Feature Requests

Description Scale Insights supports bid boosts for AMC audience segments, but there is no accessible view showing how each audience segment actually performs. Users are pulling bulk files from Amazon to get the numbers. The request is an Audiences view in Ad Insights Performance, showing per-segment stats with the same metrics available elsewhere: impressions, clicks, spend, orders, conversion rate, CPC, ACOS. It should be filterable across the full catalog or by selected products and campaign types, matching how the Match Types view already works. It could either sit as its own tab or be added as a breakdown within the existing Match Types view. Use case Users applying AMC audience bid boosts are currently boosting bids without in-platform evidence of which segments convert. The segments perform very differently by product and category, and the correct bid boost for each depends entirely on that performance data. For a seller managing thousands of SKUs, exporting bulk files to evaluate audience performance is not something that gets done regularly, so audience bid boosts end up set once and left alone. Native stats would close the loop between the bid boost feature and the decision that drives it, and would let users tune boosts the same way they tune placement modifiers and keyword bids.

Description Ads Insights > Performance > Placements already breaks each campaign into Top of Search, Rest of Search and Product Pages rows, and placement values can be edited there directly. The data exists, but it is hard to act on: The tab reads as an adjustment screen rather than a reporting screen. Users land on it, see Placement and Placement value columns, and conclude there are no stats. The performance metrics are not where the eye lands. Placement performance cannot be cross-referenced with match type. Users who structure campaigns around match type need to see how a given match type performs at each placement, and today that means comparing across views or exporting. There is no filter path from an ASIN or campaign type straight to its placement breakdown. Requested changes: Surface the core metrics (impressions, clicks, CTR, spend, orders, conversion, CPC, ACOS) prominently on the Placements tab rather than trailing the adjustment columns Allow the placement breakdown to be viewed within, or filtered alongside, the Match Types view Add ASIN and campaign type filtering to the placement view, consistent with how other Performance views filter Use case Placement modifiers get set without users being confident they are reading the placement's actual performance, because the numbers are not surfaced where the decision is made. A concrete example from a seller managing $200k monthly spend: on an exact match campaign, Top of Search converted at 23 percent with 18 percent ACOS, while Rest of Search converted at 13 percent with 20 percent ACOS. Top of Search converts far better and sits below the ACOS target, so there is roughly 2 points of headroom to bid that placement more aggressively, take more volume, and hold overall campaign performance flat. He built that comparison in an external spreadsheet rather than in Scale Insights, despite the platform already holding the data, because getting match type and placement into one view was easier outside the tool. That is the gap worth closing: the data is there, the analysis path is not.

Description Status rules can already trigger on Minimum and Maximum Inventory Stock, but the only available action is pausing or enabling ads. There is no way to throttle bids down as inventory tightens. The request is an inventory-aware bid action that works like the Dayparting hourly bid adjustment, but triggered by stock cover instead of hour of day: Calculate days of stock remaining using a configurable moving average of sales, for example 180 days Set thresholds, for example when cover drops below 45 days, reduce bids by X percent Support tiered steps, so bids step down further as cover shrinks Restore bids automatically when stock is replenished This should act on bids, not budgets. Cutting budget caps volume; cutting bids lowers acquisition cost while keeping the ads live. Use case A seller with a large catalog and significant logistics lead times constantly has SKUs approaching low cover. Today they work around it by building status rules that pause and resume ads at manually defined stock levels, per SKU, per campaign type. With thousands of SKUs and varying sales velocities, defining and maintaining those thresholds by hand is a continuous drain, and pausing throws away sales that could still be captured profitably. If inventory is scarce, there is no need to keep paying full price per click. Bidding down still captures the traffic and still converts, just at a lower acquisition cost, which stretches the remaining stock further at better margin. Pausing does not achieve that, it just stops sales. This is available in competing tools and was named as one of two features determining whether the account stays on Scale Insights while doubling spend next year.

Description Scale Insights already supports bulk import for creating rules, and an Unassign option exists in the UI for removing them one at a time. What is missing is bulk assignment: there is no way to attach an existing rule to many campaigns, ad groups, or ASINs in one action. The request is a bulk file upload that assigns and unassigns existing rules at scale, covering all rule types: Bidding rules Status rules Placement rules Daily budget rules Negative and negative word rules The file would map rule name to campaign, ad group, or ASIN, the same way Amazon bulk operations work. Ideally it supports unassign as well as assign, and returns a validation report before committing so users can catch mapping errors before they hit live campaigns. Use case A seller running roughly 2,500 SKUs across about 15,000 campaigns, scaling toward 10,000 SKUs, has one full-time team member whose entire job is assigning and reassigning rules to campaigns. That is a headcount spent on data entry. Every new product launch, every catalog expansion, and every strategy change means manually attaching bidding, status, placement, and budget rules campaign by campaign. At this catalog size the manual path does not scale, and it is the single biggest reason the account cannot expand faster. Everything else on Amazon is done through bulk files precisely because manual work at this volume is not viable. The assignment logic already exists in the back end. Exposing it through a bulk file would free a full-time person to work on optimization instead of data entry, and would remove the main blocker for large-catalog sellers and agencies scaling within Scale Insights.

We need a solution fo an automated COGS import

I know rules can only use 90-days of historical data, but is there any potential to extend this window?

API we can have access to to be able to call data and set data points

Dear Scale Insights Team, I'm reaching out to propose the development of an MCP (Multi-Cloud Protocol) server that would integrate Scale Insights' powerful PPC management platform with conversational AI capabilities. Project Scope I'd like to request the development of an MCP server that would: Connect to Scale Insights API: Establish secure connections to access campaign data, rule configurations, and performance metrics from Scale Insights. Enable AI-Powered Strategy Creation: Allow users to create and modify automation rules and strategies through natural language conversations. Transform Conversations to Rules: Implement a system that can interpret conversational requests and transform them into properly configured Scale Insights automation rules. Use Cases This integration would enable: Simplified Rule Creation: "Create a bidding rule that increases bids by 30% for keywords with less than 100 impressions in the last 7 days" Strategy Development: "Set up a complete new product launch strategy for my product in the health niche" Performance Analysis: "Analyze my campaign performance and suggest optimization rules" Quick Adjustments: "Update my existing bidding rules to be more aggressive during the upcoming holiday season" Technical Requirements The MCP server would need: Secure Authentication: OAuth or similar secure authentication with Scale Insights Structured Data Exchange: Well-defined schema for passing rule configurations Conversational Processing: NLP capabilities to interpret PPC-specific requests Rule Validation: Logic to ensure generated rules follow Scale Insights' parameters and best practices User Confirmation Flow: Preview and confirmation steps before applying changes Benefits This integration would significantly enhance the Scale Insights platform by: Making advanced automation more accessible to users of all experience levels Reducing the learning curve for new Scale Insights users Enabling faster strategy implementation Providing an innovative differentiator in the Amazon PPC tool market Next Steps I would appreciate your feedback on this proposal and would be happy to discuss: Technical feasibility Development timeline Potential pricing models Required permissions and access levels Testing and implementation process Thank you for considering this request. Scale Insights' powerful rule-based automation combined with conversational AI would create a truly next-generation PPC management experience. Looking forward to your response.

The current Automation Dashboard does an good job of showing automation savings, actions performed and time saved. However I believe it could become even more valuable by adding an Automation Health section that helps users quickly identify issues requiring attention. I often come across the following issues when auditing new accounts. In many cases these issues are unintentionally created by us while optimizing the account. I've categorized them into two groups: Warnings and Alarms. 🟡 Warnings: Warnings are issues that require attention and may lead to problems in the future if left unresolved 1) Paused campaigns are not linked (affects data collection) 2) Active campaigns have no active advertised ASIN (creates dummy campaigns and clutters Campaign Manager) 3) Active campaigns have no active targets (creates dummy campaigns and clutters Campaign Manager) 4) Active campaigns have received zero impressions in the last 7 days (may require bid adjustments or further investigation) 5) Products are out of stock or nearing out of stock, but campaigns remain active (requires updating the linked ASIN or pausing campaigns in a timely manner) 🔴 Alarms: Alarms are critical issues that require immediate action because they can significantly impact automation or PPC performance. 1) Active campaigns are not linked (affects automation and data collection) 2) Active campaigns have no automation assigned (may have been missed during setup, especially Sponsored Brands campaigns) 3) The linked ASIN is inactive (requires updating the linked ASIN to restore automation) 4) Active Discovery campaigns have no Negative Rule assigned (Auto, Broad & Phrase) (may result in wasted ad spend on irrelevant search terms) Why this would be valuable As accounts grow to hundreds or even thousands of campaigns, manually identifying these issues becomes difficult. An Automation Health Dashboard would help users: 1) Quickly identify automation gaps 2) Prevent wasted ad spend 3) Ensure every active campaign is properly managed 4) Catch configuration issues before they impact performance 5) Spend less time auditing accounts manually out of SI I've also created a simple UI concept (attached) showing how this could fit naturally into the existing Automation Dashboard without adding complexity. I believe this would be especially useful for PPC agencies and sellers managing big accounts.

Currently, "Days to Observe Change" works at the campaign level – if any Placement Rule fires on a campaign, all other Placement Rules are frozen for the configured number of days, including rules for different placements. This creates a critical problem when running separate rules for Top of Search and Rest of Search on the same campaign. In practice, Rest of Search automation may never fire at all – because ToS rule conditions are met regularly and keep the campaign permanently frozen. Priority settings don't help here – Priority determines which rule executes when there's a conflict, but it doesn't lift the freeze for other placements. The only alternative is to remove Days to Observe Change entirely – but this leads to chaotic daily modifier changes, which damages campaign stability. This creates a dead end: either RoS automation doesn't work, or campaigns become unstable. Requested change: make "Days to Observe Change" placement-specific, so that: A ToS rule firing only freezes other ToS rules A RoS rule firing only freezes other RoS rules PP rules remain independent as well This would allow independent, simultaneous automation for each placement on the same campaign without sacrificing stability.