The classic tug-of-war between marketing and sales is an age-old tradition. Marketing blames sales for not following up on leads, while sales complain that the leads aren't qualified. The real problem? There is a disconnect between how marketing defines an MQL (Marketing Qualified Lead) and how sales accept and nurture an SQL (Sales Qualified Lead).  

This disconnect is frustrating and costly. Marketing might be hitting its MQL targets, but if those leads never convert into SQLs, what's the real ROI? MarTech bridges the gap by providing real-time data and automated lead scoring that keeps both teams in sync.  

The article will explain how Martech bridges the gap between marketing and sales.  

Here is a breakdown of MQL and SQL and where the gap exists.  

1. What is an MQL (Marketing Qualified Lead)? 

An MQL is a leader who has shown interest but is not yet ready to talk to sales. 

These leads have taken actions such as downloading an eBook, signing up for a newsletter, or attending a webinar.  

Example: A product manager at a SaaS company downloads a whitepaper on "cloud migration." They're showing interest but are not ready yet.  

Role of MarTech: Tools like HubSpot can track this activity and assign a lead score based on engagement, helping identify potential MQLs.  

2. What is an SQL (Sales Qualified Lead)? 

An SQL is a lead that is ready for a sales conversation. This person has shown high intent and fits your ideal customer profile (ICP).  

SQLs take actions like requesting a demo, asking for pricing, or replying to a sales outreach. 

Example: That same product manager now books a meeting with your team to discuss integrating your solution into their tech stack.  

Role of MarTech: Platforms like Salesforce notify the sales team and provide a full engagement history.  

Here are the key reasons why the gap exists.  

1. Different KPIs and Goals 

Gap: Marketing might celebrate hitting an MQL target, even if those leads do not convert.  

Example: A cybersecurity company's marketing team delivers 1,000 MQLs from a gated webinar campaign. However, sales converted only 2% of them because the leads were early in the buying process.  

2. Lack of Data Transparency 

Gap: Without integrated systems, sales cannot see how a lead engages with marketing, and marketing cannot understand the post-handoff process. 

Example: In a SaaS company, the marketing team doesn't realize that most MQLs haven't been contacted for over a week because the sales team didn't get notified in time.  

3. Misalignment on Lead Scoring Criteria 

Gap: Marketing may consider someone an MQL after downloading a whitepaper, while sales may not qualify them unless they've shown buying intent, like requesting a demo.  

Example: An IT solution firm identifies friction between marketing and sales. Marketing hands over leads based on content engagement, but sales reject them because they lack decision-making power.    

Following is the MarTech process of bridging the gap between MQL and SQL.  

1. Lead Scoring Automation 

What it does: scores lead based on behavior (like email opens, downloads, or webinar attendance) and profile fit (job title, company size).  

Why it matters: It ensures that only relevant MQLs move to the SQL stage.  

Example: A cloud services company uses Marketo to score leads. A CIO who visits the pricing page twice and downloads a case study gets prioritized for sales.  

2. Seamless Lead Handoff Between Teams 

What it does: When a lead meets pre-set criteria, MarTech triggers an alert or task for sales.   

Why it matters: Faster handoff reduces the chances of leads going cold. 

Example: A SaaS company uses HubSpot to assign an MQL to a sales rep once the lead reaches a specific score. The rep is notified with context about the lead's journey. 

3. Unified Data and Visibility 

What it does: Integrates marketing tools with CRM, creating a single source for both teams. 

Why it matters: Sales see what actions led to the MQL stage, and marketing can track what happens post-handoff. 

Example: A fintech startup connects Pardot with Salesforce so sales can see that a lead attended a webinar and opened a pricing email.  

4. Funnel Reporting and Attribution 

What it does: Tracks the entire journey from first touch to closed deal, showing which channels delivered the SQLs. 

Why it matters: Helps marketing improve campaign targeting and allows sales to focus on leads with proven potential.  

Example: A software company uses a dashboard in HubSpot to see that leads from LinkedIn ads convert to SQLs 2x faster than leads from email newsletters.  

Challenges & Solutions in Bridging the Gap 

Here are the challenges and solutions that help bridge the gap between MQL and SQL.  

Challenge 1: Over-Reliance on Tools Without a Clear Strategy 

The issue: Many teams buy MarTech tools hoping for instant results, but the tech is not appropriately used without aligning goals and processes.  

Example: A SaaS company invests in an advanced marketing automation tool, but marketing and sales still argue over what defines SQL.  

Solution: Start with clear definitions of MQL and SQL. Build a shared lead scoring model and document the lead handoff process. 

Challenge 2: Siloed Data and Systems 

The issue: When marketing and sales use different platforms, visibility is lost.  

Example: A cybersecurity firm uses HubSpot for marketing and a homegrown CRM for sales. The sales team can't see what content a lead engaged with before the handoff.  

Solution: Integrate systems to create a unified view of the customer journey. Use APIs to sync key data between platforms.  

Challenge 3: Low-Quality MQLs Frustrating Sales 

The issue: Marketing often hands over leads based on engagement, but sales want leads that show clear buying intent.  

Example: An enterprise software company sends leads who download a whitepaper to sales, but they are not decision-makers. 

Solution: Use MarTech to enrich leads with firmographic data (job title, company size, etc.) and behavior signals. Update lead scoring rules based on qualified prospects who fit the ICP.  

Challenge 4: Lack of Feedback Loop 

The issue: Sales don't always provide feedback on lead quality, and marketing cannot improve future targeting.   

Example: An e-learning platform does not have a system for sales to mark leads as unqualified, so marketing continues sending similar ones. 

Solution: Set up MarTech workflows where sales can rate lead quality. Use that input for scoring.  

Bridging the MQL to SQL gap is about teamwork, shared goals, and a customer-centric approach to growth. MarTech makes that partnership smarter and more scalable. With the right technology, you can bring structure, clarity, and efficiency to the lead lifecycle. Ready to align your marketing and sales teams for better lead conversion? Start by evaluating your MarTech stack and defining a shared MQL-to-SQL strategy. 

Discover how the right MarTech stack can turn leads into revenue. Talk to Us

A new strategy combining ABM and Demand Gen helps you attract an audience, accounts worth pursuing based on intent and fit, and targeted campaigns. But when the two strategies work in isolation, you either get volume without value or value without enough volume.   

This article explores combining ABM and Demand Gen to create a unified strategy.  

Here's why the hybrid approach between ABM and Demand Gen matters.  

1. Reach Broadly, Then Focus  

Demand Gen educates the market, creates awareness and pulls in leads starting their journey. 

ABM identifies high-value accounts and tailors you're messaging for the accounts.  

Example: A cybersecurity SaaS company runs a Demand Gen campaign targeting IT leaders through webinars. From the leads captured, they segment accounts for an ABM follow-up with custom case studies and 1:1 outreach.  

2. Improve Funnel Efficiency 

Many Demand Gen leads don't convert because they aren't the fit. 

With ABM, you can filter your inbound pipeline for engaged and qualified accounts. 

Example: A fintech firm uses website traffic and intent data to identify which engaged leads belong to target accounts, then moves to personalized ABM for conversion.  

3. Align Marketing and Sales Teams 

ABM collaborates with marketing and sales to agree on target accounts and shared KPIs. 

Meanwhile, Demand Gen provides the volume and insights to fuel the ABM efforts.  

Example: A SaaS CRM platform's marketing team shares weekly Demand Gen insights with sales to identify new ABM prospects to refine the message.  

4. Create Personalized Experiences 

Demand Gen gives you reach through content, ads, and campaigns.  

ABM helps you tailor journeys and account-specific outreach.  

Example: A MarTech company uses marketing automation to nurture audiences, but once an account shows strong buying signals, it switches to ABM with email sequences and live demos.    

5. Accelerate Pipeline and Deal Velocity 

ABM focuses efforts on deals to close, while Demand Gen keeps filling the top of the funnel.  

Example: A data analytics provider saw faster deal closures after integrating ABM into their Demand Gen and aligning their follow-ups.  

Here's the whole journey of ABM and Demand Gen's combined efforts.  

1. Awareness Stage: Drive Market Education (Demand Gen) 

Start with content that attracts an audience — blog posts, webinars, social ads, and SEO.  

It will generate interest and capture engagement.  

Example: A SaaS HR platform launches a LinkedIn ad campaign promoting a guide on the "Future of Work." The campaign captures leads from an audience such as HR or People Ops.  

2. Interest Stage: Qualify and Segment Accounts 

Use intent data and behavioral signals to identify accounts from the leads generated through Demand Gen. 

Segment leads into ABM tiers — high-value (1:1), mid-tier (1: few), and long-tail (1: many). 

Example: A cloud infrastructure company sees repeat visits from CTOs at Fortune 500 companies. These accounts are flagged for 1:1 ABM campaigns, while mid-market accounts are grouped into themed 1: 1:few.  

3. Consideration Stage: Engage with Personalized ABM Campaigns 

You can prioritize and build tailored messaging and campaigns for each tier.  

Use multi-channel touchpoints — email, direct mail, custom landing pages, and SDR outreach.  

Example: An analytics vendor sends personalized case studies and scheduling links to healthcare CIOs and runs webinars to nurture interest.  

4. Decision Stage: Align Sales and Marketing  

Sales take insights from marketing, such as what content they engage with, what questions they ask, and who iswho's involved in the decision-making.  

Continue with demos, ROI calculators, or briefings.  

Example: For a high-intent ABM account, a marketing automation firm invites decision-makers to a tailored product demo relevant to their existing stack. 

5. Post-Sale: Expand and Nurture 

Keep using ABM to onboard, upsell, and retain. Demand Gen continues nurturing broad prospects. 

Example: A SaaS company offers workshops for key ABM accounts while continuing to run Demand Gen campaigns for a new pipeline.   

Here are the metrics to measure your ABM and Demand Gen campaigns.  

1. Marketing Qualified Accounts (MQAs) 

For ABM, the goals are engaged accounts. 

MQAs are accounts that meet your fit criteria and show buying signals.  

Example: A cybersecurity company tracks accounts visiting key pages (like pricing or demo) and labels them as MQAs if multiple stakeholders engage within a week.  

2. Account Engagement Score 

Measures how much a target account is engaging with your brand across touchpoints.  

Factors include website visits, content downloads, webinar attendance, and email opens.  

Example: A software firm creates a scorecard that gives higher weight to visits on solution pages than blog visits, prioritizing which accounts are ready for ABM outreach.  

3. Pipeline Velocity 

It tells you how quickly leads move through the sales funnel. 

Combining Demand Gen volume and ABM targeting leads to faster conversion times.  

Example: A MarTech platform shrunk its sales cycle after integrating ABM for high-intent leads sourced via inbound Demand Gen.  

4. Cost Per Opportunity (CPO) 

Tracks how much you're spending to generate qualified opportunities. 

With Demand Gen, you optimize for lower CPL (cost per lead), but CPO gives a clearer view of ROI in ABM.  

Example: A fintech company noticed that its ABM campaigns had higher upfront costs, but its CPO was lower than that of its Demand Gen efforts due to better win rates.  

5. Win Rate by Segment 

Compare win rates for accounts targeted via ABM vs. those sourced through Demand Gen. 

It shows how effective your hybrid strategy is at closing deals.  

Example: A SaaS CRM firm found that Tier-1 ABM accounts closed at 30% vs 12% for general inbound leads.  

6. Influence on Revenue 

It tracks how both ABM and Demand Gen influence pipeline and revenue.  

Use multi-touch attribution or engagement scoring to connect marketing impact to sales outcomes.   

Here are the roadblocks in the hybrid strategy and how to overcome them.  

1. Siloed Teams and Misaligned Goals 

Marketing runs Demand Gen campaigns for MQLs, while sales focus on a different set of ABM accounts.  

Solution: Create a shared account list and align KPIs. Sync the target accounts and campaign performance. 

Example: A SaaS firm created a "revenue council" with sales, marketing, and RevOps to align on ICPs, campaign plans, and pipelines.  

2. Technology Overload  

Running ABM and Demand Gen means juggling multiple platforms such as CRMs, ad tools, and marketing automation.  

Solution: Consolidate tools and integrate key platforms for data visibility. Ensure intent data, engagement signals, and lead scoring flow into your CRM.   

Example: A cybersecurity startup used HubSpot for Demand Generation and 6sense for ABM. By syncing them through Salesforce, it got a unified view of the buyer journey.  

3. Scaling Personalization Without Burning Resources 

ABM demands personalized experiences, but personalization doesn't scale easily across many accounts. 

Solution: Use a tiered ABM model. Reserve 1:1 personalization for top accounts and automate 1:1, 1: few, or 1: many campaigns for other accounts.  

Example: A MarTech firm created email sequences and landing pages for mid-market ABM accounts and used custom outreach for its top enterprise accounts.  

4. Measuring Success Across Two Different Motions 

Demand Gen is volume-driven; ABM is value-driven. Tracking performance without context can mislead. 

Solution: Define shared metrics like pipeline influence, opportunity rate, and deal velocity.  

Example: An analytics company moved away from MQL targets and focused on the marketing-influenced pipeline.  

Combining ABM and Demand Gen creates a growth engine that aligns teams, improves ROI, and accelerates pipeline velocity. What is the reward? A scalable approach to growth that adapts to the modern B2B buyer journey. Now's the time to rethink your strategy; it's the key to scaling up in the competitive landscape.  

Get weekly insights that scale to stay ahead in B2B marketing.

A marketing team launches a campaign using a marketing automation platform. New leads pour in within a week, are nurtured through automated emails, and are tracked across multiple touchpoints. But a few weeks later, when these leads are handed off to the sales, there's a disconnect. The feedback? "These leads aren't ready," or worse, "They're not even a fit." It highlights the question: Should we focus on lead quantity or quality?   

With the help of Martech stacks, you can segment audiences, identify buying intent, personalize outreach, and score leads, allowing you to scale lead gen. A high volume of leads may look good, but if they don't convert or align with your ICP, they will burden your sales team.   

This article explores how to use MarTech to strike a balance between lead quality and quantity.  

Here's a breakdown of the trade-off between quantity and quality and how Martech can help.  

1. High Quantity = Broader Reach, But Lower Relevance 

When lead gen efforts prioritize volume, marketing teams often run broad campaigns to attract as many leads as possible.  

Example: A SaaS company runs a paid campaign targeting all mid-size companies across multiple industries. It generates 5,000 leads in two weeks.  

Problem: Most leads aren't decision-makers or don't fit the company's ICP, leading to wasted sales efforts.  

Martech solution: Marketing automation tools can segment and score these leads post-capture, but filtering out irrelevant leads still costs time and effort.  

2. High Quality = Better Fit, But Slower Funnel Growth 

Quality leads are more likely to convert but sourcing them involves research and precise targeting.  

Example: A cybersecurity company uses intent data to identify 200 companies actively searching for endpoint protection.  

Result: Though fewer in number, these leads convert faster and show higher engagement.  

Martech benefit: Platforms help zero in on buying signals and prioritize outreach based on intent, behavior, and firmographics.  

3. Sales-Market Misalignment Grows Without Balance 

Quantity-heavy approaches frustrate sales with unqualified leads. On the other hand, filtered lists may not meet pipeline goals.  

Martech tools and lead-scoring algorithms can bridge the gap, helping both teams align on what defines a "good lead."  

4. Cost Efficiency vs. Long-Term ROI 

Volume campaigns often have a lower cost-per-lead (CPL), but higher churn and lower LTV (Lifetime Value).  

Quality-focused strategies may cost more upfront (e.g., ABM tools), but deliver better ROI over time.  

5. Scalability Becomes a Bottleneck Without Martech 

Martech platforms automate the process of qualifying, nurturing, and scoring leads, helping businesses maintain both volume and precision as they grow.   

Here are keyways Martech supports and elevates lead gen.  

1. Smarter Targeting with Data Enrichment 

Martech tools enrich incoming leads with firmographic and technographic data.  

Example: A SaaS company identifies company size, industry, and tech stack of form-fill leads, filtering out non-decision makers.  

It ensures sales teams only work with leads that fit the Ideal Customer Profile (ICP).  

2. Real-Time Buying Intent Detection 

intent data platforms help identify which companies are actively researching your product.  

Example: A cybersecurity firm identifies healthcare companies searching for "cloud data protection" and triggers outreach campaigns.  

It helps marketing focus on leads that are already in a buying mindset.  

3. Automated Lead Nurturing 

Marketing automation tools help nurture leads through personalized email workflows, website content, and retargeting ads.  

Example: A logistics platform sends automated emails tailored to industry pain points based on earlier content downloads. 

This keeps leads engaged and informed without manual follow-up.  

4. Lead Scoring and Qualification 

Martech assigns lead scores based on actions like email opens, website visits, or demo requests. 

Example: A fintech company uses Salesforce's Einstein AI to prioritize leads who visit pricing pages or request case studies.  

High-scoring leads are routed directly to sales, while low-scorers are nurtured further.  

5. Multi-Channel Campaign Orchestration 

Martech platforms manage email, social, paid ads, and SEO efforts in one place, providing a unified view.  

Example: An HR software firm uses HubSpot to run integrated campaigns across LinkedIn, Google Ads, and email tracking, which channels drive the best leads.  

6. Attribution and Analytics 

Martech tools provide attribution models that show which touchpoints lead to conversion. 

Example: Using Google Analytics, a SaaS firm discovers that webinars contribute to pipeline quality, leading it to invest more in live events.   

Here's how to build that balance through MarTech.  

1. Data Enrichment & Segmentation 

Tools like ZoomInfo enrich raw lead data with job title, company size, industry, and revenue. 

Example: A SaaS platform targeting tech startups uses ZoomInfo to filter contacts by funding stage and employee count, ensuring every lead fits the ICP.  

Impact: You generate more leads from companies that match your core target audience.  

2. Intent Data for Prioritization 

Platforms track online behavior to detect when companies are researching solutions similar to yours.  

Example: A cloud infrastructure company uses intent data to identify companies searching for "hybrid cloud solutions" and then pushes these leads into an outbound campaign. 

Impact: You don't just get more leads; you get leads ready to talk.  

3. Lead Scoring & Predictive Models 

CRMs and AI tools score leads based on behavior (e.g., email clicks, demo requests) and profile fit. 

Example: A fintech firm assigns scores to leads who attend webinars and visit the pricing page multiple times. 

Impact: Sales teams focus on high-potential leads, while marketing nurtures the rest—maximizing volume and conversion. 

4. Marketing Automation & Nurturing  

Marketo helps nurture cold or mid-funnel leads through tailored content and drip campaigns. 

Example: An HRTech company runs industry-specific nurturing emails for leads who downloaded whitepapers but haven't booked a demo.   

Impact: You grow your lead database while increasing leads over time for better conversion rates.  

5. Attribution & Optimization Tools 

Use tools like Google Analytics to track which campaigns drive volume and quality. 

Example: A payments firm learns that LinkedIn ads generate lower volume but higher conversion, so it allocates its budget accordingly. 

Impact: You refine your channels to favor quality and scale.    

The following are KPIs, which measure leads in quality and quantity.  

Quantity Metrics: Tracking the Volume Side  

1. Lead Volume 

Measures how many new leads enter your CRM database. 

Example: An IT solutions provider runs LinkedIn ads and tracks the number of leads generated from gated eBooks and webinars.  

Martech tool: HubSpot captures and tags each lead by campaign source. 

2. Cost Per Lead (CPL) 

Tracks the average cost to generate leads across channels. 

Example: A logistics company spends $5,000 on paid ads and generates 200 leads. CPL = $25. 

Martech tool: Google Ads + CRM integration calculates CPL per campaign. 

3. Click-Through Rate (CTR) 

Measures the percentage of people who clicked on your ad or email out of the total who viewed it. 

Example: A cloud software firm runs an email campaign with a CTR of 3.5%, which helps identify high-performing content. 

Martech tool: Email marketing platforms like Mailchimp track CTR in real-time. 

Quality Metrics: Tracking What Converts 

4. Conversion Rate 

Shows how many leads take the next step (demo request, sign-up). 

Example: From 1,000 leads generated, a cybersecurity company sees 150 converts to demos, which is a 15% conversion rate. 

Martech tool: Analytics platforms (e.g., Google Analytics) track conversion actions. 

5. Lead-to-Opportunity Ratio 

Measures how many leads turn into qualified sales opportunities. 

Example: A fintech startup finds that 1 in 10 marketing-qualified leads (MQLs) move to a sales opportunity. 

Martech tool: CRMs like Salesforce provide funnel visibility. 

6. Customer Lifetime Value (LTV) 

Estimates how much revenue a customer will generate over time. 

Example: A SaaS company sees that leads from organic webinars have 2x LTV compared to paid channels. 

Martech tool: Attribution platforms like Dream data combine CRM and revenue data to analyze LTV by source.  

The debate between lead quality and quantity isn't about picking sides; it's about finding the right balance. Choosing a well-structured MarTech framework makes all the difference. It turns your lead gen strategy into a scalable engine. It's not about choosing between more leads or better leads. With the right tools in place, you can do both with precision and clear visibility into ROI. 

Get the MarTech Lead Gen Framework!

A startup is launching a new product in a competitive market. Despite investing in digital ads, social media campaigns, and content marketing, leads are not converting. The impressions are amazing, but nothing seems to stick to it. Awareness without action does not drive growth, which is one of many reasons why demand generation is essential.  

As buyers become more informed, organizations need to do more than surface-level engagement and focus on driving measurable results. The science of demand gen helps blend data, strategy, and technology to build pipelines that go beyond vanity metrics.   

This article will explain what effective demand generation looks like and why it matters.  

Here’s why demand generation is important.  

1. Longer B2B Sales Cycles Require Sustained Engagement 

In B2B, decisions involve multiple stakeholders and can take time. A strong demand gen approach helps stay top of mind throughout the journey by delivering gated content such as case studies and insights at the right time of the buyer journey.   

Example 

A SaaS company targeting mid-sized enterprises uses a series of webinars and whitepapers to engage IT directors, nurturing them until they’re ready for a demo. Without nurturing, the lead would go cold or shift to a competitor.   

2. Buyers Do Their Research Before Talking to Sales 

B2B buyers prefer to self-educate. They complete 60-70% of their journey before talking to a sales rep. Your content and outreach must create interest before any sales conversation begins.  

Example 

A cybersecurity firm creates a demand gen strategy that includes SEO-optimized blog posts, explainer videos, and comparison guides long before the buyer fills out a contact form.   

3. Traditional Lead Generation No Longer Works Alone 

Old-school lead generation, where companies buy lists or rely solely on gated content, doesn’t cut it anymore. They deliver contacts but not qualified leads. Demand gen focuses on interest, not just contact info.   

Example 

Instead of just gating a whitepaper, a fintech company runs a multi-channel demand gen campaign involving thought leadership on LinkedIn, targeted email sequences, and free tools or assessments.   

4. Marketing and Sales Alignment Depends on Demand Gen 

Sales teams need warm, educated prospects. A well-structured demand gen strategy ensures that marketing delivers aware and ready-to-buy leads.  

Example  

An HR software provider might use lead scoring and behavioral triggers to pass only sales-ready leads, improving conversion rates. 

5. Scalability and Predictable Pipeline Growth 

Demand generation matters because of its ability to scale. Along with data and automation, demand gen creates a predictable flow in pipeline planning.  

Example  

A cloud infrastructure company using intent data and AI-driven email automation can nurture leads, driving scalable growth.  

Here’s how technology and automation play a key role in scaling demand generation.  

1. Marketing Automation Saves Time and Increases Efficiency 

Marketing automation platforms like HubSpot help teams create personalized email workflows, trigger follow-ups based on user behavior, and score leads based on engagement. They ensure your demand gen campaigns run without needing manual input.  

Example 

A SaaS company creates an automated email nurture series triggered when a prospect downloads a whitepaper. Based on the interaction (opening, clicking, or ignoring emails), the system adjusts the messaging, helping to warm up the lead.  

2. CRM Integration Ensures Sales and Marketing Alignment 

When CRM systems like Salesforce are integrated with marketing tools, both teams get a unified view of the customer journey. It improves lead handoff, reduces friction, and helps qualify prospects. 

Example  

An enterprise IT services provider uses Salesforce integrated with a marketing automation tool. When a lead reaches a certain score (e.g., attends a webinar and downloads a pricing guide), the system alerts the sales team, enabling relevant outreach.  

3. AI and Predictive Analytics Identify High-Intent Leads 

AI tools analyze data to identify patterns in buyer behavior. It helps teams focus on high-intent leads, making demand-gen campaigns targeted.  

Example  

A cybersecurity firm uses an AI tool to monitor digital signals such as visits to pricing pages or increased engagement on product videos. When a lead shows buying intent, the system recommends a high-priority outreach.  

4. Personalization at Scale Enhances Engagement 

With the help of technology, marketers can personalize content and messaging based on a lead’s role, industry, or stage in the funnel.  

Example  

A cloud infrastructure provider creates dynamic landing pages that change content based on the visitor’s company size and industry. For example, an enterprise visitor might see a case study about Fortune 500 clients, while a mid-market user might get ROI calculators.  

5. Data and Reporting Drive Smarter Strategy Decisions 

With advanced analytics and dashboards, you can track the performance of every demand generation strategy, from CTR to pipeline contribution. It helps optimize campaigns in real-time and justify ROI.  

Example 

A firm runs multiple campaigns across email, LinkedIn, and webinars. Using a centralized analytics dashboard, they identified that webinars convert 3x better than cold emails for C-level executives, so they adjusted their strategy.  

Below are the key performance metrics you need to track to scale demand gen.  

1. Marketing Qualified Leads (MQLs) 

MQLs are leads who have shown enough interest to be passed from marketing to sales. They’ve downloaded resources, attended events, or interacted with your content. 

Example  

A HR software company considers leads who attend a live demo and visit their pricing page as MQLs. Tracking the number generated monthly helps measure the demand generation campaigns. 

2. Sales Qualified Leads (SQLs) 

SQLs are leads vetted by sales and deemed ready for the sales pitch. Monitoring the conversion rate from MQL to SQL shows how aligned your demand generation strategy is with sales objectives. 

Example   

If a cybersecurity firm generates 500 MQLs in a quarter but only 50 become SQLs, that could signal a disconnect between the messaging or lead scoring criteria.  

3. Cost Per Lead (CPL) 

CPL tells you how much you’re spending to generate each lead. It helps you compare different channels, such as paid search, webinars, or social media. 

Example  

A fintech company spends $2,000 on a LinkedIn campaign that generates 100 leads, making the CPL $20. If another campaign generates leads at $10 each but with lower conversion quality, the team must balance cost vs. lead value in its approach. 

4. Lead-to-Customer Conversion Rate 

The metric tracks the percentage of leads that convert into revenue. It is a sign of whether your demand generation strategy is driving revenue. 

Example  

A SaaS provider sees a 5% lead-to-customer conversion rate. After introducing targeted content and better lead-nurturing emails, that rate improves to 8%.  

5. Pipeline Contribution 

It is the percentage of the sales pipeline influenced or sourced by marketing. It shows how much demand gen is driving opportunities for sales to close.  

Example  

If a data analytics company sees that 65% of its $5M pipeline originated from demand gen campaigns, it shows the marketing’s value to the business. 

6. Customer Acquisition Cost (CAC) 

CAC tells you how much you’re spending to acquire each customer. When combined with Customer Lifetime Value (CLTV), it helps evaluate the long-term success of your demand gen campaign.  

Example  

A logistics platform tracks CAC across all channels and discovers that webinars, while costly upfront, produce the highest-value customers with the longest retention. It helps shift the demand generation strategy.  

With the right strategy, technology, and metrics in place, demand generation becomes the engine that fuels your sales pipeline. Ready to move from impressions to real business impact? Start building a smarter, scalable demand generation strategy today.  

Contact Us to Future-Proof your Marketing with MarTechEdge!

The B2B lead generation has evolved from cold calls, trade shows, and mass emails. The sales team would often struggle with contact lists where closing leads would require a lot of manual resources. But now, a B2B marketing team uses an integrated MarTech stack that tracks user behavior, personalizes content, automates email workflows, and scores leads based on engagement data.   

MarTech in lead generation helps businesses better understand their audiences by collecting and analyzing data from various touchpoints, such as website visits, email interactions, and social media engagement. In B2B, where the sales cycle is longer, investing in the right MarTech stack leads to higher lead quality and better engagement rates.   

This article will talk about the role of MarTech in lead generation.  

Here’s why MarTech is essential for lead generation.  

1. Data-Driven Decision Making 

MarTech platforms provide detailed analytics and reporting on lead behavior, campaign performance, and ROI.  

Example: A marketing manager can see that leads from LinkedIn ads convert better than those from email campaigns, so they reallocate the budget accordingly.  

2. Marketing and Sales Alignment 

MarTech tools like CRMs and lead scoring systems ensure sales teams focus on the most qualified leads, improving collaboration and conversion rates.  

Example: When a lead downloads a whitepaper and visits the pricing page, the system notifies a sales rep with that information. 

3. Real-Time Lead Nurturing 

With MarTech, companies can engage with leads at critical touchpoints with personalized content based on real-time interactions. 

Example: A potential client browsing a software site receives a chatbot message offering a free demo. 

4. Improved Lead Quality and Conversion 

MarTech helps filter out leads that will not convert immediately by analyzing lead behavior and intent.  

Example: A lead scoring tool gives high marks to users who attend webinars, view case studies, and submit contact forms.  

Here’s how MarTech powers B2B lead generation.  

1. Identifying and Attracting the Right Audience 

MarTech helps you identify your ideal audience through industry, location, and job title. It also offers SEO tools, ad platforms, and social listening tools.  

Example: A software company uses LinkedIn Ads to target decision-makers in mid-sized healthcare companies based on job role and company type. 

2. Driving Traffic with Targeted Campaigns 

MarTech tools like Google Ads, HubSpot, or Marketo can help create and run personalized marketing campaigns to drive relevant traffic to landing pages. 

Example: A cloud services firm launches an email campaign offering a free cybersecurity checklist to IT managers, driving them to a landing page.  

3. Capturing Leads Through Smart Forms and CTAs 

MarTech enables dynamic web forms and CTAs that are optimized for conversions. These tools collect relevant lead data.  

Example: A visitor downloads an eBook on a MarTech website by filling out a form that adds them to the CRM.   

4. Scoring and Qualifying Leads 

Lead scoring tools evaluates and rank leads based on how closely they match your ideal customer profile and how they engage with your content.  

Example: A lead who visits the pricing page and opens multiple emails gets a higher score and is sent for immediate sales follow-up.

5. Nurturing Leads with Automation 

MarTech platforms automate nurturing through personalized emails, drip campaigns, and retargeting to keep leads engaged.  

Example: A lead who downloads a whitepaper receives emails with case studies, testimonials, and product guides.   

6. Handing Over to Sales at the Right Time 

MarTech tools alert the sales team and pass the lead along with behavioral data when a lead reaches a specific score or shows strong buying intent.  

Example: A sales rep is notified that a lead has attended a webinar requested a demo, and a timeline of interactions.  

Here are the tools that contribute to B2B lead generation.  

1. Customer Relationship Management (CRM) Tools 

CRMs store and manage your lead and customer data in one place. They track every interaction, helping sales and marketing.  

Example: Salesforce allows sales teams to view a lead’s journey—from the first email click to a product demo request to engage.  

2. Marketing Automation Platforms 

They automate tasks like email follow-ups, lead nurturing, and campaign scheduling, saving time.  

Example: HubSpot or Marketo sends a personalized email series to new leads based on the content they viewed, nurturing them over time.  

3. Lead Scoring and Qualification Tools 

These platforms use AI to score leads based on behavior and engagement, prioritizing high-quality prospects. 

Example: A lead who attends a webinar and views pricing pages gets a higher score in tools, which makes them a hot prospect.   

4. Intent Data Platforms 

These tools track buyer behavior online to identify companies actively researching your product or service.  

Example: Bombora shows that a group of logistics company leads are searching for “warehouse automation,” signaling strong intent.  

5. Chatbots and Conversational Marketing Tools 

AI chatbots engage website visitors to collect lead information and route them to the right sales contact. 

Example: Drift greets a site visitor and offers to schedule a call or answer product questions.  

6. Data Enrichment Tools 

These tools fill in the blanks on lead profiles with additional data like company size, revenue, and job role.  

Example: Clearbit adds info to your CRM so your outreach can be personalized and effective.   

1. Smarter Personalization at Scale 

MarTech will help to create personalized experiences across the buyer journey. AI will analyze behavioral and intent data to customize emails, web content, and ads for each lead.  

Example: businesses will use dynamic content that changes based on a lead’s industry, job title, and past interactions.   

2. Predictive and Intent-Based Lead Generation 

MarTech tools will focus on predictive analytics to spot potential leads before they express interest.  

Example: Platforms like 6sense offer intent data, but future tools can forecast when a lead will likely enter the buying phase.   

3. Hyper-automation of the Sales Funnel 

With AI-driven MarTech, the entire lead journey—from identifying leads to nurturing them and even scheduling meetings—will be automated.  

Example: A lead who visits a product page gets a chatbot message offering a demo, followed by a tailored email campaign.  

4. Deeper Sales and Marketing Integration 

MarTech will align marketing and sales teams by providing unified data, real-time insights, and shared dashboards.  

5. Privacy-First Lead Generation 

MarTech will prioritize ethical and permission-based lead-generation methods. Tools will focus on zero-party and first-party data to build trust and compliance.  

If your business is ready to generate better leads, shorten your sales cycle, and drive growth, it is time to invest in a smarter MarTech strategy. Explore the right tools, align your teams, and let technology do the heavy lifting—your future leads are already waiting.  

Take your lead strategy to next level.  Schedule a call with our MarTech expert.  

For businesses still holding out, the risks of delaying migration are significant. Here's what could happen: 

Data Loss: While Microsoft will provide tools to migrate chat history and contacts, failing to act before decommissioning will result in permanent data loss.  

Security Vulnerabilities: Continuing to run an outdated version of Microsoft Skype Business, especially server-based, exposes your systems to security threats.   

Compliance Issues: Organizations in regulated industries could face compliance risks if communications aren't archived or transitioned.  

2. Transition Experience for Current Users  

Guided Migration: Microsoft provides a framework that will help transition from Skype for Business to Teams.  

Data Portability: Chat history, contacts, scheduled meetings, and user preferences can be migrated from Skype for Business to Teams.  

Improved Features: Teams offer threaded conversations, file collaboration via SharePoint and OneDrive, and better video meeting experiences.   

With Microsoft Skype Business's retirement, Microsoft has taken steps to ensure users aren't left in the dark.  

Signing In and Accessing Skype Data via Teams 

Users can sign in to Microsoft Teams using their Skype credentials, making the transition seamless. Early access was granted, allowing them to switch to Teams immediately after the February shutdown announcement.  

All your key data, such as chats and contacts, will appear in Teams. You don't need to recreate conversations or rebuild your contact list, reducing migration disruption.  

Skype users were also allowed to export their chat history, contacts, and call history. It offered an extra layer of control for those who wanted to keep archived communications outside the Teams environment.   

The most significant change is in the form of paid Skype calling plans. Microsoft discontinued Skype Credit and international calling subscriptions, but ensured that existing subscriptions would remain active for their full duration.   

Additionally, the Skype Dial Pad remains available for paid users, and it is now embedded in the Skype web portal. Users who still need dial-out capabilities can access them without juggling multiple platforms.     

As Microsoft Skype Business officially retires, it leaves behind a legacy.  It redefined how teams connected across time zones, supported remote work, and laid the groundwork for other platforms.  

For many businesses, Microsoft 365 Skype for Business introduced virtual collaboration. Before remote meetings became the norm, Skype enabled one-on-one video calls, team chats, screen sharing, and even international voice calls.  

While nostalgic, its retirement is also a natural evolution. Communication is no longer just meetings; we're co-creating in real time across projects and collaborating across departments. That's the direction Microsoft Teams is built for, and that's why Skype is stepping aside.   

As we move forward, it's important to embrace the mindset of adaptability, efficiency, and human-centered technology. Still, for those who spent years with Skype for Business as a daily companion, the farewell feels personal. The sound of that familiar ringtone and the reliability of the service all played a part in shaping the modern workplace.   

Rethinking your strategy? Get in touch with our MarTech Experts.

A marketing team is gearing up for the launch of a new product. In earlier times, they used to rely on historical campaign data and customer segments to craft the messaging and choose the right channels. With predictive analytics and AI, they can anticipate when customers will engage, what messages will resonate, and where to target for maximum impact. Predictive analytics processes data—from CRM, social media, and website behavior— to reveal insights that drive optimization and growth.  

This article will showcase the relationship between MarTech and predictive analytics.  

Predictive analytics refers to the use of historical data, ML, and algorithms to forecast marketing predictions. In MarTech, this means understanding customer behavior to create a marketing strategy. From personalized email campaigns to targeted ads and churn prevention, marketers use AI-powered models to enable brands to engage better. 

AI predictive analytics can tailor experiences to each customer's unique behavior and intent. This leads to better conversion rates, ROI, and loyal customer relationships. 

Here are key reasons why predictive analytics is essential in MarTech.  

1. Identifying High-Value Leads 

Predictive analytics helps distinguish serious buyers from casual shoppers by scoring leads based on historical data and behavior patterns.  

Example: If a potential client visits pricing pages multiple times and downloads a whitepaper, predictive analytics can assign a high score, signaling the sales team to follow up.  

2. Personalized Content Recommendations 

Buyers are looking for tailored content that speaks to their pain points. AI predictive analytics analyzes the industry type, past interactions, and stage in the buyer journey to recommend content.  

Example: A mid-sized software firm researching data security might be shown case studies or blog posts related to compliance. It increases engagement and nurtures the lead. 

3. Optimizing Email Campaigns 

Predictive analytics can determine the best time to send emails and messages most likely to resonate.  

Example: A SaaS company uses AI models to predict which subject lines will get attention from IT managers vs. procurement officers.  

4. Forecasting Customer Churn 

Losing customers can be expensive, as sales cycles are longer. AI predictive analytics monitors usage patterns and customer support interactions to spot signs of dissatisfaction.  

Example: A marketing platform noticing a client's drop in logins and email campaign usage can trigger a retention campaign or personal outreach.  

5. Enhancing Account-Based Marketing (ABM) 

Predictive analytics helps identify which accounts are most likely to convert based on lookalike modeling and behavioral analysis.  

Example: A cloud service provider could use predictive models to target healthcare companies showing similar characteristics to their lookalike clients. 

1. Personalized Content and Campaigns 

AI predictive analytics analyzes which type of content relates to different personas and predicts what will perform best for each user.  

Example: A cloud services provider sends industry-specific white papers to healthcare CTOs, while financial sector leads receive data security benchmarks.  

2. Campaign Optimization 

You can use predictive analytics to forecast how different messages and channels will perform before launching a campaign.  

Example: A SaaS company uses AI to test various subject lines and send times before launching an email campaign. Predictive models show that emails sent on Tuesday mornings to IT managers have the highest open and conversion rates.  

3. Sales and Marketing Alignment 

Predictive analytics enables marketing and sales teams to collaborate, ensuring that both functions focus on the most valuable opportunities.  

Example: Marketing uses predictive analytics to find the top 100 prospects showing high buying intent based on web activity and content engagement. Sales teams prioritize outreach to these accounts, shortening sales cycles.  

4. Budget Allocation and ROI Forecasting 

Predictive analytics models forecast the expected return from different campaigns, channels, and audience segments. 

Example: A digital agency uses AI to predict that LinkedIn ads targeting mid-market HR professionals will outperform Google Ads. The marketing team shifts the budget, increasing lead volume.  

Here are some common hurdles faced by marketers while implementing predictive analytics.  

1. Data Quality and Incomplete Data 

Challenge: Customer data is spread across multiple systems in many organizations or may be outdated or incomplete.  

Solution: Invest in data cleaning and integration tools and use data governance practices to ensure data accuracy across platforms.  

Example: A CRM software provider found that leads had inconsistent job titles and missing firmographic details. By integrating a data enrichment tool, they improved lead scoring accuracy. 

2. Lack of Skilled Talent 

Challenge: Predictive analytics requires expertise in data science and AI modeling. Organizations often lack skilled staff for it.  

Solution: Upskill internal teams or partner with external analytics firms. You can also use MarTech platforms that offer built-in AI predictive analytics tools.  

Example: A mid-sized logistics company adopted a marketing automation platform with built-in predictive lead-scoring tools.  

3. Misalignment Between Marketing and Sales 

Challenge: Even when predictive analytics identifies high-value leads, sales teams may ignore them due to poor communication.  

Solution: Involve sales teams early when designing predictive models. Share scoring logic, define lead quality, and how predictions are made. 

Example: A cybersecurity firm used AI predictive analytics to rank leads, but the sales team was skeptical. After collaborative workshops, adoption increased, and lead conversion improved.  

4. Over-Reliance on Historical Data 

Challenge: Predictive models often rely on past behaviors, which may not reflect new market realities or shifts in buyer behavior. 

Solution: Update models with recent data and combine quantitative data with qualitative insights from sales and customer support teams.  

Example: An HR software provider relied on pre-COVID data to predict customer interest. Post-pandemic, buyer behavior changed. Updating their models with recent webinars and content engagement data made predictions accurate.  

5. Difficulty Measuring ROI 

Challenge: Attributing success to predictive analytics can be hard, especially in long sales cycles. 

Solution: Define clear KPIs (e.g., lead-to-opportunity rate, deal velocity) and use A/B testing to compare predictive vs. non-predictive strategies. 

Example: An email marketing agency tested AI-driven content personalization vs. standard emails. It helped to track higher click-through rates, justifying investment. 

Data Privacy and Compliance Risks 

Challenge: Predictive analytics often involves sensitive data. Misuse can lead to compliance issues, especially with regulations like GDPR. 

Solution: Use anonymized data and ensure all data sources are compliant. Work closely with compliance teams when implementing predictive tools. 

Example: A fintech firm developed an AI predictive analytics tool to forecast customer needs but had to rework their model to comply with GDPR.  

Here are some key future trends that will shape marketing.  

1. Predictive Analytics Integrated into All MarTech Tools 

Trend: Predictive capabilities will be embedded into core MarTech platforms—CRMs, email tools, and content management systems—making insights more accessible without needing data science teams. 

Example: A B2B email platform integrates AI predictive analytics to suggest the best time to send emails to specific leads, reducing the reliance on manual testing and improving open rates automatically. 

2. Real-Time Predictive Decision-Making 

Trend: Predictive analytics will power decisions like changing the website experience or retargeting visitors.  

Example: A SaaS company offers a live demo pop-up when AI detects a prospect exhibiting buying behavior such as visiting the pricing page multiple times in one session.    

3. AI-Powered Creative Optimization 

Trend: AI predictive analytics will help marketers with "who" to target and "how" to communicate, optimizing subject lines, ad creatives, and content types.  

Example: A marketing agency uses AI to predict which ad formats and copy styles work best for CFOs in tech companies versus operations heads in logistics.  

4. Increased Focus on Predictive Customer Lifetime Value (CLV) 

Trend: Predictive analytics will help transition from short-term lead conversions to long-term customer value prediction to determine which leads will be long-term clients.   

Example: A legal software company's scores lead not just to the likelihood of buying but also to predicted subscription length and upsell potential. 

5. Cross-Channel Predictive Orchestration 

Trend: Predictive analytics will coordinate customer experiences across email, social, paid ads, and websites. 

Example: An event software company uses AI predictive analytics to identify a decision-maker's interest and triggers a coordinated email, LinkedIn ad, and chatbot follow-up to increase event sign-ups. 

By understanding their customers through predictive analytics, marketers can create valuable experiences at every stage of the buyer journey. Organizations that harness AI predictive analytics will lead the way in creating marketing experiences.  

Ready to take your B2B marketing to the next level? Start integrating predictive analytics into your MarTech strategy.  

Read the Latest Insights on Analytics!

Programmatic advertising is changing how digital ads are bought and sold, and one tool is making it possible: the Demand-Side Platform (DSP). DSPs automate ad buying, allowing advertisers to reach their target audience. Through DSPs, you can tap into real-time bidding, precise targeting, and data-driven decision-making.  

What exactly is a Demand-Side Platform, and how does it work?  

A Demand-Side Platform (DSP) is software that enables advertisers and media buyers to purchase digital ad inventory. Advertisers use a DSP to access multiple ad exchanges and networks in one place. DSPs harness data from first-party and third-party sources, helping advertisers gain insights on performance and user behavior to optimize campaigns.  

This article will talk about the impact DSP brings in programmatic advertising.  

Here's how DSP works with ad exchanges and Supply-Side Platforms.  

1. A User Visits a website or Opens an App 

When someone visits a website or opens a mobile app, the ad inventory becomes available for advertisers to bid on in real-time.    

2. The Publisher's Side Sends a Request 

The publisher uses a Supply-Side Platform (SSP) to manage and sell its ad inventory. It then signals ad exchanges for interested buyers.  

Example: A sports news app has a user open an article. The app's SSP sends a bid request with data like the user's location, device type, and browsing behavior.  

3. The Ad Exchange Receives the Bid Request 

Ad exchanges connect SSPs to DSPs. They receive the bid request and forward it to multiple Demand-Side Platforms.  

Think of this as an auction house sharing the same product with many bidders simultaneously. 

4. The DSP Evaluates the Bid Opportunity 

Each Demand-Side Platform analyzes the data from the bid request. It looks at the user's demographics, browsing history, and relevance to the advertiser's campaign goals. 

If the user matches the advertiser's target audience, the DSP places a bid on behalf of the advertiser. 

Example: A DSP working with a fitness brand sees users often visit health blogs. The DSP bids $2.50 to show an ad promoting protein bars. 

5. The Highest Bid Wins & the Ad is Displayed 

The highest bid wins, and the ad is immediately displayed to the user. 

6. Reporting & Optimization 

Once the ad is shown, the DSP tracks its performance (clicks, views, conversions). This helps advertisers refine their advertising strategies in real-time.   

The following are the key features that make DSP advertising effective.  

1. Real-Time Bidding (RTB) 

DSPs use Real-Time Bidding to purchase ad impressions. It means advertisers only pay for users who match their persona.  

Example: A travel company bids in real-time to show ads only to users who recently searched for flights to Europe.  

2. Cross-Channel Buying 

A Demand-Side Platform can run campaigns across multiple webs, mobile, video, in-app, CTV, and audio channels.   

Example: A clothing brand runs the same seasonal campaign on mobile apps, YouTube, and Spotify, managed entirely through one DSP dashboard.   

3. Advanced Audience Targeting 

DSPs allow advertisers to define specific audiences using demographic, behavioral, geographic, and device-based data.  

Example: A luxury car brand targets users aged 35–55 who have visited competitor websites and live in high-income zip codes.   

4. Lookalike and Retargeting Capabilities 

DSPs can identify users similar to your lookalike audiences and retarget users who interacted with your site or app.  

Example: An e-commerce site shows follow-up ads to users who added products to their cart but didn't complete the purchase.  

5. Budget and Bid Control 

Advertisers can set daily and weekly budgets and maximum bid amounts per impression or user action. 

Example: A startup with a $5,000 ad budget sets its DSP to prioritize impressions with the highest engagement potential.   

6. Performance Tracking and Optimization 

DSPs offer real-time analytics, helping advertisers adjust targeting or bidding strategies.  

Example: An online education company pauses underperforming creatives and shifts its budget toward video ads with higher conversion rates.  

7. Integration with Data Providers and DMPs 

Many Demand-Side Platforms integrate with Data Management Platforms (DMPs) and third-party data providers, giving access to insights. 

Example: A DSP pulls in third-party data on consumer shopping habits to refine audience targeting for a retail campaign. 

Benefits of Using a DSP for Advertisers  

Using a Demand-Side Platform gives you an edge in advertising.  

1. Unified Campaign Management Across Channels 

DSPs allow advertisers to manage and run campaigns across display, video, mobile, and CTV, all from one platform.  

Example: A beauty brand launches a new product and uses a DSP to run ads on YouTube, lifestyle blogs, and mobile apps, all through a single interface.  

2. Smarter Audience Targeting 

A Demand-Side Platform helps advertisers reach audiences based on demographics, behavior, and device usage.   

Example: A pet food company targets dog owners in urban areas who recently searched for "organic dog treats." 

3. Real-Time Optimization for Better Results 

DSPs provide analytics to track performance and make adjustments such as shifting budgets, changing creatives, or updating targeting.  

Example: An online bookstore sees better engagement from carousel ads than static banners, so it boosts its spending on the top performer mid-campaign.   

4. Cost Efficiency Through Real-Time Bidding 

DSP advertising costs less than traditional buying because you only bid on impressions that match your target audience.  

Example: A local gym avoids showing ads to people outside their service area, saving budget and increasing sign-up conversions.   

5. Scalable Reach 

A Demand-Side Platform connects advertisers to multiple ad exchanges and inventory sources, giving access to millions of websites and apps.  

Example: A startup uses a DSP to scale a product launch campaign nationwide without negotiating with individual publishers.  

6. Transparent Reporting and Insights 

DSPs offer clear performance reports that help marketers understand campaign impact and ROI. 

Example: A fintech brand uses a DSP's reporting tools to see which ad placements drive app installs and reallocates the budget accordingly.  

Here is the breakdown for different kinds of DSPs.  

1. Self-Serve DSPs 

These platforms are designed for advertisers who want to manage everything in-house—targeting, bidding, budgets, creatives, and reporting. 

Example: A marketing manager at an e-commerce brand uses a self-serve DSP to launch retargeting ads, adjusting bids and creatives daily.  

Best for: Experienced advertisers or agencies who want hands-on control.

2. Full-Service DSPs 

In this model, the DSP provider manages your campaigns, including setup, optimization, reporting, and creative development. 

Example: A hotel chain uses a full-service DSP to run seasonal campaigns targeting travelers in different countries.  

Best for: Brands that lack in-house expertise or want a more "done-for-you" approach. 

3. White-Label DSPs 

These DSPs can be rebranded and customized to offer programmatic advertising solutions under their brand.   

Example: A digital agency rebrands a white-label DSP to offer tailored programmatic services to its clients.  

Best for: Agencies or platforms looking to expand their offerings with programmatic advertising.  

Data plays a major role in making DSP smarter. Here's how data impacts DSP advertising.  

1. First-Party Data Integration 

Advertisers can feed their own customer data, such as website traffic, purchase history, or email lists, into DSP to create customized campaigns.  

Example: An online clothing retailer retargets users who leave items in their carts using data from its own e-commerce platform.  

Why it matters: You get more conversions by re-engaging users who already know your brand. 

3. Third-Party Data for Broader Reach 

DSP advertising also uses third-party data, including data sets from lifestyle, interest, or intent signals collected across the web.  

Example: A luxury watch brand partners with a third-party data provider to find users identified as high-income individuals interested in fine jewelry.  

Why it matters: You can scale campaigns and reach new prospects who look like your ideal customer.  

3. Real-Time Data for Dynamic Bidding 

A Demand-Side Platform uses real-time data to decide how much to bid for each ad impression based on the engagement or conversion. 

Example: A food delivery app bids higher during lunch hours and in metro areas with higher-order activity. 

Why it matters: You spend your budget where and when it matters most. 

4. Performance Data for Optimization 

DSPs track ad performance—impressions, clicks, conversions—and feed that data back into the system for optimization. 

Example: A travel company adjusts its bidding strategy mid-campaign when it sees that users in one region convert more than others.  

Why it matters: Campaigns get smarter over time, helping you maximize ROI. 

Let's break down the challenges while using DSP and how to solve it.  

Challenge 1: Complex Setup and Steep Learning Curve 

DSPs have advanced interfaces that can overwhelm new users. 

Solution: 

Start with a full-service DSP, or choose a provider that offers hands-on onboarding and training. 

Example: A small business uses a full-service DSP during the first few campaigns while learning to manage the dashboard and read performance metrics.  

Tip: Choose platforms with user-friendly dashboards and strong customer support if you're new.  

Challenge 2: Wasted Ad Spend Due to Poor Targeting 

Without proper targeting, you may show ads to the wrong audience or overpay for low-quality impressions.  

Solution: 

Use first-party data and create tight audience segments. Always run A/B tests and monitor performance closely.  

Example: A SaaS company narrows targeting to users in tech roles who recently visited competitor websites, cutting cost-per-lead prices.  

Tip: Avoid broad targeting unless your goal is awareness—not conversions.  

Challenge 3: Ad Fraud and Low-Quality Traffic 

DSP advertising can sometimes result in impressions from bots, especially when buying at scale. 

Solution: 

Work with DSPs that partner with trusted fraud detection vendors and offer inventory quality filters.  

Example: A finance brand activates a fraud filter and sees an improvement in click-to-conversion rates.  

 Tip: Ask your DSP provider what fraud prevention tools they have before you commit. 

Challenge 4: Limited Transparency on Where Ads Appear 

Some DSPs don't offer detailed visibility into the websites or apps where your ads are shown. 

Solution: 

Use allowlists and blocklists, and select DSPs that offer domain-level reporting. 

Example: An automotive brand allows only premium auto news sites, ensuring their ads appear only on them.  

Tip: Always monitor placement reports and block irrelevant domains.  

Challenge 5: Measuring the True ROI 

Attribution can be tricky in DSP advertising, especially across multiple devices and channels. 

Solution: 

Use DSPs that support cross-device tracking and multi-touch attribution models. 

Example: An online learning platform sees that most conversions happen after users see both video and display ads, leading them to adjust their strategy.  

As technology evolves, so does DSP advertising. Here are some of the ways. 

1. AI and ML for Smarter Bidding 

DSPs use AI to adjust bids, optimize ad placements, and predict user behavior. 

Example: A DSP identifies which users are more likely to convert and increases their bid amounts.  

Why it matters: AI helps reduce manual work while increasing campaign efficiency. 

2. More Transparent and Ethical Advertising 

DSPs are responding with better reporting, fraud prevention, and brand-safe inventory to cater to transparency needs.  

Example: A health brand chooses a DSP with advanced fraud detection and full visibility into where every ad appears.  

Why it matters: Trust and transparency are key to long-term success in DSP advertising. 

3. Growth in CTV, Audio, and Emerging Channels 

DSP advertising expands through CTV, digital audio, and even in-game ads.  

Example: A beverage brand uses a DSP to run ads on Spotify, reaching audiences in non-traditional spaces.  

Why it matters: Omnichannel reach is essential to meet consumers wherever they are.    

Choosing the right DSP takes more than just plugging in a budget. It requires strategy, experimentation, and a clear understanding of your goals. Ready to take your advertising to the next level? Now's the time to explore the world of DSP advertising and unlock data-driven campaigns that deliver results.   

Schedule a Call to Reach out to our MarTech Professionals.