blocks of time for what drives real progress. Say no quickly and directly, explaining why, so you and others can focus on the work that truly matters.

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Episode Recap

Nadia learned early that attribution keeps you in business. It’s what lets you prove to executives why you need the budget, why your team exists, and why the work matters. In every role, she carried it like part of her standard kit. So when she saw people declaring “attribution is dead,” she paid attention. The loudest voices were often those who had a bad run and gave up, while bigger teams quietly kept making it work with structure and discipline. She wanted to show what that looked like.

That became her Attribution Periodic Table, a visual map of how the pieces fit together. Data modeling, measurement rules, and cross-team alignment weren’t separate tasks. They were elements of one connected system. Each had a defined place and purpose, so you could spot gaps, diagnose problems faster, and avoid chasing surface metrics.

In B2B, the pressure to prove return is constant and exhausting. Your budget is often treated like an investment portfolio, not a cost of keeping the lights on or something that’s mission critical. Investors and CFOs want to see how each dollar grows the business. Even if your multi-touch attribution model isn’t perfect, it’s something that many non-marketers expect to connect brand and demand to revenue in their language. That means learning the metrics they care about and building reporting they believe.

For Nadia, multi-touch attribution is an analytics tool, not a scoreboard. In her past govtech experience, she found one sequence that consistently moved deals forward: an event conversation followed by an on-demand webinar. Without both touches, momentum died. She brought executives these stories, not just MTA charts, so budget choices were grounded in actual buyer behavior.

Chain-based attribution helped her go deeper. Markov models showed which ‘visible’ touches kept deals moving and which were just noise. She cut out metrics with no purchase intent and checked her findings against sales feedback. But no model works if key moments aren’t logged, so she pushed for a culture where they always were, or at least as much as possible.

Perfection is the enemy of good, but with MTA sometimes inaccurate can be the corruption of good. Perfect data doesn’t exist, but good enough does. That happens when marketing and sales share one system, log meaningful touches, and focus on what will guide the next decision. Missing a few points that wouldn’t change the outcome is fine. Stalling or not doing anything because you can’t see everything is not.

Ultimately, the throughline is strong marketing operations. For Nadia, clear funnels, shared definitions, aligned metrics, and skilled ownership make multi-touch attribution useful. Without that, the smartest model becomes a spreadsheet nobody trusts.

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Follow Nadia 👇

• LinkedIn
• 2025 State of Marketing Attribution Report

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Intro music by Wowa via Unminus
Cover art created with Midjourney (check out how)

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