Exporting Data: CSV Downloads and Analysis Workflows

Everything Bonesaw tracks is visible in the app: price changes, activity feeds, anomalies, weekly reports. For most day-to-day monitoring, you never need to leave the dashboard. But there are moments when you need the raw data outside the app.

Maybe you want to build a pricing comparison for a quarterly review. Maybe a team member who does not use Bonesaw needs to see what happened across your competitive set last month. Maybe you want to sort and filter changes in ways the app does not support. That is what CSV exports are for.

This guide covers what you can export, when it is worth doing, and how to build simple analysis workflows that turn a CSV download into something useful.

What You Can Export

Bonesaw offers two exports, both available on PRO plans:

Stores CSV. A snapshot of your watched stores with their platform, URL, product count, check interval, last successful scan, and active status. This is your monitoring coverage inventory. It answers the question: "What exactly am I monitoring and when was each store last checked?"

Price History CSV. The full change log for a specific store. Each row is a price event: timestamp, product title, product ID, event type (price drop, price increase, new product, restock, out of stock, removed), old price, new price, currency, product URL, and availability status. You can filter by store and date range before downloading.

Both exports download as standard CSV files that open directly in Excel, Google Sheets, Numbers, or any spreadsheet tool. No special formatting or conversion needed.

When Exporting Is Worth It

The in-app views handle most monitoring tasks. The activity feed shows changes in real time, the weekly report summarizes everything, and anomaly detection flags unusual patterns automatically. You do not need to export data to do effective monitoring.

Export when you need to:

Share with someone who does not use Bonesaw. A buyer, a client, a team lead. A CSV attached to an email is universally readable. No login, no context needed. Just data.

Do analysis the app does not support. The app shows you changes chronologically and by type. If you want to pivot by product category, calculate average discount percentages, or compare two stores side by side in a spreadsheet, export is the path.

Build a deliverable. Quarterly competitive reviews, pricing audits, category analyses, client reports. These usually require data in a format you can annotate, chart, and present. CSV gives you that starting point.

Investigate something specific. An anomaly fired and you want to see the full price history for that store over the past 90 days. The in-app view shows recent events. An export gives you the complete picture with every data point.

Create a persistent record. In-app data is always current. If you want a snapshot of competitive pricing at a specific point in time for future reference, download it.

When the In-App View Is Enough

Do not export out of habit. If you are looking at the data and making a decision right there, the dashboard is faster than downloading a file, opening it, and scrolling through rows.

The in-app view is better when you are:

• Doing a quick daily or weekly check
• Reviewing your weekly report summary
• Triaging a specific alert or anomaly
• Checking one store's recent activity
• Deciding whether a single price change matters

Export is better when the question is bigger than a glance or when the audience is someone other than you.

Practical Workflows

Weekly Pricing Review

If your team does a structured weekly review (the weekly review playbook covers the process), exports can supplement the in-app report with raw data.

1. Open Bonesaw on Monday morning.
2. Review the weekly report for high-level trends.
3. For any store that showed unusual activity, export its price history for the past 7 days.
4. In your spreadsheet, filter to price drops and sort by percentage change.
5. The top rows are the biggest competitive moves of the week. Discuss with your team.

This takes about ten minutes and gives you a concrete artifact to share after the meeting.

Monthly Competitive Snapshot

Once a month, pull a broader view:

1. Export the stores CSV to see your full monitoring coverage.
2. For your top 3 to 5 competitors, export price history for the past 30 days.
3. In your spreadsheet, create a pivot table by event type. How many price drops vs increases? How many new products vs removals?
4. Compare the composition of activity across competitors. A store with mostly price drops is in a different strategic mode than one with mostly new product launches.

The monthly snapshot builds a record you can compare quarter over quarter.

Anomaly Follow-Up

When an anomaly flags something unusual, you might want deeper context than the dashboard provides.

1. Note which store and what type of anomaly was flagged.
2. Export that store's price history for the past 30 to 60 days.
3. Chart the price changes over time for the flagged product or category.
4. Look for patterns: is this a one-time event or part of a trend? Did prices gradually decline before the spike, suggesting clearance? Did multiple products change on the same day, suggesting a policy-level repricing?

Client or Team Reporting

If you manage monitoring for a client or report to a team that does not log into Bonesaw:

1. Export the relevant stores CSV and price histories at the end of each reporting period.
2. Build a simple summary: total changes, notable movers, new products detected, availability shifts.
3. Attach the CSV as a data appendix for anyone who wants to dig deeper.
4. Over time, standardize the format so your reports are consistent and comparable.

Working With the Data

Column Reference

The price history CSV includes these columns:

• timestamp — when the change was detected
• store — which store
• product_id — unique product identifier
• product_title — human-readable product name
• handle — the product's URL slug
• event_type — what happened (price_drop, price_increase, new, deleted, restock, out_of_stock)
• old_price — price before the change (empty for new products)
• new_price — price after the change
• currency — price currency code
• url — direct link to the product
• compare_at_price — the "compare at" or original price if the store publishes one
• availability — whether the product is currently available or unavailable

Quick Spreadsheet Tips

Filter by event type first. If you are doing a pricing review, filter to price_drop and price_increase. If you are tracking catalog changes, filter to new and deleted. This immediately reduces noise.

Sort by percentage change. Add a calculated column: (new_price - old_price) / old_price. Sort descending to see the biggest drops, ascending for the biggest increases.

Pivot by store. A pivot table grouped by store and event type gives you a quick comparison of who is most active and what kind of activity they are generating.

Watch for same-day clusters. Multiple products changing on the same timestamp at the same store usually indicates a policy change or repricing event, not individual product decisions.

Rate Limits and Practical Limits

Exports are rate-limited to 5 per hour, which is more than enough for any reasonable workflow. If you hit the limit, you are probably downloading more granularly than necessary. Use date range filters to pull exactly what you need in one download rather than making many small requests.

Price history exports cap at 250,000 rows. For most stores and timeframes, you will never approach this. If you do, narrow the date range. A 30-day window for a single store is almost always well within the limit.

Frequently Asked Questions

Is CSV export available on the free plan? No. CSV exports are a PRO feature. The in-app activity feed, weekly report, and anomaly dashboard are available on all plans. Export is for operators who need the raw data outside the app.

Can I export data for all stores at once? The stores CSV includes all your watched stores in one file. Price history is exported per store with optional date filtering. If you need price data across multiple stores, download each one separately and combine them in your spreadsheet.

What format is the file? Standard CSV with headers. Opens directly in Excel, Google Sheets, Numbers, LibreOffice, or any data tool that reads CSV.

How far back does the price history go? The export includes all recorded history for the selected store and date range. There is no hard lookback limit beyond what Bonesaw has collected since you started monitoring the store.

Can I automate exports on a schedule? Not currently. Exports are manual downloads from the Exports page. For automated reporting, the weekly report delivers summaries on a schedule without requiring a manual download.

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Bonesaw is a product of MoonsLink. Monitoring capabilities described in this guide reflect publicly accessible product data collected through standard web protocols. Bonesaw does not access private or authenticated data. All data collection respects robots.txt directives and site access policies.