Mihaela Gogota, Global Marketing Campaign Manager at UiPath, shares insights from over a decade of experience in B2B marketing, spanning execution to strategy. She discusses global campaigns, cross-functional alignment, ABM, data-driven decision-making, and MarTech. Her practical, hands-on approach highlights how simplicity, adaptability, and continuous learning drive meaningful marketing impact and sustainable business growth.

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

I’ve been in B2B marketing for over 10 years, mostly in enterprise tech. I started in email marketing and marketing automation, then moved into global campaign management. I didn’t begin with a big strategy; I learned by doing: launching campaigns, fixing what didn’t work, and understanding how everything connects behind the scenes.

Over time, I moved closer to strategy, but I’ve always stayed hands-on. That’s important to me. It’s easy to build a strategy in slides, but the real learning comes from seeing what works in the market.

What keeps me motivated is that marketing is never really “done”. Channels evolve, buyer behavior shifts, and you constantly have to adjust. It keeps you sharp and on a constant learning journey.

In global campaigns, what principles guide the balance between brand consistency and regional flexibility?

You must be clear about what cannot change and what should.

The core message, your values, and your brand identity should stay consistent everywhere. That’s how people recognize and trust you over time.

But how you bring that to life should change depending on the region. Different markets respond to different things. Even small details, like tone or channel choice, can make a big difference.

One thing I’ve learned: if you control everything centrally, campaigns can feel disconnected from the market. If you give full freedom, things can become messy. The sweet spot is giving teams a strong direction, then trusting them to adapt it.

How do you align marketing with sales, product, and other departments to drive shared goals?

Alignment must start early, at the strategy level, and then carry through into execution. If you wait until a campaign is already built to bring in sales or product, you’re already playing catch-up.

It also becomes much easier when everyone is working toward the same outcomes. Shifting the focus from “leads” to “pipeline” or “revenue” changes the dynamic, and people stop working in silos and start pulling in the same direction.

I try to bring the right people in from the start, having a quick cross-functional chat early on, especially when we’re defining the audience, the goals, and the problem we’re trying to solve. Then others get involved as it becomes relevant for their role.

And honestly, it’s not about complex frameworks or long PowerPoint plans. Simple, regular conversations make a bigger difference. A short weekly check-in can do more for alignment than a perfectly crafted strategy deck.

Within a global organization, what does an effective go-to-market and ABM framework look like in practice?

You need a clear idea of who you’re targeting, what you’re saying, and why it matters TO THEM. That’s your foundation. Then, for your most important accounts, you go deeper, with more tailored outreach, closer collaboration with sales, and more relevant content.

Not everything needs to be highly personalized. Focus your effort where it really counts.

The best framework is not the most complex one; it is the one teams can follow without overthinking.

It also requires alignment on metrics, continuous feedback loops, and agile execution to refine strategies based on real market response.

 “Shifting the focus from ‘leads’ to ‘pipeline’ or ‘revenue’ changes the dynamic, and people stop working in silos and start pulling in the same direction.”

How do you approach advanced segmentation and personalization while ensuring data quality and compliance?

I always start with a reality check: Is our data usable? Where are the gaps?

Before doing anything advanced, you need clean data and shared definitions. If you don´t have that, personalization just becomes guesswork.

Also, sometimes a clear, well-written message to the right audience works better than something personalized but confusing. So, more personalization isn’t always better; it needs to come with relevance.

On compliance, especially in global teams, you can’t treat it as an afterthought. It needs to be part of how you design campaigns from day one.

Which KPIs do you prioritize when reporting marketing performance to leadership, and why?

I focus on what the business cares about.

Pipeline is usually the main one: how much we’re generating and influencing. Then I look at how that pipeline is moving. Are deals progressing? Are we reaching the right accounts?

I still look at engagement, but more as a signal. High engagement is good, but it doesn’t always mean business impact. I also check the profile of our most engaged accounts.

At the end of the day, leadership wants a simple answer: Is marketing helping growth?
If your reporting is too complicated to answer that, it needs to be simplified.

What criteria indicate that your MarTech stack is driving scalable, multi-channel growth rather than adding unnecessary complexity?

The MarTech stack assessment. Though the reality makes it more complex than that, to me, it should come down to a few simple questions:

Are the teams using the tools?

Can we launch campaigns faster than before?

Do we trust the data we’re seeing?

If the answer is no to any of these, the stack probably needs to be reviewed.

I believe that teams with too many tools spend more time managing systems than building and working towards the business goals. That happens in big companies, and it is usually a sign to step back and simplify.

A good stack should feel almost invisible; it should support the work and the decision-making process, not slow them down.

How would you advise aspiring marketers on developing the right skill sets for today’s world?

First, understand how the business works. Marketing is about contributing to growth; that’s the goal behind the campaigns.

Second, get comfortable with data. You should understand what’s working and why.

And third, stay close to real work for as long as possible. It’s tempting to move into strategy quickly, but without hands-on experience, I think it’s harder to make good decisions.

Also, be curious and keep learning. The tools and trends will keep changing, but curiosity is what helps you adapt.

Build strong collaboration skills, seek feedback often, and learn to communicate impact clearly to stakeholders across functions and leadership teams.

About Mihaela Gogota:

Mihaela Gogota is an experienced B2B marketing leader with over 10 years of driving revenue growth through data-driven strategies, innovative campaigns, and marketing technology. She excels at leading cross-functional teams, optimizing operations, and translating complex data into actionable insights. Passionate about scaling impactful marketing programs, she thrives in fast-paced global environments, leveraging collaboration and technology to deliver meaningful business results while continuously learning and evolving.

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In this exclusive interview, Fabi Rocha, Field & Partner Marketing Manager, U.S. West Coast & LATAM at Snyk, shares how field marketing has evolved from event execution to a strategic revenue driver. She explores global strategy, sales alignment, pipeline impact, and the role of AI in building programs that deliver measurable business outcomes across regions and modern go-to-market execution.

Welcome to the interview series, Fabi. Please tell us a bit about yourself and your journey as a marketer.

I’ve spent over 15 years in marketing, with most of my career focused on field and partner marketing for B2B SaaS companies. My journey started in Brazil, where I was born and raised, and over the last 14 years, I’ve built my career in the U.S., working across North America, EMEA, and LATAM.

What has always drawn me to marketing is the blend of creativity and business impact. I love building programs that bring people together, but what really excites me is turning those moments into measurable business outcomes. Over time, I evolved from executing events into building strategic go-to-market programs that align closely with sales, accelerate pipeline, and influence deals across regions.

Today, I view field marketing as one of the most powerful growth engines in B2B when I apply it strategically.

What defines a strong field marketing strategy across regions like North America, EMEA, and LATAM?

A strong global field marketing strategy starts with one principle: global consistency and regional relevance.

What works in North America doesn’t automatically translate to LATAM or EMEA because buyer behavior, trust dynamics, urgency levels, and partner influence vary significantly by region. The mistake many companies make is assuming localization is just language translation, when in reality it’s market translation.

For me, the strongest regional strategies are built on:

• Deep partnership with regional sales leaders
• Understanding local buyer journeys
• Adapting messaging to cultural and commercial realities
• Balancing global brand consistency with local execution flexibility

Global strategy fails when it ignores regional nuance. Because at the end of the day, deals are closed in regions.

Tell us about your most memorable experience as a marketer.

One of the most memorable moments in my career was seeing a complete shift in how sales and marketing worked together around events.

Earlier in my career, events were often measured by lead volume alone. But I helped reshape that mindset by creating tighter alignment with sales before, during, and after events, focusing on target accounts, pre-booked meetings, pipeline influence, and deal acceleration instead of badge scans.

Watching sales teams begin to view field marketing not as “the events team” but as a true strategic partner was incredibly rewarding. That shift changed how the company valued field marketing and made our programs much more impactful.

What does it take to turn event engagement into real pipeline and revenue impact?

It starts before the event even begins.

Real pipeline impact happens when events are built around account strategy, not attendance volume. That means:

• Identifying target accounts in advance
• Aligning with sales on who needs to be there
• Booking meetings before the event
• Creating personalized follow-up based on buyer stage
• Measuring performance beyond lead capture

One strong conversation that accelerates a six-figure opportunity is worth more than 100 unqualified badge scans.

Events generate revenue when they are treated as pipeline acceleration moments, not standalone marketing activities.

“A strong global field marketing strategy starts with one principle: global consistency and regional relevance.”

Beyond MQLs, what metrics do you look at to gauge the success of a demand generation campaign?

MQLs are only one signal (and often an incomplete one).

I focus on metrics that show true business impact:

• Meetings booked
• Net new opportunities
• Pipeline created
• Pipeline influenced
• Opportunity acceleration

I also look at qualitative indicators like whether campaigns are moving strategic accounts closer to a decision.

The question isn’t “How many leads did we get?”

It’s “Did this field campaign help create or move revenue?”

What best practices have helped you maintain strong alignment between marketing, sales, customer success, and product teams?

Cross-functional alignment happens when everyone is tied to shared business outcomes, not departmental outputs.

What works best for me:

• Bring teams into planning early
• Align around shared revenue goals
• Create clear ownership before launching events
• Keep communication transparent throughout execution
• Share post-event performance broadly

I also believe marketing becomes far more effective when we understand what matters most to each function. Sales cares about closing deals. Customer success cares about expansion and retention. Product cares about adoption insights. Strong marketers learn how to connect their work to all three.

How have you incorporated AI into your overall marketing planning and execution?

AI has become a major force multiplier in how I work.

I use AI in several ways:

• Analyzing pipeline patterns to identify revenue leaks
• Improving account prioritization for ABM campaigns
• Speeding up event planning workflows
• Generating faster field initiatives reports
• Streamlining event operations through Slack-based automations

For example, I’ve built AI-powered Slack workflows that help reduce event prep time, centralize event briefs, automate task visibility, facilitate collaboration, and surface real-time insights for sales teams.

The biggest value of AI isn’t replacing marketers; it’s giving marketers more time to think strategically.

What would be your advice for marketers looking to grow into managerial or leadership roles?

Stop thinking only about execution and start thinking about business outcomes:

• Understand revenue metrics
• Learn how sales teams think
• Communicate strategically with executives
• Develop the ability to influence cross-functional decisions

Also, speak up. Some of the biggest growth moments in my career came from being willing to share ideas before I felt 100% ready.

Leadership starts long before the title does.

About Fabi Rocha

Fabi Rocha is a seasoned B2B SaaS marketing leader with over 15 years of experience in field marketing and demand generation. She specializes in building integrated, revenue-focused programs that drive pipeline, accelerate sales cycles, and strengthen brand presence across North America, EMEA, and LATAM. Her expertise spans event strategy, ABM, and cross-functional alignment, with a strong focus on turning engagement into measurable business impact.

The post Field Marketing as a Growth Engine: Fabi Rocha on Driving Real Business Outcomes appeared first on iTechSeries.

Bella Lai, Marketing Lead for Legal & Corporates in Southeast Asia at Thomson Reuters, shares her journey from conference producer to B2B marketing leader, shaping the full buyer journey across AI and SaaS solutions. She reflects on building confidence-driven marketing, aligning events with broader GTM ecosystems, and balancing brand, performance, and cultural nuance across diverse Asian markets.

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

Thanks for having me. I currently lead marketing for Southeast Asia at Thomson Reuters, where we work with legal and tax teams on AI and SaaS solutions. Alongside that, I’m also pursuing my MBA at Singapore Management University.

I always say I didn’t “choose” marketing. I kind of grew into it. I started my career as a conference producer. Back then, the job was simple (on paper): fill a room. But I quickly learned that the only way to do that was through the right topic and the right people. That was my first real lesson in marketing. You can’t force attention. You have to earn it.

Over time, I realised events are just one moment in a much bigger customer journey. What really shapes decisions is everything around them. That curiosity about the full journey is what pulled me into marketing. My daily role today is about designing and connecting that entire buyer journey, not just chasing individual touchpoints.

Outside of work, I’m balancing an MBA and raising a very energetic three-year-old. My days are full, but I enjoy the pace. For me, growth only feels uncomfortable when it stops.

What role does event marketing play in an overall brand and growth strategy, especially in B2B industries?

Events are a big bet in B2B. They require real investment and planning, so they’re expected to deliver.

I see events marketing less as lead generators and more as confidence checkpoints in the buyer journey.

They humanise the brand and give buyers a direct experience of your product and credibility. They also surface real insight. What buyers ask, where they hesitate, and what matters most.

But B2B decisions don’t happen in a single interaction. They build overtime across multiple touch-points & stakeholders, much of it outside direct sales.

So events don’t close deals. They create early convictions. The rest of your GTM engine carries the deal forward. Content, proof points, and consistent messaging need to reinforce the same story. In my experience, events influence around 30 to 35 percent of the decision.

That’s why alignment across online and offline matters. Buyers today aren’t short of information. They’re short of confidence proof. So your marketing machine should shift from competing for attention to competing for confidence across every touchpoint.

How do you curate conference content that is both educational for the audience and commercially viable for the business?

It always starts with conversations. Online (secondary) research is fast, but it reflects what is already known. It’s a lagging indicator. The real insight comes from speaking to people who are dealing with the problem day to day.

A good example is the ESG topic. Online, the narrative is dominated by regulations and frameworks. But when we spoke to in-house counsel, the real challenge was operational. Data collection across the supply chain, internal alignment, and limited resources. That gap between theory and reality is where meaningful content surfaces. And that gap is invisible unless you talk to real people.

Audiences today are extremely selective with their time, so relevance has to come first.

And how do you measure your content? In B2B, shareability is one of the clearest signals of value. People only share content when it helps them look informed or prepared. Case studies, practical reports, peer discussions, and webcast videos consistently outperform abstract messaging.

What does effective collaboration between marketing and sales look like in your experience?

For me, good collaboration starts with remembering that marketing and sales are serving the same clients and working toward the same goal.

Collaboration works best when there’s curiosity in both directions. Marketing can understand what happens in a real sales conversation. What questions come up when deals slow down? Sales, in turn, benefits from what marketing sees at scale. Campaign signals, channel performance, and patterns across accounts that aren’t always visible in a one-to-one conversation.

I personally enjoy working closely with sales. Many of my most successful GTM strategies are built from input across all client-facing teams. Sales, pre-sales, customer success, and trainers each see different parts of the customer journey.

Marketing’s role is to connect those insights and scale them. When that collaboration flows well, it’s not just more effective. It’s genuinely enjoyable, because everyone feels aligned and focused on the same goal – delighting the customers.

“Your marketing machine should shift from competing for attention to competing for confidence across every touchpoint.”

When working across different markets in Asia, how do cultural nuances influence your event and marketing strategy?

When you work across Asia, cultural nuance shows up in very practical ways.

The biggest one is clarity. In many markets, English is a second language, so people scan quickly and focus on what’s immediately relevant. People want to know straight away why this matters and how it helps them do their job better.

That’s why what works best here is directness. Clear value. Reliable outcomes. Clear positioning. You don’t need more words. You need solid ones.

Trust and familiarity are the second layer. Buyers pay attention to social proof, peer references, and established brands. In regulated or high-stakes environments, familiarity often outweighs experimentation.

As a result, consistency tends to outperform creativity. Saying the same clear thing well, over time, builds more trust than saying something new once.

How do you balance brand building with performance-driven metrics in your marketing approach?

This is the million-dollar question, and honestly, there’s no perfect formula. Even with the best tools and agencies, it’s almost impossible to track the full B2B funnel end-to-end.

I think of brand and performance as two sides of the same system. Brand defines what you stand for. Performance is how that gets delivered and activated. People often say brand can’t be measured, but in reality, a brand shows up in the organic pipeline, shorter sales cycles, and the spillover effect across channels that performance alone can’t explain.

The risk is over-indexing on short-term performance metrics. That’s where marketing myopia sets in, optimising for short-term results while missing how buyers actually decide. In B2B, trust and validation are built long before conversion.

The right balance depends on the maturity of the business, but as a general rule, I’d aim for at least 30 percent of the total pipeline to come from brand and organic activities.

A simple test I use is this: if performance numbers look strong but organic demand stays flat, the brand isn’t doing enough. Ultimately, strong performance is easier when the brand has already done its job.

How do you leverage the power of AI-enabled tools for your marketing programs?

I see AI as a last-mile accelerator rather than a substitute for thinking. It adds value in two areas. First, execution. It helps us move faster by adapting messages across segments, optimising formats and channels, and scaling what already works. Second, decision-making. It helps marketers process signals faster, spot patterns in performance data, surface audience insights, and make better calls on where to focus.

But the output is only as good as the input. Start with the menu and ingredients, not the tool. AI can help you execute faster, but it cannot replace customer insight, positioning, or judgement. Those ingredients are your unique advantage.

AI tools also need guardrails. I can usually tell when something is entirely AI-generated, and I’m sure audiences can feel that too.

Lastly, the bigger shift is recognising that AI is now part of the audience landscape. It’s not just your buyer researching, but their AI doing the filtering, ranking and evaluation. Marketing needs to adapt. GEO ensures your content is discoverable and credible in those AI-led journeys.

What advice would you give up-and-coming marketers on developing the right skill sets?

Buckle up and enjoy the ride. Marketing is one of the fastest-evolving professions out there, and that’s what makes it exciting. It’s a field that needs both creativity and strong number discipline. You’re constantly testing ideas, learning what works, and letting data guide your next move. You also have to be comfortable challenging your own assumptions and accepting that not every idea will land the first time.

At the same time, don’t lose sight of who you’re building for. Stay close to your ideal customer profile. A campaign that tries to speak to everyone is the campaign that speaks to no one. If you stay curious and anchored in your customer, you’ll always know what to do next, even when the tools change.

About Bella Lai:

Bella Lai is a Marketing Lead, SEA at Thomson Reuters with over 10 years of experience in B2B marketing across the legal and corporate tax and finance sectors. She leads GTM marketing and demand generation for AI and SaaS solutions globally, with a focus on owning and shaping the full buyer journey from market insight to pipeline conversion across Asia. With a background in economics and marketing from Singapore Management University, she has led cross-border campaigns across Asia, working closely with industry experts to address evolving legal and regulatory challenges.

The post Brand, Performance, and AI: Bella Lai on Scaling Impact Across the Buyer Journey appeared first on iTechSeries.

Every day, businesses generate vast amounts of data from customer interactions, transactions, websites, and digital platforms. Within this data lie valuable insights that can inform smarter strategies, enhance customer experiences, and drive growth. The real challenge is not collecting data but interpreting it effectively. Organizations that can identify meaningful patterns gain a competitive advantage. This is where data mining becomes essential. Uncovering hidden trends and relationships enables informed decision-making and strategic planning. This blog explores how data analysis works, the techniques used to extract insights, and its role in transforming raw data into actionable business intelligence.

What is Data Mining?

Data mining definition: it is the process of discovering meaningful patterns, trends, and relationships in large volumes of data using statistical techniques and machine learning methods. It involves analyzing structured, semi-structured, and unstructured data stored in databases, warehouses, or data lakes to extract useful knowledge. 

The goal of data extraction is to transform raw data into actionable insights that support decision-making and prediction. Common data mining techniques include classification, clustering, regression, association analysis, and anomaly detection. Organizations use data insights to understand customer behavior, detect fraud, improve operations, and forecast outcomes. By using advanced algorithms, powerful data mining tools, and modern data mining software together, businesses can speed up analysis and make better strategic plans in industries around the world.

Key Benefits and Common Challenges

Benefits

Data analysis offers significant benefits by uncovering hidden insights and patterns within large volumes of data, enabling organizations to make informed, strategic decisions. With the help of advanced data mining software, businesses can identify trends across functions such as marketing, finance, healthcare, supply chain, and human resources, improving planning and performance.

By analyzing data from multiple sources, organizations can detect inefficiencies, streamline operations, and reduce costs. Data extraction also supports fraud detection, risk management, and compliance by identifying unusual patterns and potential threats. Automation through specialized data mining tools enhances tasks such as data cleaning, modeling, and reporting, improving productivity and enabling better resource allocation and revenue generation.

Challenges

However, data insights come with notable challenges. Extracting reliable insights requires high-quality data, robust data mining tools, and skilled professionals proficient in technologies such as Python, R, and SQL. Poor data quality or incorrect preprocessing can lead to misleading conclusions.

There are also privacy and security concerns, especially when handling sensitive or personally identifiable information, which must comply with regulations. Costs may increase due to data acquisition, infrastructure setup, licensing of data mining software, and ongoing maintenance. In addition, risks such as overfitting, biased models, outdated datasets, and mistaking correlation for causation can reduce accuracy. Human judgment remains essential to interpret results responsibly and ethically.

Data Mining vs. Text Mining vs. Process Mining

Data mining is a broad discipline focused on discovering patterns, relationships, and trends in large structured or unstructured datasets. Using various data mining techniques drawn from statistics, machine learning, and artificial intelligence, it supports prediction, classification, clustering, and decision-making across industries such as finance, healthcare, and marketing.

Text mining is a specific type of data analysis that looks at unstructured text data like emails, documents, and posts on social media. It applies natural language processing and linguistic analysis to extract insights like sentiment, key entities, and topics. Process mining, in contrast, concentrates on analyzing business processes using event logs from systems like ERP and CRM platforms. It reconstructs actual workflows, identifies bottlenecks and deviations, and helps optimize operations. While data patterns ask what patterns exist, process mining examines how processes flow, and text mining interprets what textual data means.

The Data Mining Process Explained:

Many organizations partner with specialized data mining companies or invest in professional data mining services to manage this complex process efficiently. Below are the key steps involved in the data mining process:

1. Understanding and Gauging Data

Analysts examine data sources, structure, volume, and quality to assess relevance. This often includes reviewing information stored in a mining database, data warehouse, or cloud-based system. They identify gaps, inconsistencies, and constraints while clarifying project goals. Clear alignment between data characteristics and business questions ensures meaningful and actionable outcomes.

2. Data Preparation

This includes removing duplicates, handling missing values, correcting errors, and integrating multiple data sources within a centralized mining database environment. High-quality data preparation is a critical component of most professional data mining services. Data is transformed into consistent formats and reduced to essential variables, improving model accuracy and analytical performance.

3. Data Selection

In this step, analysts select relevant subsets of data aligned with defined objectives. Not all collected data within a mining database is necessary for analysis. Feature selection helps focus on the most meaningful variables. Choosing appropriate predictors reduces complexity, improves computational efficiency, and enhances the overall effectiveness of mining results.

4. Data Mining and Model Building

Data mining techniques such as classification, clustering, regression, and association analysis are applied to uncover patterns and relationships. Analysts build and train models using prepared data, then test and refine them. Many data mining companies leverage advanced tools and scalable infrastructure to optimize this stage. Iterative validation ensures accuracy, reliability, and alignment with the intended analytical purpose.

5. Pattern Evaluation and Presentation

Insights are interpreted, validated, and visualized using charts, dashboards, and reports. Professional data mining services often include executive reporting and strategic recommendations to help stakeholders act confidently on findings. Continuous monitoring and refinement ensure that insights remain relevant and valuable over time.

Core Data Mining Techniques

Core techniques for data mining enable organizations to uncover patterns, relationships, and predictions from large datasets. Association rule mining identifies relationships between variables using if-then logic, with support and confidence measuring the strength of these connections. It is widely used in market basket analysis to understand products frequently purchased together and to improve cross-selling strategies. Classification organizes data into predefined categories based on shared characteristics, often using historically labeled data to predict the class of new records. Clustering is similar but does not rely on predefined labels; instead, it groups data points based on natural similarities, helping businesses discover hidden segments or patterns. 

Decision trees use a tree-like structure to model decisions and predict outcomes through classification or regression. The k-nearest neighbor (KNN) algorithm classifies data points based on their proximity to similar instances. Neural networks, inspired by the human brain, process data through interconnected layers to identify complex patterns and support deep learning. Regression analysis predicts continuous outcomes by identifying relationships between variables. Predictive analytics combines statistical modeling and machine learning to forecast future trends, risks, and opportunities using historical data.

Conclusion

In today’s data-driven economy, the ability to extract meaningful insights from vast information sources is no longer optional but essential. Data exploration empowers organizations to move beyond intuition and base decisions on evidence, patterns, and predictive analysis. While it requires quality data, the right tools, and skilled expertise, its impact on efficiency, innovation, and growth is significant. From understanding customers to optimizing operations, data extraction transforms raw information into a strategic advantage. As technology continues to evolve, businesses that effectively harness data mining will be better positioned to adapt, compete, and lead in an increasingly data-centric world.

The post How Do B2B Businesses Use Data Mining to Make Better Decisions? appeared first on iTechSeries.

In today’s B2B landscape, speed and focus decide who wins deals and who misses them. Yet many GTM teams still rely on lagging, fragmented data that slows decisions and weakens execution. GTM Intelligence changes that. By unifying real-time market signals, buyer intent, and customer data, it helps revenue teams see where to focus before opportunities peak. Instead of reacting to what already happened, teams can act on what’s likely to happen next. In this blog, we explore what GTM Intelligence is, why it matters, and how it enables faster prioritization, smarter targeting, and more predictable growth across sales, marketing, and revenue operations.

What is GTM Intelligence?

Go-to-market (GTM) intelligence is the practice of turning market, customer, and competitive data into clear, actionable insights that guide where and how revenue teams should focus. Unlike traditional market research, which is often static and retrospective, GTM intelligence is dynamic and decision-oriented. It answers critical questions such as who the ideal customers are, what problems they’re trying to solve, how they buy, and which messages and channels will resonate most.

GTM intelligence connects strategy to execution by combining quantitative data (market size, growth, and competitor positioning) with qualitative context (buyer behavior, preferences, and emerging trends). The result is sharper prioritization, stronger alignment across teams, and faster, more confident go-to-market decisions that directly support revenue growth.

Key benefits of GTM intelligence in go-to-market strategy

A data-driven go-to-market strategy depends on clarity and alignment, and GTM Intelligence delivers all three. By combining traditional business intelligence with real-time market signals, GTM Intelligence unifies sales, marketing, and revenue operations around a single source of truth. This alignment strengthens your overall go-to-market plan, eliminates silos, and ensures teams are working toward shared revenue goals. When everyone tracks the same KPIs and understands how marketing activity influences pipeline and revenue, collaboration improves, and execution becomes more consistent.

GTM Intelligence also significantly improves pipeline forecasting and accuracy. Instead of relying on static historical data, teams can incorporate real-time engagement signals, sales activities, and market shifts to create dynamic forecasts. This allows leaders to spot risks early, adjust targets confidently, and allocate resources where they will have the greatest impact.

Another key benefit is smarter buyer journey management and lead prioritization. GTM Intelligence leverages artificial intelligence to identify high-intent accounts, highlight the most influential touchpoints and surface conversion-driving patterns. Sales teams spend less time chasing low-quality leads and more time engaging buyers who are ready to move forward, making the go-to-market plan more efficient and revenue-focused.

By continuously diagnosing the funnel breakdowns, stalled deals, and misaligned spending helps teams fix issues before they impact results. Combined with real-time market and competitive insights, GTM Intelligence empowers organizations to adapt faster, optimize spending, and maintain a durable competitive advantage.

What are the core pillars of GTM Intelligence?

The core pillars of GTM Intelligence form the foundation that enables modern go-to-market teams to execute a smarter, data-backed go-to-market plan. At its heart, GTM intelligence combines buyer intent, account-level signals, timing optimization, and personalization at scale to replace static planning with real-time execution. Buyer intent reveals which companies and stakeholders are actively researching solutions, signaling genuine in-market demand before buyers formally engage. 

Account signals add critical context by tracking meaningful changes such as hiring trends, funding activity, technology adoption, and leadership moves, helping teams understand why an account may be primed for action. Timing optimization then connects these insights, ensuring that sales and marketing engage buyers at the moment when relevance is highest, rather than relying on outdated lead stages or assumptions. Personalization at scale turns intelligence into impact by tailoring messaging, outreach, and campaigns to specific buyer needs, roles, and priorities without sacrificing efficiency. Together, these pillars create a connected intelligence layer that aligns sales, marketing, and revenue operations around a shared view of the market, enabling faster decisions, stronger engagement, and more predictable growth in a buyer-driven world.

How to conduct B2B market intelligence: a step-by-step approach?

Every intelligence initiative must begin with clear objectives. Before gathering data, define what success looks like and how insights will shape your GTM strategy. Then follow structured steps to create, implement, and operationalize GTM intelligence effectively.

Step 1: Define objectives and business questions

Start by clearly defining why you are conducting B2B market intelligence and how it will support your GTM strategy. A modern GTM intelligence platform can help structure these objectives by connecting data to revenue outcomes. Identify the key questions you need answered, such as where growth opportunities exist, how competitors are positioned, or which segments show buying intent. Clear objectives ensure focus, alignment, and strategic relevance.

Step 2: Align stakeholders and ownership

Market intelligence impacts marketing, sales, product, and leadership, so early alignment is critical. To maximize the value of a GTM intelligence platform, agree on shared definitions, priorities, and success metrics across teams. Assign ownership for collecting, analyzing, and distributing insights to ensure intelligence is trusted, consistently used, and embedded into everyday decision-making processes.

Step 3: Axdit data readiness and foundations

Evaluate the quality of your existing data across CRM, marketing platforms, and sales tools before activating a GTM intelligence platform. Strong business intelligence depends on clean, integrated data. AI models are only as effective as the data feeding them, so governance, accuracy, and system integration are essential to avoid misleading conclusions and maximize predictive performance.

Step 4: Collect and synthesize intelligence sources

Combine multiple sources to build a complete market view that informs your GTM strategy. Use internal performance data, external research, competitive signals, and real-time intent data. Synthesize quantitative and qualitative inputs to identify patterns, shifts, risks, and opportunities aligned with your strategic priorities.

Step 5: Activate insights and continuously refine

Turn insights into action by adjusting targeting, messaging, prioritization, and product direction. Measure impact using pipeline, conversion, and win-rate metrics. Continuously refine intelligence inputs using both business intelligence dashboards and artificial intelligence-driven insights to keep your go-to-market plan dynamic, relevant, and decision-driven.

Conclusion 

GTM Intelligence is transforming the way B2B teams approach the market by turning fragmented data into actionable insights. By combining real-time buyer intent, account-level signals, and AI-powered analysis, it allows for smarter targeting, faster decisions, and more predictable revenue growth. Implementing a structured market intelligence process ensures teams stay aligned, prioritize high-value opportunities, and adapt quickly to market shifts. Beyond driving pipeline and conversions, GTM Intelligence strengthens cross-functional collaboration, reduces inefficiencies, and uncovers hidden opportunities. In a competitive landscape where speed and insight matter, adopting GTM Intelligence is essential for sustained success.

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Nitin Bhargava, Senior Director Marketing at SAP, shares how modern marketing is evolving from demand generation into a revenue-driving function. From aligning with sales to influencing complex enterprise deals, he explores building outcome-driven strategies, high-performance teams, and context-aware campaigns across diverse APAC markets in today’s enterprise SaaS landscape.

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

My career has been an unconventional path into marketing leadership, and I think that’s been an advantage.

I started in sales, selling telecom solutions to enterprise accounts. Before running a campaign, I understood what it means to face a customer with a problem, a budget, and a reason to say no. That grounding stayed with me. It’s why I’ve always seen marketing as a business function first—not just communications.

From there, I moved into alliance and GTM management, leading a joint strategy between Mahindra Satyam and Microsoft across Southeast Asia. Then into marketing, where I chose to learn every layer: demand generation, segment, partner, and field marketing across IBM, Cognizant, and over seven years at SAP.

Today, I lead Cloud ERP marketing across APAC, working across diverse markets—from mature economies like Japan and ANZ to high-growth regions like India and Southeast Asia. That diversity pushes you beyond templates to build context-aware, outcome-driven marketing systems.

The arc of this journey is a shift from services to product, execution to strategy, and from supporting the business to being embedded in the revenue engine. In enterprise SaaS—especially ERP—marketing cannot operate only as a volume engine. It must balance reach and depth: expanding new logo coverage while influencing complex, high-value accounts with precision. That dual mandate is one of the defining challenges of the role.

The work I’m proudest of is where there’s no playbook—because that’s where you learn what marketing truly does.

How do you ensure marketing campaigns communicate both technical innovation and tangible business value to enterprise customers?

The most common mistake in enterprise marketing is leading with technology instead of transformation. Technical teams focus on features, sales on deals—but customers care about two things: does this solve my problem, and can I trust you?

We anchor campaigns on a simple principle: technology only matters if it changes a business outcome. So we structure everything in three layers:

• Business-first narrative: What is the boardroom problem—cost, resilience, growth, compliance?
• Transformation pathway: How does the enterprise move from its current state to a better future state?
• Technology as the enabler: Where cloud, data, and AI actually unlock that shift.

In global organisations, the core narrative is usually defined centrally—the positioning, messaging architecture, and campaign framework. The real skill for regional marketers lies in translating that into something relevant for a specific market, buyer, and competitive context—without diluting its strength.

When RISE with SAP launched, the global message focused on business transformation through the cloud. But across APAC—India, ANZ, Southeast Asia—that message had to adapt. Markets vary in maturity, objections, and the relationship between IT and business teams. The marketers who succeeded were not those who followed the global playbook rigidly, but those who adapted it while preserving the core idea.

Ultimately, bridging technical innovation and business value is not about a tagline. It’s about understanding both sides deeply and knowing which conversation you are in. If a campaign cannot answer, “What changes in the customer’s P&L or operating model?”, it won’t resonate.

How do you build high-performance marketing teams aligned with business and sales objectives across multiple markets?

Alignment is overused and under-practiced. Most marketing teams say they’re aligned with sales. Very few actually are.

Real alignment means shared accountability—not just shared meetings. It means marketing has a number, not just a budget. It means sitting in the same pipeline reviews as sales, not a separate marketing review where we report impressions and event attendance. Success is measured by business outcomes, not activity.

I focus on three structural elements:

• Anchor every marketing motion to named accounts, pipeline priorities, or strategic deals—not abstract targets.
• Integrate with sales execution and sales plays, not sit alongside them—marketing embedded into deal progression moments, not just pre-pipeline demand.
• Build regional frameworks that empower local autonomy—APAC is not one market. What works in Korea will not work in India or Southeast Asia.

I’ve built teams around two things: clarity on what we’re actually trying to move and ownership of the outcome—not just the activity. When a marketer owns a pipeline number for a specific segment, their decision-making changes completely. They stop asking ‘what campaign should we run’ and start asking ‘what is actually blocking the pipeline here.’

The mindset shift that matters the most: you’re not working for the business stakeholder, and you’re not working with them. You’re working to drive the business forward in your own irreplaceable way. That distinction changes how a marketer shows up—and how much impact they have.

“When marketing is accountable to revenue, it earns its seat in strategy conversations—rather than being handed one as a courtesy.”

Where do you see the biggest shift in marketing’s role within a broader go-to-market setup?

Revenue accountability and the discomfort that comes with it.

The biggest shift I see is from pipeline creation to pipeline progression and deal influence. For a long time, marketing could operate in a world of leading indicators—brand health scores, MQL volumes and share of voice. These are real metrics, but they’ve also been a comfortable buffer between activity and outcome. That buffer is disappearing.

In enterprise SaaS today, the challenge is rarely just creating a pipeline. It’s

• Deals stalling
• Buying groups are becoming more complex
• Decision cycles stretching

Marketing’s role is evolving into a strategic orchestrator within the revenue engine:

• Expanding stakeholder engagement within accounts
• Enabling higher-quality executive conversations
• Intervening at critical points to move deals forward

CEOs and CROs increasingly expect marketing to be predictable—not just directionally positive, but forecastable. What will marketing contribute to the pipeline this quarter? Not approximately. A number.

When marketing is accountable to revenue, it earns its seat in strategy conversations, rather than being handed one as a courtesy.

Could you tell us about your most memorable experience as a marketer?

Building SAP’s presence in the digital natives segment in India.

We had a defined set of target accounts: start-ups, unicorns, and high-growth tech companies. But no established presence, no clear perception, and no playbook. The starting point was understanding how these companies actually saw SAP, which, honestly, was mostly ‘not for us. Too large, too complex, built for enterprises three times their size.

The work was about changing that perception across every route to market—events, content, partner channels, and direct engagement. Building a narrative credible to a CTO at a 200-person startup, not just a CFO at a 10,000-person enterprise. We built the motion end-to-end—adapting the narrative to each sub-segment, building out the partner ecosystem, designing targeted engagement models, and creating segment-specific plays—and measured success by new logos, not just leads.

What made it memorable wasn’t just the outcomes, though those came. It was the nature of the challenge—building a market from zero, where every element had to be created. You understand the real role of marketing when you have to build a market from zero, not when you’re optimising what already exists.

As a marketing leader, how do you leverage AI-enabled tools in your marketing activities without becoming overly reliant on them?

I think about AI the way I think about any infrastructure investment—it should make execution faster and thinking sharper. Do not replace either one.

Practically, I’ve integrated AI into the parts of marketing work that are high-volume and process-driven. Event marketing is a good example—using AI to improve the conversion cycle from attendee to lead to opportunity, tightening both the time and the signal quality. Meeting summaries, research synthesis, campaign brief generation—these are areas where AI creates real efficiency gains.

I see the use of AI evolving across three areas in particular:

• Content and productivity acceleration—faster creation, iteration, and localisation across markets
• Signal detection—identifying patterns in engagement, account behaviour, and intent
• Personalisation at scale—tailoring messaging across diverse markets and buyer personas

But the guardrail is clear: AI supports thinking—it doesn’t replace it.

The real risk of over-reliance is not laziness—it’s losing the instinct that comes from doing the hard thinking yourself. The risk isn’t using AI. The risk is outsourcing thinking to AI. The leaders who will win are those who combine AI leverage with strong operator judgment.

Beyond the traditional metrics, how do you evaluate the effectiveness of your marketing initiatives?

Traditional metrics (MQLs, cost per lead, event attendance) tell you what happened. They don’t tell you why, or whether it matters.

In enterprise environments, I focus on progression and influence metrics:

• Pipeline velocity—are deals moving faster through the funnel?
• Stage conversion improvements—are we unlocking stalled opportunities?
• Buying group expansion — are we engaging the right stakeholders within strategic accounts?
• Deal influence—did marketing create or enable a critical interaction that moved a decision?
• Pipeline quality—are marketing-sourced deals actually closing?

At a broader level, I look at two signals that are harder to quantify but easy to sense. The first is whether the conversations marketing enables are getting shorter and sharper—when marketing is working, customers arrive at early sales conversations already informed, already interested, and already further along in their thinking. Sales spends less time educating from zero. The second is sales confidence in marketing: do sales teams actively want marketing involved, or do they see it as overhead? That distinction is a real signal.

At a fundamental level, I evaluate marketing on one question: Did we reduce friction in the customer’s decision-making process?

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

The shift to leadership starts when you stop thinking like a marketer and start thinking like a business operator.

Three things I’d focus on:

• Build commercial understanding—know how your company makes money, how deals are structured, and how customers actually buy. The marketers who earn strategic seats are the ones who can participate fully in business conversations, not just marketing conversations.
• Get closer to sales and customers—real learning happens in deal cycles, not dashboards. Sit in on sales conversations. Understand the objections in the room. See how decisions are actually made, not how they’re supposed to be made.
• Focus on outcomes, not activity—leadership is not about doing more. It’s about ensuring that what is done actually moves the business forward. Own the outcome, not just the execution.

And one mindset shift that matters deeply: get comfortable being accountable for outcomes you don’t fully control. Marketing leadership operates in ambiguity—across sales, product, and market dynamics. The ability to influence without authority is what defines strategic leaders.

Finally, develop a point of view. In complex organisations, it’s easy to default to consensus. But leadership requires clarity and conviction on what will work—and the willingness to stand by it.

Don’t aim to be a better marketer. Aim to be someone the business cannot operate without.

About Nitin Bhargava

Nitin Bhargava is a marketing leader with over 20 years of experience across India and APAC, currently serving as Senior Director–Marketing at SAP. He specializes in aligning marketing with revenue, driving demand generation, GTM strategy, and ecosystem-led growth. With experience across SAP, IBM, and Cognizant, he has built high-performance, outcome-driven marketing systems, led cross-functional teams, and delivered measurable business impact across diverse enterprise markets.

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In a world where products, services, and ads increasingly look the same, memorable experiences are what truly set brands apart. Experiential marketing moves beyond traditional promotion by creating immersive, real-time interactions that engage people on a human level. It’s not about transactions; it’s about moments that spark emotion, build trust, and make an indelible mark. Whether it’s a live event, an interactive demo, or a hands-on brand activation, these experiences stay with audiences long after the campaign ends. When done right, interactive marketing turns attention into connection, customers into advocates, and brand moments into measurable impact.

What is Experiential Marketing? : Experiential Marketing Definition

Experiential marketing is a strategy that creates direct, interactive experiences, allowing people to actively engage with a brand in real time. Instead of communicating value through ads or messaging alone, it immerses audiences in hands-on moments that appeal to the senses and emotions. These experiences can include live events, pop-ups, product trials, immersive installations, or digitally extended activations.

Leading experiential marketing companies offer strategic experiential marketing services that align brand storytelling with immersive engagement. These experiences strengthen emotional connections, which drive higher brand recall, trust, and long-term loyalty.

Many brands partner with experiential marketing companies or a specialized experiential marketing agency to design and deliver tailored experiential marketing services that bring immersive brand moments to life effectively. A skilled experiential marketing agency ensures strategy, creativity, and execution align seamlessly to maximize engagement and impact.

The goal is to shape perception through participation, not persuasion. By focusing on how people feel and interact, interactive marketing builds stronger emotional connections, improves recall, and encourages advocacy. It shifts marketing from passive exposure to active involvement, turning brand interactions into memorable experiences that influence behavior, loyalty, and long-term brand value.

Role of Experiential Marketing in Building Brand Value

Experiential marketing plays a critical role in building brand value by transforming how people perceive, remember, and relate to a brand. Instead of relying on messages alone, it creates direct interactions that allow audiences to experience a brand’s purpose, personality, and promise firsthand. These experiences strengthen emotional connections, which drive higher brand recall, trust, and long-term loyalty. 

As per the benefits of experiential marketing, it makes brands feel human and credible rather than abstract. It also delivers immediate feedback and rich first-party insights, helping brands refine their positioning and offerings. When experiences are shared through word of mouth and social platforms, they organically extend reach and amplify impact far beyond the moment itself. Over time, these meaningful interactions reinforce brand equity by aligning what a brand says with what people actually experience, turning customers into advocates and brand value into a measurable business asset.

Types of Experiential Marketing Campaigns

Experiential or Immersive marketing comes in many forms, each designed to create meaningful, memorable brand interactions rooted in experiential learning. These experiences allow audiences to actively engage with products and services rather than passively consume messaging.

Pop-up experiences generate excitement by appearing in unexpected locations and offering limited-time engagement through product trials or immersive environments. Exhibition stands and live events transform physical spaces into interactive brand showcases where audiences can explore, test, and connect with products firsthand. Interactive workshops and classes encourage experiential learning, positioning brands as industry experts and fostering deeper engagement.

Guerrilla marketing activates public spaces with bold, unconventional ideas that disrupt routine and spark curiosity. Kiosks and interactive installations at trade shows use technology like augmented reality or photo experiences to attract attention and encourage participation. Festivals and sports events provide high-energy platforms for large-scale Immersive marketing campaigns, increasing visibility and cultural relevance. Launch parties reward loyal customers with exclusivity, while interactive technology, such as virtual worlds and digital extensions, blends physical and digital touchpoints into seamless brand experiences.

How to Implement an Experiential Marketing Strategy 

To run a successful engagement marketing strategy, focus on key elements that build strong customer relationships and deliver measurable impact through strategic experiential marketing solutions.

Define clear objectives

Define what you want to achieve: brand awareness, engagement, lead generation, or loyalty. Clear goals guide creative decisions, align teams, and provide benchmarks for success. Without objectives, even strong experiential marketing solutions may be memorable but fail to deliver meaningful business results.

Know your consumer profile

Identify your target consumer’s interests, behaviors, motivations, and pain points. Understanding what drives them enables you to design experiences that feel authentic and relevant. Audience insight strengthens experiential learning moments and increases emotional connection and long-term brand recall.

Create powerful stories

Storytelling transforms activations into emotional journeys. A compelling narrative gives your experiential marketing campaigns purpose, helping consumers feel part of the brand story. When audiences emotionally connect, they are more likely to remember, share, and advocate for your brand.

Design personalised experiences

Tailor experiences based on audience preferences, demographics, or behavior. Personalized experiential marketing solutions make participants feel valued, driving deeper engagement, stronger relationships, and higher conversion potential.

Apply technology strategically

Technology such as AR, VR, and interactive digital tools enhances experiential learning when used purposefully. The key is integrating technology to elevate interaction, strengthen brand perception, and create immersive, memorable experiences that support campaign objectives.

Measuring the Impact of Experiential Marketing

Tracking the impact of experiential marketing is crucial for creating memorable experiences. You can measure the success of interactive marketing through attendance, engagement rates, social media shares, and customer feedback. Surveys, post-event follow-ups, and sentiment analysis reveal how audiences connect emotionally and perceive the brand. Linking experiences to business outcomes such as leads, conversions, or repeat purchases demonstrates tangible return on investment. Analyzing these metrics helps refine future campaigns, optimize touchpoints, and ensure each activation delivers value. Measurement also identifies which experiences build advocacy and long-term loyalty. Bringing together creative ideas with data analysis makes sure that experiential marketing creates important experiences while also showing real growth and improving brand value.

Conclusion

Experiential marketing is essential for brands that want to stand out in 2026 and beyond. By creating immersive, meaningful experiences, brands can forge emotional connections, boost loyalty, and turn customers into advocates. When executed with clear objectives, audience insight, storytelling, personalization, and smart technology, experiential campaigns move beyond awareness, delivering measurable impact and lasting brand value. In a crowded marketplace, it’s the experiences that people remember that define a brand’s success.

The post How to Create Experiential Marketing Campaigns That Drive Engagement? appeared first on iTechSeries.

In this edition of iTechSeries Unplugged Interview, Jessica Jackson, Field Marketing Manager at Harness, shares insights on blending digital strategy with human connection to drive demand. From leveraging data and personalization to prioritizing events and ecosystem marketing, she explores how integrated approaches build trust, accelerate the pipeline, and align marketing closely with revenue and sales goals.

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

By my junior year of high school, I decided to study marketing in college. During my collegiate career, I had internships in sports marketing, fashion, and even Disney’s college program. Following college, I held marketing positions in staffing, engineering, bridal, and now tech.

From your experience, what role does field marketing play today in driving meaningful demand compared to purely digital channels?

Digital channels are extremely important for driving demand because we’re in a digital world, and that won’t change. Where field marketing plays a role is by adding a human touch. We may have acquired a contact from a digital campaign, but a field marketing event allows us to connect with that contact in person and develop a human connection, which nurtures relationships. When a genuine relationship is established, trust is built, which further advances the relationship and the deal cycle. This combination of digital reach and in-person engagement helps create more meaningful interactions, improves buyer confidence, and ultimately leads to stronger conversions and long-term customer relationships.

How do you leverage data, intent signals, and personalization to target accounts and enhance the effectiveness of your marketing campaigns?

I use data from interactions—the material a contact has engaged with, the pain points they identify with, and the responsibilities of their role—to be sure that we’re inviting them to the right events and targeting them with intentional content. I analyze intent signals and behavioral trends to refine audience segmentation, ensuring messaging is timely and relevant. This allows us to create more personalized experiences, improve engagement rates, and ultimately drive higher-quality interactions that move prospects further along the buyer journey.

With experience across digital, social, events, and ecosystem marketing, how do you prioritize channels and tactics for maximum impact?

Digital is important for strategic targeting and coincides with social to amplify a brand and messaging. I recommend prioritizing event marketing to introduce specialized in-person campaigns, nurture the community, and get in front of prospects where a conversation is already happening. Ecosystem marketing can go hand in hand with events but is especially helpful when you have engaged partners and partner programs to make introductions to new accounts to exhibit credibility.

“Digital channels are extremely important for driving demand because we’re in a digital world, and that won’t change. Where field marketing plays a role is by adding a human touch.”

What’s been your most memorable experience as a marketer?

Being a part of Harness’ user conference roadshow, Unscripted, during my first year at the company. This was such a strenuous labor of love that combined all aspects of marketing: digital, social, community, customer marketing, events, and ecosystem. It was rewarding to see our marketing team work together to create something so impactful, reaching new cities and target accounts and opening the door to expansion in current accounts.

What strategies do you use to ensure marketing stays closely aligned with sales goals and revenue outcomes?

Joining weekly sales calls, having quarterly field marketing planning sessions with sales leaders, and sharing consistent updates for upcoming events and marketing activity. I focus on maintaining open communication and feedback loops with sales teams to understand evolving priorities and pipeline needs. Aligning on shared KPIs, tracking performance metrics, and collaborating on account strategies ensures marketing efforts directly contribute to revenue growth and measurable business outcomes.

How do you maintain cross-regional marketing alignment while adapting programs and campaigns to local market dynamics?

There will always be a balance between what the market demands, what your vertical is doing, and what’s best for the business. How I balance this is by maintaining brand consistency while considering pain points and desired results for each region, and, where possible, their current use cases. I also collaborate closely with regional teams to gather on-the-ground insights, ensuring campaigns feel locally relevant while still aligning with global strategy, messaging, and overall business objectives for consistent impact.

What advice would you offer to marketers looking to build a career in field, event, and integrated marketing roles?

Get familiar with all aspects of marketing projects within the company, beyond your own, to understand how they can tie into field events. Nurture relationships with your teammates and vendors in the industry. Treat vendors with respect and think of them as partners. Also, stay adaptable and open to learning, as these roles often require cross-functional collaboration and quick problem-solving. Building strong communication skills and being proactive in taking ownership of projects can significantly accelerate your growth in this space.

About Jessica Jackson

Jessica Jackson is a Field and Events Marketing Manager with over nine years of experience across tech, engineering, staffing, and retail. Currently at Harness, she drives demand through integrated marketing strategies spanning digital, events, and ecosystem partnerships. She has generated a multimillion-dollar pipeline, improved conversion rates, and executed high-impact events. With a background in ABM and sales alignment, she focuses on building human-centered marketing experiences that accelerate pipeline and revenue growth.

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As the digital marketing landscape continues to evolve in 2026, third-party data has shifted from a broad targeting tool to a strategic source of market intelligence. With growing privacy expectations and the decline of third-party cookies, businesses are rethinking how external data can be used responsibly to support audience understanding, intent identification, and competitive benchmarking. When combined with first- and second-party data, it enables brands to improve personalization, sharpen messaging, and analyze through data science techniques. This blog explores the key trends, insights, and best practices shaping third-party data in today’s privacy-first marketing environment.

What is third-party data? 

Third-party data refers to information collected by organizations that do not have a direct relationship with the consumer. This data is gathered from multiple external sources, such as websites, apps, publishers, and platforms, then aggregated and packaged by data providers for business use. Unlike simple contact lists, third-party data offers anonymized insights into consumer behavior, interests, and intent across the broader digital ecosystem.

In a comparison of first-party data vs third-party data, first-party data is collected directly by a brand from its own customers through touchpoints like websites, apps, and transactions. Second-party data is another company’s first-party data shared through a direct partnership. Third-party data provides the widest reach, enabling businesses to identify new audiences and market trends beyond owned channels. Third-party data is collected using privacy-compliant data collection methods such as behavioral tracking, contextual signals, surveys, and public data sources. When used responsibly, it helps businesses improve targeting, personalization, and competitive intelligence in a privacy-first marketing environment.

Benefits of Using Third-Party Data

Third-party data offers significant benefits by helping businesses build richer insights, expand reach, and improve decision-making. One of its primary advantages is enriching customer profiles by filling gaps left by first- and second-party data, giving companies a more complete view of customer demographics, behaviors, interests, and intent. This deeper understanding enables more personalized messaging, better product recommendations, and improved customer experiences. In addition, businesses can leverage third-party logistics insights alongside third-party data to optimize supply chain efficiency.

Third-party logistics data complements this by providing operational insights, such as delivery performance, regional distribution efficiency, and partner reliability. Integrating logistics intelligence with marketing data ensures that product availability and promotional campaigns are aligned, improving customer satisfaction and conversion rates. In addition, it enhances ad targeting capabilities by supporting more precise segmentation, reducing wasted ad spend, and improving ROI. With data analysis tools alongside, third-party data delivers revenue-driving consumer insights, supports innovative location-based campaigns, and strengthens machine learning models by improving feature accuracy. Overall, it empowers smarter marketing, stronger targeting, and more informed business strategies.

The Role of Third-Party Data in Today’s Marketing Landscape

Implementing third-party data in today’s marketing landscape enables brands to move beyond limited internal insights and build a more complete, actionable understanding of their audiences. By analyzing third-party intent data using advanced data analysis tools, marketers can identify who their ideal customers are, what topics matter most to them, and where they are in the buying journey. These insights help shape relevant content generation, improve personalization across industries or roles, and deliver messages that address real pain points. 

Third-party data also plays a critical role in benchmarking brand performance, allowing companies to compare market share, sentiment, visibility, and campaign effectiveness against competitors using trusted research platforms, review sites, and data analytics tools. When applied with data science strategically, it supports smarter targeting, competitive conquesting, and the discovery of new customer segments based on life events or behavioral signals. Successful implementation starts with clear business objectives, followed by selecting reliable data providers that prioritize accuracy, freshness, and compliance with privacy regulations. 

Integrating this data seamlessly across marketing channels ensures consistency and relevance, while ongoing measurement of metrics such as engagement, conversions, and return on ad spend helps refine performance. Beyond marketing activation, third-party data is also essential for powering AI and machine learning models, enriching first-party datasets with greater breadth and depth to improve predictions and insights. As privacy expectations rise globally, working with ethical, consent-based data partners is critical. When used responsibly, third-party data strengthens customer understanding, fuels AI-driven decision-making, and drives measurable business growth without compromising trust.

Best Practices for Using Third-Party Data

B2B Marketers should first define specific use cases tied to business objectives, avoiding the temptation to acquire data without a clear purpose. Selecting reliable, reputable data providers is critical, with close evaluation of data sources, data collection methods, freshness, and governance standards. Seamless integration with existing systems is equally important, requiring platforms that can break down data silos while maintaining security, scalability, and compatibility with internal datasets. Data privacy and regulatory compliance must remain a top priority, with strict adherence to frameworks such as GDPR and CCPA, transparent consent practices, and regular consultation with legal teams. 

To maintain effectiveness, third-party data should be continuously audited, cleansed, and updated, as external data can decay quickly over time. Combining third-party data with first-party insights delivers the greatest impact, enabling richer audience understanding, stronger personalization, and more accurate intent signals. Starting with pilot projects allows teams to test value before scaling, while ongoing monitoring of performance metrics helps optimize ROI. When managed responsibly, third-party data becomes a powerful asset that enhances decision-making, strengthens targeting, and supports long-term, trust-based marketing strategies.

The Future of Third-Party Data in a Privacy-First World

The future of third-party data in a privacy-first world will be defined by trust, transparency, and intelligent technology. As regulations such as GDPR and CCPA continue to evolve, marketers must adopt consent-driven, compliant data practices that prioritize consumer privacy while still delivering value. This shift is pushing organizations to work with trusted data providers and strengthen governance around data sourcing and usage. 

At the same time, advances in data science, AI and machine learning are reshaping how third-party data is analyzed, enabling predictive analytics, automated insights, and more precise audience understanding without exposing sensitive information. Real-time data integrations are also gaining momentum, allowing businesses to respond faster to changing consumer behavior and market conditions. Techniques such as synthetic data and privacy-safe modelling will further support innovation while reducing risk. In this new landscape, third-party data will remain essential, but its success will depend on ethical use, regulatory compliance, and the ability to turn data into actionable intelligence that builds long-term customer trust.

Conclusion  

In 2026, third-party data continues to play a critical role in modern marketing when applied with purpose, accountability, and transparency. As privacy standards tighten and technology advances, its true value comes from delivering high-quality, actionable insights rather than sheer scale. By aligning third-party data with clear business goals, trusted providers, and robust governance, brands can deepen audience understanding, strengthen AI-driven decisions, and remain competitive. When thoughtfully combined with first- and second-party data, it enables more relevant personalization, precise targeting, and long-term growth. The future favors organizations that innovate responsibly while earning and maintaining consumer trust.

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Search is no longer limited to blue links and keyword rankings. As AI-powered search engines like ChatGPT, Gemini, and Perplexity reshape how people discover information, brands must rethink how visibility works. This shift has given rise to the Generative Engine Optimization (GEO) approach, designed to help content appear in AI-generated answers, not just traditional search results. GEO focuses on context, credibility, and expertise that large language models rely on when responding to user queries. In this guide, we explore how GEO is changing SEO, why it matters, and how businesses can stay discoverable in an AI-driven search landscape.

1. What Is Generative Engine Optimization (GEO)?

Generative Engine Optimization definition: It is the practice of optimizing content so it is discoverable, referenced, and surfaced within AI-powered search and answer engines that rely on large language models. Instead of focusing on keyword rankings in traditional search results, GEO prioritizes clear context, authoritative insights, and natural language relevance so AI systems can understand and cite your content syndication in conversational responses. As platforms like ChatGPT, Perplexity, Gemini, and Claude combine information from various sources, GEO helps brands become more visible, trustworthy, and influential in AI-generated answers, boosting awareness and engagement in the new AI-driven search landscape.

2. Why Generative Engine Optimization Matters?

Generative Engine Optimization services matter because the way people search, discover, and trust information is fundamentally changing. Users are increasingly relying on AI-driven platforms to deliver instant, conversational, and highly contextual answers rather than scrolling through traditional search results. In this environment, visibility is no longer defined by rankings alone but by whether your content is selected, synthesized, and referenced within AI-generated responses. GEO helps brands adapt to this shift by aligning content with how large language models interpret intent, context, and authority across every digital campaign.

When done well, GEO enhances the user’s generative search experience by providing clear and complete answers in a single interaction, improving engagement and satisfaction. It also increases content visibility, as optimized brands are more likely to appear in AI responses where buying decisions and brand perceptions are formed. Most importantly, GEO future-proofs digital strategies by ensuring relevance as AI-powered search continues to grow. By prioritizing credibility, depth, and usefulness over keywords alone, GEO strengthens trust, supports long-term discoverability, and positions businesses ahead of competitors who rely solely on traditional search engine optimization approaches.

3. How to Implement Generative SEO Strategies for Your Business?

Now that we’ve covered the fundamentals of generative engine optimization services, let’s explore the actionable steps you can take to optimize your content for AI-powered platforms and deliver a strong generative search experience.

Manage your brand narrative

In the generative search experience, AI engines summarize your brand instead of sending users to your site. Ensure consistent messaging across all digital touchpoints. Audit existing mentions, clarify your value proposition, publish FAQs, and monitor how generative AI tools describe your brand to influence recommendations accurately.

Conduct AI-focused keyword research

GEO prioritizes conversational, question-based queries over short keywords. Identify prompts that trigger AI overviews, map natural-language variations, and cluster long-tail questions around core topics. Optimize each page for one main intent while answering related follow-up questions naturally and clearly.

Create comprehensive, authoritative content 

Generative engines favor depth and credibility. Build content that demonstrates E-E-A-T through expert insights, original data, real-world examples, and clear guidance. Address actual user questions thoroughly, include quotes or statistics, and update content regularly to maintain relevance and trust.

Optimize content structure for AI parsing

Well-structured content helps generative AI extract and synthesize information. Use clear heading hierarchies, short paragraphs, bullet points, and highlighted key facts. Implement schema markup for FAQs and products, and organize content in a logical flow that mirrors how users ask and explore questions.

Track and measure GEO performance

Success in GEO goes beyond rankings. Monitor AI citations, brand mention sentiment, share of voice, and conversion quality from AI-driven traffic. Test prompts in tools like ChatGPT and Perplexity, track AI referrals, and use graders to assess how visible and accurate your brand appears in generative AI results.

4. Best Practices for Optimizing AI-Driven Search Engines

Optimizing for AI-driven search engines requires recognizing that each generative AI platform interprets and prioritizes content differently. Tools like ChatGPT favor comprehensive topic coverage, strong expertise signals, and a conversational yet authoritative tone. Content that performs well typically includes in-depth explanations, practical examples, step-by-step guidance, and expert insights, with products or services positioned naturally within the broader context of problem-solving.

Platforms such as Perplexity place greater emphasis on credibility and freshness. Content that is well-structured, citation-friendly, and contains verifiable data, recent statistics, and links to authoritative sources is more likely to receive references. Clear, factual statements and expert commentary help to establish trust, which is critical for visibility in citation-led AI responses. Google AI Overviews combine traditional SEO signals with generative AI preferences. Content that already ranks well, delivers concise answers to specific questions, includes local relevance where applicable, and uses schema markup is more likely to surface. Structured data, reviews, and clearly defined FAQs help Google extract and summarize information accurately.

Across all platforms, avoiding common GEO mistakes is essential. Keyword stuffing, thin or outdated content, poor structure, missing citations, and weak technical foundations can reduce visibility. A good approach that combines great content, solid technical search engine optimization, clear brand messaging, and regular performance checks helps maintain success in AI-driven search settings.

Conclusion

Generative Engine Optimization services represent a major shift in how SEO operates within AI-driven search environments. Platforms such as ChatGPT, Gemini, and Perplexity now influence discovery through synthesized answers rather than traditional rankings. Visibility depends on context, credibility, and clarity. By adopting GEO, businesses can align their content with how AI engines interpret intent, expertise, and trust. Brands that invest in authoritative content, strong structure, and consistent messaging will stand out. With AI-powered search continuing to evolve, GEO has become a critical part of staying visible, relevant, and competitive in the future of search.

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