Pricing Analyser is designed to provide practical pricing guidance inside WooCommerce, but there are some important limits to how certain data is collected, displayed and interpreted. These are part of the current scope of the plugin and should be understood when reviewing recommendations.

Sales data is generally product-level

Sales signals are primarily based on product-level sales history. This means that for variable products, sales used in recommendations may reflect parent-level sales behaviour rather than variation-specific sales behaviour.

As a result, a variation-level recommendation may sometimes be influenced by broader product-level sales performance rather than by that variation alone.

Interest is tracked at parent-product level

Interest is based on product view activity and is generally tracked at parent-product level rather than separately for each variation.

For many stores, this still produces useful results because customer interest usually begins at the product page rather than at a variation-specific URL. In practice, this means Interest can still be a helpful signal for variable products, but it should not be interpreted as variation-specific attention.

Conversion for variations inherits product-level context

Because sales and interest are generally evaluated at product level, variation-level conversion insight is limited. Variation recommendations can still be useful, especially when pricing or stock differs by variation, but conversion should not be treated as a fully variation-specific measurement.

Floor and ceiling prices are primarily product-level

Boundary pricing is primarily designed around the main product workflow. Depending on the product structure, floor and ceiling pricing may not offer the same level of variation-specific control in every case.

Where boundary pricing is important, merchants should review how the product is structured and how stock and pricing are being managed.

Price history is intentionally limited

Price history is limited to a fixed number of stored changes rather than acting as an unlimited archive. This keeps the feature practical and lightweight, but it means very old price movements may no longer be shown once newer changes have replaced them.

Price history sparklines are summarised

For products with many variations, the price history sparkline is a summary view and may not show every variation line at once. The larger modal chart provides more detail, but the inline sparkline is intentionally compact.

The dashboard highlights the primary recommendation

When more than one rule matches a product, Pricing Analyser highlights the strongest current recommendation. Other matched rules can still be reviewed, but the dashboard is designed to prioritise the most significant action rather than show every matched rule with equal prominence at all times.

The daily briefing is selective

The Daily Briefing is designed as a useful summary, not a complete dump of dashboard data. It may not include every recommendation or every notable product from the store on every run. Its purpose is to surface the most relevant highlights and pricing opportunities in a manageable format.

Today’s chart data is partial

Charts and sparklines can include today’s sales and interest, but today is still an incomplete day. The final point or bar should therefore be read as partial-day activity rather than a complete daily total.

Signals are simplified by design

Pricing Analyser is intended to support practical pricing management, not to model every nuance of merchandising, attribution or customer behaviour. Signals such as Trending, Interest and Conversion are designed to be useful operational indicators rather than perfect analytical measures.

Bot filtering improves Interest data but cannot make it perfect

Pricing Analyser can filter known bot traffic from Interest data, which improves the quality of the signal. However, no filtering approach can remove all non-human traffic or guarantee that every recorded product view represents genuine purchase intent.

Unsupported product types are intentionally excluded

Pricing Analyser is designed primarily for simple and variable products. Unsupported product types, such as grouped and external products, are intentionally excluded from the main recommendation workflow where pricing recommendations would not make practical sense.

Cron-driven features depend on WordPress cron unless real cron is configured

Features such as the Daily Briefing depend on WordPress cron unless the store is configured to use a real server-side cron job. On low-traffic or test sites, scheduled tasks may not run exactly on time if WordPress cron is not triggered regularly.

Recommendations support judgement rather than replace it

Pricing Analyser provides guidance based on store data and merchant-defined rules, but it cannot know every commercial factor affecting a product. Supplier changes, seasonality, branding considerations and strategic promotions may all justify decisions that differ from the recommendation.