Find email address and write personalized copy from press clips and recent personal or professional posts.
In this workflow, you'll be able to:
- Find target companies (and their LinkedIn profiles) based on specified criteria like location, size, employee count
- Enrich companies with pain points, feature sets, and company narratives
- Use AI to turn enriched data into personalized product demo snippets
- Send those personalized product demos to your prospects at scalee
In this template you can:
- Find company domains and enrich them with funding data
- Find and scrape these companies' Crunchbase URLs
- Generate a summary of the reasons for the fundraising and what the company is most excited to do next
This workflow uses Claygent to enrich school districts based on numerous data points. Specifically, you'll be able to find out:
- School district URL
- School superintendant
- School district size
- Student-teacher ratio
- Average SAT score
- Average ACT score
- City Locations
This campaign pulls in an existing list from the client's Hubspot or Salesforce Account. This list can be imported either from a CSV or from the native import feature in Clay.
Low Hanging Fruit is a term commonly used in sales that refers to leads that are most likely to convert. In theory, it'd be much easier to book a meeting with somebody who is already familiar with a client's company and their offering. This is why the re-engage can work well.
It's also a strong campaign because there's most likely existing data points about each prospect/lead that we can utilize in the copy. We'd run this campaign for a client pretty early on in our engagement with them especially if they have an existing inbound work-flow. The copy will reference whatever activity led to the prospect to be on the HS / SFDC list, whether that be a form-fill, a demo/meeting request, a meeting held, etc. A brief 1-liner overview of the client's offering should also be worked into the campaign. Additionally, referencing recent news or a problem the prospect may be facing that the client solves should be worked into the copy using AI prompting.
This generated us roughly 30 positive replies over the course or 2.5 months and 15 of those replies were meeting requests.
