10/04/2024
Dealing with low quality booked calls?
Low show rates?
Well after generating over 150,000 booked calls the past 6 years for over 300 coaches, experts, & consultants, we've developed a quant based process to fix these problems.
Our process identifies, tags, & feeds back high-quality booked calls into each respective advertising pixel, creating a feedback loop that continually optimizes lead quality over time.
This approach generates a powerful flywheel effect: as we feed the pixel with more qualified leads, the algorithms refine their targeting to attract similar high-value prospects, ultimately improving the overall quality of booked calls.
We call it the 'Qualified Call Engine' and here's exactly how we do it..
1. Developing Qualifying Questions:
The first step in establishing the MQL (Marketing Qualified Lead) process is to create effective qualifying questions.
This can be done in two ways:
Client Interview: During our Creative Call, we ask clients to categorize their customers into three tiers.
Tier 1 represents clients with the highest lifetime value and the easiest to achieve results, while Tier 3 comprises clients with the lowest LTV, who are hardest to obtain results for, and are more prone to churn or cancellation.
Here's an example of the tiers we mapped out for a 'Performance+' client this week..
By segmenting clients in this manner, we can design quantitative qualifying questions based on the key characteristics of their top-performing customers.
2. Cross-Rank Analysis:
We analyze the client's internal onboarding forms and track the amount spent across each segment. By cross-referencing this data with demographic profiles, we identify commonalities between their best clients, enabling us to refine our qualifying questions further.
Step 1: Import Google Sheet Data into ChatGPT
You need to export the Google Sheet data into a format that can be processed by ChatGPT, such as CSV. Here’s how:
Export Google Sheet to CSV:
Open the Google Sheet.
Click on File > Download > Comma-separated values (.csv, current sheet).
Upload the CSV to ChatGPT:
Upload the CSV file here, and I can read it to extract client data.
Step 2: Analyzing the Data in ChatGPT
Once the data is uploaded, we can analyze it in the following way:
Identify Top Clients:
Define "top clients" based on your criteria, such as highest lifetime value (LTV), most revenue generated, or highest engagement.
Filter and rank the data to identify these top clients.
Find Common Traits:
Analyze common attributes among top clients, such as:
Demographics
Purchase history
Average order value
Industry verticals
Campaign success rates
Create Qualifying Questions: Based on the common traits and patterns observed, create qualifying questions such as:
"Does the client have a product/service that matches the high-performing categories?"
"What is the client's average order value, and does it align with top-tier customers?"
"Is the client experiencing similar challenges or objections as our top clients?"
4. ChatGPT Prompt:
"Based on the historical data of our top clients, including their demographics, key challenges, and goals, generate three qualifying questions. Each question should have 3-4 answer options, and each option should be assigned a score ranging from -1 to 3 points based on how closely it aligns with a high-quality client profile.
Use the following criteria to guide the scoring:
Answers indicating high lifetime value or engagement should be scored as 2-3 points.
Neutral or average-fit responses should receive 0-1 point(s).
Responses that indicate a poor fit should receive -1 point.
Please ensure that the questions reflect insights derived from past data trends and are designed to effectively screen and categorize new leads according to these profiles."
Example Output from ChatGPT:
Question 1: What is your current business revenue level?
Less than $75,000 (-1 point)
$75,000 - $199,000 (0 points)
$200,000 - $499,000 (1 point)
$500,000 - $1M (2 points)
More than $1M (3 points)
Question 2: What is your biggest business challenge right now?
Generating consistent leads (-1 point)
Converting leads into paying customers (0 points)
Scaling operations and team management (1 point)
Expanding into new markets or launching new products (2 points)
Maintaining profitability and managing growth (3 points)
Question 3: How long have you been in business?
Less than 1 year (-1 point)
1 - 3 years (0 points)
3 - 5 years (1 point)
5 - 10 years (2 points)
More than 10 years (3 points)
Example of how we used this for a recent client in the Relationship Niche:
We worked with a consultant in the relationship coaching niche who needed a method to effectively qualify client income levels without asking directly about annual earnings.
Through an in-depth analysis of their existing customer base and performance metrics, we discovered that approximately 67% of their highest-converting clients, with the highest LTV, were categorized in one of three career stages: Self-Employed, Management, or Retired.
Using this data, we developed a strategic multiple-choice question to include in their booking form, asking, “What stage are you at in your career?”
The options provided were:
Student (-1 point)
Entry Level (-0.5 points)
Management (+2 points)
Self-Employed (+3 points)
Retired (+2 points)
Unemployed (-1.5 points)
Disabled (-1 point)
Using this qualifying question & two others we developed, we were able to improve the following metrics on their call funnel:
- Increase their Marketing Qualified Booked Call Rate by over 30%
- Decrease their No Show rate by 12%
- Increase their close rate by over 24%
- Increase overall return on ad spend by 178% over a 120 day period.
Hopefully this guide helps some of you put together better quant based qualification questions, qualify your prospects, and increase your profits on your call funnels :)
As always, if you have any questions, drop them below.
