# Getting recommended by AI tools feels unclear and unmeasurable
> Source report: https://painfinder.app/reports/getting-recommended-by-ai-tools-feels-unclear-and-unmeasurable

## 1. What we're building
Build an “Answer Engine Visibility” workspace that does two things end-to-end: (1) continuously measures whether your brand is being mentioned/recommended across multiple AI answer engines, and (2) converts those findings into concrete, implementable on-site and off-site actions specifically intended to increase citation/recommendation probability. The must-have feature set is: an AEO capability to get your business recommended/cited by AI tools; multi-engine brand citation tracking that shows where you’re mentioned (not just directional monitoring); and an affordable pricing option suitable for small teams.

To address the measurement gap called out repeatedly, include a prompt-tested monitoring layer with “query fan-out” style coverage and logged prompt runs, while acknowledging that mentions can vary by model/provider. Your dashboard should provide an AI-era Search Console analogue focused on citations: reference frequency/share-of-model style reporting, per-provider results, and representation/accuracy checks (e.g., whether what the model says is consistent with your stated facts). Then add a task generator that turns findings into weekly/monthly, post-level deliverables and proof requirements—explicitly distinguishing what’s “extra beyond SEO” (e.g., structured Q&A sections, schema/FAQ deployment, entity clarity, and evidence assets like quotable stats and third-party mention targets). This directly targets the strongest feature asks: an affordable clean GEO/AEO dashboard, concrete chargeable workflows with proof, and guidance on the specific AEO/GEO steps that matter.

Finally, include an integration-lite approach for teams without data specialists: run brand setup checks (is the site extractable/indexable and does the page contain the explicit machine-readable info AI needs) and produce “what to change on which page” output rather than generic audits. Include a lightweight “success confirmation” workflow for audit submissions/testing runs (so users know the monitoring/prompt job actually completed), and provide an operator-in-the-loop review layer to reduce trust risks when results are inconsistent across providers. This should position the product as the practical alternative to agencies/tools that only produce pretty reports or provide unstable, non-actionable recommendations.

**Working name:** AEO Cite Monitor
**Tagline:** Measure AI brand citations across engines and generate implementable on-site/off-site AEO tasks.
**Main goal:** Prove an AEO measurement loop that produces credible “citation/share-of-model” signals and turn them into weekly tasks users can ship.
**Target users:** Small SEO teams and founders trying to win AI Overviews/answer engines recommendations for their brand or business.

**Main user result:** A clear AEO “citation share” view per prompt theme and per engine, plus a task list of exactly what to change next.
**5-minute outcome:** Create a brand + first prompt set, run it once, and receive an initial citation report with next-step recommendations.
**What we solve first:** Repeatable prompt-run monitoring that produces a measurable citation/share trend and actionable AEO fixes.
**Out of scope for MVP:**
- Full multi-tenant team workflows
- Automated large-scale schema generation across thousands of pages
- End-to-end community reply tooling and anti-spam controls

## 2. Why this is worth building
- Verdict: **HIGH** (70/100)
- Across the corpus, posters consistently report that being ranked in classic search does not guarantee being cited or recommended by AI tools, even when competitors are mentioned. The dominant frustration is lack of deterministic measurement (no reliable equivalent of Search Console for AI recommendations) and lack of repeatable, actionable steps for improving citation/mention likelihood. Many solutions are described as monitoring-first or generic/template audits that don’t provide verifiable proof or concrete deliverables. The combination of trust/ROI uncertainty and operational complexity makes the problem high urgency for practitioners.

**Current pain:** Users can rank well in search but still don’t know whether AI tools are actually citing/recommending their brand. Monitoring is inconsistent and results are hard to validate across models/providers.
**Current workaround:** They manually track a consistent prompt sheet weekly and look for brand mentions/citations over time, often “hoping for the best.”
**Why existing tools fail:** Existing tools either overcharge, miss the citation transparency needed for AEO, or provide dashboards without clear implementable guidance; users also worry that GEO/AEO is still vague and not truly measured. Manual prompt testing doesn’t scale and can’t guarantee consistent measurement.

## 3. Must-have capabilities
- Prompt-set monitoring with logged fan-out runs across selected engines
- Multi-engine citation tracking that shows where brand is mentioned
- Citation/share-of-model scoring and trend chart by prompt theme
- Drop detection that flags prompt themes with reduced citation rates
- Task generator: produce implementable next changes based on findings
- AEO/GEO explanation block for internal alignment
- Scalable template outputs for JSON-LD FAQ/Organization (generate, don’t bulk deploy)

## 4. Use cases & user stories
Web SaaS that continuously measures whether the user’s brand is mentioned/cited by selected AI answer engines using versioned prompt sets and logged runs. It converts monitoring results into an implementable AEO task plan (e.g., quote-ready sentences, FAQ/Schema placement hypotheses, and monitoring deltas).

- Set brand name and site
- Create a prompt set
- Choose AI engines
- Run monitoring now
- Review citation report
- Generate next-step tasks
- Export a weekly plan

## 5. Pages & form factor
**Form factor:** Web SaaS dashboard (with optional browser companion later)
**Why:** Users need recurring workflows (LLM prompt tracking, citation/share-of-voice monitoring, audits, and actionable implementation reports) and want “not manual bs every week.” A web SaaS centralizes monitoring + execution and supports affordable onboarding for small teams evaluating AEO/GEO tools.

### Pages
**5.1 Visibility Dashboard**
Single-pane “AEO health” view: brand citations/share-of-model trends, prompt-set performance, and alerts when results drop.
Key elements:
- Brand citations/share-of-model trend chart
- Prompt sets list with last run status
- LLM coverage matrix (ChatGPT/Perplexity/Claude/Gemini)
- Anomalies/alert feed for drops or missing citations
- Export button for monthly reporting

**5.2 Prompt & Model Monitor**
Define standardized prompt sets, run them on a schedule, and log which LLMs cite/mention the brand per query theme.
Key elements:
- Prompt set builder (topics + audience intent labels)
- Model selectors (per LLM)
- Run scheduler (daily/weekly)
- Per-prompt mention/citation scoring
- Audit trail of prompt versions

**5.3 AEO Report Builder**
Turn monitoring outcomes into an implementation plan: page-level what-to-change, schema updates, and GEO/AEO content fixes.
Key elements:
- Auto-generated “what changed” summary from latest run
- Page-level recommendation list with priority
- Proof-of-impact worksheet (before/after citation rate)
- Exportable deliverable (deck or PDF)
- Confidence/risk notes per recommendation

**5.4 GEO & Schema Audit**
Audit and generate scalable, per-page schema and content-structure fixes (FAQs, Organization, breadcrumbs) with correct targeting per page type.
Key elements:
- Page-type selector (blog/service/local/product, etc.)
- Schema coverage checklist (FAQ, Organization, breadcrumbs)
- Dynamic schema preview per sample URL
- Robots/Javascript rendering risk warnings
- Batch “generate JSON-LD” action

**5.5 Reply Composer**
AI-assisted drafting that can feel non-ad-like for community posts/replies, with anti-spam/shadow-ban guidance.
Key elements:
- Community reply editor (thread context + product/offer tokens)
- Tone controls (helpful, concise, non-promotional)
- CTA/link placement guidance panel
- Shadow-ban risk checklist
- One-click copy/export

**5.6 Citation Explorer**
Investigate where brand mentions/citations appear per LLM response and correlate them with content/structure changes.
Key elements:
- Prompt → response → extracted citations view
- Standalone-sentence/quote detection hints
- Comparison mode (before vs after content/schema changes)
- Notes/verification tags (trust level)
- Referral-attribution view (directional measure)

**5.7 Settings & Integrations**
Connect SEO inputs, define brand/entity details, manage exports, and configure affordability constraints (prompt limits, schedules).
Key elements:
- Brand/entity profiles (name variants, locations, offers)
- Integration connections (GSC baseline truth, site CMS exports)
- Run limits (affordable tiers)
- Export formats (CSV, PDF, deck)
- Safety/consent toggles for outreach/feeds

### Key functions
- **Create prompt set** *[on: Prompt & Model Monitor]*
  - Trigger: User clicks “New Prompt Set” and saves a topic + model combination
  - Creates a versioned set of prompts grouped by query theme so later runs can measure citation/share-of-model changes over time.
- **Schedule model runs** *[on: Prompt & Model Monitor]*
  - Trigger: User selects frequency (weekly/monthly) and clicks “Start schedule”
  - Automates recurring prompt testing across selected LLMs without weekly manual effort.
- **Score brand citations** *[on: Visibility Dashboard]*
  - Trigger: After a run completes, user views per-prompt mention/citation outcomes
  - Computes a directional citation/share-of-model score per prompt theme and per LLM for trend tracking.
- **Detect citation drops** *[on: Visibility Dashboard]*
  - Trigger: Automated threshold check after each scheduled run
  - Flags prompt sets where brand citations/mentions drop compared to prior runs so users can focus on what broke.
- **Generate AEO content recommendations** *[on: AEO Report Builder]*
  - Trigger: User clicks “Generate recommendations” after selecting a URL set
  - Produces implementable fixes aligned to AEO: direct first sentence answer, FAQs, bullet/numbered structure, and quote-ready claims.
- **Create before/after proof worksheet** *[on: AEO Report Builder]*
  - Trigger: User selects a “baseline run” and “post-change run”
  - Creates a report comparing citation rates so users can show evidence of impact (not just dashboards).
- **Batch generate FAQ JSON-LD** *[on: GEO & Schema Audit]*
  - Trigger: User clicks “Generate JSON-LD” for selected pages
  - Generates FAQ schema that matches visible FAQs and positions it consistently (e.g., bottom-of-post) for AEO/GEO extraction.
- **Generate Organization JSON-LD with state details** *[on: GEO & Schema Audit]*
  - Trigger: User enables “Include Organization schema” and chooses location/state fields
  - Creates Organization structured data with the correct location/state fields plus FAQ schema as requested.
- **Audit robots.txt and JS-rendered sections** *[on: GEO & Schema Audit]*
  - Trigger: User clicks “Run crawlability audit”
  - Identifies crawler-blocking issues and JS-dependent content risks that can prevent LLM crawlers from seeing important text.
- **Generate standalone quote candidates** *[on: Citation Explorer]*
  - Trigger: User clicks “Find quote-ready sentences” for a page
  - Highlights or suggests at least five standalone, quote-ready sentences/claims to increase extractability.
- **Draft non-ad-like community reply** *[on: Reply Composer]*
  - Trigger: User clicks “Draft reply” after pasting the thread/context
  - Generates a helpful reply that avoids disguised promotion style and follows anti-spam guidance.
- **Validate Substack consent strategy** *[on: Settings & Integrations]*
  - Trigger: User opens “Audience/feeds” settings and chooses import vs restart
  - Shows a consent-implication checklist so users decide whether to import lists or start fresh under guidance.

### UX details
- **AEO definitions onboarding:** Use a dedicated “GEO vs AEO” explainer card in the first-run setup (with exact definitions and a simple mental model link).
- **Recommendation shaping:** First recommended change is always audience-value (not “rewriting content just for AI systems”) and shows a “why this matters” line.
- **Schema output targeting:** Schema generation is page-type aware and includes a “sample URL preview” so teams don’t misapply JSON-LD across templates.
- **Citation measurement approach:** Default view uses relative trend measurement (directional) rather than claiming exact “true citation counts,” with a switch to referral-based directional attribution.
- **Content structure linting:** If a page lacks a direct first-sentence answer, show a blocker-style lint: “Start with the actual answer in the first sentence.”
- **Stat density guidance:** When content performs poorly, recommend adding 3–5 stats per ~1000 words (only changing the stat blocks first for isolated testing).
- **Quote/claim extractability:** Run a “standalone quotes” heuristic and display the count of candidate quote-ready sentences (target: 5+).
- **Audit crawlability:** Show a crawlability warning when robots.txt or JS-dependent content could hide text from AI/gen-AI crawlers, with a “what to change” checklist.
- **Anti-spam UX:** In Reply Composer, provide a CTA/link placement coach (e.g., “move links to end, keep single CTA, avoid disguised promotion”).
- **Submission feedback:** After GEO audit form submission, show an explicit confirmation step with next actions (baseline run scheduled, expected turnaround time).

## 6. Monetization
**Model:** (unspecified)

## 7. Competitors to beat
| Name | Why it fails | Price | Mentions |
|---|---|---|---|
| Semrush | Users report declining recommendation accuracy and incorrect recommendations (schema, keyword/content issues, noindex/canonical misflags, and unhelpful search volumes), leading to double-checking across tools. | - | 11 |
| Gusto | Not framed as failing; characterized as pricier in the respondent’s comparison: “felt pricier and more U.S.-centric.” | - | 6 |
| Rely on SEO fundamentals because AI tools ground/cite from search index (no separate AI index) | Some commenters argue GEO/AEO is still mostly SEO and that “GEO isn’t real”; also there’s uncertainty about upstream reranking layers that filter results before an LLM sees them. | - | 5 |
| AI Max (Google Ads feature) | Multiple commenters/users report inflated leads, poor intent matching, overly broad targeting, and cumbersome negative keyword upkeep; lead quality is described as rough for B2B tech. | - | 5 |
| Focus ads on top-selling/profitable SKUs/categories instead of many ad groups for a large catalog | Proposed broad/segmented structure is considered unaffordable and leads to budget dilution and low impressions/ineffective spend. | - | 5 |
| In-house SEO (do it in-house) | Concerns about capacity/bandwidth for a small team: “he's probably gonna drown in work” and “a legit in house SEO is not cheap.” | - | 5 |
| Ahrefs (Brand Radar / GEO-AEO-LLMO-SEO positioning) | Pricing criticized as too high for Brand Radar / AI mentions. | - | 4 |
| Profound (AI visibility / Share of Model tracking) | Seen as specialized and not perfect for exact citation transparency; also described as limited in showing why you’re being picked up (and only part of the solution). | - | 4 |

## 8. Distribution
- Top subreddits to launch in: r/smallbusiness, r/Entrepreneur, r/SEO, r/analytics, r/DigitalMarketing, r/marketing, r/PPC, r/startups, r/bigseo, r/kpop

## 9. Users & roles
**Primary persona:** small SEO team running AEO experiments
**Secondary personas:**
- solo SEO / founder
- marketing manager

**Roles:**
- **Workspace owner** — Create prompt sets, schedule runs, view citation dashboards, and export AEO task plans.

## 10. Data model & integrations
- (no data model extracted)

## 11. States
**Empty state:** User sees a blank dashboard with a “Create first prompt set” CTA and explanation of GEO vs AEO.
**Error state:** User sees which engine/prompt failed, with a retry button and an error summary (e.g., rate limit, timeouts).

## 12. Analytics & metrics
- (not synthesized for this report)

## 13. Risks & open questions
- (no risks/questions extracted)

## 14. Post-launch
- See https://painfinder.app/reports/getting-recommended-by-ai-tools-feels-unclear-and-unmeasurable for DM-able hot leads (workarounds × buying intent).
- See https://painfinder.app/reports/getting-recommended-by-ai-tools-feels-unclear-and-unmeasurable for verified key quotes you can use as landing copy.

## 15. Suggested build order (3-week MVP cut)
- Week 1: §3 must-haves + §5 page 1.
- Week 2: §5 remaining pages + auth/persistence if needed.
- Week 3: §6 monetization wiring + analytics + launch checklist.

## 16. Setup hints (your stack overrides these)
- `pnpm create next-app . --typescript --tailwind --app`
- `npx shadcn@latest init`
- The agent SHOULD ask the user before committing to a stack.

## 17. How to use this file
You're an AI coding agent reading this in AGENTS.md. Your job:
1. Confirm the stack with the user (their preferences override this file).
2. Scaffold an MVP covering §3 + §5 page-1 first.
3. Defer §6 (monetization) and §14 (post-launch) until §3 ships and works.
4. Re-fetch the live PRD anytime via:
   curl https://painfinder.app/api/public/reports/getting-recommended-by-ai-tools-feels-unclear-and-unmeasurable/export.json?size=compact

## 18. Verbatim key quotes (top 10)
> "Everyone’s talking about SEO, AIO, GEO, and AEO lately  and honestly, it’s getting hard to keep up."  
> — General research & advice, post #23855

> "What are you using right now  or planning to use? Which one’s actually getting results in 2025?"  
> — GEO/AEO concepts & terminology, post #23855

> "AEO seems focused on making content that directly answers user queries maybe the next level of SEO"  
> — GEO/AEO concepts & terminology, post #23855

> "Its all just search optimization. Marketing gurus pull new acronyms out of their ass every year."  
> — General research & advice, post #23855

> "Can someone explain GEO and AEO in a very simple and easy way?"  
> — General research & advice, post #24157

> "They are both sort of BS science experiments right now based on synthetic testing."  
> — General research & advice, post #24157

> "The concept is that you can gain the system to trick LLMs into recommending you more or talking about you."  
> — GEO/AEO concepts & terminology, post #24157

> "The problem is there is 0 proof this effectively works."  
> — General research & advice, post #24157

> "Until LLMs give out data on what prompts people are using GEO is a nice snapshot of information but hardly anything I would pivot my entire business on."  
> — General research & advice, post #24157

> "AEO = Answer Engine Optimization"  
> — GEO/AEO concepts & terminology, post #24157

## 19. Manual workarounds users cobble together (top 15)
1. **AEO/citation tracking automation** — *Guessing whether they show up in LLM answers by using random prompts instead of reliable tracking.*
   > "just been guessing with random prompts."
2. **Automated AEO measurement across LLMs** — *Weekly manual prompt tracking with a consistent prompt sheet across LLMs, then looking for brand mentions/citations over time.*
   > "track a consistent prompt sheet weekly and look for brand mentions or citations over time."
3. **Reliable attribution/measurement for AEO** — *Measure impact indirectly by tracking referrals rather than direct citation/mention tracking.*
   > "Your best bet is to track via referrals."
4. **Ad copy generation workflow tailored to Google Ads** — *DIY tool creation to compensate for perceived poor AI writing/workflow mismatch.*
   > "I built an ad writing tool since I found ChatGPT and Google's writing a little off, and the workflow just felt bad: https://30chars.com"
5. **Managed workflow automation for keyword/search term management** — *Use free scripts for search term management as a workaround/solution approach.*
   > "I'm all about free scripts. Search term management is a big one there."
6. **AEO attribution / measurement tooling that is trustworthy vs synthetic dashboards** — *Set up UTM tracking to measure leads attributable to ChatGPT.*
   > "I set up full UTM tracking for one of my clients and they get 5-10 ChatGPT leads (quote requests) every week."
7. **AI visibility tracking / monitoring** — *Use a spreadsheet and manually prompt each LLM on a weekly cadence to see results/mentions.*
   > "You can track this yourself with a spreadsheet and prompt each LLM manually, we just got tired of doing that every week."
8. **End-to-end AEO/GEO workflow automation** — *Manually test prompts, track brand mentions, improve content structure, and build authority across platforms.*
   > "Most teams i have seen are manually testing prompts , tracking the brand mentions , improving the content structure and trying to build authority over diffrent platforms.."
9. **GEO analytics/reporting dashboards** — *Manually track brand mentions in ChatGPT/Perplexity as a temporary reporting approach.*
   > "you can track brand mentions manually in chatgpt/perplexity for now"
10. **audit deliverable format** — *The author says they deliver audits as a 12-15 slide deck in Gamma where each slide names a specific page/issue/recommendation to force specificity and decisions.*
   > "each slide names a specific page, the specific issue, the specific recommendation."
11. **template-to-specific audit generation** — *They generate most of the audit report structure via an AI report generator prompt plus pasted Ahrefs export, then manually name pages and write the consolidation map.*
   > "an ai report generator prompt with the actual ahrefs export pasted in gets you 80% of the structure"
12. **consolidation mapping** — *Manual creation of page-level naming and consolidation mapping after using AI for the initial structure.*
   > "then i spend the rest of the time naming pages and writing the consolidation map."
13. **AEO/GEO analytics + AI-Overviews citation recommendation & outreach tooling** — *Built an internal tool that exports question-type queries from Google Search Console, runs a query fan-out simulation, extracts AI Overviews citations, returns citation websites with contact details for outreach, and scores content match via vector embeddings and cosine similarity.*
   > "My theory is that we're not often mentioned in other websites, and our content might not be too relevant to the queries, so my data team built a small internal tool to help the marketing team optimize for AI Overviews / AI Mode visibility."
14. **Query fan-out simulation** — *Synthetic query generation to simulate Google’s query fan-out.*
   > "The tool performs a query fan-out simulation, generating a series of synthetic queries similar to those produced by Google during its own query fan-out process."
15. **Fan-out visibility tooling** — *Reverse engineering fan-out by analyzing ChatGPT page titles.*
   > "In Chatgpt - you have to look at the page titles for consistent keywords and reverse engineer the fan out."

## 20. "I would pay for…" quotes (top 10)
1. **would_pay** — wants: Assess whether to buy/subscribe to the agency's AEO service on top of SEO.
   > "please let me know if it's worth going for it"
2. **would_pay** — wants: Find/derive an AEO/AI-visibility strategy (tool-assisted research) for experiments.
   > "I asked Perplexity (which is fed by Google) for an SEO strategy for AI Visibility tools for an experiment"
3. **wishing** — wants: Tools or tweaks that can fix AI citation/mention issues.
   > "tools or tweaks that actually fixed this for you?"
4. **wishing** — wants: Which AEO/GEO tools are useful.
   > "If yes, which ones and are they actually useful?"
5. **wishing** — wants: Advice on choosing an AI tool for business and willingness to trial nexos.
   > "I’m thinking of going with nexos first and testing the free trial. What do you think?"
6. **wishing** — wants: Willingness to pay for guidance/coaching on AEO workflow (quote request).
   > "DM me a quote if you’re interested in learning to do it together."
7. **would_pay** — wants: Whether paying an agency for 'super targeting' is worth it for their beauty salon.
   > "Worth paying an agency for it?"
8. **would_pay** — wants: Confidence that GEO agencies actually deliver results before committing again.
   > "Did they deliver or did you end up with another beautiful report and nothing to show for it?"
9. **would_pay** — wants: Real user confirmation to justify paying GEO providers.
   > "Tell me honestly  has anyone actually used any of these three?"
10. **would_pay** — wants: Workforce management/HR platform that covers payroll, onboarding, and employee feedback with intuitive UX for 10–20 people.
   > "I'm willing to pay for something solid, but I need it to actually work."

## 21. Hot leads summary
- 63 hot leads identified (users who BOTH built a workaround AND signaled buying intent)
- Tier breakdown: 6 hot / 15 warm / 42 cold
- DM-able usernames available at: https://painfinder.app/reports/getting-recommended-by-ai-tools-feels-unclear-and-unmeasurable#hot-leads (kept off this file for privacy — see live report)

## 22. Full competitor list (top 10)
| Name | Why it fails | Price | Mentions |
|---|---|---|---|
| Semrush | Users report declining recommendation accuracy and incorrect recommendations (schema, keyword/content issues, noindex/canonical misflags, and unhelpful search volumes), leading to double-checking across tools. | - | 11 |
| Gusto | Not framed as failing; characterized as pricier in the respondent’s comparison: “felt pricier and more U.S.-centric.” | - | 6 |
| Rely on SEO fundamentals because AI tools ground/cite from search index (no separate AI index) | Some commenters argue GEO/AEO is still mostly SEO and that “GEO isn’t real”; also there’s uncertainty about upstream reranking layers that filter results before an LLM sees them. | - | 5 |
| AI Max (Google Ads feature) | Multiple commenters/users report inflated leads, poor intent matching, overly broad targeting, and cumbersome negative keyword upkeep; lead quality is described as rough for B2B tech. | - | 5 |
| Focus ads on top-selling/profitable SKUs/categories instead of many ad groups for a large catalog | Proposed broad/segmented structure is considered unaffordable and leads to budget dilution and low impressions/ineffective spend. | - | 5 |
| In-house SEO (do it in-house) | Concerns about capacity/bandwidth for a small team: “he's probably gonna drown in work” and “a legit in house SEO is not cheap.” | - | 5 |
| Ahrefs (Brand Radar / GEO-AEO-LLMO-SEO positioning) | Pricing criticized as too high for Brand Radar / AI mentions. | - | 4 |
| Profound (AI visibility / Share of Model tracking) | Seen as specialized and not perfect for exact citation transparency; also described as limited in showing why you’re being picked up (and only part of the solution). | - | 4 |
| Audit Shopping/Feed/merchant center/account-level issues; try switching payment method / recreate ad / relink feed | OP reports ongoing zero impressions; commenters propose multiple troubleshooting paths rather than a confirmed fix. | - | 5 |
| Schema/UI alignment for rating scale (match best/worst or display inline 1–5) + ensure it’s visible/timed | Comments frame the mismatch as potentially causing rich results to be skipped; not guaranteed but offered as fix hypotheses. | - | 5 |

## 23. Where this conversation lives (top subreddits)
- r/smallbusiness (70 posts)
- r/Entrepreneur (60 posts)
- r/SEO (59 posts)
- r/analytics (55 posts)
- r/DigitalMarketing (54 posts)
- r/marketing (49 posts)
- r/PPC (49 posts)
- r/startups (42 posts)
- r/bigseo (34 posts)
- r/kpop (4 posts)