FAQ
Questions before you trust us with a price change.
Direct answers about what Parbat does, what can go wrong, and what stays under your control.
A short list of specific price changes worth considering. Each recommendation includes the expected revenue and gross-profit impact, a confidence level, and the reasoning behind it. You decide whether anything ships.
They may notice a price change, but Parbat is not surge pricing or per-customer pricing. We recommend deliberate catalog changes within your constraints. Nothing goes live without your approval.
We compare the result with what we projected and show you where we were wrong. You keep control of rollback. The point is not to pretend every forecast is perfect; it is to make smaller, measured decisions and learn from the outcome.
That’s exactly what the beta is for. We have one design partner in production and early results we’re happy with. We’ll share the accuracy number as it develops, including if it’s worse than we hoped.
No. Parbat recommends; you approve. There are no automatic changes and no silent updates. Your team remains the final decision-maker.
Parbat reads catalog and performance data, finds the strongest opportunities, estimates what each move is worth, and checks the result after an approved change ships. The beta is designed around two to four deliberate moves, not constant repricing.
No. Keep the tools that show what happened. Parbat uses your data to help decide what to do next, then measures the result against the projection.
Price changes propagate with the change. Matching displayed, landing-page, and checkout prices is a hard requirement, not an afterthought.
Not in this beta. Subscription pricing affects existing cohorts as well as new customers. One-time-purchase SKUs only for now.
Traffic, conversion, sales velocity, inventory position, margin or COGS, and price history. We also need your pricing constraints, including MAP agreements or retail-partner rules. We will tell you if the available data is not sufficient.
We use it to analyze your catalog and measure approved changes. Anonymized, aggregated outcomes are a condition of the free beta; the named case study and reference calls stay opt-in with written approval.
Traditional tests need enough orders per group and several weeks per move. That works for a few high-traffic products, not an entire catalog. Parbat helps narrow the field before exposure, then learns from the real result after an approved change ships.
No. The beta is designed for Shopify brands doing $2M–$20M a year, with 15+ products that each take 20+ orders a month — roughly 1,500+ product-page sessions each — and 40%+ gross margin on the products we would work on. A good fit also does 80%+ of revenue through its own Shopify storefront, has cost-per-item populated for most of the catalog or can add it in week one, and can make pricing decisions without a three-month process. If you are not a fit, we will say so plainly.