Posts | Marketing with Dave

August 2026 Digital Marketing Roundup: What Changed and Why It Matters

Reading Time: 5 minutes

1. Google rolled out its third spam update of 2026, now covering AI-answer manipulation

What happened: On August 18, 2026, Google began the August 2026 spam update, a global ranking change applying to all languages, which finished rolling out on August 21 after two days and sixteen hours, the longest of the three spam updates Google shipped this year.

Key players: Google

Why it matters: This is the first spam update since Google formally extended its spam policies to cover manipulation of AI Overviews and AI Mode responses, so enforcement now reaches tactics built purely to get cited in AI answers.

Implications:

Marketers and SEO teams should audit for both legacy spam tactics and AI-citation manipulation, since one update now covers both categories.
Researchers tracking ranking volatility should note that several unconfirmed traffic swings in late July and early August preceded the announced update and may not share the same cause.
Sites with unexplained August traffic or ranking losses should isolate pre-August 18 changes from the spam update itself before attributing cause, since Google confirmed no new policy categories accompanied the rollout.

2. Apple launched ads on Apple Maps

What happened: On August 21, 2026, Apple began letting businesses run ads on Apple Maps to drive calls and visits, offering new advertisers a $150 credit to start.

Key players: Apple

Why it matters: It extends Apple’s ad business beyond the App Store and Apple News into local intent moments, giving small and local businesses a new paid placement tied directly to navigation and search behavior.

Implications:

Local and multi-location businesses should evaluate Apple Maps ads as a lower-cost test given the initial credit, alongside existing Google Business Profile and Google Ads local campaigns.
Marketers should expect reporting gaps at launch, since a new ad surface typically starts with limited attribution and benchmarking data compared to mature channels.
Startup operators building local marketing tools should expect Apple to keep expanding ad inventory across Maps, News, and the App Store into a connected local advertising stack.

3. AdRoll opened a ChatGPT advertising pilot for small and mid-market advertisers

What happened: On August 11, 2026, AdRoll launched a pilot letting a select group of AdRoll and AdRoll ABM customers test sponsored placements inside ChatGPT, six months after OpenAI began selling ads in the assistant.

Key players: AdRoll

Why it matters: It gives small, mid-market, and B2B advertisers, who typically lack the resources to test a brand-new channel independently, guided access to conversational AI advertising instead of leaving early access to enterprise buyers only.

Implications:

Marketers without in-house AI advertising expertise should treat this as a lower-risk way to test ChatGPT placements before committing standalone budget.
Startup operators building attribution or ad tech tools should watch how AdRoll measures ChatGPT performance, since cookie-independent measurement is central to its pitch.
Researchers tracking AI-referred traffic should note this pilot remains gated to select customers, so early performance data will not yet represent typical results across the wider advertiser base.

4. Instacart’s advertising revenue kept outpacing its overall growth

What happened: On August 6, 2026, Instacart reported second quarter 2026 advertising and other revenue of $297 million, up 16 percent year over year and equal to 2.9 percent of gross transaction value, again outpacing its 14 percent overall transaction growth.

Key players: Instacart

Why it matters: It confirms that mid-tier retail media networks beyond Amazon and Walmart can sustain advertising growth that outpaces transaction volume, reinforcing retail media as a durable revenue line rather than a pandemic-era spike.

Implications:

Marketers running grocery or CPG campaigns should expect continued pressure on Instacart ad inventory pricing as advertising demand keeps outgrowing order volume.
Investors evaluating retail media exposure should compare ad revenue as a share of gross transaction value across networks, since Instacart’s 2.9 percent ratio gives a disclosed benchmark few competitors publish.
Startup operators building retail media tooling should note that grocery and delivery verticals are proving non-Amazon retail media can scale profitably, widening the addressable market for ad tech vendors.

5. Nielsen quietly rewrote the numbers behind TV and CTV measurement

What happened: On August 19, 2026, Nielsen announced seven simultaneous methodology changes to its Big Data and Panel measurement, effective August 31, including updated universe estimates it says had been based on outdated 2024 survey data.

Key players: Nielsen

Why it matters: With seven changes landing at once and no published breakdown separating their individual effects, buyers and programmers lose the ability to tell whether a post-August ratings shift reflects real audience behavior or a methodology adjustment.

Implications:

Marketers and agencies negotiating CTV and linear buys should flag any comparisons spanning August 31 as not directly comparable until Nielsen publishes more detail.
Researchers and analysts building longitudinal audience trends should treat late August 2026 as a break point in the data series, similar to a rebasing event.
Media planners relying on Nielsen currency for upfront negotiations should ask sellers directly which of the seven changes affected their reported numbers, since no single cause is identifiable from the announcement alone.

6. Meta disclosed removing over 750,000 underage accounts under Australia’s social media ban

What happened: Meta reported in August 2026 that it removed more than 750,000 Facebook and Instagram accounts in Australia it assessed as belonging to users under 16, as part of ongoing enforcement of the country’s minimum age law that took effect in December 2025.

Key players: Meta

Why it matters: It is the clearest public data point yet on the real-world scale of a minimum age social media law, and Australia’s government is separately moving to double penalties for non-compliance, giving other jurisdictions a concrete enforcement precedent to reference.

Implications:

Marketers targeting Australian audiences should expect continued shrinkage of under-16 reach across Facebook, Instagram, TikTok, YouTube, Snapchat, and other covered platforms as enforcement continues.
Policymakers in other countries, including the UK, which has proposed similar restrictions, gain real enforcement data to cite when drafting comparable legislation.
Researchers studying platform demographics should treat Australian audience data from December 2025 onward as structurally different from prior periods, given the scale of account removal.

7. IAB released the first standardized framework for measuring AI visibility

What happened: On August 3, 2026, the Interactive Advertising Bureau published standardized guidelines for measuring brand and publisher visibility on AI platforms, after identifying more than 20 vendors offering AI visibility tools with little consistency in methodology or results.

Key players: IAB

Why it matters: It is the first attempt at a shared measurement language for AI search visibility, an area that previously left marketers unable to compare vendor scores with any confidence.

Implications:

Marketers evaluating AI visibility vendors should check whether a tool aligns with IAB’s presence, prominence, and portrayal framework before comparing scores across platforms.
Researchers and agencies building AI visibility benchmarks should expect vendor consolidation around this framework over time, similar to how MRC standards shaped attention measurement.
The IAB has scoped this first framework to organic visibility only, so marketers should not yet expect standardized measurement for paid placements or AI-driven commerce attribution.

8. Consumer use of agentic shopping is outrunning brand readiness

What happened: An August 2026 survey of 80 brands, retailers, and agencies by Swap and Glossy found 70 percent are testing or deploying an agentic storefront, while more than a fifth of respondents reported declines in both upper and lower funnel search traffic they attribute to AI.

Key players: Swap

Why it matters: The survey suggests brand-side adoption of agentic commerce is following consumer behavior rather than leading it, and that gap does not typically close in the brand’s favor.

Implications:

Marketers should treat agentic storefront readiness as a discovery and full-funnel investment, not just a conversion play, since brands cited discovery ahead of conversion as their top objective.
Startup operators building commerce infrastructure should note that data requirements and ROI measurement, not data quality, are the top cited barriers among the 30 percent of brands not yet adopting.
Researchers should be cautious generalizing from this survey’s 80-respondent sample size, even though its direction aligns with broader market forecasts from Bain and McKinsey on agentic commerce growth.

August 2026 Digital Marketing Roundup: What Changed and Why It Matters Read More »

Comparison of AI search visibility scores from Nuxtonic, Visby, SnowSEO, and Subsig across different tools for trustworthiness analysis.

Can You Trust AI Search Visibility Scores? I Tested the Same Prompt Across 6 Tools

Artificial Intelligence (AI), Search Engine Optimization (SEO)

Reading Time: 7 minutes

AI Search visibility tools give marketers wonderfully precise-looking numbers: 67% visibility, 90% visibility, position #1, 100% share of voice.

How much confidence should you put in those numbers?

I had an unusual opportunity to find out. I currently have access to several AI Search visibility platforms, so I tracked the exact same prompt for the exact same brand across six tools. All the results below were collected within less than one week.

The tools often agreed that Marketing With Dave was visible. They did not agree on how visible it was, where it ranked, or even whether it appeared at all on a given AI platform. One tool gave me a 90% visibility score while the underlying AI response got my name wrong.

How I Ran the Test

I used one of the 10 prompts I benchmark AI Search visibility platforms with:

Who is Marketing With Dave?

This is intentionally a branded prompt. It isn’t a difficult query for Marketing With Dave to appear for, which is exactly what makes it useful for comparing how different tools measure the same basic outcome.

I pulled results from Nuwtonic, SnowSEO, Subsig, Visby, ZeroRank, and iGEO. iGEO is included because it was part of the test, although it never returned any results during the testing period.

This isn’t a scientific study. The tools may query different model versions, run prompts at different times, and use different sampling methods, locations, or scoring formulas. AI responses themselves are also non-deterministic. That’s not a weakness in the experiment. Those differences are part of the measurement problem. I also didn’t repeatedly rerun every tool to determine how much these results would change from one run to the next. That’s an important limitation, but also part of the larger problem: a single visibility measurement may not be particularly repeatable.

That uncertainty isn’t theoretical. SparkToro and Gumshoe recently ran nearly 3,000 AI responses and found substantial inconsistency in brand recommendations even when prompts were repeated. Their research tested the stability of the AI responses themselves. My test looks at another layer of the problem: whether the commercial tools measuring those responses agree on what the measurement means.

The Same Prompt, Four Different Scores

Here’s what the tools reported for the same prompt on OpenAI alone:

Tracking Tool	Mentioned	Position	Visibility
Nuwtonic	Yes	#1	100%
SnowSEO	Yes	#1	75%
Subsig	Yes	#1	16.7%
Visby	Yes	—	90%
ZeroRank	Yes	#2	Not shown separately

One platform, one prompt, one week. Four visibility numbers: 100%, 90%, 75%, 16.7%. That doesn’t mean three of the four tools are wrong. It means a number labeled “Visibility” isn’t automatically measuring the same thing from one product to another.

Semrush, one of the largest companies in the SEO space, defines AI Visibility in its Visibility Overview as a 0-100 benchmark comparing how often a brand appears against competitors, but defines visibility differently inside Prompt Tracking, where it reflects a site’s standing within the top citations for specific tracked prompts. Related concepts, not identical measurements. Semrush also warns that AI responses are fast-changing and personalized, so no platform can produce exact visibility numbers, only directional signals. Semrush explains where its AI visibility data comes from here.

That’s also why I question the precision implied by some of these dashboards. A visibility score reported as 16.7% looks remarkably exact for a measurement built on AI responses that may change the next time the prompt runs.

That context matters when a dashboard hands you a 96% or a 67% without making its methodology equally visible.

100% Visible in Gemini. Or 0%. Or, Actually, It Depends What You Mean By Gemini.

The most dramatic disagreement in the data isn’t about scoring formulas. It’s about whether Marketing With Dave appeared at all, and it gets stranger the closer you look.

Nuwtonic reported the Gemini result as mentioned, position #1, visibility 100. Visby reported the same prompt on Gemini less than a day later as not mentioned, visibility 0%.

That’s not a rounding error.

But Visby’s own dashboard adds a wrinkle worth sitting with: alongside that 0% Gemini score, Visby separately tracks Google’s AI Overview, and there it reports Marketing With Dave at 100% visibility with 7 citations. Same brand, same week, same company (Google), two different surfaces, opposite results, from a single tool that at least has the honesty to report them separately.

Gemini the chatbot and AI Overview the search feature are genuinely different products built on related but distinct retrieval behavior. A visibility score that doesn’t specify which one it’s measuring, or that quietly folds both into “Google,” isn’t just imprecise. It’s answering a different question than the one you think you’re asking.

A 90% Visibility Score Can Still Be Wrong

Visby gave Marketing With Dave a 90% OpenAI visibility score. The answer cited MarketingWithDave.com and correctly connected the site to digital marketing, A/B testing, martech, measurement, and SiteSqueeze.

It also said Marketing With Dave was run by Dave Harrell.

It isn’t.

So I could check off every box that usually signals success: brand mentioned, high visibility score, website cited, relevant topics identified. And the answer still contained a material entity error. What good is a visibility score if the AI doesn’t accurately understand the entity it’s making visible?

Marketing With Dave probably makes this problem easier to spot than most brands would. Dave is a common name, “marketing” is a common word, and there’s no shortage of marketers and agencies using similar language. A highly distinctive SaaS brand may resolve more cleanly. But that’s exactly why the ambiguity doesn’t invalidate the test: real companies share names, terminology, and categories constantly. Entity resolution is part of AI visibility, and a high score can hide a wrong answer.

Even One Tool Isn’t Internally Consistent

You don’t need to compare across vendors to find volatility. Nuwtonic’s own results for this single prompt run remarkably tight across five platforms: 95 to 100 visibility and position #1 on Perplexity, OpenAI, Gemini, Grok, and Meta. Then Anthropic drops to 80 and position #5.

Same tool, same prompt, same week, same methodology. The one variable that changed is the model being queried. If a single vendor’s own numbers can swing that much platform to platform, treating any one visibility score as a stable fact about your brand, rather than a snapshot of one model on one day, is asking more of the number than it can deliver.

The Tools Don’t Even Report the Same Things

The differences go beyond how visibility gets calculated. The six products vary considerably in what they expose to the user at all:

Tool	Visibility	Position	Share of Voice	Sentiment	Citation Count	Cited Pages
Nuwtonic	✓	✓	–	–	✓	✓
SnowSEO	✓	✓	✓	✓	✓	⚠
Subsig	✓	✓	✓	✓	✓	✓
Visby	✓	–	–	–	✓	✓
ZeroRank	✓	✓	–	✓	✓	✓
iGEO	–	–	–	–	–	–

✓ Available – Not available in testing ⚠ Unknown / not verified

More metrics isn’t automatically better; it can just mean more numbers to misread. But the gaps matter once you start comparing products or trying to build a repeatable process.

Mentions, Citations, Accuracy: Three Different Questions

A brand can appear in an AI answer without its website being used as a source, and a page can get cited without the brand being prominently recommended. Semrush draws the same line: mentions are appearances of the brand in AI answers, citations are linked references to the content behind them. Semrush’s AI Share of Voice guide explains that distinction.

I’d add a third category: accuracy. Did the AI get the brand, the person, and the positioning right? That may matter more than whether your visibility score moved from 67 to 72, and Visby’s citation counts for this one prompt ranged from 0 (Gemini) to 7 (AI Overview) without any change in whether the brand was “visible” by the tool’s own definition. Citation volume and visibility score aren’t the same thing either.

Platform-Level Data Beats a Blended Score, and I Have Proof

ZeroRank reports an “Overall, 3 models” figure alongside its per-platform numbers: 67% visibility, position #1.5. It’s a real, sensible-looking average. It also sits neatly between the individual platform numbers and, in doing so, erases the exact volatility this entire test was built to surface: a #1 on Perplexity, a #2 on OpenAI, and no mention at all on Google/Gemini, blended into one tidy 67.

Semrush reaches a similar conclusion in its own methodology, reporting AI platforms separately for cross-platform comparisons rather than combining them into a single weighted score, because aggregation can hide insights. See Semrush’s AI Visibility Index methodology. After watching one tool’s blended score paper over a #1-to-absent spread within its own data, that conclusion doesn’t need much convincing.

What I Actually Trust

Not any single visibility score. Before I’d act on one, especially a low one, I want to know:

Do I already have a page that genuinely answers this prompt, or is this a content gap rather than a visibility problem?
Does the result hold up if I check again next week?
Does the brand show up on more than one platform, or is this one model’s quirk?
Do multiple tools tell a broadly similar story?
Which specific pages are actually getting cited?
Are competitors consistently showing up where I’m absent?
Is the AI’s description of my brand actually correct?

I’d apply a similar standard when choosing the software itself. If a platform won’t let me see the underlying AI response, platform-level results, and citations behind its score, I’m much less interested in the score. I want enough raw evidence to question the metric, not just a dashboard asking me to trust it.

That first question turned out to be the most useful one in this whole exercise. Some of the prompts I want Marketing With Dave to appear for don’t yet have an obvious page on the site that deserves to be cited. That’s not an AI visibility problem. That’s a content gap, and it calls for a different response than chasing a better score.

Bottom Line

AI Search visibility measurement is useful, and I’m going to keep using it. But the problem isn’t that these tools are wrong. It’s that their precise-looking scores can imply a level of standardization and certainty the underlying measurements don’t support yet, whether that’s one vendor’s 0% sitting next to another vendor’s 100% for the same platform, or one vendor’s own results shifting from position #1 to #5 depending on the model.

The question isn’t simply “What’s my AI visibility score?” It’s “What decisions does this measurement actually justify?”

Treat AI visibility scores as directional evidence, not ground truth. The real work isn’t chasing a perfect number. It’s using repeated measurements, actual AI responses, citations, competitor patterns, and content gaps together to figure out where your brand is genuinely becoming part of the answer, and right now the industry is much better at producing these scores than at explaining how much confidence you should put in them.

Can You Trust AI Search Visibility Scores? I Tested the Same Prompt Across 6 Tools Read More »

Subsig Review: A Brand Intelligence Platform Wearing an AI Visibility Trench Coat

Artificial Intelligence (AI), Martech

Reading Time: 9 minutes

Most AI Search visibility tools follow a similar playbook: enter prompts, run them through AI models, and see if your brand shows up. Subsig does that too, but prompt tracking isn’t the most interesting part of the platform.

Subsig combines AI visibility with citations, competitors, reviews, brand mentions, sentiment, keyword intelligence, and AI Agents designed to turn that data into recommendations. It’s less a dedicated AI Search tool than a broader brand-intelligence platform that includes AI visibility.

That makes Subsig one of the more unusual platforms I’ve tested, and also harder to evaluate. Some features were genuinely impressive, others need more explanation, and several make far more sense for an established brand generating regular reviews and online conversations than they do for Marketing With Dave.

Subsig Overall Score

Why 3.8/5: Subsig combines strong AI Search reporting with unusually broad brand intelligence, but confusing UX, opaque data processing, and restrictive Tier 1 limits hold it back.

See how I rate software tools

See the current AppSumo deal for Subsig

Affiliate disclosure: If you purchase through my link, I may earn a small commission at no additional cost to you. I only share tools I have used myself.

The 30-Second Decision

Buy Subsig if: You run a SaaS company or established brand with products people actively review, mention, and discuss online. It makes the most sense when you have recognizable brand or product names to track and want AI visibility, citations, competitors, reviews, mentions, sentiment, and keyword conversations in one platform.

Skip it if: Your brand is still too small to generate meaningful reviews or online discussion, or you primarily want high-volume prompt tracking across multiple AI models. Tier 1 limits you to 10 prompts, 5 keywords, and ChatGPT, making a dedicated AI Search tool a better fit for that use case.

The bottom line: Subsig gets more compelling as your brand gets more visible. Its advantage isn’t simply tracking whether AI mentions you. It’s connecting AI visibility with what customers, review sites, competitors, and the broader web are saying about your brand.

What Subsig Actually Does

Subsig splits into several connected areas:

AI Visibility tracks whether your brand appears in AI answers, how often, where it ranks, share of voice against competitors, citations used, and sentiment.
Review Monitoring pulls in reviews from supported platforms for reputation and feedback analysis.
Brand Mentions tracks conversations about your brand across supported online and social sources.
Keyword Intelligence tracks conversations around topics you care about, even without a brand mention.
Agents analyze the collected data and turn it into recommendations instead of leaving you with more dashboards.

That combination separates Subsig from tools focused entirely on AI Search Visibility. It’s part AI Search visibility platform, part reputation manager, part brand monitor. Whether those pieces add up to more than the sum of their parts is still an open question, but the idea is compelling.

The Wait for Data Needs to Be Clearer

Setup itself wasn’t hard. Not knowing what was happening after setup was the problem.

I added Marketing With Dave, configured what to monitor, added prompts and competitors, then waited. Some sections populated hours later, with almost no indication of how long that should take. That’s a UX problem, not a performance one.

If Subsig told me “we’re analyzing your brand, initial results typically take 2-4 hours,” I’d close the browser and come back later. Without it, an empty dashboard just leaves you guessing whether it’s processing, misconfigured, or broken. Subsig needs better processing indicators and a real estimated timeframe.

Once data started arriving, the product got a lot more interesting.

What I Liked

1. The AI Visibility Reporting Is Surprisingly Complete

I ran the same set of 10 prompts I use to benchmark every AI Search platform I test. Marketing With Dave appeared in only one of them, which is my visibility problem, not Subsig’s.

For “Who is Marketing With Dave?”:

Brand rank: #1
Brand mentions: 16.7%
Brand sentiment: Positive
Multiple MarketingWithDave.com URLs retrieved as sources

Subsig also preserved the actual AI response, not just an aggregate score. ChatGPT described Marketing With Dave as a digital marketing resource led by an experienced professional with 25+ years in the field, focused on SEO, analytics, and software reviews. A fair representation.

More importantly, that result exposed the real gap. ChatGPT recognizes Marketing With Dave, retrieves the site directly, and understands what the brand is about. I’m just not yet showing up for broader category questions around AI Search resources, marketing software reviews, or measuring AI traffic. That’s more actionable than simply knowing my visibility score is 6.7%.

The sentiment score was 80/100, entirely positive, but based on a single response. Subsig does display the response count, so you can weigh the number appropriately, but an 80/100 built on one data point isn’t something to make decisions around yet.

2. Citation Analysis Goes Deeper Than a List of Links

Across tracked responses, Subsig identified 52 domains and 62 URLs, with MarketingWithDave.com contributing 5.3% of both. It categorizes citation sources by type (corporate, UGC, editorial, owned, institutional, and other), and corporate sources dominated my early dataset at 72.4% of retrievals.

That surfaces a useful question: if AI systems keep retrieving certain sites to answer the questions I care about, what can I learn from those sources? One Subsig recommendation pointed toward getting Marketing With Dave included on a site that was already being retrieved frequently. Whether every recommendation holds up remains to be seen, but the underlying citation intelligence is genuinely useful.

3. Competitor Reporting Gives the Numbers Context

A 6.7% visibility score means little on its own. Adding competitors (AppSumo, HubSpot, RevLocal, Search Engine Journal, Search Engine Land, WebFX) changed that:

Brand	Visibility	Share of Voice	Avg. Position
AppSumo	20%	60%	2.1
Marketing With Dave	6.7%	20%	2.0
HubSpot	6.7%	20%	2.5
RevLocal	0%	0%	—
Search Engine Journal	0%	0%	—
Search Engine Land	0%	0%	—
WebFX	0%	0%	—

Ten prompts and one AI model is too small a sample to treat this as real competitive intelligence, but it shows the reporting’s value: Marketing With Dave trailed AppSumo on visibility but had the best average position of any brand that appeared. That’s a far more useful signal than a bare visibility percentage. Subsig also suggests additional competitors worth monitoring.

4. The Prompt-Level Drilldown Is Where the Real Story Lives

The dashboard reported a 6.7% AI Visibility Score, #2 visibility rank, 20% share of voice, and #2 share-of-voice rank. Useful for tracking change over time, but not for optimization decisions on their own.

That distinction matters more than I initially realized. I tested the same prompt across six AI Search tools, including Subsig, and found dramatically different visibility scores depending on the platform measuring it. The experiment reinforced why I put more weight on the underlying responses, citations, and prompt-level data than any single visibility score.

The real value is in the individual prompts. Nine told me I have a visibility problem. One told me something better: when someone directly asks who Marketing With Dave is, ChatGPT knows the brand, describes it accurately, ranks it first, and pulls pages straight from the site. That’s a starting point. The real opportunity isn’t “raise 6.7%,” it’s figuring out why the site has strong entity recognition on a branded query but isn’t surfacing for the broader category questions I actually want it associated with. That’s exactly what I want AI Search software to help me investigate.

5. The Agents Are One of Subsig’s Most Interesting Features

Subsig doesn’t stop at dashboards. Its Agents turn your visibility data into standalone reports: root-cause analysis, prompt discovery, competitive benchmarking, even a 180-day AI visibility roadmap.

The Root Cause Analysis Agent was the standout. It examined where Marketing With Dave was and wasn’t appearing, dug into citation sources, and prioritized actions. The Prompts Discovery Agent suggested new questions to track, organized by buyer intent, which matters because deciding what to track is half the battle. The reports are polished, too: my 180-day roadmap broke recommendations into phases with separate tactics for ChatGPT, Perplexity, and Gemini.

subsig-root-cause-analysis Download

But polished doesn’t always mean correct.

I hadn’t added competitors to this project. The Competitive Benchmark Agent correctly flagged that it couldn’t run a true comparison, then speculated that the absence of tracked competitors might mean Marketing With Dave operates in a niche without real rivals. Another report skipped the caveat entirely and invented generic “Competitor A,” “Competitor B,” and “Competitor C” profiles. Neither conclusion is supported by my data.

Treat the Agents as AI-assisted analysis, not a finished analyst’s report. Read it, question it, and verify before acting on it.

Several more Agents are marked “coming soon”: Social Mentions Plan, Reputation Playbook, Review and Listings Plan, Keyword Strategy, Authority Building, Review Response, and a Custom Agent for building your own workflows. If those deliver, the Agents could become the platform’s biggest differentiator.

6. Review Monitoring Could Be Valuable for the Right Business

Marketing With Dave showed zero review data at first, unsurprising for a site that doesn’t generate hundreds of customer reviews. I added AppSumo as a second brand specifically to see what Subsig does when review data exists. A local business, SaaS company, eCommerce brand, or established product with real review volume will get far more out of this feature than I can demonstrate here, which is part of why I wouldn’t judge Subsig purely against dedicated AI visibility platforms.

Screenshot of a review table for AppSumo on Marketing with Dave website, showing ratings, review dates, and user roles, emphasizing app deals and customer feedback.

7. Keyword Intelligence Has Potential, but I Couldn’t Properly Test It

Keyword Intelligence is one of Subsig’s more interesting features because it can track conversations around topics even when your brand isn’t mentioned. I configured all five keywords available on Tier 1, including “AI Search Visibility,” “SiteSqueeze,” and “Marketing With Dave,” but Subsig had not returned any results by the time I completed this review.

That leaves a significant part of the platform I can’t fairly evaluate yet. The setup also needs clearer guidance around how keyword matching works, particularly the distinction between “All of these keywords” and “Any of these keywords.” I’ll revisit this feature once I have enough data to judge what it actually finds.

See the current AppSumo deal for Subsig

What I Didn’t Like

1. Tier 1 Has Some Significant Limits

Tier 1 gives you 10 tracked prompts, ChatGPT as the only AI provider, 5 keywords, 5 brands, 2,000 reviews, and 1,000 mentions. Ten prompts were enough to run my standard benchmark, but not much more, and meaningful cross-platform AI visibility requires moving up to Tier 3.

Some limits also aren’t obvious until you hit them. Review and mention capacities don’t reset, and deleting records doesn’t restore capacity. Subsig also limits monitoring to its supported platforms rather than letting you add any source. None of these are dealbreakers, but buyers should understand them before choosing a tier or configuring broad monitoring.

2. Data Processing Needs Much Better Communication

Data taking time is fine; making users guess whether the system is processing, broken, or misconfigured is not. During my testing, some data took hours to appear with little indication of what was happening. Something as simple as “7 of 10 prompts processed” or “next mention scan scheduled for…” would solve much of the problem.

3. The Product Needs More Explanation and Guidance

Subsig has a lot going on, and it sometimes assumes you understand how everything works together. Metrics such as Citation Rate, Retrieval, and Contribution need better explanation. Keyword Intelligence’s All/Any matching wasn’t clear. The distinction between competitors configured in different parts of the platform can also be confusing.

The same applies to the Agents. They’re promising, but I’d like to see more context showing which underlying data produced a recommendation, especially after seeing some reports make conclusions the data didn’t support.

Pricing and Tier 1

Subsig is currently on AppSumo across four lifetime tiers, all with unlimited users and all Agents included:

	Tier 1	Tier 2	Tier 3	Tier 4
Price	$49	$119	$269	$499
Workspaces	2	3	10	20
Tracked prompts	10	25	50	100
Lifetime AI credits	240	450	900	1,500
Review capacity	2,000	3,500	7,000	13,000
Mention capacity	1,000	3,000	5,500	12,000
Keywords	5	15	25	50
Brands	5	15	25	50
Trackable review platforms	12	18	32	64
Supported review platforms	4	6	10	15
Social platforms	5	7	7	7
AI providers	OpenAI	OpenAI	OpenAI, Perplexity, Google AI Overview	OpenAI, Perplexity, Google AI Overview
Notifications	Email & Slack	Email & Slack	Email & Slack	Email & Slack

Tier 3 is where the biggest unlock happens. It’s the first tier to add Perplexity and Google AI Overview alongside OpenAI, while increasing prompt capacity to 50. If cross-platform AI visibility is a priority, that’s the tier I’d look at.

Tier 1 makes more sense if you’re interested in Subsig’s broader brand-intelligence capabilities and can live with ChatGPT-only visibility and 10 tracked prompts. That’s an important distinction: the value looks considerably different depending on whether you’re buying Subsig as an AI Search tracker or as a broader brand-monitoring platform.

Bottom Line

Subsig is hard to put in a neat category, and that’s what makes it interesting. As a pure AI Search visibility tracker, its reporting, citation analysis, competitor tracking, and prompt-level data hold their own, but Tier 1’s 10-prompt limit and ChatGPT-only access are restrictive.

The bigger story is the combination of AI visibility with reviews, brand mentions, sentiment, keywords, citations, competitors, and Agents designed to turn that data into recommendations. Not every part was a natural fit for Marketing With Dave today, and the platform still needs better guidance and transparency around data processing.

Most AI visibility platforms essentially ask, “Does AI mention my brand?” Subsig is aiming at a bigger question: “What does the digital world know and say about my brand, and what should I do about it?” If that’s the problem you’re trying to solve, Subsig is considerably more interesting than another AI visibility dashboard.

See the current AppSumo deal for Subsig

This content is for educational purposes and reflects my hands-on experience using the product and the information publicly available at the time of writing. Always evaluate tools based on your specific business needs, goals, and workflows before making a decision.

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What Pinterest Automation Tools Don’t Let You Do

Social

Reading Time: 3 minutes

Pinterest automation sounds great in theory. Create the image, generate the description, schedule the pin, move on with your life.

But while working with Pinterest automation tools, I noticed something that doesn’t get talked about nearly enough: automating a Pin can mean giving up functionality available when you publish directly through Pinterest.

And sometimes the limitation isn’t the tool. It’s Pinterest’s API.

The two AI disclosure checkboxes are new

Two fields in that screen are recent additions: “Mark as AI-Modified” and “This Pin includes an AI-generated person.” Pinterest’s AI labeling system launched globally on April 30, 2025. I’ve used Pinterest daily long enough to know these two checkboxes didn’t show up together though. They rolled out separately, just days apart.

This fits a broader pattern. Meta started applying “Made with AI” labels to Facebook and Instagram content in May 2024. More recently, New York became the first state to legally require AI disclosure in ads: its synthetic performer law took effect in June 2026, mandating a clear label any time an ad uses an AI-generated person. Whether it’s platform policy or now state law, the direction is the same. Flagging AI involvement is becoming standard, not optional.

And that’s where automation becomes more complicated: as Pinterest adds new native publishing controls, third-party tools may not immediately, or ever, have access to them.

What’s missing when you’re not using Pinterest directly

Tagged Topics. Pinterest’s native pin creation flow lets you attach up to 10 predefined categories to help classify your pin. This field isn’t part of Pinterest’s documented API for creating pins, and multiple users have reported in Pinterest’s own community forums that tagged topics only work through Pinterest’s native scheduler, not third-party tools. I couldn’t find an official Pinterest statement confirming this outright, but I’ve never been able to set it through anything but Pinterest’s own interface either. My most-used categories tend to be Marketing, Business Marketing, Small Business Marketing, and Marketing Tools and while I don’t have proof that choosing topics helps your pin perform better, I feel better doing it.

Allow comments and Show similar products toggles. Same story. These two switches don’t appear anywhere in Pinterest’s documented API fields, so there’s no way for a third-party tool to set them on your behalf.

Product tagging. If you’re an Amazon affiliate or run any kind of shopping content, this one can matter. Pinterest’s Create Pin API doesn’t expose a field for attaching product tags to a standard pin. That functionality seems to live inside Pinterest’s Shopping/Catalog features rather than the basic pin creation endpoint.

Alt text is a different story. I want to correct something I initially assumed: alt text actually is part of Pinterest’s official API. Whether a specific tool lets you set it comes down to whether that tool’s developers built the field into their product, not a Pinterest restriction. So don’t assume every third-party tool is missing this. Check the specific tool.

Why this matters for your workflow

With over 500 billion Pins already on the platform, you need every edge you can get. Depending on your business, these gaps range from trivial to a genuine roadblock. If you rely on Amazon affiliate links, losing product tagging in your automated workflow is a real problem. If you don’t run comments on purpose anyway, that toggle won’t bother you.

There’s also a scale question worth knowing before you build your workflow around automation. Pinterest’s API caps you at 1,000 write operations per day and 300 requests per minute per account, there’s no bulk endpoint so every pin is its own API call, and you can’t schedule more than 30 days out. If you’re pinning at a normal pace, none of that touches you. If you’re trying to batch-schedule a season’s worth of content in one sitting, it will.

There are constant trade-offs among speed, quality, and effectiveness, and figuring out what’s worth automating on Pinterest is no different. I use a product called Pin Generator, and here’s specifically where it’s helped:

Saves time on pin creation by handling a chunk of the repetitive design work for me
Fills in alt text with AI wherever it’s missing, closing a gap most people never notice until it’s pointed out
Audits my Pinterest account so I can track whether I’m actually making progress cleaning it up month over month
Flagged that board categories need a description, something I had no idea about and that Pinterest doesn’t even show you when you’re first creating a board

These are just a few of the most actionable benefits I have enjoyed using this platform. If interested, you can read my full Pin Generator review.

The takeaway isn’t that you shouldn’t automate Pinterest. I do. It’s that automation isn’t a perfect substitute for publishing natively. Know which features matter to your workflow, know which ones your tool supports, and decide where the time savings are worth the trade-off.

What Pinterest Automation Tools Don’t Let You Do Read More »

Marketing with Dave infographic emphasizing customer communication, including questions about name, email, company, role, and marketing budget, with tips for better customer engagement.

Stop Asking Customers What They’ve Already Told You

SiteSqueeze, Strategy

Reading Time: 5 minutes

Most companies occupy an unremarkable place in our lives. We pay the bill. We renew the registration. We deal with the HOA. We file the taxes.

The relationship is low engagement and low trust. We don’t expect much. We just want it to require as little attention as possible.

When one of those relationships suddenly demands more attention, it’s usually because something that was supposed to happen automatically stopped working.

Then there’s a much smaller group of companies: the ones we actually choose.

We like what they do. We trust them. We’ve used them for years, maybe decades. We recommend them to other people. In marketing terms, we’ve moved past being customers and become fans.

That’s an incredibly valuable relationship.

Which is why it’s strange when companies that have earned that trust keep reminding me they aren’t paying attention.

You Already Know This About Me

I’ve been an Audible customer since before it was part of Amazon. Over the years I’ve bought and rated hundreds of books.

Netflix is similar. I was a customer back when the experience meant a DVD in the mail. I’ve rated an enormous number of movies and shows there too.

In both cases, I’ve spent years explicitly telling these companies what I like and don’t like. That’s valuable data.

My expectation isn’t ambitious. I’d love for that history to help these companies recommend things I haven’t discovered yet. But let’s set the bar lower.

Even if you don’t know what to recommend, just don’t recommend something I’ve already consumed and rated.

Yet recommendations are still routinely filled with content I already own, have watched, or have rated. I’m not asking for futuristic AI. I’m asking the company to remember what I already told it.

Sometimes the Data Doesn’t Even Survive the Transaction

Costco gives me another version of the same experience. I like Costco. I trust Costco. I spend plenty of money there.

But when I pick up a prescription, I’m routinely asked for my birthdate twice. I provide it once. Something happens while my prescription is located and prepared. Then I’m asked again.

The employees sometimes seem almost as aware of the absurdity as I am.

This isn’t sophisticated personalization. It isn’t a recommendation engine. It isn’t even remembering something from six months ago.

The information couldn’t make it from one step of the same interaction to the next.

Every time that happens, the company is communicating something about the systems behind the experience: we heard you, then we forgot.

Marketers Are Guilty Too

It would be easy to blame legacy systems, disconnected databases, or slow-moving organizations. I’m not sure we have room to criticize.

There are marketing companies whose webinars I’ve attended repeatedly. Every month, I fill out the same registration form: name, email, company, role, marketing budget, maybe another qualification question. Then the next webinar comes along and we start over as though we’ve never met.

That’s a missed opportunity. If you already have my name, company, role, and budget, why ask again? More importantly, isn’t there something else you’d rather learn about me?

That’s the idea behind progressive profiling: instead of repeatedly asking for the same information, use what you already know and gradually learn something new.

If you know my role, ask about my biggest challenge. If you know my company size, ask which channels I manage. If you know my budget, ask what I’m trying to improve this quarter.

Each interaction can make the relationship richer. Or you can ask me for my marketing budget a fifteenth time.

Personalization Doesn’t Have to Be Impressive

The marketing industry makes personalization sound complicated: customer data platforms, predictive models, AI, identity resolution, real-time engines.

Those technologies can create powerful experiences. But there’s a more basic level many companies still haven’t mastered:

Remember what the customer already told you.

Before predicting what someone wants next, make sure you aren’t asking a question they already answered. Before recommending a product, make sure they don’t already own it. Before showing an offer, check whether they already responded to it. Before asking what they’re interested in, look at what they’re currently doing.

Sometimes the smartest personalization decision isn’t showing someone something more relevant. It’s simply not showing them something obviously irrelevant.

Ignoring Customer Data Quietly Erodes Trust

None of these are catastrophic experiences. I haven’t canceled Audible over a duplicate recommendation. I haven’t canceled Netflix over a rated show resurfacing. I haven’t stopped shopping at Costco over a repeated birthdate question.

That’s exactly why this problem is easy to dismiss. These aren’t dramatic failures. They’re tiny withdrawals from the trust account. Each one says, in some small way: we don’t remember you, we aren’t connecting what you told us yesterday with what we’re asking today, we’re collecting your data without using it to improve your experience.

Trusted brands have goodwill to absorb those moments. But customer expectations don’t stand still. Eventually another company enters the market and makes the old experience feel unnecessarily difficult.

The incumbent hasn’t gotten worse. The expectation got better.

This Is a Philosophy I Want to Protect

I’ve been thinking about this while building SiteSqueeze, which is designed around a simple idea: help website owners do more with the traffic they already have.

As the product evolves, one principle stays near the center: use the context you already have before asking the visitor to do more work.

That leads to three simple rules I want to keep in mind whenever SiteSqueeze asks a visitor for information:

Know it before you ask it. If the visitor has already given you the information, don’t make them provide it again.
Infer it before you ask it. If the page, referral source, campaign, behavior, or previous interaction already gives you enough context, use that first.
Only ask when the answer is worth the effort. Every field creates friction, so the information should meaningfully improve what you can do for the visitor.

Put another way: know it → infer it → ask it.

A website already knows a lot about the current interaction. What page is someone viewing? What category does it belong to? How did the visitor arrive? What promotion have they already seen? What have they clicked, and what have they ignored?

There can eventually be much richer context than that. But the goal isn’t collecting every possible piece of data. It’s using the information that’s genuinely useful to create a better interaction.

If someone is reading their fourth article about analytics, you probably don’t need to ask if they’re interested in analytics. If they arrived through a campaign URL, you probably don’t need to ask how they heard about you. If they’ve already responded to an offer, you probably shouldn’t keep showing it. If they repeatedly ignore a promotion, that’s information too.

This isn’t about creepy personalization. It’s about respecting the visitor enough to pay attention.

The Question Every Marketer Should Ask

We spend a lot of time trying to collect more customer data: more form fields, more surveys, more tracking, more behavioral signals, more integrations.

Before collecting another piece of information, there’s a more useful question: what are we already collecting that we aren’t using?

And a less comfortable one: what are you doing today that reminds your customers you’re either deaf or lazy about their world?

Your customers may have already told you what they need. The next step isn’t always asking another question. Sometimes it’s proving you were listening.

Stop Asking Customers What They’ve Already Told You Read More »

FeedBoss Review: AI-Powered LinkedIn Content, Analytics and Optimization

Martech, Social

Reading Time: 9 minutes

AI tools that write LinkedIn posts are everywhere, so I went into FeedBoss skeptical. The usual pattern: you give it a topic, it spits out something vaguely post-shaped, and you spend the rest of your time making it sound like you.

FeedBoss broke that pattern. Its best features barely involve generating content at all. It analyzes your LinkedIn history, builds a detailed writer profile, refines drafts conversationally, creates images and editable slide decks, audits your profile, and delivers analytics that beat what LinkedIn gives you natively.

It also has real gaps. Some AI editing functions failed outright, navigation buries key features, and Tier 1 skips functionality teams will want. I also encountered an error with Trending Topics, but the support team corrected it within minutes of my reporting it.

Net result: more interesting than I expected, with rough edges that need fixing. I also put FeedBoss through my six-question framework for evaluating social media software, adapted for one important distinction: FeedBoss is built entirely for LinkedIn.

FeedBoss Overall Score

Why 4/5? Inconsistent AI editing, frustrating navigation, launch-day bugs, and Tier 1 limitations keep an otherwise impressive LinkedIn platform from feeling fully polished.

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Affiliate disclosure: If you purchase through my link, I may earn a small commission at no additional cost to you. I only share tools I have used myself.

The 30-Second Decision

Buy FeedBoss if: You want an AI-powered content and optimization platform built specifically for LinkedIn. It combines writing assistance, scheduling, carousels and images, LinkedIn analytics, profile auditing, engagement tools, and content research in one workspace, with surprisingly strong personalization to your brand and writing style.

Skip it if: You need a multi-channel social media management platform, or LinkedIn is only one small part of your social strategy. FeedBoss is not trying to manage Facebook, Instagram, X, TikTok, or your other social channels. It is a LinkedIn tool.

The biggest reason to consider FeedBoss is how much of the LinkedIn workflow it brings together. It can learn how you write, help develop and refine content, create supporting visuals and carousels, schedule posts, analyze what is working, audit your LinkedIn presence, and surface ideas for what to do next. Some features still have rough edges, but FeedBoss is much more than another AI LinkedIn post generator.

The Onboarding Analysis Nailed Who I Am

FeedBoss spent real time analyzing my LinkedIn history before generating anything, and the output showed it. It labeled me “The AI-Search Auditor” and correctly identified my recurring strengths: original frameworks, first-hand testing, consistent publishing.

It pulled my actual background (nearly 30 years in digital marketing, Verisk, Nelson Nexus, my MBA, MarketingWithDave.com, my book) and correctly mapped the intersection of SEO, AI-search visibility, and analytics that drives my recent content.

The writing-style analysis was the strongest part. It recognized that I open with tension or a contrarian claim, then back it with my own testing data. It cited real examples and flagged specific patterns: short declarative hooks, functional over decorative formatting, minimal emoji, named frameworks, a preference for evidence over vendor claims.

Best of all: it described me as an “anti-hype analyst of AI-search marketing” and correctly picked up on my documented/observed/inferred/unknown distinction, plus my recurring warning against chasing AI visibility while ignoring the traffic that still comes from everywhere else.

The broad topic tags (“AI,” “Leadership,” “Innovation”) felt generic next to that detailed persona work, but overall this is the best onboarding analysis I’ve seen from an AI content platform.

The Writing Workflow Asks Instead of Inventing

I tested it with a real idea: before asking a customer to fill in a field, ask whether you’re requesting information they already gave you or information your company should already know.

The first draft was polished but leaned into data-governance and enterprise CRM language, more consultant than me. Then FeedBoss did something I liked: instead of calling the draft done, it told me the post stayed general because it needed a specific example only I had, and it wouldn’t invent one. It asked me for a real customer story.

I gave it a rant involving Audible, Netflix, Costco prescriptions, the IRS, and my HOA. FeedBoss worked those into the piece. The result sounded substantially more like something I would publish.

Most AI writing tools work like this: prompt, generate, here’s your post, good luck. FeedBoss works like this: understand the writer, draft, identify what’s missing, ask for it, revise, keep refining. That difference matters.

One Gap: No Easy Way to Trim for LinkedIn’s Limit

The revised post landed at 3,762 characters. LinkedIn caps posts at 3,000. FeedBoss flagged the overage correctly but gave me no one-click way to send it back through and say “cut this to 3,000 without losing the point.” If FeedBoss knows the destination is LinkedIn, it should either respect the limit automatically or surface a prominent “Trim to 3,000” option.

Slide Decks: Enough to Finally Get Me Publishing a Carousel

I’ve never published a LinkedIn carousel because the production work never felt worth it. FeedBoss got me closer than any tool has.

I prompted it to build a myth-busting carousel separating documented, observed, inferred, and unknown claims in AI-search visibility reporting, and to explain why confusing those categories leads to bad decisions. It returned an eight-slide deck that got the framework right: each category defined correctly, a slide on how reporting breaks down when observation becomes fact and inference becomes certainty, and a closing slide that turned the framework into a four-step process.

That’s a strong first draft from a short prompt.

The Slide Editor Struggles With Images

The slide deck itself was a strong starting point, but the AI editing tools were much less capable than I expected. My logo rendered at roughly 50×36 pixels on every slide, far too small to read. I asked FeedBoss to enlarge it across all eight slides, then tried again with a specific instruction to triple its size. Neither request changed the logo. I saw the same limitation when asking the AI to remove image elements.

Text edits worked much better. FeedBoss could resize, rewrite, and replace text, so the editor is useful for copy and typography. Visual-element editing is where it currently falls short. If I have to manually resize the same logo across eight slides and then verify that every slide matches, some of the time savings disappear.

I still see real potential here. FeedBoss can import an existing PDF or slide deck, so once I create a polished MarketingWithDave carousel template with the right logo size, typography, and spacing, I should be able to reuse that structure instead of fixing the same design issues every time.

One smaller UX issue: finished decks appear in a Your decks section below the creation interface. Because that section is off-screen, a newly created deck can briefly feel like it disappeared until you know to scroll down.

Image Studio: Good Output, One Credit Bug

I asked FeedBoss to build an infographic around another idea I’m writing about: AI search visibility has a sampling problem, where checking too few prompts across too few models produces misleading scores. It generated a clean, on-brand infographic using my blue/navy/white kit, correctly incorporating prompts tested, models tested, test frequency, and response variation, with a solid closing line about sample size.

Output: a 1024×1024 PNG, about 1.38 MB, text rendering cleanly. Good first-generation quality, though some text ran small for mobile.

One problem: the infographic illustrated the sampling issue with invented numbers (72% visibility from 10 prompts and 2 models, 38% from 100+ prompts and 6+ models). Those weren’t my real numbers. For a graphic specifically about evidence quality, fabricated stats are the wrong move. I’d strip the percentages or label them illustrative before publishing.

I also hit a credit error trying to remove an element from the image, despite having 2,355 credits available. Likely a bug or an undocumented Tier 1 limit, but it shouldn’t happen with that much headroom.

LinkedIn Analytics Is Probably the Best Feature

This nearly stayed hidden. Analytics, Engagement, Popular Posts, Authority Map, and LinkedIn Audit all sit under an “Optimize” section buried deep in the left nav. I almost finished testing without finding it.

Once I did, it became my favorite part of the product. Native LinkedIn analytics are thin. FeedBoss turns your posting history into an actual dashboard: total engagement, reactions, comments, shares, engagement-rate trends, follower growth, performance by date, and it goes further by turning that data into decisions.

It identified Thursday afternoon as my strongest posting window and faded out results where it didn’t have enough data yet, rather than presenting a thin sample with the same confidence as a strong one. Good instinct.

It also broke down engagement rate by format across my last 25 posts: articles at 0.24% (13 posts), text at 0.21% (11 posts), image at 0.11% (1 post). I wouldn’t act on that last number, and neither should you, since one post isn’t a sample. Showing the post count next to the rate is exactly the right call.

It also analyzes posts you published outside FeedBoss, checking for new activity every 24 hours. You get useful analytics immediately instead of waiting weeks for FeedBoss to build its own history.

The LinkedIn Profile Audit Delivers Real Substance

The audit generated a four-page PDF, gave me a Personal Brand Score (73/100), and broke that down across Core Health, SEO/Discoverability, Content/Engagement, and Authority/Leadership. It then produced specific recommendations for my headline, About section, experience, skills, and profile image, correctly referencing real accomplishments from my history like eCommerce results and Google Ads ROAS.

I don’t agree with every call. It flagged zero skill endorsements as a major red flag and tied that to a lack of recommendations, but endorsements and recommendations aren’t the same thing. Treat the audit as input for judgment, not a checklist to follow blindly. Even so, it’s one of the strongest features in the product.

Two annoyances: the audit asked me to re-enter my LinkedIn URL, even though FeedBoss already has my connected profile, imported posts, and follower count. And there’s no obvious place to find your most recent audit or track score changes over time. Both should be easy fixes.

feedboss-linkedin-audit-report Download

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What Still Needs Fixing

Navigation buries Analytics, Audit, and Engagement below the fold with no clear signal there’s more to scroll to.
Slide Deck editor can resize and rewrite text but can’t reliably resize or remove images and logos.
Generated posts can exceed LinkedIn’s 3,000-character limit with no one-click way to trim them.
Image Studio invents illustrative data without labeling it as such.
Image editing hit a credit error despite a large available balance.
Emoji picker is missing basic bullet characters, a real gap for a LinkedIn-focused tool.
Tier 1 excludes Video Studio, Lead Magnets, Authority Map, API access, and post-approval workflows, the last of which matters most for agencies and teams.

Most of these are fixable UX and QA issues, not fundamental design problems. In fact, I initially encountered an error that prevented Trending Topics research from working at all. I reported it, the support team responded, and the issue was corrected within minutes. That’s exactly the kind of responsiveness I want to see when testing a newly launched product. The remaining issues are worth knowing before you buy, but they’re not reasons I’d write the product off.

Tier 1 and Credits

Tier 1 ($59) includes 400 monthly AI credits, 2,000 lifetime credits, 5 profile audits per month, 1 LinkedIn connection, 1 workspace, 1 member, 10 tracked engagement profiles, 25 GB storage, 2K image resolution, voice/text Knowledge Base, Bring Your Own AI Key, and profile voice matching.

Credits by action: a post costs 5, an image costs 25, a slide deck costs 20 regardless of slide count, an image carousel costs 50 regardless of slide count, and video costs 100 regardless of length. Editing individual slides is free. Charging decks and carousels as a flat fee instead of per-slide is the right model.

FeedBoss draws from monthly credits before touching your lifetime balance, and the dashboard shows the split clearly (355/400 monthly, 2,000 lifetime, 2,355 total after my test). That transparency is well done.

Brand Kit and Content Styles

The Brand Kit (handle, tagline, logo, fonts, colors) applies consistently to generated images but inconsistently to slide decks, where my logo rendered too small despite the same settings. One central brand config is the right idea; the execution needs to actually honor it everywhere.

FeedBoss also auto-generated writing styles from my LinkedIn history (My LinkedIn Voice, Lesson Learned, Build in Public, Observational, Reflection, Milestone, Tactical, Story, Contrarian). Starting from a predefined style beats re-explaining tone in every prompt. I didn’t test each style deeply enough to say how distinct the outputs really are.

Bottom Line

I expected another AI LinkedIn writer. That undersells it. The strongest parts of FeedBoss are where it works with information that already exists: my LinkedIn history becomes a real persona, my published posts become analytics, my profile becomes an audit, my writing patterns become reusable styles, and one real example turns a generic article into something I’d actually publish.

The product still has launch-stage bugs, and several Tier 1 gaps will matter more to teams than solo creators. But between the analytics, the profile audit, and a writing workflow that asks for missing information instead of fabricating it, there’s enough real value here to keep using it and watch where it goes.

See the current AppSumo deal for FeedBoss

Looking for more marketing software reviews? See my full list of marketing tools and software I recommend.

FeedBoss Review: AI-Powered LinkedIn Content, Analytics and Optimization Read More »