Analytics & Trends

Overview

The Analytics and Trends pages transform your snapshot data into visual insights. While individual snapshots tell you what happened in a single moment, Analytics and Trends show you how your AI visibility is changing over days, weeks, and months.

AEO Optima provides two complementary views: the Analytics page for detailed breakdowns and the Trends page for high-level performance tracking.

Analytics Page

The Analytics page provides three primary visualizations that help you understand the shape of your AI visibility data.

Visibility Trend

A line chart that plots your brand visibility percentage over time. Each data point represents the percentage of snapshots that mentioned your brand on a given day.

Use this chart to:

• Identify upward or downward trends in brand mention frequency.
• Spot sudden drops that may indicate changes in AI model behavior.
• Correlate visibility changes with your content publishing or SEO activity.

LLM Comparison

Engines are no longer compared here on a figure blended across the four question types. The Platform comparison page (Performance › Platform comparison) compares every answer engine inside each question type and leads with the spread between the best and worst engine; this section now shows those spreads in one line each and links to it.

Sentiment Distribution

A pie chart showing the breakdown of sentiment — positive, neutral, and negative — across the answers that both named your brand and were judged. An answer that never mentioned you has no opinion of you and is not counted, and an answer nothing judged is not "neutral". The CSV export carries the same three rows and, since 4 September 2026, the split per question type beside them; the Performance review shows that split on each question type's card.

Changed 4 September 2026. From that date an answer that does not discuss your brand carries no sentiment at all. Before it, such answers were stored as Neutral — the field could not hold "not judged", so an answer nobody had assessed was indistinguishable from one assessed as balanced. Two figures are now reported separately beside the chart: not judged (your brand was named, but no verdict was recorded) and did not name the brand (there was nothing to have an opinion about). Neither is a slice of the pie, because a fourth slice would read as a fourth opinion.

This is a large change for projects whose answers rarely discuss them by name. A neutral share measured before 4 September 2026 is not comparable with one measured after it.

Use this chart to:

• Get a quick read on overall AI perception of your brand.
• Track whether the sentiment balance is shifting over time.
• Identify when negative sentiment spikes and investigate the cause.

Intelligence Scores

The Analytics page surfaces six proprietary Intelligence Scores — composite metrics that distill your snapshot data into actionable numbers. Each score is displayed as a KPI card with a 0-100 value and trend indicator.

These scores update automatically as new snapshots are captured. Together, they provide a comprehensive view of your AI brand health that goes beyond simple visibility percentages.

Tip: Use CMCS to identify models that describe your brand inconsistently, then investigate the specific prompts where discrepancies appear. A low CMCS often indicates opportunities to improve your content for specific AI engines.

For a deep dive into each score's methodology and how to improve them, see the dedicated Intelligence Scores page.

Share of Model

Share of Model is the platform's name for its core visibility measurement: the percentage of sampled AI runs (per prompt and engine) in which your brand appears. It is the same computation as Mention Rate — runs mentioning your brand divided by total sampled runs — presented under the name the AEO/GEO method uses for it.

The name matters because of how answer engines actually behave:

• AI answers are a stochastic distribution. Independent multi-run studies find the same prompt almost never returns an identical brand list twice, so a single answer or a "rank" is not a measurement.
• The distribution has structure. The consideration set is stable even when order is not — strong brands still appear in a majority of runs (often 55-80%). Inclusion frequency across many runs is therefore the honest unit of measurement.
• Sample size is part of the number. The measurement discipline targets 30-50 runs per prompt and engine. The per-prompt trends page flags any prompt-engine pair with a smaller sample: fewer than 10 runs is marked low confidence, and 10-29 runs is marked below target sample — treat those readings as directional until more runs accumulate.

You will see Share of Model on the Trends page (the headline KPI and the main chart) and per engine on the per-prompt trend detail, always with the run count alongside so a thin sample is never presented as truth.

How a brand mention is detected

An answer counts as mentioning your brand when your name appears in it as a whole word. Because AI answers rarely write a company's full legal name, the detector also matches shortened forms of it — "Captain Steel India Limited" is recognised as "Captain Steel", and a one-word form is used only when it is distinctive enough to stand alone. A short or ordinary word is never matched on its own: on a project tracking Tata Tiscon, a mention of Tata Steel is not a mention of your brand.

Search-surface answers — Google AI Overviews and AI Mode — go through the same detector as chat answers since 8 September 2026. Before that date the search leg counted a mention only when the exact configured name appeared as a whole word, so a shortened form that counted on ChatGPT did not count on an AI Overview. Answers captured before the change keep the reading they were given; their algorithm version records which rule produced it.

Competitors are matched the same way, from the name and the website you configured, so a competitor entered as a web address is still recognised by the name an engine actually writes.

Correction applied 3 September 2026. Brand-name matching previously relied on a fixed list of company-name endings, so a name it could not shorten — anything ending in an unlisted word, or containing a typo — matched only in full. Answers that plainly named those brands were recorded as not mentioning them.

The detector no longer depends on that list, and the correction was applied to all captured history, not only to new captures. Affected projects will see their historical Share of Model rise; that step is the measurement being corrected, not a change in performance. Fixing it only going forward would have put a false improvement into every affected trend line on the day of the change.

Answers that came back empty are now excluded from these figures entirely. An empty answer is not evidence that your brand was absent, and counting it as such understated visibility on the engines that produce them.

Second correction, applied 4 September 2026 — what the first one got wrong. The 3 September detector could accept a shared word as a company name. A competitor called "Sify Technologies" was recorded on answers that only contained the word "Technologies" (from a different company's name); "Ireland" or "Nicotine" could stand in for Nicotinell; "International School" for a named school. On one project a competitor appeared on 141 answers whose text named it 19 times.

The detector now requires the identifying part of a name — never a sector word, a place, or a common word one letter away — and, for a company whose domain abbreviates its name, recognises the name people actually write. The correction was again applied to all captured history. What changed, in full: 178 brand mentions were removed (answers whose text does not contain the brand at all — 133 of them on one project where "nicotine" had been read as the brand), 1 was added, and competitor attributions were corrected on 2,305 answers. Of the 1,013 brand mentions added on 3 September, at most 38 were reversed today; the rest stand. Answers from Google where no AI Overview was generated are never counted as a brand mention — the text of such a row is the ordinary search results, not an AI answer.

Every stored answer now records how each brand and competitor was matched and at what confidence, so a mistake of this kind can be found by a query rather than by reading answers by hand. Before today that record did not exist, which is why the size of the 3 September error could only be measured by re-running the correction.

The AI Surfaces split

The Analytics page also splits Share of Model by surface family — chat LLMs vs Google AI Overview vs Google AI Mode — so movement on one surface is never hidden inside a blended average. Each surface on the card discloses its capture method (native browser capture for the Google AI surfaces, provider APIs for chat LLMs). See SERP & AI-Surface Tracking for how the Google AI surfaces are captured.

Trends Page

The Trends page takes a higher-level view, designed for quick weekly reviews and executive-level reporting.

KPI Cards

Four key performance indicators are displayed at the top of the page:

Visibility Area Chart

A filled area chart showing brand visibility over the selected time range. The area fill makes it easy to see the overall trajectory at a glance, while the line edge shows daily fluctuations.

LLM Performance Bars

The per-model bars are gone from Trends: engines are compared inside each question type on the Platform comparison page, and this slot shows those spreads in one line each, with a link to it.

Top 5 and Bottom 5 Prompts

Two ranked lists that highlight your best-performing and worst-performing prompts within the selected date range:

• Top 5 Prompts: The prompts with the highest brand visibility. These represent your strongest areas of AI presence.
• Bottom 5 Prompts: The prompts with the lowest brand visibility. These represent your biggest opportunities for improvement.

Tip: Check the Bottom 5 Prompts list weekly. These are your biggest improvement opportunities. Focus your content strategy on the topics where AI models are not yet mentioning your brand.

How rates are counted (from 4 September 2026)

Four rules apply to every rate on the Performance review page and the home page's pool cards, and they are being extended to the rest of the product.

Four kinds of question, never added together. Every tracked question sits in exactly one of four pools — whether the asker already named a company (brand / non-brand) and whether they are choosing or learning (transactional / informational). Each pool has its own headline measure: named-in-answer rate for non-brand transactional, cited rate for non-brand informational, own-source share and entity accuracy for brand informational, sentiment and accuracy for brand transactional. There is no combined score across pools anywhere in the product, because a question about you and a question about your category are different contests.

Rates count questions, not answers. A question is asked many times per cycle — several runs on each engine — and answer engines are stochastic. Mention, citation and top-three rates therefore divide questions by questions: a question counts toward the numerator when it qualifies in the majority of its runs in the window. "Total mentions" is the one figure that stays at answer level; it is a count, shown beside the rate and never as a percentage, because it can exceed the number of questions.

The denominator is the contest you can enter. Non-brand rates divide by the pool's contestable questions — those where at least one company was named or one source cited. A question no engine answers with any company is open ground, reported as its own figure, never as a loss. Brand-pool questions name you by construction, so their rates divide by the pool's tracked questions.

Movement smaller than the noise floor is not reported as change. With a working depth of three runs per engine per cycle, a share metric has to move more than 6 points (4 points for sentiment) before the product calls it a change; smaller moves are labelled as inside the noise floor. A trend direction needs at least four captured cycles; coverage is judged against the captures your schedule actually promised, not against calendar days.

An answer nobody judged is never counted as neutral. From 4 September 2026 an answer that does not discuss your brand carries no sentiment; before that date such answers were stored as Neutral. Every sentiment share divides by the answers that named your brand and carry a verdict, and the two residuals — not judged, and did not name the brand — are reported beside the shares rather than folded into them.

Tone is reported per question type, over the answers that judged you. Each question type's card on the Performance review shows positive / neutral / negative shares over the answers that both named your brand and carry a judged verdict — never over every answer. Below four judged answers the card shows the count and says the sample is too small; three shares drawn from two answers look exactly like three shares from two hundred, so the picture is what is withheld.

A question type's trend is plotted per capture cycle, on your own cadence. The "named in the answer, per capture cycle" line on each card has one point per capture cycle — a week for a weekly project, a month for a monthly one — and each point is the share of that type's questions captured that cycle where you were named in the majority of runs. A cycle with no captures is a gap, never a zero, and the line does not appear until two cycles have been captured.

Engines are compared inside one question type, and the spread is the diagnostic. The Platform comparison page measures every answer engine on each question type's headline rate separately — never on a figure pooled across the four — and reports the spread: the best engine's rate minus the worst engine's rate, in points. Every engine is divided by the same contestable questions of that type; an engine that answered fewer than five of them is listed with its count and left out of the spread. A spread under the six-point noise floor is reported as inside the noise floor, not as a gap, and there is no spread across question types.

Segment Filter

Both the Analytics and Trends pages include a Segment Toggle — the segment pills of the lens filter bar, which also offers Intent, Journey, Topic, Tag, Group, and Orbit filters — that lets you filter all data by prompt type:

The Non-Branded view is particularly valuable because it shows your true organic AI visibility — how often AI models recommend you when users aren't specifically asking about you. The Competitor view reveals how you perform in direct comparison queries.

The segment toggle is URL-synced (?segment=branded), so filtered views are bookmarkable and can be shared with team members.

Tip: Compare your Branded visibility (should be high) against your Non-Branded visibility (the real signal). A large gap means AI models know your brand but don't organically recommend it — this is your biggest opportunity for improvement.

Date Range Filters

Both the Analytics and Trends pages support date range filtering to focus your analysis on the time period that matters most.

Select a date range using the filter controls at the top of either page. All charts and KPIs update immediately to reflect the selected period.

How to Use Analytics Effectively

Weekly Review Workflow

1. Open the Trends page with a 7-day filter.
2. Check the Trend Direction KPI — is visibility moving up or down?
3. Review the Bottom 5 Prompts — are there any new entries since last week?
4. Switch to the Analytics page for deeper investigation if you see unexpected changes.

Monthly Review Workflow

1. Open the Trends page with a 30-day filter.
2. Compare Avg Visibility and Avg Rank to the prior month's values.
3. Check the LLM Performance Bars — has any model's behavior changed significantly?
4. Review the Sentiment Distribution on the Analytics page — is the positive/negative balance stable?

After a Content Campaign

1. Set the date range to cover the period before and after your content was published.
2. Watch the Visibility Trend line chart for inflection points.
3. Check the LLM Comparison to see if the new content improved visibility across all models or just specific ones.

Analytics and Other Features