We detected 1,479 customers using Shoplift.ai, 486 companies that churned or ended their trial, and 45 customers with estimated renewals in the next 3 months. The most common industry is Retail (40%) and the most common company size is 2-10 employees (50%). Our methodology involves detecting JavaScript snippets or configurations on customer websites.
Note: Our data tracks companies with Shoplift installed on their website and may not capture stores running tests only on checkout flows/gated pages
About Shoplift.ai
Shoplift.ai provides A/B testing and conversion rate optimization specifically for Shopify stores, enabling merchants to run split tests on product pages, prices, themes, and landing pages without coding.
๐ง What other technologies do Shoplift.ai customers also use?
Source: Analysis of tech stacks from 1,479 companies that use Shoplift.ai
Commonly Paired Technologies
i
Shows how much more likely Shoplift.ai customers are to use each tool compared to the general population. For example, 287x means customers are 287 times more likely to use that tool.
I noticed that Shoplift.ai users are clearly sophisticated e-commerce companies, specifically Shopify merchants running data-driven direct-to-consumer brands. The presence of Triple Whale, Klaviyo, and Elevar together tells me these companies are obsessed with tracking performance and optimizing every step of the customer journey. They're not just selling products online. They're building proper growth engines with multiple feedback loops.
The pairing of Gorgias with Rebuy Engine is particularly revealing. Gorgias handles customer support for e-commerce, while Rebuy Engine powers personalized product recommendations and upsells. This suggests these companies view customer service as a revenue channel, not a cost center. Support agents are probably trained to suggest complementary products during conversations. The fact that Heatmap appears so frequently, despite being in fewer total companies, tells me they're serious about conversion rate optimization and actually watch how users interact with their sites. When you combine this with Elevar's enhanced tracking capabilities, you get companies that want pixel-perfect data flowing into their analytics.
The full stack reveals these are marketing-led companies in growth stage, probably doing between $5 million and $50 million in annual revenue. They've graduated past basic Shopify setups but haven't necessarily moved to enterprise platforms yet. They're willing to invest in best-of-breed tools rather than all-in-one solutions, which means they have dedicated marketing and analytics teams who can manage integration complexity. The emphasis on attribution tools like Triple Whale and Elevar suggests they're running paid acquisition across multiple channels and need to prove ROI.
๐ฅ What types of companies is most likely to use Shoplift.ai?
Source: Analysis of Linkedin bios of 1,479 companies that use Shoplift.ai
Company Characteristics
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Shows how much more likely Shoplift.ai customers are to have each trait compared to all companies. For example, 2.0x means customers are twice as likely to have that characteristic.
Trait
Likelihood
Industry: Retail Health and Personal Care Products
29.2x
Industry: Personal Care Product Manufacturing
23.5x
Funding Stage: Series A
19.5x
Funding Stage: Series unknown
14.4x
Industry: Retail Apparel and Fashion
11.5x
Funding Stage: Seed
7.2x
I noticed these companies are predominantly direct-to-consumer brands selling physical products across consumer categories. They make everything from coffee and energy drinks to skincare and apparel to outdoor gear and pet supplies. What stands out is how tangible and lifestyle-oriented their offerings are. These aren't software companies or service businesses. They're companies that ship actual products to people's homes, often with strong brand narratives built around quality, wellness, sustainability, or performance.
These companies span the growth spectrum but cluster in the scaling phase. I see a mix of 2-10 and 11-50 employee counts dominating, with some reaching 51-200. Funding stages range from bootstrapped to Series B, but many show no institutional funding at all. They're past the pure startup phase but not yet massive enterprises. They have enough traction to need sophisticated e-commerce tools but are still building their operations and distribution.
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