Leads in B2B Marketing: Insights from Thomson Reuters, Palo Alto Networks, and Trystar
In the new episode of B2B Marketing Leaders, Olga Bondareva, founder of ModumUp Agency, talks leads in B2B marketing with experts from enterprise companies:
Ralff Tozatti, Sr Director of Marketing Strategy - Analytics and Opps Americas at Thomson Reuters
Rahul Agarwal, Strategic Marketing Manager at Trystar
Hitendar Sethi, Principal Product Marketing Manager - AI Cybersecurity at Palo Alto Networks
Key themes discussed:
🔸 Why “lead” remains the default metric: familiar, simple, and easy to count
🔸 Quality vs. volume tension: sales targets rise, forcing both at once
🔸 Moving from MQL counts to pipeline value and conversion through stages
🔸 The need for shared definitions across teams, tools, and acquired business units
🔸 Buying groups and account-level intent signals for complex B2B decisions
🔸 Data hygiene as the prerequisite for AI: enrichment, validation, normalization
🔸 AI in practice: faster content production and scalable cohort personalization
A practical discussion on why B2B teams still disagree on “good leads,” and how the path forward looks: align on one language and funnel, prioritize pipeline outcomes, adopt buying-group signals where it fits, and use AI only after fixing data foundations.
In a recent episode of the B2B Marketing Leaders Podcast, Olga Bondareva, founder of ModumUp, talks about leads in B2B marketing with experts from different B2B companies:
Hitendar Sethi, Director Product Marketing AI Security at Palo Alto Networks
Ralff Tozatti, Sr Director of Marketing Strategy - Analytics and Opps Americas at Thomson Reuters
Rahul Agarwal, Strategic Marketing Manager at Trystar
Why leads still matter in B2B marketing
Hitendar Sethi at Palo Alto Networks said the lead metric remains common because it is familiar and gives marketing a clear way to show activity, while sales is focused on revenue. That difference can create tension when lead volume does not translate into pipeline.
Rahul Agarwal at Trystar agreed that leads are easy to count. Teams may use that number as a simple indicator of progress, even though the actual customer journey is often more complex than the dashboard suggests.
Ralff Tozatti at Thomson Reuters pointed to another source of pressure. Sales targets increase every year, so once lead quality improves, teams are expected to deliver both quality and volume. Lead definitions can also vary across CRM systems.
How each company defines a lead
At Thomson Reuters, Ralff’s team built a marketing measurement framework using Gartner and Forrester frameworks to create greater consistency across business units and acquired companies.
The framework gives teams shared definitions for MQLs, sales-accepted leads, and opportunities across the customer journey, from awareness through renewal. The main outcome they track is pipeline conversion, not lead count alone.
At Trystar, Rahul described a classic path: marketing-qualified lead, sales-qualified lead, and then opportunity.
In manufacturing, where sales cycles are long, a website form for a product such as a medium-voltage transformer can already signal late-stage intent. Sales then evaluates capacity, customization requirements, pricing, and margin.
At Palo Alto Networks, Hitendar said the team does not use MALs or MQLs at the top of the funnel. Instead, it uses marketing engaged leads, or MELs, and requires more than one engagement signal.
A single ebook download or form submission is not enough in today’s noisy buying environment.
Moving from lead lists to opportunity-level views
Hitendar sees sales asking less often for lead lists and more often for account-level opportunity views: who is engaging, which products they are exploring, and which topics are attracting their attention. This is especially important within existing customer accounts.
Ralff sees disagreement between marketing and sales as useful when it remains respectful.
Marketing does not always provide enough context behind a lead source. Organic search, for example, converts differently from early-stage awareness content. Without that context, sales may reject a lead based on its score alone.
A shared plan and shared metrics can turn that friction into an opportunity to improve the process.
Rahul noted that teams should not treat lead criteria as fixed. If definitions are not revisited as buyer behavior and sales priorities change, debates about lead quality will continue.
Buying groups vs. the classic funnel
Asked whether MQL and SQL models are outdated, Ralff said it depends on the offer.
Buying-group thinking is well suited to complex B2B purchases with long sales cycles. The same company may also sell transactional products that close within a day. For those products, highly personalized one-to-one account programs may not be cost-effective.
Rahul still finds MQL and SQL models useful for complex manufacturing deals. He also sees room for a hybrid approach that adds account-level intent signals.
Hitendar focused less on labels and more on outcomes: separating noise from intent, understanding whether engagement comes from one person or several people within an account, and using those patterns to understand where the account is in the buying journey.
Clean data first, then AI
Ralff prioritized data quality before expanding AI use.
Working with the customer data platform team, his team uses AI to fill gaps, validate fields, and reduce duplicate records. Ralff said that across five pilots, cleaning existing data improved conversion rates by approximately 12% to 15%.
Rahul has a dedicated team responsible for cleaning pricing and configuration data. His technology stack includes ChatGPT, Copilot with internal agents, Clay and Apollo for data enrichment, ConstructConnect for project intent, and CRM as the central hub.
Ralff’s scoring model currently relies on seniority and first-party engagement. The team plans to add market intent data so scores can increase when buyers begin researching the category.
Hitendar’s team has also tested fully AI-generated campaign assets at a fraction of the traditional cost. The work is still experimental.
Hitendar encouraged teams to understand buying groups and move beyond single-lead thinking.
Rahul encouraged marketers to move beyond feature-focused messaging and bring more creativity into B2B marketing.
Ralff asked marketers to stay curious, continue learning about AI, and lead change while keeping human judgment at the center.
You can check out the full episode on the B2B Marketing Leaders Podcast: