Scaling demos & improving lead quality at Guider

Here’s a detailed approach I used to scale the number and quality of leads at Guider. This process combined targeted campaigns, continuous experimentation, and strategic optimisation across multiple touchpoints in the client acquisition funnel.

I started by mapping out the existing customer journey, identifying key data points in core stages such as awareness, consideration, and decision. This allowed me to pinpoint specific areas where targeted experiments could be most effective, ensuring that each stage of the funnel was addressed with precision. As part of this, I utilised GA4 for a backwards exploration from previous leads to understand what pages were viewed in these conversion journeys, producing valuable insights for the next step.

Based on insights from analytics, marketing principles, and user feedback, I developed hypotheses for each funnel stage. For example, I hypothesised that reducing lead friction could significantly boost conversions. To test this, I experimented with dynamic CTA sections in popular content areas and surveyed web users on key pages to understand their intent. I structured these experiments into both ‘Ideal Experiments’ (IDE) and ‘Minimal Viable Experiments’ (MVE), allowing for quick insights and later scaling based on performance.

I defined clear KPIs for each experiment to align with our strategic goals. These KPIs—such as conversion rates on the pricing page or ROI calculators—were critical for measuring the success of each initiative and directly tied to increasing the number of qualified leads and sales.

Using the ICE (Impact, Confidence, Ease) prioritisation method, I focused on experiments with the highest potential impact. For instance, experiments that promised significant improvements in lead generation with minimal implementation difficulty were prioritised, allowing us to achieve quick wins and build momentum.

I launched experiments in phases, starting with those that were likely to yield the most valuable insights. For example, I rolled out each element of our Always-On campaigns separately, optimising them to reach a good performance level, before testing them. This phased approach enabled real-time adjustments and continuous refinement of our strategies.

Continuous monitoring of experiment outcomes was crucial. I used advanced analytics tools to track KPIs and make data-driven decisions. For example, optimising CTA placements and page structure led to a 41% increase in views on high-intent website pages. I also used Google Search Console to identify SEO growth opportunities, resulting in a 15% increase in organic search traffic between Q3 and Q4 2023. This iterative process allowed for ongoing improvements and informed future experiments.

Once an experiment proved successful, I scaled it up to maximise impact. This often involved extending the experiment to additional channels or broader audiences. For instance, a multi-channel demand generation campaign on LinkedIn and Google Ads was scaled for greater reach, lead to 80 ICP demos in Q3 2023, and resulting in >£600k in pipeline opportunities.

Finally, I compiled all findings into a comprehensive playbook on Notion and recorded multiple Loom videos when I left the company. This documentation served not only to guide future strategies but also as a resource for other teams within Guider. By sharing these insights, we ensured that our learnings could be leveraged across the organisation for even greater success.

This structured approach led to substantial improvements in lead generation at Guider, driving significant revenue and ensuring that our marketing efforts were both scalable and sustainable.