> For the complete documentation index, see [llms.txt](https://resource.consumr.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://resource.consumr.ai/release-notes/release-notes-library/october-2026.md). # October 2026 Updates on releases for the month of October

## Table of Contents for the Release


Features	Current Environment	Release Date
Brand Guidelines - Auto Update	Test	Oct-19-2026
In App Notifications	Test	Oct-31-2026
eTail Intelligence & Module - app.consumr.ai & Maven	Test	Oct-19-2026
Digital Study - Creatives	Test	Oct-19-2026
Content Optimization	Test	Oct-19-2026

## Brand Guidelines Auto Update ### What’s New Brand Guidelines can now be created by uploading an existing company brief. consumr.ai identifies available elements such as colors, fonts, imagery and voice, then organizes them into a structured draft for the user to review and upload. The upload supports PDF, PPTX, DOCX and image files up to 10 MB. If you brand guideline document is larger than 10MB, you are requested to kindly reprint the document including the pages that not core to the guidelines. Please note, not all nuances of brand guidelines will be managed by consumr.ai but the core aspect will be handled. To know the aspects that will be covered by consumr.ai please try creating a manual brand guideline. For earlier release note on Brand Guidelines, please refer to the release note of [September 2026](https://resource.consumr.ai/release-notes/release-notes-library/september-2026#brand-guidelines-manual)

### From Brand Document to Research Context Most organizations already document how their brand should look, sound and communicate. However, this information often remains inside a brand book or presentation designed for people to read. It is not automatically available as structured context when research is conducted. Researchers may therefore need to interpret the document manually, transfer individual details into the platform or repeatedly explain the same requirements across different studies. Brand Guidelines Auto Update turns this existing knowledge into a reusable foundation within consumr.ai. Instead of treating the uploaded brand book as a separate reference file, the platform identifies its principal visual and verbal elements and prepares them as a structured Brand Guideline. The extracted information remains a draft, allowing users to correct missing or inaccurate details and confirm that the interpretation represents the brand before it is used in research. This expands the Manual Brand Guidelines capability introduced in September. Teams with an established brand book can now import it instead of recreating the guideline field by field, while teams without a formal document can continue entering their brand information manually. Both routes create the brand context that consumr.ai can carry into relevant research and evaluation. ### Why Brand Context Matters Audience response and brand alignment answer different research questions. An idea may be clear, relevant or persuasive to consumers while still using the wrong tone, conflicting with the brand’s positioning or failing to follow its visual identity. Similarly, a direction that follows every brand rule may still fail to connect with the intended audience. Effective evaluation therefore requires both perspectives: how consumers respond and whether the work remains consistent with the brand it represents. By translating an existing brand document into structured context, Brand Guidelines Auto Update reduces manual setup and helps different users work from the same foundation. Teams do not need to reinterpret the same document for every study, important requirements are less likely to be overlooked, and recommendations can account for both consumer response and the identity the brand intends to maintain. ## In-App Notifications (Releasing on Oct 31st 2026) ### What’s New In-App Notifications now tell users not only what happened, but also how urgently it needs attention and what they can do next. Each notification is categorized according to its importance and, where an action is available, includes a button that takes the user directly to the relevant next step. ### Communication That Leads to Action A notification is useful only when the user can quickly understand what it means. If every update is presented in the same way, a technical issue that blocks work can appear no more important than a product announcement. Users must read and interpret each message individually before deciding whether to act and may still need to search through the platform to find the appropriate response. The new notification structure separates updates into critical or warning, non-critical, and informational categories. Critical and warning notifications draw attention to issues that may prevent a user from continuing or require a timely response. Non-critical notifications communicate updates that may be relevant without immediately blocking work, while informational notifications keep users aware of announcements and other platform activity. This categorization gives each message a clearer role. Users can distinguish something that requires intervention from something they should know, reducing the chance that an important issue is overlooked, or that a routine update creates unnecessary urgency. ### Why This Matters Clear communication inside a research platform is part of keeping the research workflow moving. Users need to know whether an update affects their ability to continue, requires a decision or is simply available for their awareness. The notification therefore becomes a direct route into the next action rather than a message the user must resolve independently. In-App Notifications bring the message, its importance and the relevant response together. Users can understand what happened, judge whether it needs attention and move directly to the next step without searching for the affected setting, feature or resource. eTail Intelligence in consumr.ai and Maven ### What’s New eTail Intelligence introduces a dedicated way to understand the consumers behind an Amazon product or category. Users can enter an Amazon URL/product category and generate Behavior, Intent, or Mentions Intelligence directly within consumr.ai. The resulting intelligence can be used to build AI Twins representing the relevant shoppers and further explored through Maven. ### Connecting the Digital Shelf to Consumer Intelligence Consumer research often begins with a defined audience and then examines its behaviors, interests or intentions. eTail Intelligence provides a different starting point. It begins with a product or category on Amazon and works outward to identify the consumers, adjacent opportunities and patterns of demand surrounding it. This product-led approach is useful when a team knows what it wants to study or which category it may enter but has not yet established the most relevant audience. A product URL provides a focused view of the shopper context surrounding a particular listing, while a category URL supports a broader investigation of the market. The URL establishes the commercial context, but users still choose the type of intelligence required. Behavior Intelligence identifies related audience and category opportunities, separating consumers who are People Looking to Buy from those who are Interested In. Category Recommendations are compared using Reach and Uniqueness, then positioned across Growth, Idea, Niche and Ignore areas. This helps teams distinguish opportunities with broader scale from narrower but more differentiated relationships, without treating general affinity as equivalent to purchase relevance. Intent Intelligence examines how demand is expressed through search. It identifies the relevant category, core keywords and leading search terms, then shows their search volumes and movement across the selected period. Top Themes and the Keyword Table reveal the language surrounding the product or category, helping teams understand what consumers are actively seeking, how strongly those interests appear and how demand is developing. Together, the two reports provide complementary perspectives. Behavior Intelligence helps explain which audiences and category relationships surround the product, while Intent Intelligence shows what consumers are searching for and how that demand changes over time. ### Extending the Intelligence Through AI Twins and Maven The resulting intelligence can be carried forward in two ways. Maven allows users to question the report, investigate relationships within the findings and examine what the evidence may mean for a particular commercial or research decision. AI Twin creation turns the identified shopper context into reusable audience respondents for subsequent quantitative and qualitative research. This means teams do not need to begin with a completed audience definition. The eTail report provides a product-led foundation from which relevant AI Twins can be developed and used to evaluate concepts, creatives, messages, products and other research questions. Maven supports deeper interpretation of the intelligence, while AI Twins allow the identified audience to participate in further research. ### Why This Matters An Amazon listing can show what is being sold, its price, reviews and position within a category, but it does not independently explain the wider consumer and demand landscape surrounding it. eTail Intelligence closes that gap by connecting the digital shelf with behavioral opportunity, search intent, report exploration and audience development. Teams can move from an Amazon URL to a structured understanding of who the opportunity may involve, what those consumers are seeking and where further investigation may be valuable. This can support audience discovery, category analysis, product positioning, campaign development and digital retail strategy. The findings should still be interpreted alongside sales data, market conditions and the organization’s commercial objectives. ## eTail Intelligence in consumr.ai and Maven ### What’s New eTail Intelligence introduces a dedicated way to understand the consumers behind an Amazon product or category. Users can enter an Amazon URL/product category and generate Behavior, Intent, or Mentions Intelligence directly within consumr.ai. The resulting intelligence can be used to build AI Twins representing the relevant shoppers and further explored through Maven.

### Connecting the Digital Shelf to Consumer Intelligence Consumer research often begins with a defined audience and then examines its behaviors, interests or intentions. eTail Intelligence provides a different starting point. It begins with a product or category on Amazon and works outward to identify the consumers, adjacent opportunities and patterns of demand surrounding it. This product-led approach is useful when a team knows what it wants to study or which category it may enter but has not yet established the most relevant audience. A product URL provides a focused view of the shopper context surrounding a particular listing, while a category URL supports a broader investigation of the market.

The URL establishes the commercial context, but users still choose the type of intelligence required. Behavior Intelligence identifies related audience and category opportunities, separating consumers who are People Looking to Buy from those who are Interested In. Category Recommendations are compared using Reach and Uniqueness, then positioned across Growth, Idea, Niche and Ignore areas. This helps teams distinguish opportunities with broader scale from narrower but more differentiated relationships, without treating general affinity as equivalent to purchase relevance.

Intent Intelligence examines how demand is expressed through search. It identifies the relevant category, core keywords and leading search terms, then shows their search volumes and movement across the selected period. Top Themes and the Keyword Table reveal the language surrounding the product or category, helping teams understand what consumers are actively seeking, how strongly those interests appear and how demand is developing. Together, the two reports provide complementary perspectives. Behavior Intelligence helps explain which audiences and category relationships surround the product, while Intent Intelligence shows what consumers are searching for and how that demand changes over time. ### Extending the Intelligence Through AI Twins and Maven The resulting intelligence can be carried forward in two ways. Maven allows users to question the report, investigate relationships within the findings and examine what the evidence may mean for a particular commercial or research decision. AI Twin creation turns the identified shopper context into reusable audience respondents for subsequent quantitative and qualitative research. This means teams do not need to begin with a completed audience definition. The eTail report provides a product-led foundation from which relevant AI Twins can be developed and used to evaluate concepts, creatives, messages, products and other research questions. Maven supports deeper interpretation of the intelligence, while AI Twins allow the identified audience to participate in further research. ### Why This Matters An Amazon listing can show what is being sold, its price, reviews and position within a category, but it does not independently explain the wider consumer and demand landscape surrounding it. eTail Intelligence closes that gap by connecting the digital shelf with behavioral opportunity, search intent, report exploration and audience development. Teams can move from an Amazon URL to a structured understanding of who the opportunity may involve, what those consumers are seeking and where further investigation may be valuable. This can support audience discovery, category analysis, product positioning, campaign development and digital retail strategy. The findings should still be interpreted alongside sales data, market conditions and the organization’s commercial objectives. ## Digital Study - Creatives ### What’s New Digital Study can now give AI Twins performance context from existing advertising activity when they evaluate a creative. Users can supply data such as click-through rate, conversion or return on ad spend from an account, campaign or ad set, allowing the Twins to consider not only the creative itself but also what happened when related work reached the market. A creative can also be evaluated without performance data when no relevant campaign history exists. Supported formats include landing pages, ad copy, images and videos.

### Giving AI Twins Performance Context A performance metric shows the outcome of a campaign, but it does not independently explain the consumer response behind it. A decline in click-through rate may coincide with weak attention, an unclear message, poor relevance or a mismatch between the creative and its audience. Similarly, strong performance can show that something worked without identifying which elements should be preserved in the next creative. Digital Study adds this performance information to the context available to the AI Twins. The Twins can examine the creative alongside the selected metrics, reason about what may have contributed to a decline, identify the elements that appear to have worked well and explain how the intended audience may have interpreted the execution. This makes their feedback more specific to the advertising environment being studied. Instead of evaluating a creative only against general principles or audience preferences, the Twins can connect their interpretation with actual patterns from the organization’s existing activity. Those learnings can also provide context when a new creative is evaluated, helping the team understand whether it retains effective elements or addresses problems observed previously. Performance may also be influenced by targeting, placement, bidding, timing, budget, competition, and other campaign conditions. ### Selecting the Relevant Level of Context Users can choose whether the performance context should come from the account, campaign or ad-set level. Account-level context provides a broader view across campaigns and ad sets. Campaign-level context narrows the analysis to activity within a selected campaign, while ad-set-level context focuses on a specific ad set. The appropriate level depends on the question being investigated. A broader account view can reveal patterns across a larger body of creative activity, while a campaign or ad-set view can provide context that more closely reflects a particular objective, audience or execution. The platform checks whether sufficient performance data is available at the selected level before it is used in the study. When no suitable data is available, users can continue without performance context. The AI Twins then evaluate the creative using their existing audience grounding, the research questions and the selected assessment framework. ### Why This Matters Advertising platforms are designed to report performance. Digital Study uses that performance as additional research context, allowing teams to investigate what the numbers may mean from the audience’s perspective. The objective is not only to label a creative as strong or weak, but to understand which parts of the execution may have supported the result, which parts may have created friction and what those findings imply for future work. By giving AI Twins access to existing performance context, Digital Study connects observed campaign results with audience-based reasoning. Teams can investigate why performance may have risen or fallen, understand what appears to have worked and use those findings to make the next creative decision more informed. ## Content Optimization ### What’s New Content Optimization brings audience testing to blogs, webpages, landing pages and emails. Teams can now see how selected AI Twins respond to written content, understand what strengthens or weakens the experience, and generate improved variations from the same workflow.

### Content Is Only Effective in Context A piece of content cannot be evaluated only by asking whether it is well written. Its effectiveness depends on where it appears, who encounters it and what that person is expected to do next. * A blog may need to make a complex subject understandable and sustain attention. * A webpage must help visitors understand the brand or offering and find the right next step. * A landing page usually needs to move a defined audience toward a focused action, * An email has limited time and space to earn attention and motivate a response. Content Optimization accounts for these differences by asking the user to identify the content style before the evaluation begins. The report then interprets the content according to the role it is meant to perform instead of applying one generic standard to every format. This distinction can materially change the conclusion. Strong storytelling may make a brand webpage engaging, but the same amount of editorial content can frustrate purchase-ready visitors on a landing page if it hides product details or the path to purchase. An email may benefit from being direct and concise, while a blog using the same level of detail may feel incomplete. The evaluation considers whether the content serves its intended purpose, not simply whether the language sounds polished. ### See the Content Through the Audience’s Perspective Internal reviewers already know what the brand intends to communicate. That familiarity can make missing information, unclear transitions or weak calls to action harder to notice. AI Twins approach the content from the perspective of the selected audience, bringing their needs, motivations and expectations into the evaluation. The scores provide a quick reading of the result, while the qualitative feedback explains why the content received it. * Audience Resonance shows how strongly the message connects with the selected Twins. * Visual Appeal considers how the presentation and organization support the experience where applicable. * The Overall Score brings these findings together, but it is not intended to stand alone. The surrounding explanations identify the specific content choices that helped or hindered the response. ### From Feedback to a Better Version Content Optimization translates the evaluation into an action plan rather than leaving teams with a collection of reactions. Recommendations are prioritized around the issues identified in the report and may address message hierarchy, audience relevance, missing information, content depth, calls to action, purchase friction or other elements affecting the experience. Users can then generate new variations informed by these findings. This connects diagnosis with revision: the report explains what should change and why, while the generated variation provides a practical direction the team can review and develop further. The final content should still be checked for factual accuracy, brand alignment and the organization’s commercial requirements before publication. ### Why This Matters Content is often reviewed according to what the organization wants to say. Content Optimization adds the other side of the decision: how the intended audience may actually receive it. By combining audience-grounded evaluation, explainable scores, individual feedback, prioritized recommendations and generated variations, the feature turns content optimization into a r