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Reducing unqualified leads with personalisation

MQL definitions impacting performance metrics?

Tracking where and why prospects typically drop off may open the door to closing more deals. 

Here's how Vanessa Hili reduced unqualified leads and increased conversions by 15% by aligning activity with buyer intent.

Industry and company size
  • 5000 employees
  • E-commerce
Funnel stage
  • MQL > SQO
Playbook impact
  • 20% reduction in unqualified leads
12x headshot images for fix your funnel playbooks_Meg copy 12

Vanessa Hill

Marketing Operations Manager

Optimising conversion rates
Designing impactful nurtures
Moving from low-intent prospects to high intent buyers

Let's jump in 👇🏻

💡 What was the problem?

Vanessa tracks everything; she speaks to the marketing team to understand what campaigns are going on, the target audience for that campaign, what channels they are using, and how much is being spent. 

This is then reported in the dashboard so she can understand where MQLs are dropping off and why. As she explained:

“Recently, we’ve been struggling to convert low-intent MQLs. The problem was that we were classifying a lead as an MQL when it wasn’t.”

“We did a campaign with a content syndication tool and reached out to a list of our target audience with a report we thought might attract their attention. After we got their data, we had an SDR reach out to them. As soon as we got these leads, we classified them as MQLs but really struggled to progress them down to SQL.”

💡 Tracking drop-off points

To better understand why this might happen, Vanessa set up regular meetings with the demand generation managers and the SDRs and created a feedback loop to identify the drop-off points and at which stages they occur through data analysis.

That way, she can determine which leads need nurturing and which campaigns are working.

A day before that meeting, she’ll look at the data and pinpoint which areas to highlight to the team so that they can add insights and help provide solutions.

Here, customer journey mapping and identifying all the touchpoints with those customers is key.

She uses Zoho as a CRM tool. Zoho Analytics links with Google Analytics and syncs it with their CRM. 

This then syncs with her company’s website to analyse these drop-offs. As she explained:

“We set benchmarks to understand what the disqualification rates are and why they’re happening. We kept track of that progress.”

💡 Improving MQLs to SQLs

After analysing the data, the problem became clear: the leads weren’t nurtured enough because the content wasn’t high intent enough. 

Vanessa started tracking the pain point relevant to that customer when it was converted so that she could use content to try and push them down the funnel. As Vanessa shared:

“We began to ensure that if we target a specific campaign to an inbound lead, that campaign relates to a pain point the customer would face.”

“So if the lead came to us because our solution was more cost-effective, we would use content similar to that theme.”

Here, email marketing campaigns, LinkedIn and PPC campaigns helped send these leads more informative content to help them understand who the company was and push them further down the funnel.

💡 Tracking the key metrics

To test these campaigns, Vanessa implemented automation to help identify engagement scoring.

The CRM tracked website visits and how long a person has been on the website for. After they’ve spent more than two and a half minutes on a website, she added this as a touchpoint. As she explained:

“We track form submissions and downloaded reports or contact forms and demo requests.” 

Vanessa also used a tool called Content Square, which provides metrics on drop-off rates on the company website and for specific landing pages.

She also A/B tests landing pages to test new versions and tactics.

💡 What were the results?

By refining the MQL criteria and improving communication between teams to tweak and improve paid media channels, Vanessa achieved a 20% reduction in unqualified leads entering the pipeline, increasing overall conversion rates by 15%. 

By tracking each customer journey stage, Vanessa could also identify drop-off points and why leads failed to progress to SQL. 

This allowed Vanessa to determine which leads needed nurturing and how to improve personalisation through their preferences. As she explained: 

“Through our reporting, every quarter, we can analyse the campaigns, the content, and the job titles that converted from those campaigns. That way, we can match and ensure our personas are aligned and are converting.” 

Following this, Vanessa saw a huge decrease in drop-off rates and lead leakage, which ultimately increased the number of MQLs converting to SQLs.

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