Compound Leverage

Issue #008

Do you wish you could get more sales meetings?

I just completed a head-to-head experiment comparing LinkedIn messaging vs. cold email outreach with AI research and message writing to see if I could increase a sales meeting booking rate.

Here's a summary of the strategy goal and experiment:

The Challenge:

Booking sales meetings was slower and time-consuming, with cold emails and referrals.

I want to find a faster, more efficient way to connect with potential clients with better conversion rates.

The Experiment:

I leveraged AI tools and a revamped workflow to create personalized campaigns on LinkedIn and cold email, tracking every step in the THINK process.

The Process:

• Think: I defined the task (booking more sales meetings) and my strategic goal (faster, more efficient outreach).
  

• Hypothesis: I set up two AI-powered experiments: one for LinkedIn connection requests and personalized messages and another for highly personalized cold emails.
  

• Invest: Invested time in setting up campaigns, crafting AI templates, and researching prospects for maximum personalization.
  

• Network: Ran both campaigns concurrently, tracking key metrics like connection rates, responses, and booked meetings.
  

• Knowledge: Analyzed the data to uncover insights and refine my approach for even better results

Let’s walk through the process.

Think (T): Define the Task and Strategic Goal

I began by identifying the specific task to achieve and what strategic goal it was linked to:

• Task: Conduct a head-to-head experiment using LinkedIn messaging and cold email outreach campaigns for booking sales meetings.
  

• Strategic Goal: Increase the number of sales meetings bookings each week.

Hypothesis (H): Set Up the Experiments

Next, I outlined the workflow hypothesis for each experiment.

AI Experiment 1: LinkedIn Connect and Message

• Use Sales Navigator to create lists.

• Filter persona list for 2nd connection, location, activity

• Send 20 to 40 connection requests per day.

• Send 2nd Level connection request (No message)

• Upon acceptance, send a short, highly personalized thank you message using AI templates:

  • Sentence 1: The highly personalized reason for reaching out.

  • Sentence 2: Highlight common ground or shared interest.

  • Sentence 3: Present a value offer related to AI.

  • Sentence 4: Ask a question to encourage engagement. (Based on AI profile and web research analysis)

• Engage in back-and-forth dialogue, then request a chat

• Track response and booking rates.

AI Experiment 2: Highly Personalized Cold Email

• Build prospect lists with a Google Sheet tab.

• Use a Chrome GPT for Work extension to automate online research and email personalization.

• Run research on leads and companies using AI.

• Craft a similar AI message as in the LinkedIn experiment (Add one free AI tip based on the research results)

• Schedule campaigns with GMASS.

• Track open rates, responses, and meeting bookings.

Once done, I move to the next step.

Invest (I): Time and Tools

Then, I set up and ran daily experiments over five days to see which AI experiment many booked meetings I secured from both experiments.

I spent time:

• Setting up Sales Navigator and LinkedIn campaigns.

• Building prospect lists.

• Creating and refining AI message templates.

• Researching prospects for personalization.

• Sending messages and emails.

List the tools used:

• Sales Navigator

• LinkedIn

• Google Sheets

• ChatGPT (optional) - Analyze and recommend message

• GPT for Sheets and Docs Chrome extension for research and email creation.

• GMASS to schedule and report campaign performance

• Email sending platform (Gmail or Outlook)

Network (N): Run the Experiments and Track Rate

I checked the results of each experiment daily and tracked my success rate.

• Ran both experiments concurrently over a set period of five days.

• Tracked key metrics for each experiment:
  

  • LinkedIn: Connection request acceptance rate, message response rate, meeting bookings.
    

  • Cold Email: Open rate, response rate, meeting bookings.

• Monitored prospect engagement to document success.

Knowledge (K): Analyze the Data and Modify

I analyzed patterns and time invested in each process by:

• Analyze the booking data to determine:
  

  • Which approach generated more meeting bookings?

  • Which approach had a higher response rate?

  • What types of leads were most responsive to each approach?
    

• Review data for insights into message winners and losers.

• Identify patterns and trends to understand what led to success.

• Based on the analysis, double down on the most effective strategies and refine your approach to achieve the strategic goal faster.

Takeaways

The takeaways were this:

• Higher conversion than standard messaging.

• One sales meeting was booked off LinkedIn.

• More follow-up with email.

• Faster booking cycle with LinkedIn

• They both performed higher than standard email outreach.

• Personalization insights were key.

• Sales Navigator made it easy to engage with leads.

• Two meetings to date, One for a partnership secured and one for a new customer.

The next step is to improve experiments using the data to see if the current results remain, then explore further automation with a lead generation AI Agent.

Taking away more manual steps to accelerate bookings.

Marvin

Here are three ways to begin doubling your output with AI automation experiments whenever ready.

1. AI Strategy Session: For $200, Book a one-hour coaching session to identify quick wins, set goals, and AI integration tips for a strategic goal.

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