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Sales & revenue reporting

Consolidate Shopify, Amazon, and POS sales into one clean report. Normalize SKUs, strip refunds, and roll up revenue by product and period automatically.

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The prompt

I want to consolidate revenue across Shopify, Amazon Seller Central, and our POS into a single clean sales report. Can you build me a flow that pulls order line items from all three sources, removes cancelled and refunded activity, normalizes SKUs and dates, and rolls everything up by product category and period?

Just copy and paste the prompt into a new Parabola flow to get started.

What Parabola builds

A workflow with seven steps you can edit:

1. Pull each channel. Shopify orders, Amazon Seller Central transactions, POS exports, NetSuite invoice lines, marketplace sales. One pipeline per source.

2. Filter out the noise. Cancelled orders, refunds, test transactions, internal sample shipments. Each gets tagged at the top of the flow so the downstream math stays clean.

3. Standardize the SKU. Trim, uppercase, strip retailer prefixes, map between code systems. The same SKU sold on Shopify and Amazon lines up to one row.

4. Standardize the date. Channel timezones, settlement date vs order date, fiscal vs calendar period. Whichever convention finance uses, the flow applies it consistently.

5. Join to the category map. SKU rolls up to product, product to category, category to brand. The mapping table lives in the flow, owned by the team.

6. Calculate the metrics. Gross revenue, net revenue, units, average order value, refund rate. Per category, per channel, per period.

7. Output the report. Roll-up dashboard for the leadership cut, detail table for the analyst dig, optional Slack alert when a channel diverges from the forecast.

Why teams stop doing this manually

Every channel exports sales differently. Shopify has its own order schema. Amazon Seller Central uses settlement reports that arrive two weeks late. The retail POS spits out a CSV with a different column layout each quarter. The analyst job is to stitch them all together by noon every Monday.

The manual version works fine at one channel. Add a second, and the SKU codes stop matching. Add a third, and refund handling becomes its own job. By the time finance asks for revenue by category by channel by month, the analyst spends Monday morning rebuilding the report, then half of Tuesday answering questions about why the numbers do not tie to NetSuite.

The work is repeatable. Same pull, same cleanup, same roll-up, every period. The math is straightforward once the data is clean. That is exactly the kind of work that lives in a flow. The analyst stops being the data plumber and starts being the analyst.

How it works

Step 1. Paste the prompt.

Open Parabola, paste the prompt in section 2, and let it ask follow-up questions about your channel mix, your fiscal calendar, and the category structure finance uses.

Step 2. Connect your data.

API connections to Shopify, Amazon Seller Central, your POS, and NetSuite. Plus the SKU and category mapping tables.

Step 3. Run it on a schedule.

Daily, weekly, or on-demand. The flow refreshes against the latest data each time. Add a new channel by adding a pipeline. Update the category map without rebuilding anything else.

Related prompts

FAQ

How does the flow handle Amazon Seller Central settlement timing?

Settlements arrive on a delay. The flow tags each transaction by its source date and reconciles back to NetSuite when the settlement file lands. The category roll-up stays consistent across both views.

What about returns and refunds that span periods?

A return logged this period against an order from last period gets attributed per the policy. Configurable: net out at the order period, or recognize the refund in the period it processed.

Can the flow show revenue by retail customer or wholesale account?

Yes. Add the customer dimension at the join step. The roll-up then splits by customer alongside channel and category.

How does it handle promotional discounts and coupons?

Each discount line gets categorized at the standardization step. The roll-up shows gross revenue, promotional discount, and net revenue per category.

How is this different from a BI dashboard?

A BI tool reads from clean data. This flow is what produces the clean data. Connect the BI tool to the flow output and the dashboard refreshes against numbers everyone trusts.
One sales report, every channel, every period.
Paste the prompt, point it at your channels and category structure, and let the consolidation refresh on schedule.
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