A SaaS company launches a paid LinkedIn campaign targeting mid-market CFOs. The AdTech platform captures intent signals such as job titles, engagement behavior, and even dwell time on specific product features. These insights are pushed into the company’s marketing automation platform.
The lead enters a nurture track tailored to their pain points. The prospect receives a personalized email with a case study, webinar invites, and an ROI calculator. A few days later, they fill out a demo form, and the lead becomes the Marketing Qualified Lead (MQL).
This article will discuss how integrating MarTech and AdTech helps generate MQLs.
Understanding AdTech vs. Martech in the Funnel
Here’s how ADTech and MarTech work in a funnel and how they overlap.
1. AdTech: Driving Awareness and Acquisition
Purpose: AdTech focuses on grabbing attention at the top of the funnel.
Goal: Drive traffic, capture interest, and generate inbound leads.
Functions: Paid search ads, display ads, social media campaigns, retargeting, and programmatic advertising.
Example: A cybersecurity firm uses LinkedIn Ads to target IT directors in the finance sector. These ads highlight a free industry report, drawing clicks from decision-makers showing interest.
2. Martech: Engaging and Nurturing Leads
Purpose: Martech supports mid- and bottom-funnel activities, converting raw leads into MQLs.
Goal: Nurture, educate, and qualify leads based on behavior and intent.
Functions: Email marketing, CRM integration, lead scoring, content personalization, and marketing automation.
Example: The cybersecurity firm uses HubSpot to send a personalized email sequence to leads who downloaded the report. The sequence includes case studies, FAQs, and demo invites based on their industry and engagement level.
3. Key Distinctions
Intent Data: AdTech collects anonymous user data (e.g., ad clicks), while Martech uses known data (e.g., email opens, and website behavior) for insights.
Timing: AdTech operates before a lead is in your database; Martech takes over once the lead is captured.
Output: AdTech generates potential leads; Martech turns them into MQLs through structured engagement.
4. Where AdTech and Martech Overlap
Data Enrichment: Integration allows leads from AdTech campaigns to enter Martech systems with detailed behavioral tags.
Journey Tracking: When synced, AdTech click data can inform how leads are scored and segmented in Martech tools.
Personalization: Insights from ad interactions can shape email content, CTAs, and nurturing workflows for better MQL conversion.
The Integration Advantage: Why You Need Both AdTech and Martech
Here’s why both are critical and how they complement each other.
1. AdTech Drives Discovery, Martech Drives Qualification
AdTech helps you reach the right audience at the right time with targeted ads across social, search, and programmatic platforms.
Martech nurtures those leads with personalized content, scoring models, and automated workflows.
Example: A software company runs a Google Display Network campaign (AdTech) targeting procurement heads. The click leads to a gated eBook. Once the lead is captured, their behavior is tracked in Pardot (Martech) and enrolled in a nurture journey.
2. Better Lead Scoring Through Combined Insights
Integrated systems enrich lead profiles with both ad engagement data and on-site behavior.
It improves MQL accuracy, ensuring only interested leads are passed to sales.
Example: If a lead clicks a pricing-focused ad and later spends time on your comparison page, Martech can assign a higher lead score due to combined AdTech activity signals.
3. Seamless Personalization Based on Ad Engagement
AdTech insights (like which message or creative drove the click) can guide Martech platforms in delivering relevant content.
This increases email open rates, content engagement, and overall trust.
Example: If a lead clicked an ad titled “Reduce IT Downtime by 30%,” your email campaign (via Marketo) can follow up with a case study on how another client achieved that same outcome.
4. Faster Sales Handoff
When sales receive a lead from Martech, they also get insight into the AdTech journey—what campaign attracted them, what content they interacted with, and how engaged they were.
This speeds up the sales cycle and makes outreach more informed.
Example: A rep sees that a lead came through a CMO-targeted campaign and has attended a product webinar, making the pitch relevant.
Metrics That Matter: Measuring MQL Impact Post-Integration
Here are the key metrics that matter most post-integration.
1. Improved Lead Scoring Accuracy
Why it matters: Without integration, lead scores rely only on limited engagement data from the Martech side.
With integration: AdTech data (e.g., ad interactions, time spent on landing pages) enriches lead profiles, enabling real-time scoring.
Example: A fintech company uses 6sense (AdTech) to track anonymous buying signals. Once a lead submits a form, this data flows into Salesforce (Martech), boosting lead scores based on intent and known engagement.
2. Increased MQL-to-SQL Conversion Rates
Why it matters: MQLs only matter if they become Sales Qualified Leads (SQLs). Post-integration, alignment improves.
With integration: Leads nurtured based on unified AdTech and Martech insights will convert as the content and timing align with their interests.
Example: A cloud services firm integrates Google Ads with HubSpot. Leads who engage with a pricing ad and later download a product guide receive a higher MQL score. As a result, their sales team sees a jump in MQL-to-SQL conversions.
3. Reduced Lead Leakage and Sales Misalignment
Why it matters: When AdTech and MarTech systems aren’t integrated, leads fall through the cracks, either poorly timed or not followed up at all.
With integration, every lead interaction, from the first ad click to the final email open, is tracked and scored, so sales knows exactly when to engage.
Example: A cybersecurity company uses LinkedIn Ads (AdTech) and Marketo (Martech). Once a lead hits a specific score, sales are notified with the full context of their engagement history.
4. Tools to Measure These Metrics
Lead Scoring Models: Custom scoring in HubSpot, Marketo.
Multi-Touch Attribution: Tools like Dreamdata help track how AdTech and Martech Touch contribute to conversion.
Engagement Heatmaps: They show how leads engage with landing pages and content.
Common Pitfalls and How to Avoid Them
Below are the major pitfalls and how to avoid them.
1. Siloed Teams Between Marketing and Performance
The Problem: AdTech teams focus on media buying and paid campaign performance (impressions, clicks, CPM), while Martech teams handle lead nurturing and automation. Often, they don’t align.
The Impact: Leads generated by AdTech are handed off to Martech with little context, reducing the chances of them becoming qualified MQLs.
Example: A SaaS company’s performance marketing team runs Google Ads but doesn’t align messaging with the content used in email nurturing by the Martech team.
Solution: Schedule brainstorming sessions between both teams to align goals, messaging, and campaign timelines.
2. Misaligned KPIs (e.g., CPM vs. MQL Quality)
The Problem: AdTech is often judged by top-of-funnel metrics like cost-per-click (CPC) or cost-per-thousand-impressions (CPM), while Martech is evaluated on downstream metrics like MQL-to-SQL conversion.
The Impact: This creates a disconnect, such as campaigns that may look “successful” by media metrics but deliver low-quality leads.
Example: An HRTech company celebrates a low CPM campaign but later discovers that none of the leads from that campaign engaged with their follow-up content.
Solution: Define shared KPIs such as cost-per-MQL, engaged lead rate, or pipeline contribution that bridge both systems.
3. Data Integration Challenges
The Problem: AdTech and Martech often run on separate data sets, leading to poor identity resolution and bad attribution.
The Impact: Leads may be duplicated, scored incorrectly, or lost in disconnected platforms.
Example: A marketing team captures ad leads via LinkedIn, but due to lack of integration, they aren’t appropriately tagged in Salesforce, missing out on automated nurture workflows.
Solution: Invest in data normalization tools, customer data platforms (CDPs), and workflow audits to ensure clean, synced data across systems.
Conclusion
In today’s B2B landscape, generating leads isn’t the challenge; qualifying them is. From campaign launch to lead handoff, an AdTech-Martech integration means fewer leaks and meaningful buyer experiences. If you’re ready to elevate your MQL strategy, assess where your AdTech and Martech systems connect and where they don’t.
