Siftmo vs WorthSignal

Siftmo is a web app that analyzes Shopify order history to report on customers, products, and store performance. It groups customers by behavior into two to eight named personas. Ask AI turns plain-English prompts into customer filters that can be inspected, edited, saved, shared, and exported to CSV.

Customer analytics include six lifetime value views, customer-level percentiles and order histories, and 18 live segments. Product and variant analytics cover gross profit, net quantity, discounts, returns, percentile rank, and frequently bought-together products. Siftmo provides 15 reports with interactive charts, tables, prior-period and year-over-year comparisons, and CSV export. Integrations listed include Shopify, Google Analytics 4, and Facebook Ads. Organization-level reporting can span unlimited stores and users, with no per-seat pricing. The free Starter plan covers up to 50 monthly orders; paid plans start at $29/mo. All plans include a 7-day trial without a required credit card.

WorthSignal is a free, open-source toolkit that turns spreadsheet and data files into customer value analyses for marketers. It reads XLSX, XLS, XLSM, CSV and JSON files, suggests column mappings and lets users correct them without changing the uploaded file. Its analyses cover nine areas, including RFM targeting, customer lifetime value, customer equity, acquisition and retention budgets, Markov switching ROI, contractual retention, BG/NBD, BG/BB and complaint recovery. Results can be downloaded as Excel or JSON. In local mode, analysis runs in memory on the user's computer; WorthSignal says uploaded customer data is not intentionally sent to its maintainer or a third-party API. Hosted deployments process files on the chosen server, and that operator is responsible for access controls, logs, retention and privacy obligations. The app requires Python 3.10 or newer and can be launched on Windows or Mac, through terminal instructions for any operating system, or with Docker. Its documented methods are described as classic, deliberately simple models. The project uses the AGPL-3.0-or-later license, with commercial use subject to its conditions.