LTV Analytics ‑ ScaleX AI

Name: LTV Analytics ‑ ScaleX AI
Rating: 5 (4 reviews)
Author: Digital Mozarts

by Digital Mozarts

View on Shopify

LTV & Retention Analytics

Analytics

Rating: 5.0
Reviews: 4
Pricing from: —
Launched: Jan 2024
Est. installs: Unlock
Est. lifetime revenue: Unlock

First seen 18 Sep 2026 · Data updated 1 week ago

AI review summary

Mostly positive

Merchants are broadly positive about the app and single out fast onboarding and responsive support. The main frustrations concern pricing tiers and occasional sync delays, and recent reviews suggest sentiment is stable.

What merchants praise

Responsive support
Many merchants mention quick, helpful replies.
Easy setup
Several got started within minutes.

Common complaints

Pricing tiers
Several feel key features sit behind the higher plans.
Sync delays
A few report data taking hours to update.

Feature requests

Bulk editing
A few want to change many items at once.

Competitor opportunities

Transparent pricing
A rival with simpler plans could win merchants who feel nickel-and-dimed.

Based on 4 reviews · generated 1 second ago

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Worst recent reviews

1- and 2-star reviews reveal what this app gets wrong: the gaps a competitor could fill.

★★★★★

The app stopped syncing after the last update and support took a week to reply. We lost orders during the outage and had to switch to a competitor.

★★★★★

The app stopped syncing after the last update and support took a week to reply. We lost orders during the outage and had to switch to a competitor.

★★★★★

The app stopped syncing after the last update and support took a week to reply. We lost orders during the outage and had to switch to a competitor.

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About LTV Analytics ‑ ScaleX AI

Are you fully utilizing your store's data? Do you know your customers' true lifetime value and how to leverage it for acquisition optimization? Which products are best at retaining customers? Are you tracking loyal, promising, and other customer segments to boost repeat purchases?

Highlights

Optimize marketing spend by focusing on high-value customers
Forecast future revenue based on customer behavior
Improve product recommendations by understanding customer preferences
Improve promo planning by understanding the performance of past promos

Languages

English