Introduction:

Simply put, more leads mean more customers. The more customers there are, the higher a business' profits will be. 

However, in reality, lead generation isn't always easy. And finding qualified leads is even trickier.

To help you in your quest to find better leads, try Ari Meisel's own automated lead generation process.

As the founder of Less Doing says himself:

"What if I could show a machine learning algorithm a bunch of my current clients and it could create a model based on them to compare against any new potential lead? Would the machine be able to “pick a winner” in a way that I never could?

Spoiler Alert: It can, and it does it with 93% accuracy." - Ari Meisel, How to Teach a Machine Learning Algorithm to Identify Your Customers

This checklist will guide you through beginning tasks - where you'll gather your customer data and use applications to enrich it - all the way through to later steps, where you'll be making the very same automatic Zap (via Zapier) that Meisel uses.

With the Zap, once a new email newsletter signup is tagged as a customer, your team will be notified. The sales team can then go in and do what they do best: Follow up with the lead and secure the sale.

Follow Ari Meisel's steps to upgrade your standard lead generation process to a successfully automated one. 

Get your customer data:

To kick-off Ari Meisel's process for better lead generation, you're first going to need to obtain your customer data.

The next task will inform you of how to do that effectively.

This checklist makes use of stop tasks. This means that critical tasks need to be completed before the user can move on. Watch the video below to learn more about how stop tasks work on Process Street.

Gather customer data

Gather customer data from your systems.

The first step in the process is to gather customer data. This can be found in systems such as email systems, like CovertKit or Mailchimp

As Ari Meisel says:

"You probably have a list of your customers somewhere, people who have actually paid you money for something. It might be in your email system (Convertkit, Mailchimp, etc…) or in your payment process (ACH, Stripe, Recurly, etc…) or maybe you have them written in a ledger in your desk drawer, doesn’t matter to me as long as you have at least ten of them. You want a list that includes as equal of a number as possible of customers and non-customers. You can just grab random ones from your newsletter for the latter group." - Ari Meisel, How to Teach a Machine Learning Algorithm to Identify Your Customers

Enrich the data:

The next section in this process focuses on enriching the data. That is, using ClearBit to find important data points.

The following tasks will guide you through email data enrichment.

Use ClearBit to gather more data

Use ClearBit and upload your file of emails to gather more data.

ClearBit is a marketing data engine which helps you better understand your customers.

Ari Meisel suggests using ClearBit as it can enrich your emails with up to 85 points of publically available data, such as company size, company description, location, and more.

Upload your file of emails so ClearBit can gather the data.

"I like a tool called ClearBit which can “enrich” your emails with up to 85 points of publicly available data like location, company name and size, seniority, Twitter bio, LinkedIn company description, etc…you upload your file of emails and it will return one with all the data added to it." - Ari Meisel, How to Teach a Machine Learning Algorithm to Identify Your Customers

Save the CSV file ClearBit generates

Save the CSV file which ClearBit creates in an easy-to-remember location.

The CSV file created via ClearBit should contain the information which you saw on ClearBit: Company size, company description, location, and other kinds of integral data.

You will want to save your CSV file from ClearBit in an accessible location on your computer, as you'll be uploading the file to another program in the next step.

Teach the machine:

The next section is focused on the machine learning aspect of Ari Meisel's process. This is made possible by utilizing a tool called MonkeyLearn.

Follow the next step to start using MonkeyLearn effectively.

Use MonkeyLearn to upload the CSV file

Use MonkeyLearn to upload the CSV file.

The next step in the process is to use business software MonkeyLearn, a tool which creates more value from your data by using custom machine learning.

You'll want to create a new classifier, upload the CSV, and choose the data points you want to analyze.

Like Ari Meisel himself says:

"Now go to MonkeyLearn (use coupon code lessdoing for 50% off your first three months), create a new classifier, and upload the csv. It will let you choose which of the data points you want to look it. That part is up to you depending on what's most relevant to what you’re looking for. Then it’s just a matter of telling it which ones are customers and which ones aren’t." - Ari Meisel, How to Teach a Machine Learning Algorithm to Identify Your Customers

Use the subchecklist and the GIF below as reference points.

  • 1
    Log into MonkeyLearn
  • 2
    Create a new classifier
  • 3
    Upload the CSV file
  • 4
    Choose the data points
  • 5
    Define who are customers and aren't customers via tags

Build the automation:

Now that the classifier has been built, it's time to create the automation itself. 

Follow the next steps to fully set up the lead workflow automation in Zapier.

Create a Zap for new email newsletter signups

Create a Zap in Zapier for new email newsletter signups.

In this Zap creation stage, you're going to want new newsletter signups to be tagged as customers or not and then be notified about customers.

Follow Ari Meisel's text below and the two GIFs he's created to set your Zap up correctly.

"We’re going to create a Zap using Zapier that takes any new signup to our newsletter, enriches the data with Clearbit, shows it to MonkeyLearn to tag it as a customer or not, and then if it is a customer, we need to be notified somehow.

For the classification text, you’ll have to choose whichever data points you decided to build the model based on. So at the point the [first] image finishes, Clearbit has pulled in all the info on the person's email but you need to tell MonkeyLearn just to look at the relevant data points." - Ari Meisel, How to Teach a Machine Learning Algorithm to Identify Your Customers

Choose where and how you want to be notified about signups

Choose where and how you want to be notified about new signups.

With Ari Meisel's lead generation Zap, the endpoint is being notified about signups tagged as customers. 

The last step in building the Zap is to choose specifically how you'll be notified.

As Ari Meisel suggests:

"I chose to get notified with a Slack message but you could make it an email, a text message, a Trello card, whatever works best for you." - Ari Meisel, How to Teach a Machine Learning Algorithm to Identify Your Customers

Extra steps:

The Zapier automation has now been fully set up, and it's ready to work its magic in the background. Make sure you turn the Zap on in the Zapier editor.

Thanks to process improvement, you've now bettered your lead generation process.

For ideas on how to develop your lead generation even further with business process automation, read Ari Meisel's suggestions in the next task.

Consider additional automations

Consider the additional automations suggested by Ari Meisel below.

You've now set up the process for a better lead generation. However, you can take the process one step further by creating additional automations.

Read Ari Meisel's suggestions below. If they sound usefully applicable to you and your business, try building them yourself.

"1. Create an additional set of automations to continue to improve the model by comparing the model's prediction against what actually ended up happening.

2. Automate the outreach to the person identified as a good prospect." - Ari Meisel, How to Teach a Machine Learning Algorithm to Identify Your Customers

Sources:

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