Validating Growth Pathways: How We Turned 10,000+ Leadership Quiz Responses into Revenue-Driving Insights

The Wiseman Group’s digital presence reflects Liz Wiseman’s* commitment to advancing leadership development through research-backed approaches. Their website offers valuable resources for leaders and organizations looking to enhance team performance and unlock collective potential. Visitors can explore Liz’s influential works, including Multipliers and Impact Players, along with practical insights drawn from her extensive research. The site also details their comprehensive development programs, from focused workshops to personalized coaching and strategic consulting partnerships.

Building the Foundation: Customer Lifecycle Dashboard

Like many growing businesses, The Wiseman Group initially came to us with a fundamental need: they wanted clear visibility into their customer lifecycle. While they had built an impressive range of offerings, they first needed to understand the basic journey their customers were taking – from initial attraction through to loyalty and referrals.

Our first step was building a comprehensive data dashboard that could track customers through each crucial lifecycle stage:

• Attraction: How customers were finding them
• Capture: Initial engagement points
• Nurture: Ongoing relationship building
• Convert: Purchase decisions
• Loyalty: Quality of service and continued engagement
• Upsell/Referral: Extended customer value

This foundation of lifecycle tracking would prove crucial. By connecting various data sources to measure each stage effectively, we began revealing patterns in how customers were moving through their journey. Even this initial view provided valuable insights about where customers were engaging strongly and where they might be dropping off.

In this diagram we can see all their data sources mapped to where we want to track them in the customer lifecycle. At each stage of the funnel we then track one or two primary KPIs to monitor how that lifecycle stage is doing in relation to the overall health of the entire lifecycle.

You don’t want to track more than one or two metrics at this top-level view because then you get too deep into the details. So careful consideration at this top-level report needs to be placed on what are the most important metrics for your customer, that help them achieve their goals.

When more specific questions then surface, you can always dive deeper into drill down reports and look at all the underlying numbers to validate or invalidate your questions and assumptions.

The data sources we are using to pull in the data in this case include:

• Google Search Console
• Google Analytics
• Google Tag Manager
• Google Ad Words
• Mailchimp
• HubSpot CRM
• WordPress – WooCommerce
• QuickBooks
• Amazon Author Central
• HotJar
• And Google Sheets
• Social media accounts (many)

Once we had all the data sources identified, you can pull them all into a reporting tool. We use Google Looker Studio for this. The nice thing about this platform is it is very agnostic when it comes to sources and you can select from a multitude of visualizations to create clear and useful overviews.

Understanding The Wiseman Group’s Value Ladder: From Books to Keynotes

With a clear view of customer lifecycle stages established, we needed to understand how these stages intersected with The Wiseman Group’s diverse service offerings. Their business operates across multiple tiers of engagement, creating multiple pathways for customer growth and development.

The Value Ladder

Their value ladder consists roughly of eight key tiers:

1. Quiz participation – A free but valuable self-assessment tool
2. Newsletter subscription – Regular engagement through content
3. Book purchases – Often a customer’s first tangible investment
4. Premium Assessments – Deeper, more personalized insights
5. Certifications – Professional development and credentials
6. Coaching – Professional guidance
7. Workshops – Intensive group learning experiences
8. Keynotes – High-impact speaking engagements

While these tiers generally progress from lower to higher value engagements, customer journeys aren’t always linear. Some might start with a workshop and later purchase books for their team. Others might take the quiz, subscribe to the newsletter, and jump directly to coaching. The complexity of these pathways makes tracking and understanding customer movement between tiers particularly crucial.

The interesting dynamic here is that while books represent a paid product, they often serve as an entry point, whereas free offerings like the quiz and newsletter can lead to higher-value engagements. This creates an intriguing challenge: how do we identify which free-tier participants are most likely to benefit from and engage with premium services?

This complex web of offerings and customer pathways highlighted why we needed more sophisticated data analysis. Understanding not just where customers are, but how they move between these tiers – and why – would prove crucial for optimizing their entire business model. At the heart of this customer journey was their self-assessment quiz – a critical touchpoint that could provide invaluable insights into customer needs and potential.

Value Ladder Data Integration: Quiz Development and Implementation

Technical Infrastructure: Strengthening the Quiz Foundation

With the customer lifecycle dashboard in place, we turned our attention to this foundational piece of The Wiseman Group’s infrastructure: their quiz system. While the quiz was already successful in engaging users – generating responses in the tens of thousands – it was facing several technical challenges that needed addressing before it could serve as the robust data collection tool we needed.

Working closely with their team, we identified and addressed four key technical challenges:

1. Scalability & Performance

• The existing Watupro system was hitting storage limits
• Response times were slowing during high-traffic periods
• Server capacity needed expansion to handle growing audience

2. User Experience Enhancement

• Streamlined the quiz interface for better engagement
• Improved mobile responsiveness
• Enhanced accessibility features
• Result in higher completion rates and more accurate responses

3. Analytics Infrastructure

• Built robust data collection pipelines
• Created structured storage for quiz responses
• Implemented better data organization systems
• Enabled integration with other business metrics

4. System Resilience

• Implemented stress testing for traffic spikes
• Set up New Relic monitoring for real-time performance tracking
• Created automated alerts for potential issues
• Established backup and recovery protocols

Data Integration Strategy: Connecting Quiz Insights

With a stronger technical foundation in place, we could now focus on making quiz data more valuable for business decisions. Our integration strategy focused on three key areas:

1. Data Collection Enhancement:

• Standardized data formats across systems
• Added metadata collection for deeper insights
• Implemented better tracking of user progression
  

2. Cross-System Connections:

• Connected quiz responses to customer profiles
• Linked assessment results with purchase history
• Integrated with email marketing systems
• Established connections to sales data

3. Reporting Framework:

• Created automated data pipelines
• Developed standardized reporting templates
• Built custom dashboards for different stakeholders
• Enabled real-time data access for key metrics

With a more robust quiz system in place and better data collection capabilities, we could now begin exploring deeper connections between quiz responses and other aspects of their business.

From Quiz Responses to Business Intelligence

We approached data analysis in three progressive stages, each building on the last to create deeper insights:

Stage 1: Basic Trend Analysis

We started with Google Looker Studio to visualize straightforward patterns:

• Quiz completion rates over time
• Popular quiz sections and responses
• Basic conversion patterns
• Time-based trends

This gave us immediate insights into general patterns and quick wins for improvement.

Stage 2: Linear Relationship Analysis

We then moved to analyzing relationships between different data points:

• How quiz scores correlate with purchase likelihood
• Which quiz responses indicate higher engagement
• What patterns emerge in the customer journey

The graph below shows a simple example: the relationship between website visitors and eventual purchases. This helps answer basic “what-if” questions like “If we get X more visitors, how many sales might we expect?”

This graph shows a linear relationship between number of customers and number of purchases (exact data hidden for privacy of our client). Through data modeling like this we can then answer questions like: “If we pay/work to get X more visitors to take the free quiz, how many more sales of product X should we expect?” This then helps us answer key questions like: “Is this investment worth it? Will we make an ROI?”

Stage 3: Advanced Pattern Recognition

Now we’re developing more sophisticated analysis using machine learning, but in practical, business-focused ways:

What It Is:

• Custom algorithms that look for complex patterns in customer behavior
• Models that can predict likely next steps in a customer’s journey
• Systems that can identify which customers might need different types of follow-up

How It Works:

• The system analyzes thousands of past customer journeys
• It identifies patterns that humans might miss
• It learns which combinations of actions tend to lead to specific outcomes

Practical Example:

The scatter plot below shows how we can predict customer engagement levels based on multiple factors. Each dot represents a customer, and the rising pattern shows how certain combinations of behaviors (like quiz completion time, score patterns, and engagement with follow-up materials) correlate with higher levels of program participation.

Jupyter data model with single and polynomial relationships between different customers and their predicted activities based on the relationship between different parameters, for example between different purchases in the value ladder. Note: Real data here is obscured for privacy. This graph is for illustrative purposes only.

This helps the sales team prioritize outreach by identifying which quiz participants are most likely to benefit from specific programs or services.

Connecting the Dots: From Quiz Data to Customer Journey Insights

While our initial data analysis is revealing valuable patterns, we know there is an even richer story waiting to be told by connecting quiz responses with customer purchase history and lifecycle data. By integrating their CRM system (HubSpot) with our existing analytics, we can now start mapping the complete customer journey from initial engagement through to purchase decisions and beyond.

This integration opens up an entirely new level of strategic insight. We can now identify patterns that answer more complex business questions like:

• How does quiz performance correlate with eventual service purchases?
• If someone engages with coaching services, how does self assessment completion impact their likelihood of pursuing certification?
• Which early indicators best predict a customer’s potential for higher-tier service engagement?

For their sales team, this means transforming thousands of quiz responses into prioritized outreach opportunities. Rather than treating all quiz participants equally, the team can focus their energy on prospects showing the highest likelihood of benefiting from their services.

The impact is particularly powerful when analyzing the relationship between different service tiers. For example, with this connection in place, we can validate whether encouraging a 20% increase in coach certifications would lead to corresponding growth in workshop participation. Insights like these help The Wiseman Group make more informed decisions about where to invest their resources for maximum impact.

By connecting all these data points, we aren’t just looking at isolated metrics anymore – we’re seeing the complete story of their customer journey. This comprehensive view is enabling their team to make strategic decisions based on validated patterns rather than assumptions.

Leveraging AI for Pattern Recognition: The Next Frontier

While our current data integration and analysis is already providing valuable insights, we’re excited about the next phase of this journey: incorporating AI to uncover even deeper patterns and opportunities. By setting up a private, controlled environment with an LLM (Large Language Model) specifically trained to query The Wiseman Group’s connected data sources, we can push the boundaries of what’s possible with their business intelligence.

Here are examples of how AI will transform their data strategy:

Pattern Discovery: AI can analyze quiz results and assessment data to identify subtle patterns among customers who progress to higher-tier services. These insights could reveal previously unnoticed indicators of customer readiness for advanced offerings.

Outreach Optimization: By reviewing historical performance data, AI could help refine outreach strategies based on comprehensive analysis of past successes – incorporating everything from quiz scores to engagement patterns to purchase history.

Question Generation: Perhaps most intriguingly, AI can help identify questions we haven’t thought to ask yet. By analyzing relationships across their entire data ecosystem, customer input, reviews and feedback it can surface new insight-opportunities that warrant integration.

The key to success with AI integration will be building on our existing foundation of clean, connected data. By maintaining a controlled, private environment for these AI tools, we will ensure both data security and accuracy while unlocking powerful new capabilities for data analysis.

This next step isn’t just about adding another tool – it’s about creating a system that continuously learns and adapts to help The Wiseman Group’s team make increasingly sophisticated strategic decisions.

Transform Your Data into Growth Intelligence

While The Wiseman Group’s journey shows what’s possible, we know many businesses face similar challenges:

Our methodical approach to data validation and analysis can help transform these challenges into opportunities. Through our proven process, we help businesses like yours:

Ready to turn your data into actionable growth strategies? Let’s explore how our validation-first approach can help you achieve your business goals.