The questions that come up every Monday in commerce

- answered without a SQL ticket.

Checkout conversion, second orders, CAC, promo incrementality, stockouts, returns. Pick one and see where a generic agent gets it wrong, and what ClarityQ does instead.

You ask:

“Why did checkout conversion drop for mobile traffic last week?”

How it works

Why other agents break

Other agents mis-join sessions to orders, count bot traffic, and can't separate a payment failure from a shopper leaving.

How ClarityQ resolves it

Aligns sessions by device and traffic source, filters bots and failed payments, and isolates exactly which step lost shoppers.

You ask:

“Which first-time buyers come back for a second order, and what did they buy first?”

How it works

Why other agents break

They can't bridge order history with the product catalog and customer identity across guest and logged-in checkouts.

How ClarityQ resolves it

Resolves customer identity across orders, groups by first-purchase category, and projects repeat curves by cohort.

You ask:

“Which channels deviate from our LTV forecast - creative or customer quality?”

How it works

Why other agents break

They fail to cross-reference ad platform spend, attribution and downstream order margin.

How ClarityQ resolves it

Reconciles channel aliases across platforms and splits variance into CAC shifts vs. post-purchase drop-off.

You ask:

“Did the 20% promo bring new customers or just discount the ones who would have bought anyway?”

How it works

Why other agents break

They report promo revenue and miss cannibalization, margin and the pull-forward from next month.

How ClarityQ resolves it

Compares promo buyers against matched non-promo cohorts, measures incremental orders and nets out the margin given away.

You ask:

“Which SKUs will stock out before the holiday peak at current sell-through?”

How it works

Why other agents break

They only report historical sales and can't project demand against inventory on hand and inbound.

How ClarityQ resolves it

Joins sell-through velocity with stock levels and lead times, projects stockout dates and flags reorders by priority.

You ask:

“Which products drive returns, and is it sizing, quality or expectations?”

How it works

Why other agents break

They can't connect return reasons to product attributes, reviews and the shopper's prior returns.

How ClarityQ resolves it

Links return reasons to SKU attributes and review text, and separates serial returners from product problems.

ClarityQ has been a real game-changer for us! It lets our entire gaming team work with data at scale. We’re querying billions of events and dozens of tables, with in-depth analysis delivered in seconds. It empowers both analysts and game owners with 5× greater efficiency and real data superpowers - saving countless hours of analysis and enabling faster, better decisions.

Guy Tomer

Co-founder & COO, CrazyLabs

ClarityQ became a critical decision layer for us, turning complex behavioral data into clear actions. We identified friction points in our onboarding and loyalty mechanics, which led us to remove low-impact features and focus on real revenue growth.
‍

Avner Azulay

Director of Product, Zaffic

ClarityQ lets our Marketing and Product teams get answers in minutes instead of hours. We used to juggle Firebase, GA, AdMob, Adjust - now everything lives in one place. Our analysis is easily 10x faster, saving us hours every time. But the real game-changer is the insights we wouldn't have found on our own - things we didn't even know to look for.

Eduardo Carqueja

CEO, Appgeneration

ClarityQ (or "Clara" as we refer to it) functions as a co-pilot analyst providing me and other stakeholders (including users who are resistant to use the standard reporting tools available in the company) with business insights via very friendly and convenient interface and visual outputs.

Mark Shohat

Head of Business Analysis, Spiral Interactive

Thanks to the ClarityQ Agent and strong team support, we found the perfect analysis wingman. It has significantly sped up our deep-dive analyses, is expected to cut analysis time by at least half and has freed up time to focus more on strategic work.
‍