Lookalikes
Find new accounts similar to a set of reference accounts and fill gaps in your TAM.
Last updated
Find new accounts similar to a set of reference accounts and fill gaps in your TAM.
Last updated
Our Lookalikes feature is designed to identify and deliver new accounts that closely resemble a specified set of reference accounts. It leverages AI to analyze the website content of these reference accounts, examining their offerings, target markets, and other publicly available attributes. Using embedding-based mathematical similarity modeling, Lookalikes then finds companies in Gradient Works’ database that exhibit high similarity to these reference accounts.
GTM teams utilize Lookalikes for various scenarios, including:
Filling ICP Gaps in Your CRM: Provide a list of ideal or target customers that meet your Ideal Customer Profile (ICP) criteria, and use Lookalikes to discover any similar companies you may be missing in your CRM
Expanding Your ABM Campaign Account List: Share a selection of accounts from your existing Account-Based Marketing (ABM) campaign to identify additional relevant accounts.
Finding Lookalikes for Recent Closed/Won Deals: Input a set of recently closed-won accounts to uncover more similar accounts to assign to your sales team, helping them build on existing momentum.
Identifying Accounts for a Market Map Cluster: Lookalikes complements Market Maps by assisting you in identifying as many potential accounts as possible within a selected high-potential market cluster that your team aims to pursue.
Use Case: Accelerating Client Acquisition for Industrial Cleaning Services
An industrial cleaning company specializing in servicing buildings with consistently high foot traffic faced significant challenges in identifying and segmenting potential clients using traditional NAICs codes, which proved time-consuming and inefficient. To overcome this barrier, the company implemented a lookalike modeling approach, targeting new accounts that closely resemble their most successful customers, such as Enchantment Resort Gallery, Green Source EPC, and CMS East. This strategic shift enabled the company to rapidly source niche clients without the extensive weeks of research previously required. As a result, they successfully added 1,200 lookalike accounts in under 15 minutes, significantly accelerating their client acquisition process and enhancing their market reach.
Lookalikes prevents the import of duplicate accounts by comparing the lookalike account names and website URLs to existing CRM records. It specifically looks for an exact match of the values in the Name and URL fields. Potential matches are scored based on their likelihood of being true duplicates and assigned a Likelihood Ranking. You can filter results based on this ranking to align with your sensitivity to duplicates, ensuring that you import accounts without creating data conflicts or assignment issues within your sales team.
CRM Duplicate Likelihood Rating
High
Both Name and Website match.
Medium
Only Website matches.
Low
Only Name matches.
This ranking system allows you to quickly assess the relevance of each suggested company and decide whether to engage with them or avoid duplicate entries in your CRM.
Through a partnership with People Data Labs, Lookalike accounts are enriched with key firmographic data you can utilize to filter the lookalike accounts to align to your ICP criteria:
Enriched Firmographic Attributes
Employee Count
Founding Year
The founding year of the company
LinkedIn URL
The main URL of the company, eg: "linkedin.com/company/gradient-works"
Last Funding Date
The date of the company's most recent funding event, eg: "2021-11-16"
Last Funding Stage
Primary Locality
Locality or City name for the company's primary location
Primary Region
Region or state name for the company's primary location
Primary Country
Country name for the company's primary location
Primary Street Address
Street address for the company's primary location
The current number of employees working at the company. For more details see
The stage of the company's most recent funding event. Must be one of People Data Lab's .
