Jonathan Levanon, VP of Growth Marketing at Sapiens, shares how an engineering mindset shapes scalable, predictable growth. He discusses building disciplined operating models, using data as a decision framework, aligning teams across the pipeline, and applying AI to drive sustainable demand, leadership accountability, and long-term revenue performance.

Welcome to the interview series, Jonathan. Could you tell us a bit about yourself and your journey as a marketer?

Hello, and thank you for having me!

My professional background is actually in engineering, not marketing, and that has shaped how I think about growth from day one. I was trained to break complex problems into systems, understand constraints, and rely on evidence rather than assumptions. When I moved into marketing, I didn’t see it as a creative function but as a growth problem that needed structure and accountability.

As my career evolved, I transitioned from execution into building and leading global growth organizations in complex B2C & B2B environments. Today, as VP of Growth Marketing, my focus is on designing operating models that connect strategy, data, people, and execution across regions, long sales cycles, and multiple business lines. The goal is not activity, but consistency – creating systems that perform predictably over time.

Data has always been my primary reference point. not as a reporting exercise, but as a way to bring clarity to decisions and remove emotion from debates. In environments where growth expectations are high and margins for error are small, data becomes the light that guides prioritization, tradeoffs, and long-term direction. It’s what allows growth to scale without losing discipline.

What does a truly scalable and predictable demand generation engine look like in practice, and where do most organizations get it wrong?

A scalable and predictable demand generation engine is built on shared data and shared understanding, not on individual channels or isolated funnel stages. In practice, it means the organization has a single, trusted view of the entire pipeline – from first signal to closed revenue – and uses that view to make coordinated decisions.

It’s also important to recognize that scalability looks very different depending on the company’s stage. In startups, speed and learning matter more than predictability. In SMBs, the challenge is moving from founder-led intuition to repeatable processes. In enterprise environments, predictability becomes critical because growth depends on coordination across regions, functions, and long sales cycles. Treating these stages the same is one of the most common mistakes organizations make.

Data plays a central role, but not as a reporting layer. It functions as a common language across marketing, sales, and revenue teams. When everyone is working from the same definitions, assumptions, and performance indicators, tradeoffs become explicit and decisions become faster and more accurate.

Where many organizations struggle is in treating demand generation as a collection of disconnected activities. Teams optimize their own stages or channels in isolation, often with different success metrics and time horizons. That creates local improvements but global inefficiency. Pipeline gaps are discovered too late, and forecasts rely more on hope than evidence.

Predictability emerges when the entire pipeline is analyzed as a system, understanding how volume, quality, velocity, and capacity interact across functions. When data is used to synchronize execution rather than justify results, demand generation becomes something you can manage and scale with confidence.

You often say that data without a strategy is just noise. What’s your framework for turning raw data into decisions that drive revenue?

For me, the role of data is not to prove a point – it’s to listen. If you come to the data already knowing what you want it to say, it usually becomes a justification exercise. That’s when it turns into noise.

I start by being clear about what I care about and what I’m willing to act on. In growth, there is always too much data. The real challenge is deciding which signals matter and deliberately ignoring the rest. If a metric doesn’t change a decision, it doesn’t belong in the conversation.

From there, I work backward from the outcome we’re trying to influence – pipeline quality, velocity, or revenue predictability – and focus only on the signals that reflect movement in those areas. When those signals change, we respond. When they don’t, we don’t overreact.

Used this way, data becomes a guide rather than a report. It helps teams stay aligned, removes emotion from debates, and keeps decisions grounded in what the business is actually telling you, not what you hope to hear.

Digital transformation is an overused term. From your experience, what does real digital transformation in growth marketing actually look like?

Real digital transformation has very little to do with adopting new technology and everything to do with changing how an organization makes decisions. You see it when digital capabilities stop being layered on top of existing problems and instead reshape how teams work together.

In growth marketing, this often starts with visibility across the entire pipeline. What that visibility looks like varies by scale. In smaller organizations, it might be about finally connecting marketing and sales data. In larger enterprises, it’s about aligning multiple regions, products, and teams around the same definitions and decision cadence. For example, when marketing, sales, and operations work from the same data definitions and timelines, conversations shift. Instead of debating lead quality or attribution after the fact, teams can identify where momentum slows, where handoffs break, and where capacity (and not effort) is the real constraint. That alignment alone often delivers more impact than any new platform.

Another clear sign of real transformation is when automation and analytics are used to reduce friction, not add complexity. I’ve seen organizations unlock scale simply by redesigning how demand signals flow across regions and teams,  so prioritization is consistent, follow-up is timely, and forecasting reflects reality rather than optimism. In those cases, technology enables discipline, not speed for its own sake.

When digital transformation is real, teams spend less time explaining results and more time acting on them. Data becomes a shared reference point, workflows become intentional, and growth becomes something the organization can manage with confidence rather than chase reactively.

In a market obsessed with rapid wins and new trends, how do you defend focus, patience, and sustainable growth at the leadership table?

I don’t defend patience as a principle; I defend it as an economic reality. Short-term wins are valuable, but only when they don’t undermine long-term efficiency, credibility, or pipeline health. In complex B2B environments with long sales cycles, every rushed decision has a delayed cost.

At the leadership table, focus becomes easier to defend when conversations are grounded in unit economics, capacity constraints, and long-term impact. For example, it’s easy to push aggressively for more top-of-funnel volume, but if sales capacity, regional readiness, or follow-up quality doesn’t scale at the same pace, the result is friction rather than growth. The pipeline may look healthier in the short term, but conversion and trust suffer downstream.

I’ve seen this play out in global organizations where regions operate at different maturity levels. Accelerating campaigns without aligning processes, data definitions, and ownership across teams often creates more noise than value. In those moments, discipline means slowing one part of the system so the rest can keep up.

My role is to help leadership see not just what we can accelerate, but what acceleration might break and whether that tradeoff is worth it. When those implications are visible, patience stops being a “marketing ask” and becomes a business decision rooted in protecting future growth.

“A scalable and predictable demand generation engine is built on shared data and shared understanding, not on individual channels or isolated funnel stages.”

How are you practically applying AI today to improve targeting, personalization, or forecasting—and where do you see the biggest real ROI?

Today, we apply AI most actively where content and data scale create complexity. We use AI to identify patterns across content formats, messages, campaigns, user journeys, and data tables to understand what truly resonates – across audiences, regions, and different stages of the pipeline. AI helps us iterate faster by suggesting messaging variations and testing directions based on historical performance and emerging signals. It also highlights potential bottlenecks and crucial conversions junctions and suggests potential optimization. It doesn’t replace strategy or creativity, but it significantly shortens feedback loops and reduces uncertainty in execution.

The ROI here comes from efficiency – wasting less budget on underperforming ideas (and processes) and scaling winning narratives with more confidence. Many of these capabilities are powered by the AI already embedded in third-party platforms we use, which process volumes of engagement and intent data that would be impossible to analyze manually.

Looking ahead, especially toward 2026, the focus shifts from content, data and process optimization to decision support at scale. This is where AI delivers its most durable value. Rather than thinking in terms of standalone tools, we think in terms of agents aligned to real roles and real decisions.

Growth teams will leverage agents that act as social listeners, surfacing intent signals, narrative shifts, and in-market accounts, as well as buying-persona simulators that help stress-test messaging and positioning before it reaches the market. SDRs will rely on AI as a research and prioritization layer, helping them focus effort where it has the highest likelihood of impact. Analysts will use AI to surface patterns, anomalies, and risk signals across the full pipeline, not just isolated stages.

For leaders, the value is especially tangible. AI helps expose early signs that plans are drifting off course – whether that’s a mismatch between pipeline growth and sales capacity, regional performance diverging from assumptions, or forecast confidence eroding before it shows up in revenue. Instead of reacting late, leadership can intervene earlier, with context and clarity, while decisions are still reversible.

Forecasting is another area where AI becomes increasingly important. Instead of relying solely on historical averages, AI helps identify shifts in velocity, regional dynamics, and capacity constraints earlier, making planning more resilient and less reactive.

What would be your advice to marketers looking to step into leadership roles?

The most important shift is moving from proving that marketing works to demonstrating that you understand how the business works. Leadership is not about having the best ideas; it’s about making decisions that hold up under pressure and uncertainty.

Many marketers (I’m one of them) grow up professionally in startup or SMB environments, where speed and ownership are everything. Stepping into leadership, especially in enterprise contexts, requires a different mindset: patience, systems thinking, and the ability to scale decision-making through others.

Marketers who successfully step into leadership roles are the ones who connect execution to outcomes, ask better questions, and take ownership beyond their functional boundaries. Just as importantly, they know how to listen to their teams, to the signals coming from the market, to what the data is actually telling them and to other company functions feedback (specifically Sales and Product). That listening ability often reveals constraints, tradeoffs, and risks long before they show up in results.

Growth leadership is ultimately about accountability. Being willing to stand behind decisions, learn from outcomes, and adjust course when needed without losing direction or credibility with the people executing the work.

About Jonathan Levanon

Jonathan Levanon is a strategic growth executive with 15+ years of experience leading global demand generation, ABM, and growth operations for enterprise organizations. As VP, Global Growth Marketing at Sapiens, he drives pipeline acceleration, digital transformation, and market expansion. A recognized thought leader and advisor, Jonathan specializes in scaling growth through disciplined, data-driven, and AI-powered strategies.

The post Building Predictable Growth at Scale: Data and Discipline with Jonathan Levanon appeared first on iTechSeries.

In this exclusive interview, Jessica Pantages, Vice President of Corporate Marketing at Egnyte, shares how modern marketing has evolved from a creative support function into a revenue-driving business system and why governed AI, operational velocity, and strategic storytelling are critical to building trust, aligning GTM teams, and scaling sustainable growth in today’s digital-first B2B environment.

What has been the single most important leap in marketing’s role as it transformed from a cost center to a primary driver of revenue and business growth?

From my perspective, the single most important leap was the fundamental shift in mindsets, moving Marketing from a ‘Creative Service’ to ‘Revenue Infrastructure.’

For decades, marketing was viewed as a cost center because it was viewed as the ‘arts and crafts’ department—responsible for making things look good or generating vague ‘awareness.’ The transition to marketing as a revenue driver happened when we stopped viewing content as a disposable output and started treating it as a business asset.

We recognized that in today’s digital-first world, content is the only way a customer experiences the product before buying it. Therefore, the team that controls the narrative drives the pipeline.

Specifically, this transformation relies on three areas:

1. From Broadcasting to Enabling: We stopped just ‘shouting’ at the market and started building the systems that equip sales teams to win.
2. From Chaos to Governance: We recognized that you cannot scale revenue on shaky ground—content must be solid and factual. By implementing processes and managing the internal risks involved with content creation, we turned our content from a compliance risk into a trust accelerator for our potential and current customers.
3. From ‘Renting’ to ‘Owning’: Instead of relying solely on third-party media channels and partnerships, we built owned publishing engines that allow us to control the story, the timing, and the data.

When marketing became accountable not just for ‘likes’ but for the integrity and velocity of information moving through the organization, we stopped spending money and started generating trust, which is the ultimate currency of revenue.

In which areas do you see AI shaping and transforming the marketing landscape overall?

I see AI shaping the marketing landscape in three critical areas: Content Intelligence, Democratized Automation, and Trust Architecture.

First, we are moving past simple ‘content generation’ into Content Intelligence. It’s no longer just about asking AI to write a blog post; it’s about using AI to instantly mine years of historical assets—videos, webinars, PDFs—to find the exact clip or insight you need in seconds.

Second, we are seeing Democratized Automation. AI is empowering non-technical marketers to build their own workflows. Instead of waiting for IT, a field marketer can now spin up a specialized ‘agent’ to research competitors or draft an RFP response, exponentially speeding up their time-to-market.

And finally, Trust Architecture, an area where Egnyte is leading the charge.

We are solving the biggest barrier to AI adoption: Safety. Most C-Suite members are concerned that using AI will leak proprietary data or produce ‘hallucinations.’

We built our Egnyte AI Intelligence to allow teams to ‘chat’ with private company data (summarizing documents, transcribing video, or finding assets) without that data ever leaving our secure governance boundary. We recently launched the AI Agent Builder, a no-code tool that lets our marketing team build their own custom agents—like a ‘Content Generator’ or ‘Web Search Agent’—to automate repetitive research and writing tasks safely.

In short, our teams are proving that governance doesn’t slow AI down-it’s the only way to safely turn it on. And this is proving critical for executives and CMOs.

As a marketing leader, how do you decide which marketing channels deserve the greatest share of the budget?

I don’t decide based on trends or the lowest cost-per-lead. In our B2B environment, the ‘spray and pray’ approach is dead. I allocate budget based on Revenue Influence and Asset Lineage, using strict criteria:

1. Vertical Specificity (The ‘Right’ Room):

We don’t try to be everywhere for everyone. We invest heavily in channels that allow us to demonstrate deep, industry-specific value. For example, we prioritize budget for specialized verticals like our AEC initiatives or for our Financial Services Summit. I would rather spend more to be in a smaller room of qualified C-suite decision-makers than spend less for a broad, unqualified reach.

2. Owned vs. Rented Land:

I prioritize channels where we control the experience. We skew our budget toward ‘Owned’ events—like the Egnyte Global Summit—rather than just renting temporary attention on third-party platforms. When you invest in your own summits and community, you aren’t just buying impressions; you are building an asset that appreciates over time.

3. Sales Utility:

Finally, I look at the downstream impact. Does the channel produce assets that our sales team uses? We fund the channels that drive the creation of high-quality collateral and presentation decks that help close deals. If we spend money on a channel that generates ‘buzz’ but gives Sales nothing to use in a meeting, it’s a vanity metric. We fund what bridges the gap to revenue.

“When marketing became accountable for the integrity and velocity of information—not just likes—we stopped spending money and started generating trust.”

In a data-driven world, how has storytelling become the critical lever for change, whether that’s changing a customer’s mind, a market’s perception, or an employee’s behavior?

In my experience, data provides the evidence, but storytelling provides the urgency.

We live in a world where everyone has access to the same data; the competitive advantage lies in who can translate that data into a compelling vision for change. I have seen this clearly when navigating high-stakes uncertainty. For example, when managing the communications plan for our private equity acquisition, the financial data was just the baseline. The real work was the narrative. We had to craft a story that translated a complex transaction into a message of stability and opportunity for employees and customers alike. The positivity coming from investors, employees, reporters, and customers proves that a clear story is the only way to manage high-stakes change.

Storytelling is also the primary lever for elevating market perception. Data alone doesn’t get you into Forbes or Fast Company. By wrapping our technical metrics in a broader industry narrative, we drove strong executive visibility and a massive 40.2% follower growth for leadership. We shifted from talking about features to telling a story about market authority.

Finally, narrative is what drives actual business results. You can’t achieve your attendance goals for a Global Summit just by listing agenda items. You get those numbers by telling a story that resonates with the specific pain points of that customer, turning a standard event into a ‘must-attend’ industry moment. Ultimately, data validates the decision, but storytelling triggers it.

How do you ensure insights from customer data actually translate into better campaigns and experiences?

I believe the gap between ‘insight’ and ‘action’ is usually a speed problem, not a data problem.

We don’t just sit on customer data; we use AI-driven agility to turn it into immediate campaign adjustments. When we see a signal—whether it’s a shift in topic interest from a specific vertical or a drop in engagement on a channel—we don’t wait for a quarterly review. We pivot.

Here is my framework for translating insights into better experiences:

1. Radical Efficiency: By implementing a significant AI program across marketing, we have removed the operational friction that usually slows down response times. This allows us to take a data insight and spin up a new asset or campaign variation in hours, not weeks.
2. Vertical Precision: We don’t treat data generically. If the data shows our Financial Services audience is struggling with a specific compliance pain point, we immediately tailor our narrative to that reality. This approach helped us drive a 107% year-over-year increase in attendance for our Financial Services Summit because the content spoke directly to what the data told us the audience needed.
3. Feedback Loops: We ensure that the ‘story’ isn’t just broadcast out but validated by what comes back. If a campaign isn’t driving the ‘measurable business impact’ we expect, we kill it fast. We only scale what the data proves is working.

Ultimately, data tells us what is happening, but our ability to execute quickly is what ensures the customer feels the difference.

In an age of real-time behavior, how do you move customer segmentation and journey mapping from a static, planning exercise to a dynamic, always-on system?

The shift from ‘static’ to ‘dynamic’ isn’t a strategy problem; it’s a latency problem. Traditional segmentation fails because by the time you build the persona deck, the customer has already moved on. To build an ‘always-on’ system, you must eliminate the lag between signal and content delivery. This  is how we made that shift:

1. AI as the Velocity Engine:

Dynamic segmentation is impossible if your content supply chain is slow. You cannot serve unique journeys to five different personas if it takes you weeks to write one email. By implementing an AI content generation program across marketing, we gained the major efficiencies needed to match the speed of our customers. This allowed us to produce the volume of variations required for real-time personalization.

2. Operationalizing the ‘Content Factory’:

We moved away from ad-hoc creation to a structured manufacturing model. We established an entirely new Content Department that improved our written output by over 99%. This surge in capacity means we aren’t just planning for different segments; we have the assets ready to deploy the moment a user shows intent.

3. Vertical-Specific Triggers:

We stopped treating ‘customers’ as a monolith. We look for specific behavioral signals within key verticals. This is how we achieved a 107% year-over-year increase in attendance for our Financial Services Summit. We didn’t use a static list; we dynamically targeted users who were engaging with financial compliance topics and served them relevant, high-value programming immediately.

What would be your advice for up-and-coming marketers on cultivating the right skill sets?

My advice is to stop thinking of yourself as just a ‘creative’ or a ‘specialist’ and start thinking of yourself as a business operator driving growth. The marketers who rise to the top aren’t necessarily the ones with the flashiest ideas; they are the ones with the best endurance and adaptability.

First, cultivate Operational Agility. The industry is volatile, and budgets will fluctuate. You need to learn how to pivot immediately without losing momentum. When resources are tight, the most valuable skill is the ability to manufacture your own efficiency. Whether it’s mastering AI tools or restructuring workflows, you must learn how to deliver exceptional performance regardless of the constraints placed on you.

Next, develop Cross-Functional Fearlessness. Don’t stay in your silo. The most indispensable team members are those willing to step into the void to solve business problems, even if it falls outside their job description. Whether it’s jumping in to help execute a major event or managing high-stakes communications late at night, your value is defined by your willingness to bridge gaps across the organization.

Finally, cultivate Relational EQ. Technology changes, but people don’t. When friction occurs, you need the maturity to handle it through direct, private conversation rather than public debate. Deepening organizational trust is what allows you to move fast when it matters most; you can’t execute at speed if your relationships are slowing you down.

About Jessica Pantages

Jessica Pantages is an award-winning marketing and communications leader with over 20 years of experience transforming global organizations through strategic storytelling, brand leadership, and GTM alignment. Known for rebuilding high-performing teams and guiding companies through acquisitions and change, she brings deep expertise across enterprise marketing, communications, and organizational transformation. Her work has been recognized by Ragan Communications, PR News, and Direct Marketing News, among other industry leaders.

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Michelle Slevin, Head of Marketing for UK & Ireland at Workato, shares her 20+ year journey across Oracle, Ericsson, Indeed, and Bank of America. She discusses driving growth through customer-centric marketing, scaling global demand generation into regional impact, balancing personalisation with scale, leveraging AI for smarter execution, and aligning marketing with customer success to build long-term business impact.

Welcome to the interview series, Michelle. Could you tell us about yourself and your journey as a go-to-market leader?

Thanks for having me. It’s a pleasure to be here.

I began my career after completing a Master’s in Spanish and Sociology at Trinity College Dublin. My first role was in sales at Oracle, which was a pivotal foundation. It showed me firsthand what sales teams need from marketing: not just campaigns, but true partnership in creating demand and moving opportunities forward.

From there, I made the move into marketing with Ericsson, where I drove demand for products across the LATAM region, including Brazil, Chile, and Mexico. That experience sparked my enthusiasm for regional and global marketing, learning how to balance scale with local nuance and how cultural context shapes engagement.

Since then, I’ve spent over two decades in leadership roles at organisations like Indeed and Bank of America, leading global demand generation, communications, and go-to-market strategy across regions including EMEA, APAC, ANZ, and North America. Along the way, I’ve built a strong belief in the role of marketing as a growth driver and a connector-bringing customers, partners, and teams together around shared outcomes.

I’m also a passionate lifelong learner. Most recently, I completed a second master’s-an MSc in Cyberpsychology focusing on AI, virtual reality, and human behaviour in digital environments. That work sharpened my perspective on how technology influences decision-making and how marketers can use insight responsibly to create meaningful experiences.

Today, I lead marketing for the UK & Ireland at Workato, helping enterprises harness automation and AI to orchestrate their businesses in smarter ways. The most rewarding part of my journey has always been seeing customers succeed and knowing marketing played a role in enabling that success.

How do you showcase customer success in a way that’s both engaging and backed by measurable results?

For me, customer success is the most powerful story a company can tell, but only if it’s framed around outcomes. It’s not about the technology implemented, it’s about the business impact. Did it drive growth? Improve productivity? Reduce costs? Accelerate innovation?

The best stories strike a balance. On one side, you highlight the human impact on how teams feel empowered, how work has been transformed. On the other, you validate with metrics that prove ROI and scalability. And because different stakeholders consume information in different ways, format matters: executives may prefer succinct ROI snapshots, while practitioners value detailed case studies, and digital audiences respond to short-form video.

The critical piece is integration. Customer success shouldn’t sit in isolation; it should run through everything like campaigns, events, PR, and sales conversations. When the customer’s voice is consistently at the centre of marketing, it builds trust and demonstrates value more effectively than any brand message ever could.

As a marketing leader, how do you balance regional priorities with global campaigns while ensuring measurable business impact?

The key is alignment. Global campaigns provide consistency, efficiency, and scale, but they’re only effective when adapted for the local context. Regional execution is what makes the message resonate.

In practice, that means taking a global framework and tailoring it with proof points, messaging, and activation strategies that reflect local realities. In the UKI, that might involve highlighting local customer successes, reflecting regulatory considerations, or anchoring campaigns in events where our decision-makers are most active. Copy-paste doesn’t work; it’s about interpretation, not translation.

Measurement is what closes the loop. We track how global investments perform in-market and share insights back to global teams. That creates a cycle of continuous improvement, where global provides the foundation and regional execution brings it to life. The result is campaigns that are both scalable and impactful.

Having led global demand generation and campaign management, what do you see as the key to building repeatable, scalable demand gen programs?

Scalable demand generation starts with clarity and structure but succeeds through adaptability. The foundation is a consistent campaign architecture: clear objectives, messaging frameworks, KPIs, and execution flows that can be easily understood and replicated.

Repeatability is enabled by toolkits such as playbooks, messaging, creative assets, and enablement materials, which give teams what they need to execute quickly without reinventing the wheel. This creates consistency and efficiency across markets.

But no program can be entirely standardised. What engages in one region may need recalibrating in another. That’s why feedback loops are essential. By continuously learning from performance data and local input, demand gen programs evolve into living growth engines—global in ambition, local in execution, and dynamic enough to adapt to changing market conditions.

“Customer success is the most powerful story a company can tell, but only if it’s framed around outcomes. It’s not about the technology implemented; it’s about the business impact.”

With the rise of AI in marketing, how do you see technology reshaping the way we understand and influence consumer behavior?

AI is reshaping marketing by giving us a much deeper, more immediate understanding of customer behaviour and the ability to influence it in real time. Where we once relied on retrospective analysis, we can now anticipate intent, predict needs, and deliver experiences that are relevant in the moment. I see this transformation play out across four key pillars:

• Improved productivity: AI automates manual analysis and campaign execution, giving marketers more time to interpret behavioural signals and design strategies that resonate.
• Cost efficiency: By analysing patterns at scale, AI helps us identify the behaviours that truly signal intent, so spend is directed to the audiences most likely to convert.
• Faster decision-making: Real-time insights allow us to adapt quickly, responding to shifts in buyer behaviour, testing what influences decisions, and adjusting campaigns with agility.
• Enhanced customer experience: Most importantly, AI enables personalisation at scale. Buyers receive timely, contextual interactions that make them feel understood rather than targeted.

In short, AI moves us from descriptive marketing (“what happened”) to prescriptive marketing (“what should we do next?”). It deepens our understanding of behaviour while giving us new levers to shape it, though the human element of trust, ethics, and context remains essential.

From your perspective, how are personalisation and data-driven insights changing the landscape of B2B marketing?

Personalisation and data-driven insights are where the theory of AI meets practical application. If AI gives us the ability to understand and predict behaviour, personalisation is how we act on that understanding to influence decisions in meaningful ways.

B2B buyers now expect the same tailored, seamless experiences they get as consumers. With data-driven insights, we can design engagement strategies that are precise and timely: a personalised nurture track for an early-stage buyer, a targeted executive roundtable for a key account, or dynamic website content that adapts to a visitor’s industry and role.

The real power lies in using insights not just for acquisition but across the full customer lifecycle, improving onboarding, identifying upsell opportunities and reinforcing loyalty. In that sense, personalisation is no longer a tactic, but rather it’s becoming a strategic driver of customer lifetime value.

Of course, balance is critical. Buyers recognise when personalisation is relevant versus when it crosses into intrusion. The companies that get it right are those that apply data ethically and transparently, using insights to add value to the customer’s experience rather than overwhelm it. Done well, personalisation transforms marketing from a one-to-many activity into a driver of competitive advantage.

When evaluating campaign success, what key indicators help you connect marketing performance to revenue outcomes?

Once you’ve used AI to understand behaviour and personalisation to influence it, the final question is: how do we prove the business impact? For me, campaign success has to be measured in terms of commercial outcomes, not just activity.

I look at both leading and lagging indicators:

• Leading indicators: engagement, account penetration, event participation and pipeline velocity. These show whether we’re building momentum and enabling quality conversations.
• Lagging indicators: pipeline sourced, pipeline influenced, deal acceleration, and revenue closed. These demonstrate whether marketing has translated momentum into measurable growth.

Attribution is the bridge between the two. By linking touchpoints to opportunities and revenue stages, we can prove how marketing has influenced buyer behaviour and driven business results.

The final piece is alignment. Metrics only matter when sales and marketing share them and are accountable for them together. When both teams operate as one revenue engine, marketing is no longer seen as “generating activity”; it’s clearly contributing to opportunity creation, influencing decisions, and accelerating revenue.

What would be your advice to aspiring marketers on building and leading high-performing marketing teams?

First, hire for mindset and potential as much as skillset. The best marketers are curious, adaptable, and collaborative qualities that future-proof teams in a constantly changing landscape.

Second, create clarity of purpose. High-performing teams know not just what they’re doing, but why it matters to the business and to customers. Then empower them, give them frameworks, tools, and autonomy. Innovation and performance thrive where there is freedom alongside accountability.

Third, foster cross-functional collaboration. Marketing doesn’t succeed in isolation; it succeeds when it works seamlessly with sales, product, and customer success. Those connections turn good campaigns into business growth.

Finally, never stop learning. I’m a passionate believer in lifelong learning and encourage teams to seek out opportunities to grow, whether through formal training, stretch assignments, or mentorship. As a leader, your role is to set the vision, remove obstacles, and create an environment of psychological safety where people can contribute ideas and take risks.

When you invest in people building trust, clarity, and empowerment, you don’t just create high-performing teams; you cultivate leaders, innovators, and a culture that drives lasting business growth.

About Michelle Slevin

Michelle is a seasoned marketing leader with over 20 years of experience in driving demand generation and integrated marketing strategies for companies, including Indeed, Workato, Oracle, Salesforce, Bank of America, and Ericsson. Skilled at building scalable programs and high-impact partner ecosystems, she bridges global campaigns with localized strategies to accelerate growth. With an MSc in Cyberpsychology, Michelle offers unique insights into consumer behavior, AI, and digital engagement to deliver measurable business impact.

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