🆚 Drip vs. LimeSpot

Type

Marketing automation platform
Personalization software

About

Drip is a marketing automation platform for e-commerce that focuses on email marketing to help businesses automate customer outreach.

It offers visual workflow automation with behavior-based triggers, flexible audience segmentation and tagging, personalized email campaigns, performance analytics and A/B testing, and integrates with e-commerce platforms such as Shopify, WooCommerce, Magento and BigCommerce.

LimeSpot is an AI-driven personalization engine built for Shopify and BigCommerce stores to improve conversion rates.

Headquarters

Minneapolis, Minnesota, United States
Vancouver, British Columbia, Canada

Website

Pricing

Free version ❌Free 14-day trial
$39+/month
Free version ✔️ 1 order per 30 days
Turbo 6.99+/month
Max $50+/month

Categories

Marketing Automation › Rank #21
Email Marketing › Rank #27
Personalization › Rank #14
Personalization › Rank #39

Popularity

Determined by the number of sites using each technology.

The Drip marketing automation platform is 3 times more popular than LimeSpot.
Total websites

Market share

Personalization

Popularity by country

Determined by the number of sites detected from each country.

Drip is more popular in the United States, the United Kingdom, and Australia, while LimeSpot is more popular in India, Singapore, and Mexico.
United States
United Kingdom
Australia
Denmark
Canada
Netherlands
Germany
India
South Africa
Sweden

Awards

Popularity by domain category

Determined by the number of sites in each category.

Drip is more popular among sites focused on marketing and merchandising, business, and blogs and wiki, while LimeSpot is more commonly used on online shopping and fashion and beauty sites.
Online Shopping
Marketing/Merchandising
Business
Blogs/Wiki
Internet Services
Travel
Fashion/Beauty
Education/Reference
Sports
Software/Hardware

Top sites

Top-ranked sites that use these technologies.

Name
Rank
#7,528
#15,212
#18,790
#20,332
View more ➝
Name
Rank
View more ➝

See also

🗃️ About This Data