Lead prioritization

You’ll need a Faraday account to use this template. It’s free to sign up and you can use sample data to start.

The opportunity

For brands in industries like home services, financial services, and software, the lead stage of the customer lifecycle is critical. Successfully converting leads into paying customers can have a huge impact on a brand's growth and profitability.

But what exactly is the "lead stage"? In simple terms, it's the time between:

A potential customer sharing their contact information
That potential customer making a purchase or signing up for your service

This stage is particularly important in industries where the sales process involves significant interaction with the customer. Companies often spend considerable resources on converting leads — think call centers, in-home consultations, and creating personalized quotes. As you can imagine, these efforts add up.

However, not all leads are equal. Some are much more likely to convert than others. If a brand can predict which leads are most likely to become customers, it can prioritize its efforts and maximize the return on its investments.

The Solution

This is where Faraday comes in. Using advanced AI, Faraday helps brands optimize their lead conversion process by scoring leads based on their likelihood of converting. This scoring starts the moment a lead identifies themselves — when they first share their contact information — and continues throughout the entire lead-to-customer journey.

With Faraday’s lead prioritization, brands can:

Identify high-potential leads early on
Focus resources on the leads most likely to convert
Maximize their sales team’s effectiveness and efficiency

How it works

Faraday's lead scoring is continuous, meaning brands can track a lead's potential at any point during the lifecycle. Once you know which leads are most likely to convert, you can prioritize your efforts. This could mean calling or emailing high-potential leads first, setting appointments faster, or offering personalized deals to those most likely to buy.

The value of lead prioritization

The benefits of prioritizing leads with Faraday are straightforward:

Your resources are limited — whether that's your sales team’s time, your call center’s capacity, or your marketing budget
Pursuing leads randomly results in average conversion rates
Prioritizing high-potential leads results in better conversion rates

In other words, Faraday gives you more conversions for the same amount of effort, leading to a better return on investment.

Conclusion

If your brand is serious about improving lead conversion, Faraday’s AI-driven lead prioritization is a game-changer. By focusing on the leads most likely to convert, you can significantly improve your conversion rates and make your resources go further. Ready to see the difference Faraday can make for your business?

If you're using both Faraday and Iterable, integrating Lead prioritization predictions can be really helpful. With Faraday's insights, you can identify which leads are most likely to convert and then use Iterable to engage them more effectively. This way, your marketing efforts are more focused and efficient, ensuring you don't miss out on potential customers. It’s a straightforward way to enhance what you're already doing in Iterable, making your campaigns more successful without any extra hassle.

Step 1
Connect your data sources
Use the link below to connect Iterable to Faraday. You can also skip this step and use CSV files to get started instead.
Iterable
Step 2
Ingest your data into event streams
This allows Faraday to understand what your data means. These links will guide you through ingesting the data necessary to power this template.
Signup Transaction
Step 3
Organize your customer data
You'll create groups, called cohorts, that are the essential building blocks of Faraday and allow you to easily predict any customer behavior.
Leads Customers
Step 4
Declare your prediction objectives
With your cohorts defined, it's easy to instruct Faraday to predict the necessary behaviors. Follow the docs with the link below.
Likelihood to convert
Step 5
Define your lead scoring pipeline and deploy to Iterable
Finally, deploy your prediction with the link below.
Lead scoring
Step 6
Deploy to Iterable
Create a deployment target using the Iterable connection you created above. Or, get started by simply deploying to CSV.
Iterable