How To Improve MQL to SQL Conversion Rate Using Intent Data

What's your MQL to SQL conversion rate? If you're like most B2B companies, it's probably hovering around 10%. That means for every 100 leads your marketing team hands over to sales, 90 of them are going nowhere. I don't know about you, but I call that a recipe for frustration, wasted resources, and missed opportunities.

In this comprehensive guide, I'm going to share with you the strategies and insights I've gained over the years, particularly focusing on how intent data is helping bridge the gap between Marketing Qualified Leads (MQLs) and Sales Qualified Leads (SQLs) and improve conversion rates.

How MQLs, SQLs, and Intent Data Align for Better Conversion Rate

Let's make sure we're all on the same page before jumping into my MQL to SQL conversion rate tactics.

When we talk about Marketing Qualified Leads (MQLs), we're referring to those prospects that your marketing team has identified as more likely to become customers compared to other leads. These are the people who've shown some level of interest in your product or service. Maybe they've downloaded a whitepaper, attended a webinar, or repeatedly visited your pricing page.

But, while MQLs have shown interest, that doesn't mean they're necessarily ready to buy. Basically, they're window shoppers. They're interested, but not yet committed. And this is where many sales and marketing teams make mistakes. Isn't any interest good interest? Well, not exactly, and I'll tell you why in a moment.

Sales Qualified Leads (SQLs) are the leads that have been vetted by your sales team and marked ready for the next stage in your sales process. They've typically shown a higher level of interest and better fit your ideal customer profile (ICP).

The journey from MQL to SQL is important in your sales process. But all too often, there's a disconnect. Marketing thinks they're handing over hot leads, while the sales team feels like they're getting time-wasters at best. I've been on both sides and it's not a fun place to be.

BUT this is where intent data can help. Intent data is information collected about a person or company's online behavior that helps predict their likelihood to purchase a product or service.

Think about it this way: if someone's been reading reviews about project management software, comparing different options, and looking at pricing pages, there's a good chance they're in the market for a new project management tool. That's intent data in action.

There are two main types of intent data you should be aware of:

First-party intent data is what you collect directly from interactions with your own digital properties – your website, email campaigns, social media, etc. This is great stuff, but it's only a small part of the picture.

Second-party intent data is essentially someone else's first-party data that you've acquired through a direct relationship. For example, you might partner with a industry publication or a complementary software provider to gain access to their audience's behavior data.

Third-party intent data is collected from other sources across the web, giving you insights into what your potential customers are doing when they're not on your site.

Why Should Marketing and Sales Teams Care About MQL to SQL Conversion Rate?

Kinda obvious, huh? Well, yes and no.

First and foremost, of course it's about revenue. The more efficiently you can convert MQLs to SQLs, and ultimately to customers, the more revenue you'll generate. It's simple math. Even small improvements in SQL conversion rates could lead to significant boosts in revenue. Going from 5% to 6% doesn't seem like much but for simplicity sake that's roughly 20% increase in revenue OR even more if those new conversions coming from higher CLV (Customer Lifetime Value) clients.

Let me give you a real-world example. We had a client who was struggling with a measly 3% MQL to SQL conversion rate. After implementing some of our intent data strategies I'm going to share with you today, they saw that rate jump to 6% within nine months. Essentially doubling their revenue just from increased MQL to SQL conversion rate. Not only did they triple their conversion rate, but their average deal size also increased by 20%!

But improving your conversion rate isn't just about making more money (although that's certainly a nice benefit!). It's also about using your resources more efficiently. Every hour your sales team spends chasing an unqualified lead is an hour they're not spending closing deals with hot prospects. Every dollar your marketing team spends on attracting leads that will never convert is a dollar that could have been spent on more effective marketing campaigns.

By focusing on improving your MQL to SQL conversion rate, you're essentially optimizing your entire sales and marketing funnel. You're ensuring that your team's time and your company's resources are being used in the most effective way possible.

Here's something I'm particularly passionate about: improving your MQL to SQL conversion rate can significantly enhance the alignment between your sales and marketing teams. When I co-founded CustomerBase AI, one of our key goals was to help align marketing, sales, and leadership around a unified data layer. Why? Because I'd seen what happens when these teams are truly in sync.

You know what happens when your conversion rates are low and marketing and sales teams keep missing their quotas? Finger-pointing. Marketing blames sales for not following up properly, while sales blames marketing for sending over poor quality leads. Those meetings are no fun.

Last but certainly not least, improving your MQL to SQL conversion rate can significantly enhance the experience for your potential customers. Just think about it, when you're better at identifying high-intent leads, you can provide them with more relevant, timely info. You're not bombarding uninterested people with sales pitches. Instead, you're able to give genuinely interested prospects the attention they deserve.

This leads to a better buying experience, which in turn can improve your close rates and customer satisfaction scores. It's a win-win situation. I've seen companies transform their reputation in the market simply by getting better at identifying and nurturing the right leads.

How to Leverage Intent Data to Increase MQL to SQL conversion Rate

So how do you actually use customer intent data to increase MQL to SQL conversion rates?

Intent data is all about prediction. Intent data is about understanding not just who your potential customers are, but what they're likely to do next. This predictive power is what makes intent data so valuable in improving your MQL to SQL conversion rate.

At CustomerBase AI, we've developed a powerful data science framework that we apply to develop insights from deal data. This allows us to go beyond theoretical Ideal Customer Profiles (ICPs) and understand who a company should really be targeting and why.

Here's how you can leverage intent data to increase MQL to SQL conversion rates:

1. Prioritize leads.

One of the most immediate benefits of intent data is its ability to help you prioritize your leads. Instead of treating all Marketing Qualified Leads equally, you can use intent data to identify which leads are showing the highest levels of purchase intent.

Let me give you a real-world example. We had a client who was struggling to manage a flood of inbound leads. Practically non-existent lead scoring system. Their sales team was overwhelmed, and high-potential leads were slipping through the cracks. We helped them implement an intent-based lead scoring system. This scoring system helped them identify the 20% of leads that were responsible for 80% of their closed deals. By focusing their efforts on these high-intent leads, they saw their conversion rates skyrocket.

2. Personalize outreach.

Intent data doesn't just tell you who to reach out to. Intent data helps you understand how to approach them. By knowing what topics a prospect has been researching, what challenges they're likely facing, and what stage of the buying journey they're in, you can tailor your outreach for maximum impact.

Intent data gives you insights you wouldn't have access to otherwise.

3. Time outreach and follow ups.

Timing is everything in sales, and intent data can help you get it right. By monitoring changes in a prospect's online behavior, you can identify when they're entering an active buying cycle.

Say if a company suddenly increases their consumption of content related to your product category, there's a good chance that they're entering a research phase for a potential purchase. This is the perfect time for your sales team to reach out with helpful resources and information.

We had a client who used this approach to completely transform their cold outreach strategy. Instead of cold calling on a set schedule, they started reaching out to prospects based on intent signals. This helped them improve contact-to-meeting ratio by 300%. They were having more conversations, with more interested prospects, simply by getting the timing right.

4. Expand your total addressable market.

Intent data can also help you expand your total addressable market. Intent signals can help your marketing and sales teams spot companies that fit your ICP and are showing buying intent, even if they haven't interacted with your marketing efforts yet. This was one of the key insights that led us to develop CustomerBase AI. We wanted to create a tool that could continuously monitor the market, identifying companies that are shifting into your ICP and showing high intent signals.

One of our clients used this approach to identify a whole new segment of potential customers they hadn't been targeting before. These were companies that fit their ideal profile but hadn't engaged with their marketing efforts. This approach helped them increase their pipeline by 50% in just five months.

5. Identify best-fit customers.

Intent data can provide valuable insights into what your actual best-fit customers look like. You can refine your ICP and improve your targeting with intent signals. You want to move beyond theoretical ICPs to data-driven, validated profiles of your best customers. At CustomerBase AI, we help companies do exactly this – validate their ICP by looking into past won and lost deals to understand who they should be targeting.

6. Achieve sales and marketing alignment.

Remember how we talked about the importance of sales and marketing alignment? Intent data can help you achieve this.

We had a client who used intent data as a bridge between their sales and marketing teams. They created shared dashboards that showed intent signals for their target accounts. This allowed both teams to prioritize their efforts on the same high-potential prospects. Not only did their conversion rates improve, but the long-standing tension between sales and marketing started to go away.

7. Use predictive lead scoring.

Intent data can literally supercharge your lead scoring models. Instead of relying on demographics and past interactions with your company, you can use intent signals to create more accurate predictions of which leads are most likely to convert.

This can help you create a more sophisticated MQL definition. This way, only the highest quality leads will be passed to your sales team.

Strategies for MQL to SQL Conversion Rate Improvement

These are tactics I've seen work time and time again for improving MQL to SQL conversion rate, both in my own experience and with our clients at CustomerBase AI.

First things first, you need to implement a robust intent data strategy. This isn't just about buying some third-party data and calling it a day. You need to think carefully about how you're going to collect, integrate, and use this intent data.

The best place to start is to identify the right intent data sources for your business. This could be third-party data providers, but don't forget about your own first-party data. Your website analytics, email engagement metrics, and CRM data can all provide accurate customer intent signals.

Next, you need to integrate this intent data into your existing systems. Your intent data should flow into your CRM and marketing automation platforms. This sounds obvious but I've seen companies invest in great customer intent data, only to have it sit in a silo where no one uses it.

Finally, you need to train your teams on how to interpret and act on intent signals. You need to help your sales and marketing team understand what it means and how to use it in their day-to-day work.

We've built our platform from the ground up to replicate the way a seller does research, taking the validated ICP and monitoring the market to discover these companies.

Also, you need to refine your MQL definition. Many companies cast too wide a net when defining MQLs, leading to a flood of low-quality leads being passed to sales. It is a recipe for frustration for both sales team and marketing team.

Use customer intent data to create a more precise MQL definition. For example, this might include minimum intent score thresholds, specific high-value intent signals, or a combination of traditional lead scoring factors and intent data. Your goal is NOT to generate more Marketing Qualified Leads. Your goal is to generate better Marketing Qualified Leads.

You can also implement a Service-Level Agreement (SLA) between sales and marketing. This might be a bit of an overkill, but it's really about getting everyone on the same page. Your SLA should clearly outline what constitutes an MQL, how quickly sales should follow up on MQLs, what information marketing should provide with each MQL, and how sales should provide feedback on lead quality.

Consider developing an intent-based lead nurturing program. Not all high-intent leads will be ready to talk to sales immediately. By creating nurturing programs triggered by specific intent signals, you can provide relevant, valuable content based on the prospect's demonstrated interests and stage in the buying journey.

We had a client who implemented this strategy and saw their nurture email engagement rates increase by 150%. More importantly, the leads that came through this intent-based nurture program converted to Sales Qualified Leads at twice the rate of their traditional nurture leads.

Training your sales team on intent data is another crucial step. You really want to ensure your team understands how to interpret intent signals, how to personalize their outreach based on intent data, and when to reach out based on intent signals.

If you're not already using an Account-Based Marketing (ABM) approach, intent data provides the perfect opportunity to start. Use intent signals to identify target accounts, personalize marketing campaigns for specific accounts, and time your outreach based on account-level intent signals.

We had a client who used this approach to land their largest deal ever. They identified a target account showing high intent signals, created a personalized multi-channel campaign based on the specific topics the account was researching, and timed their outreach perfectly. They closed a deal that was ~ 4x larger than their previous average deal size.

Super important, implement a closed-loop reporting system to ensure there's a feedback loop between sales and marketing. When an MQL doesn't convert to an SQL, make sure this information (and the reason why) is captured and fed back into your systems. This data can be used to continually refine your MQL definition and lead scoring models.

How to Measure Success

First and foremost, keep a close eye on your MQL to SQL conversion rate. This is your north star metric. Calculate this by dividing the number of SQLs by the number of MQLs over a given period. Track this over time to see if your approach is working.

Look at the time to conversion. How long does it take for an MQL to become an SQL? Tracking this can help you identify bottlenecks in your process. If your sales team is taking too long to follow up on high-intent MQLs, I highly recommend you implement an automated alert system for high-priority leads. This will help you reduce the time to conversion.

Sales Qualified Lead quality is another crucial metric. Not all SQLs are created equal. We always track the percentage of SQLs that eventually become opportunities and customers.

Of course, the ultimate goal is to drive revenue. Track how changes in your MQL to SQL conversion rate impact your overall revenue.

Don't forget to look at your cost per SQL. Usually our clients see a decrease in the cost to generate each SQL when they improve MQL to SQL conversion rate. This is a great metric to check the ROI of your marketing and sales efforts.

I highly highly recommend implementing a system for your sales team to provide feedback on the quality of MQLs they receive. Nothing fancy, could be as simple as a 1-10 rating system. This feedback will help you refine your Marketing Qualified Lead definition and your lead scoring system.

Track how well your intent scores correlate with actual conversions. In my experience, depending on your business, certain intent signals are more predictive than others.

Look at which channels are producing MQLs with the highest conversion rates. This can help you optimize your marketing spend. Similarly, track which pieces of content are associated with MQLs that convert at higher rates.

Finally, keep an eye on your overall sales cycle length. As you get better at identifying high-intent leads, you should see a decrease in your overall sales cycle length. We had a client who saw their average sales cycle shorten by 20% after implementing an intent-based strategy. They were not only closing MORE DEALS but closing them FASTER.

MQL to SQL Conversion Rate Challenges and Solutions

While leveraging intent data to improve MQL to SQL conversion rates can be super powerful, it's not without its obstacles.

Data quality and integration is a big one. S how can you trust the intent data I'm getting? It's a valid concern. At CustomerBase AI, we've put a lot of effort into developing a robust data science framework to ensure the quality of our intent data. But regardless of where you're getting your data from, it's crucial to work with reputable intent data providers.

You also need to integrate it properly into your existing systems. Your intent data should seamlessly flow into your CRM and marketing automation systems.

Another common challenge is an overwhelmed sales team. The solution here is not to just dump all your intent data on your sales team. You want to provide them with actionable insights, that's it. Use lead scoring models that incorporate intent data to prioritize leads. And don't just tell them a lead has a high intent score – explain why. Context is key here!

Privacy concerns is another one. With regulations like GDPR and CCPA, you need to be careful about how you're using data. Always ensure you're complying with data privacy regulations.

Measuring ROI. You need to justify the investment. You want to establish a clear baseline before implementing your intent data strategy. Track not just conversion rates, but also metrics like deal size, sales cycle length, and customer lifetime value (CLV).

Information overload is another one. With so much data available, it's easy to get overwhelmed. The key is to focus on the signals that have historically been most predictive of success for your business. Don't try to act on every intent signal! Use AI to help prioritize and interpret intent signals.

Lack of context is a challenge I see many companies struggle with. Remember that intent data is just one piece of the puzzle. You want to combine it with other data points like firmographics, technographics, and your own first-party data to get a complete picture of your prospects.

Finally, don't fall into the trap of short-term thinking. While intent data can provide immediate benefits, the real power comes from long-term, systematic use. Don't expect overnight miracles!

Conclusion

I hope you're as excited as I am about the potential of intent data to transform your MQL to SQL conversion rates. From my experience working with clients at CustomerBase AI, I've seen the power of aligning your sales and marketing efforts around high-quality, intent-driven data.

Improving your MQL to SQL conversion rate isn't just about generating more leads or closing more deals (although those are certainly nice benefits). It's about creating a more efficient, effective, and aligned revenue generation machine. It's about providing a better experience for your prospects and customers. And ultimately, it's about driving sustainable, predictable growth for your business.

Measure, learn, and optimize. Don't be afraid to experiment and iterate based on what the data is telling you.

Never lose sight of the human element. While data and technology are powerful tools, at the end of the day, B2B sales is still about building relationships and providing value. Use intent data to enhance, not replace, the human touch in your sales process.

If you have any questions or want to learn more about how CustomerBase AI can help you leverage intent data to drive growth, don't hesitate to reach out on LinkedIn. Let's grow your CustomerBase together!

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