Predict customer purchases through hyper-personalized emails
SwiftERM uses AI to analyze customer behavior like product views and purchase history to predict what they'll buy next. It automatically sends tailored email recommendations at the optimal time, helping e-commerce businesses increase sales without manual effort. The tool focuses on maximizing customer lifetime value while reducing marketing overhead costs.
Runs 24/7 without staff input or campaign management
Identifies next likely purchase using behavioral data
Measures revenue impact with case studies showing 1,400%+ returns
Customizable email templates that auto-populate product recommendations
Works with Stripe and Microsoft Azure Marketplace
Install the plugin and start free trial immediately - works autonomously once implemented
Purchase history, product views, time spent on pages, and visit frequency
Fashion, groceries, luxury goods, pet supplies, and other repeat-purchase verticals
This tool is best for:
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SwiftERM can be integrated with these apps and services:
Recent data shows that SwiftERM has 14.0K monthly visits (25.7% increase from the previous month), 40.0% bounce rate, and average 1.58 pages per visit.
Traffic is primarily driven by 6 different sources, with users from 5 countries worldwide, led by United States contributing 21% of total traffic. SEO performance is shown by 5 tracked keywords, with "why are people so successful with ecommerce" being the top-performing keyword with 230 monthly searches. See below for more info.
Monthly Visits
14.0K
Pages per Visit
1.58
Bounce Rate
40.0%
Average Time on Site
35s
| SEO Keyword | Volume | CPC |
|---|---|---|
| why are people so successful with ecommerce | 230 | - |
| why is prediction so important in ecommerce | 80 | - |
| what is maslow's hierarchy of needs in marketing | 140 | - |
| list of biggest online retails uk | 60 | - |
| ecommerce companies uk | 40 | - |
| Source | Traffic Share |
|---|---|
| Direct | 38% |
| Search | 49% |
| Social | 3% |
| Referrals | 8% |
| Paid Referrals | 1% |
| Country Name | Traffic Share |
|---|---|
 | 21% |
 | 17% |
 | 16% |
 | 10% |
 | 9% |
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