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How to evaluate your SEO tools in 2026 – and avoid budget traps

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How to evaluate your SEO tools in 2026 – and avoid budget traps

Evaluating SEO tools has never been more complicated. 

Costs keep rising, and promises for new AI features are everywhere.

This combination is hardly convincing when you need leadership to approve a new tool or expand the budget for an existing one. 

Your boss still expects SEO to show business impact – not how many keywords or prompts you can track, how fast you can optimize content, or what your visibility score is. 

That is exactly where most tools still fail miserably.

The landscape adds even more friction. 

Features are bundled into confusing packages and add-on models, and the number of solutions has grown sharply in the last 12 months. 

Teams can spend weeks or even months comparing platforms only to discover they still cannot demonstrate clear ROI or the tools are simply out of budget.

If this sounds familiar, keep reading.

This article outlines a practical framework for evaluating your SEO tool stack in 2026, focusing on:

  • Must-have features.
  • A faster way to compare multiple tools.
  • How to approach vendor conversations.

The new realities of SEO tooling in 2026

Before evaluating vendors, it helps to understand the forces reshaping the SEO tooling landscape – and why many platforms are struggling to keep pace.

Leadership wants MQLs, not rankings

Both traditional and modern SEO tools still center on keyword and prompt tracking and visibility metrics. These are useful, but they are not enough to justify the rising prices.

In 2026, teams need a way to connect searches to traffic and then to MQLs and revenue. 

Almost no tool provides that link, which makes securing larger budgets nearly impossible. 

(I say “almost” because I have not tested every platform, so the unicorn may exist somewhere.)

AI agents raise expectations

With AI platforms like ChatGPT, Claude, and Perplexity – along with the ability to build custom GPTs, Gems, and Agents – teams can automate a wide range of tasks. 

That includes everything from simple content rewriting and keyword clustering to more complex competitor analysis and multi-step workflows.

Because of this, SEO tools now need to explain why they are better than a well-trained AI agent. 

Many can’t. This means that during evaluation, you inevitably end up asking a simple question: do you spend the time training your own agent, or do you buy a ready-made one?

Small teams need automation that truly saves time

If you want real impact, your automation shouldn’t be cosmetic. 

You can’t rely on generic checklists or basic AI recommendations, yet many tools still provide exactly that – fast checklists with no context.

Without context, automation becomes noise. It generates generic insights that are not tailored to your company, product, or market, and those insights will not save time or drive results.

Teams need automation that removes repetitive work and delivers better insights while genuinely giving time back.

Dig deeper: 11 of the best free tools every SEO should know about

A note on technical SEO tools

Technical SEO tools remain the most stable part of the SEO stack. 

The vendor landscape has not shifted dramatically, and most major platforms are innovating at a similar pace. 

Because of this, they do not require the same level of reevaluation as newer AI-driven categories.

That said, budgeting for them may still become challenging. 

Leadership often assumes AI can solve every problem, but we know that without strong technical performance, SEO, content, and AI efforts can easily fail.

I will also make one bold prediction – we should be prepared to expect the unexpected in this category. 

These platforms can crawl almost any site at scale and extract structured information, which could make them some of the most important and powerful tools in the stack.

Many already pull data from GA and GSC, and integrating with CRM or other data platforms may be only a matter of time. 

I see that as a likely 2026 development.

What must-have features actually look like in 2026

To evaluate tools effectively, it helps to focus on the capabilities that drive real impact. These are the ones worth prioritizing in 2026.

Advanced data analysis and blended data capabilities

Data analysis will play a much bigger role. 

Tools that let you blend data from GA, GSC, Salesforce, and similar sources will move you closer to the Holy Grail of SEO – understanding whether a prompt or search eventually leads to an MQL or a closed-won deal. 

This will never be a perfect science, but even a solid guesstimation is more useful than another visibility chart.

Integration maturity is becoming a competitive differentiator. 

Disconnected data remains the biggest barrier between SEO work and business attribution.

SERP intelligence for keywords and prompts

Traditional SERP intelligence remains essential. You still need:

  • Topic research and insights for top-ranking pages.
  • Competitor analysis.
  • Content gap insights.
  • Technical issues and ways to fix them.

You also need AI SERP intelligence, which analyzes:

  • How AI tools answer specific prompts.
  • What sources do they cite.
  • If your brand appears, and if your competitors are also mentioned.

In an ideal world, these two groups should appear side by side and provide you with a 360-degree view of your performance.

Automation with real-time savings

Prioritize tools that:

  • Cluster automatically.
  • Detect anomalies.
  • Provide prioritized recommendations for improvements.
  • Turn data into easy-to-understand insights.

These are just some of the examples of practical AI that can really guide you and save you time.

Strong multilingual support

This applies to SEO experts who work with websites in languages other than English. 

Many tools are still heavily English-centric. Before choosing a tool, make sure the databases, SERP tracking, and AI insights work across languages, not just English.

Transparent pricing and clear feature lists

Hidden pricing, confusing bundles, and multiple add-ons make evaluation frustrating. 

Tools should communicate clearly:

  • Which features they have.
  • All related limitations.
  • Whether a feature is part of the standard plan or an add-on.
  • When something from the standard plan moves to an add-on. 

Many vendors change these things quietly, which makes calculating the investment you need difficult and hard to justify. 

Dig deeper: How to choose the best AI visibility tool

Plus, some features that might be overhyped

AI writing

If you can’t input detailed information about your brand, product, and persona, the content you produce will be the same as everyone else’s. 

Many tools already offer this and can make your content sound as if it were written by one of your writers. 

So the question is whether you need a specialized tool or if a custom GPT can do the job.

Prompt tracking 

It’s positioned as the new rank tracking, but it is like looking at one pixel of your monitor. 

It gives you only a tiny clue of the whole picture. 

AI answers change based on personalization and small differences in prompts, and the variations are endless.

Still, this tactic is helpful in:

  • Providing directional signals.
  • Helping you benchmark brand presence.
  • Highlighting recurring themes AI platforms use.
  • Allowing competitive analysis within a controlled sample.

Large keyword databases

They still matter for directional research, but are not a true competitive differentiator. 

Most modern tools have enough coverage to guide your strategy. 

The value now stems from the practical insights derived from the data.

How to compare 10 tools without wasting your time

Understanding features is only half the equation. 

The real challenge is knowing how to evaluate specialized tools and all-in-one platforms without losing your sanity or blocking your team for weeks. 

After going through this process for the tenth time, I’ve found an approach that works for me.

Step 1: Start with the pricing page

I always begin my evaluation on the pricing page. 

With one page, you can get a clear sense of: 

  • All features.
  • Limitations.
  • Which ones fall under add-ons.
  • The general structure of the pricing tiers. 

Even if you need a demo to get the exact price, the framework should still be relatively transparent.

Step 2: Test using your normal weekly work

No checklist will show you more than trying your regular BAU tasks with a couple of tools in parallel. 

This reveals:

  • How long each task takes.
  • What insights appear or disappear.
  • What feels smoother or more clunky.

How difficult the setup is – including whether the learning curve is huge. 

I work in a small team, and a tool that takes many hours just to set up likely will not make my final list.

Not all evaluations can rely on BAU tasks. 

For example, when we researched tools for prompt and AI visibility tracking, we tested more than ten platforms. 

This capability did not exist in our stack, and at first, we had no idea what to check. 

In those cases, you need to define a small set of test scenarios from scratch and compare how each tool performs. 

Continue refining your scenarios, because each new evaluation will teach you something new.

Dig deeper: Want to improve rankings and traffic? Stop blindly following SEO tool recommendations

Step 3: Always get a free trial

Demos are polished. Reality often is not. 

If there is no option for a free trial, either walk away or, if the tool is not too expensive, pay for a month.

Get the newsletter search marketers rely on.

See terms.

Step 4: Involve only the people who will actually use the tool

Always ask yourself who truly needs to be involved in the evaluation. 

For example, we are currently assessing a platform used not only by the SEO team but also by two other teams. 

We asked those teams for a brief summary of their requirements, but until we have a shortlist, there is no reason to involve them further or slow the process. 

And if your company has a heavy procurement or security review, involving too many people too early will slow everything down even more.

At the same time, involve the whole SEO team, because each person will see different strengths and weaknesses and everyone will rely on the tool.

Step 5: Evaluate results, not features

Many features sound like magic wands. 

In reality, the magic often works only sometimes, or it works but is very expensive. To understand what you truly need, always ask yourself:

  • Did the tool save time?
  • Did it surface insights that my current stack does not?
  • Could a custom GPT do this instead?
  • Does the price make sense for my team, and can I prove its ROI?

These questions turn the decision into a business conversation rather than a feature debate and help you prepare your “sales” pitch for your boss.

Step 6: Evaluate support quality, not just product features

Support has become one of the most overlooked parts of tool evaluation. 

Many platforms rely heavily on AI chat and automated replies, which can be extremely frustrating when you are dealing with a time-sensitive issue or have to explain your problem multiple times.

Support quality can significantly affect your team’s efficiency, especially in small teams with limited resources. 

When evaluating tools, check:

  • How easy it is to reach a human.
  • What response times look like.
  • Whether the vendor offers onboarding or ongoing guidance. 

A great product with weak support can quickly become a bottleneck.

Once you have a shortlist, the quality of your vendor conversations will determine how quickly you can move forward. 

And this may be the hardest part – especially for the introverted SEO leads, myself included.

How to navigate vendor conversations

I’m practical, and I don’t like wasting anyone’s time. I have plenty of tasks waiting, so fluff conversations aren’t helpful. 

That’s why I start every vendor call by setting clear goals, limitations, a timeline, and next steps. 

Over time, I’ve learned that conversations run much more smoothly when I follow a few simple principles.

Be prepared for meetings

If you are evaluating a tool, come prepared to the demo. 

Ideally, you should have access to a free trial, tested the platform, and created a list of practical questions. 

Showing up unprepared is not a good sign, and that applies to both sides.

For example, I am always impressed when a vendor joins the conversation having already researched who we are, what we do, and who our competitors are. 

If you have spoken with the vendor before, directly ask what has changed since your last discussion.

Ask for competitor comparisons

When comparing a few tools, I always ask each vendor for a direct comparison. 

These comparisons will be biased, but collecting them from all sides can reveal insights I had not considered and give me ideas for specific things to test. 

Often, there is no reason to reinvent the wheel.

Ask how annual contracts influence pricing

Annual contracts reduce administrative work and give vendors room to negotiate, which can lead to better pricing. 

Many tools include this information on their pricing pages, and we have all seen it. 

Ask about any other nuances that might affect the final price – such as additional user seats or add-ons.

Don’t start from scratch with vendors you know

Often, the most effective approach is simply to say:

“This is our budget. This is what we need. Can you support this?”

This works especially well with vendors you have used before because both sides already know each other.

What to consider from a business perspective

Even if you select a tool, that does not mean you will receive the budget for it.

Proving ROI is especially difficult with SEO tools. But there are a few things you can do to increase your chances of getting a yes.

Present at least three alternatives in every request

This shows you have done your homework, not just picked the first thing you found. Present your leadership with:

  • The criteria you used in your evaluation.
  • Pros and cons of each tool.
  • The business case and why the capability is needed.
  • What happens if you do not buy the tool.

Providing this view builds trust in your ability to make decisions.

Avoid overselling

Tools improve efficiency, but they cannot guarantee outcomes – especially in SEO, GEO, or whatever you call it. 

Spend time explaining how quickly things are changing and how many factors are outside your control. Managing expectations will strengthen your team’s credibility.

But even with thorough evaluation and negotiation, we still face the same issue: the SEO tooling market has not caught up with what companies now expect. 

Let’s hope the future brings something closer to the clarity we see in Google Ads.

Dig deeper: How to master the enterprise SEO procurement process

The future of the SEO tool stack

The next generation of SEO tools must move beyond vanity metrics. 

Trained AI agents and custom GPTs can already automate much of the work.

In a landscape where companies want to reduce employee and operational costs, you need concrete business numbers to justify high tool prices. 

The platforms that can connect searches, traffic, and revenue will become the new premium category in SEO technology.

For now, most SEO teams will continue to hear “no” when requesting budgets because that connection does not yet exist. 

And the moment a tool finally solves this attribution problem, it will redefine the entire SEO technology market.

Read more at Read More

AI tools for PPC, AI search, and social campaigns: What’s worth using now

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AI tools for PPC, AI search, and social campaigns: What’s worth using now

In 2026 and well beyond, a core part of the performance marketer’s charter is learning to leverage AI to drive growth and efficiency. 

Anyone who isn’t actively evaluating new AI tools to improve or streamline their PPC work is doing their brand or clients a disservice.

The challenge is that keeping up with these tools has become almost a full-time job, which is why my agency has made AI a priority in our structured knowledge-sharing. 

As a team, we’ve honed in on favorites across creative, campaign management, and AI search measurement. 

This article breaks down key options in each category, with brief reviews and a callout of my current pick.

One overarching recommendation before we dive in: be cautious about signing long-term contracts for AI tools or platforms. 

At the pace things are moving, the tool that catches your eye in December could be an afterthought by April.

AI creative tools for paid social campaigns

There’s no shortage of tools that can generate creative assets, and each comes with benefits as well as the risks of producing AI slop. 

Regardless of the tool you choose, it must be thoroughly vetted and supported by a strong human-in-the-loop process to ensure quality, accuracy, and brand alignment.

Here’s a quick breakdown of the tools we’ve tested:

  • AdCreative.ai: Auto-generates images, video creatives, ad copy, and headlines in multiple sizes, with data-backed scoring for outputs.
  • Creatify: Particularly strong on video ads with multi-format support.
  • WASK: Combines AI creative generation with campaign optimization and competitor analysis.
  • Revid AI: Well-suited for story formats.
  • ChatGPT: Free and widely familiar, giving marketers an edge in effective prompting.

Our current tool of choice is AdCreative.ai. It’s easy to use and especially helpful for quickly brainstorming creative angles and variations to test. 

Like its competitors, it offers meaningful advantages, including:

  • Speed and scale that allow you to generate dozens or hundreds of variants in minutes to keep creative fresh and reduce ad fatigue.
  • Less reliance on external designers or editors for routine or templated outputs.
  • Rapid creative experimentation across images, copy, and layouts to find winning combinations faster.
  • Data-driven insights, such as creative scores or performance predictions, when available.

The usual caveats apply across all creative tools:

  • Build guardrails to avoid off-brand outputs by maintaining a strong voice guide, providing exemplar content, enforcing style rules and banned words, and ensuring human review at every step.
  • Watch for accuracy issues or hallucinations and include verification in your process, especially for technical claims, data, or legal copy. 

Dig deeper: How to get smarter with AI in PPC

AI campaign management and workflow tools for performance campaigns

There are plenty of workflow automation tools on the market, including long-standing options, like Zapier, Workato, and Microsoft Power Automate. 

Our preferred choice, though, is n8n. Its agentic workflows and built-in connections across ad platforms, CRMs, and reporting tools have been invaluable in automating redundant tasks.

Here are my agency’s primary use cases for n8n:

  • Lead management: Automatically enrich new leads from HubSpot or Salesforce with n8n’s Clearbit automation, then route them to the right rep or nurture sequence.
  • UTM cleanup: When a form fill or ad conversion comes in, automatically normalize UTM parameters before pushing them to your CRM. Some systems, like HubSpot, store values in fields such as “first URL seen” that aren’t parsed into UTM fields, so UTMs remain associated with the user but aren’t stored properly and require reconciliation.
  • Data reporting: Pull metrics from APIs, structure the data, and use AI to summarize insights. Reports can then be shared via Slack and email, or dropped into collaborative tools like Google Docs.

As with any tool, n8n comes with caveats to keep in mind:

  • It requires some technical ability because it’s low-code, not no-code. You often need to understand APIs, JSON, and authentication, such as OAuth or API keys. Even basic automations may involve light logic or expressions. Integrations with less mainstream tools can require scripting.
  • You need a deliberate setup to maintain security. There’s no built-in role-based access control in all configurations unless you use n8n Cloud Enterprise. Misconfigured webhooks can expose data if not handled properly.
  • Its ad platform integrations aren’t as broad as those of some competitors. For example, it doesn’t include LinkedIn Ads, Reddit Ads, or TikTok Ads. These can be added via direct API calls, but that takes more manual work.

Dig deeper: Top AI tools and tactics you should be using in PPC

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AI search visibility measurement tools

Most SEOs already have preferred platforms for measurement and insights – Semrush, Moz, SE Ranking, and others. 

While many now offer reports on brand visibility in AI search results from ChatGPT, Perplexity, Gemini, and similar tools, these features are add-ons to products built for traditional SEO.

To track how our brands show up in AI search results, we use Profound. 

While other purpose-built tools exist, we’ve found that it offers differentiated persona-level and competitor-level analysis and ties its reporting to strategic levers like content and PR or sentiment, making it clear how to act on the data.

These platforms can provide near real-time insights such as:

  • Performance benchmarks that show AI visibility against competitors to highlight strengths and weaknesses.
  • Content and messaging intel, including the language AI uses to describe brands and their solutions, which can inform thought leadership and messaging refinement.
  • Signals that show whether your efforts are improving the consistency and favorability of brand mentions in AI answers.
  • Trends illustrating how generative AI is reshaping search results and user behavior.
  • Insights beyond linear keyword rankings that reveal the narratives AI models generate about your company, competitors, and industry.
  • Gaps and opportunities to address to influence how your brand appears in AI answers.

No matter which tool you choose, the key is to adopt one quickly. 

The more data you gather on rapidly evolving AI search trends, the more agile you can be in adjusting your strategy to capture the growing share of users turning to AI tools during their purchase journey.

Dig deeper: Scaling PPC with AI automation: Scripts, data, and custom tools

What remains true as the AI toolset keeps shifting

I like to think most of my content for this publication ages well, but I’m not expecting this one to follow suit. 

Anyone reading it a few months after it runs will likely see it as more of a time capsule than a set of current recommendations – and that’s fine.

What does feel evergreen is the need to:

  • Monitor the AI landscape.
  • Aggressively test new tools and features.
  • Build or maintain a strong knowledge-sharing function across your team. 

We’re well past head-in-the-sand territory with AI in performance marketing, yet there’s still room for differentiation among teams that move quickly, test strategically, and pivot together as needed.

Dig deeper: AI agents in PPC: What to know and build today

Read more at Read More

Judge limits Google’s default search deals to one year

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Google is being forced to cap all default search and AI app deals at one year. This will end the long-term agreements (think: Apple, Samsung) that helped secure its default status on billions of devices. Just don’t expect this to end Google’s search dynasty anytime soon.

Driving the news. Judge Amit Mehta on Friday called the one-year cap a “hard-and-fast termination requirement” needed to enforce antitrust remedies after his 2024 ruling that Google illegally monopolized search and search ads, Business Insider reported. In September, Mehta ruled on Google search deals:

  • “Google will be barred from entering or maintaining any exclusive contract relating to the distribution of Google Search, Chrome, Google Assistant, and the Gemini app. Google shall not enter or maintain any agreement that
    • (1) conditions the licensing of the Play Store or any other Google application on the distribution, preloading, or placement of Google Search, Chrome, Google Assistant, or the Gemini app anywhere on a device;
    • (2) conditions the receipt of revenue share payments for the placement of one Google application (e.g., Search, Chrome, Google Assistant, or the Gemini app) on the placement of another such application;
    • (3) conditions the receipt of revenue share payments on maintaining Google Search, Chrome, Google Assistant, or the Gemini app on any device, browser, or search access point for more than one year; or
    • (4) prohibits any partner from simultaneously distributing any other GSE, browser, or GenAI product search access point for more than one year; or (4) prohibits any partner from simultaneously distributing any other GSE, browser, or GenAI product.”

Why we care. A more fragmented search landscape means user queries could start anywhere. If AI-powered rivals like OpenAI, Perplexity, or Microsoft make even small gains in search, you’ll face a broader and more complicated world to compete in.

Reality check. This is a speed bump, not a shake-up. Google’s cash, brand power, and user habits still give it a big edge in yearly talks.

Read more at Read More

Google denies ads are coming to Gemini in 2026

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AdWeek reported that Google told clients it plans to add ads to its Gemini AI chatbot in 2026, but Google’s top ads executive is publicly denying it.

Driving the news. Google reps reportedly told major advertisers on recent calls that Gemini would get its own ad placements in 2026, according to Adweek. This is separate from the ads already running in AI Mode, the AI-powered search experience Google launched in March.

  • Buyers said they saw no prototypes, formats, or pricing.
  • They described the conversations as exploratory and light on technical detail.

Google says that’s wrong. Dan Taylor, Google’s VP of Global Ads, disputed the report directly on X, writing:

  • “This story is based on uninformed, anonymous sources who are making inaccurate claims. There are no ads in the Gemini app and there are no current plans to change that.”

Why we care. Advertisers are watching closely for monetization inside AI assistants, which many see as the next major ad frontier. Conflicting signals about ads in Gemini hint at where Google may take AI monetization, even as the company denies any immediate plans. Any move to add paid placements to a high-engagement chatbot could reshape budgets, shift user behavior, and create a new ad surface separate from search.

Between the lines. There is a great debate over whether AI chatbots should stay pure utility tools or evolve into new ad surfaces. Even early speculation about ads inside Gemini is already prompting agencies to start planning.

What’s next. For now, Google says Gemini is still ad-free. But rivals are already testing ways to make money from AI, and advertisers are eager for new places to run ads. The debate over ads in Gemini isn’t going away – only the timeline is shifting.

Adweek’s report. EXCLUSIVE: Google Tells Advertisers It’ll Bring Ads to Gemini in 2026

Read more at Read More

November 2025 Digital Marketing Roundup: What Changed and What You Should Do About It

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November pushed the industry further into AI-shaped discovery. Search behaviors shifted. Platforms tightened control. Visibility started depending less on who publishes most and more on who earns trust across the ecosystem.

AI summaries reached Google Discover. ChatGPT released a browser. TikTok exposed true attribution paths. Meta refined placements. Google rolled out guardrails for AI-written ads. Social platforms changed how your data trains models. Streaming dominated households, and schema picked up a new strategic role.

Here’s what mattered most and how to stay ahead.

Key Takeaways

• AI is rewriting the click path. Google Discover summaries and AI Overviews are reducing CTRs across categories.
• Cross-channel influence is becoming measurable. TikTok attribution now shows how much value standard reporting misses.
• Visibility depends on authority across ecosystems, not just your site. LLMs pull from places brands often ignore.
• Platforms are tightening data controls and usage rules. Expect stricter compliance requirements across ads and content.
• Structured data has moved from “SEO extra” to critical infrastructure for AI-driven search.

Search & AI Evolution

AI is now shaping what users see before they click and in many cases, removing the need to click at all.

AI summaries hit Google Discover

Google added AI-generated recaps to Discover for news and sports stories. Users now get context from summaries instead of visiting publisher sites.

Our POV: Discover has been one of the few remaining high-intent traffic drivers untouched by AI. That buffer is gone. Zero-click consumption will rise.

What to do next: Track Discover CTR in Analytics. Refresh headline structure and imagery to compete with summaries. Expand content distribution beyond traditional articles, since Discover now surfaces YouTube, X, and other formats.

ChatGPT releases an AI-powered browser

ChatGPT Atlas launched with built-in summarization, product comparison, agent actions, and persistent memory settings.

ChatGPT Atlas's interface.

Our POV: The browser itself isn’t the threat. The shift in user behavior is. People will expect AI to interpret pages for them, not just display them.

What to do next: Strengthen structured data. Audit category and product pages for clarity. Start monitoring brand visibility inside AI-driven search using LLM-aware tools.

AI Overviews drive a drop in search CTRs

A new study shows that when AI Overviews appear, both organic and paid clicks fall sharply. They currently trigger for about fifteen percent of queries, most of them high-volume informational searches.

Paid and organic CTR trends driven by AI Overviews.

Our POV: AI Overviews function like a competitor. If your content doesn’t get pulled into the summary, discovery becomes significantly harder.

What to do next: Optimize for inclusion. Use schema, succinct summaries, and expert signals. Track performance beyond rankings. Visibility inside AI answers must become a KPI you can track through tools like Profound.

Schema’s new role in AI-driven discovery

Schema moved from a snippet enhancer to a foundational layer for machine understanding. W3C’s NLWeb group is helping standardize how AI agents consume the web.

Our POV: Schema is now infrastructure. AI agents need structured context to interpret brands, products, and expertise.

What to do next: Expand schema sitewide. Prioritize entity definitions, not just rich result templates. Add relationships between key content pieces to help machines map authority.

Paid Media & Automation

Platforms are folding more automation into ad delivery. Control now comes from strategy, not settings.

Google adds Waze to PMax

PMax can now serve location-targeted ads inside Waze for store-focused campaigns.

Our POV: This extends real-world intent targeting. For multi-location brands, Waze becomes a measurable foot-traffic lever.

What to do next: Audit store listings and geo-extensions. Monitor budget shifts once Waze impressions begin flowing. Validate whether foot-traffic lifts justify expanded proximity targeting.

Asset-level display reporting rolls out

Google Ads added per-asset reporting for Display campaigns. Marketers can now evaluate individual images, headlines, and copy.

Our POV: Better visibility helps refine creative, but it’s only part of the truth. Placement, bid strategy, and audience still determine performance.

What to do next: Organize assets with naming conventions before rollout hits your account. Use data to retire low-impact creatives and test new variants.

Meta introduces limited-spend placements

Advertisers can allocate up to five percent of budget toward excluded placements when Meta predicts performance upside.

Our POV: This creates a middle ground between strict exclusions and Advantage+ automation. It reduces risk without cutting off potential high-efficiency wins.

What to do next: A/B test manual vs. limited-spend placement setups. Evaluate cost per result and incremental conversions instead of pure CPM efficiency.

Social & Content Trends

Brands are being pushed into new storytelling styles, shaped by identity, utility, and AI-assisted behaviors.

Lifestyle branding gains momentum

Consumers are gravitating toward brands tied to identity and aspiration. Affordable luxury and status signaling are driving engagement.

Our POV: Features alone don’t move people. Identity and belonging do. If your copy focuses only on product attributes, you’re leaving impact on the table.

What to do next: Rework product messaging to show how your offering fits into a buyer’s desired lifestyle. Update CTAs, social captions, and headlines to evoke identity.

LLM-briefed CTAs redefine engagement

CXL tested CTAs that include a ready-made prompt for ChatGPT. Engagement improved because users received higher-quality AI outputs.

An example of an LLM-informed CTA.

Our POV: As users ask AI to interpret brand content, shaping the question becomes part of conversion optimization.

What to do next: Experiment with prompt-style CTAs in guides, templates, and tools. Test which phrasing drives more accurate and useful AI interpretations.

Influencer partners expand beyond typical creators

Brands are leaning into unconventional creators; think niche experts, offbeat personalities, and micro-communities.

Our POV: As traditional influencer pools saturate, originality becomes a differentiator.

What to do next: Identify unexpected storytellers your competitors ignore. Prioritize people with unique voices and strong community trust over polished aesthetics.

PR, Reputation & Brand Risk

Data control, AI training, and brand representation became major flashpoints in November.

Reddit files legal action over AI scraping

Four companies allegedly scraped Reddit content through Google search results instead of its paid API. Reddit is suing.

Our POV: Reddit is a major training source for LLMs. Legal pressure will reshape how models access user-generated content.

What to do next: Monitor how your brand appears in Reddit threads. Insights from these conversations often influence AI outputs, even indirectly.

LinkedIn will use member data to train AI

LinkedIn updated its policy to allow profile content and posts to train in-house models unless users opt out.

Our POV: This raises transparency questions and could affect brand safety for professional voices.

What to do next: Review employee account settings. Update your governance policies to clarify how team-generated content may be reused.

ChatGPT reduces brand mentions

ChatGPT lowered brand references per response while elevating trusted entities like Wikipedia and Reddit.

A graphic showing reduced brand mentions by ChatGPT.

Our POV: Authority now comes from third-party validation, not just your site. If you’re missing from high-trust platforms, AI tools won’t surface you consistently.

What to do next: Strengthen your presence on Wikipedia, industry directories, and review platforms. Build citations that AI models depend on.

AI search tools mention different brands for the same queries

BrightEdge found almost zero overlap between brands recommended by Google’s AI Overview and ChatGPT.

Our POV: Each model prioritizes different signals based on its training data. Ranking in one environment doesn’t guarantee visibility in another.

What to do next: Expand Digital PR efforts beyond search. Build authority in the sources each LLM favors.

Streaming & Media Shifts

Streaming hits ninety-one percent of U.S. households

Homes now average six subscriptions and spend over one hundred dollars per month on streaming.

Our POV: Streaming is now a core channel for shaping intent long before search happens.

What to do next: Add OTT to your awareness mix. Use it to influence demand before users reach paid search or social ads.

Conclusion

AI pushed every channel toward greater automation, heavier reliance on structure, and stricter expectations for authority. Success now depends on clarity, credibility, and presence across platforms that train and inform AI, not just traditional search engines.

Brands that adapt their data, content, and distribution strategies now will stay visible as user behavior shifts.

Need help applying these insights? Talk to the NP Digital team. We’re already working with brands to navigate these changes and rebuild visibility in an AI-first world.

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Google Shopping Ads now show merchant location labels

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Google Local Services Ads vs. Search Ads- Which drives better local leads?

Google is quietly testing a new way to make Shopping ads feel more local. Select ads using local inventory feeds now display the merchant’s city or town directly above the product title — think “London” or “Tonbridge” — giving shoppers a clearer sense of where the store is based.

Why we care. The new location labels make Shopping ads feel more local and trustworthy, helping nearby retailers stand out in crowded results. Clear city or town indicators can increase click-through rates and drive more in-store visits from shoppers who prefer buying close to home.

It also gives merchants using local inventory feeds a competitive edge by highlighting proximity without needing new ad formats or extra setup.

How it works. The label appears within Shopping ads that already use local inventory data. It joins existing formats like:

  • In-store
  • Pickup later
  • Curbside pickup

But unlike those, this label focuses purely on the store’s location, not fulfillment options.

The catch. Google hasn’t officially announced the feature. Details on rollout, eligibility, and technical requirements remain unknown.

Between the lines. Merchants using local inventory feeds may get a visibility boost if they operate in recognisable or high-trust locations. For users, it’s another nudge to choose nearby retailers over marketplace or long-distance sellers.

First seen. This update was spotted by PPC News Feed founder Hana Kobzová.

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What Is ChatGPT Shopping?

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You can now purchase products directly within ChatGPT.

That’s right, OpenAI recently announced a new feature that turns ChatGPT into a personal shopping assistant. You ask for something, and it doesn’t just recommend it. It finds it, prices it, and even helps you check out all in one chat.

They’re calling it Instant Checkout, and it’s already rolling out with help from e-commerce giants like Stripe and Walmart. The feature enables OpenAI to pull in real-time product listings and personalized suggestions.

It’s still early days, but this is a big deal for e-commerce brands. It opens up an entirely new kind of shopping experience; one where everything from product discovery and research to checkout all happens in a single interface. And with new ChatGPT ads already hitting the ecosystem, it’s clear this is a major market shift.

Key Takeaways

  • ChatGPT now supports in-chat shopping with real-time product listings and checkout through partners like Walmart.
  • Users interact with the feature using natural language prompts, making product discovery more conversational than keyword-based.
  • Product visibility depends on clean data: use schema markup, clear product names, and natural descriptions.
  • E-commerce brands must adapt fast. AI-driven recommendations are transforming the way customers browse and make purchases.
  • Optimizing for ChatGPT shopping requires mobile speed, fresh reviews, and structured product content.

What Do We Know About ChatGPT Shopping and How It Works?

Here’s what we know so far: ChatGPT can now help users discover and buy products directly in the chat interface.

The feature is called Instant Checkout, and it’s powered by OpenAI’s integration with tools like Stripe and Shopify, with Walmart also recently partnering for early rollout. The service is available to all U.S. users of ChatGPT, regardless of their tier.

What It Looks Like in Action

Let’s say you ask ChatGPT for “espresso machines under $200.” ChatGPT doesn’t just return a list of brands; it provides:

  • Curated product suggestions from across major retailers
  • Real-time pricing and availability
  • Affiliate-style product cards (think: images, links, reviews)
  • And for specific vendors, direct checkout options without leaving the chat
An example of e-commerce results in ChatGPT.

Source: RetailTouchPoints

All of this happens through integrations with online retailers and APIs that deliver live product data behind the scenes. The interesting thing is that brands don’t pay for this visibility in ChatGPT’s shopping function.

Where Google Shopping results are based on brands’ paid ad campaigns or Google’s search algorithm, ChatGPT shopping is more conversational and organic. It focuses on the people (what people are saying bout this product online, what the reviews are, etc.).

Built on Conversational Search

What makes this different is the user experience (UX). You’re not clicking through filters and category pages; you’re chatting. You refine your request like a conversation, asking questions like, “What about ones with arch support?” or “Can you find those in women’s sizes?” That’s a huge shift in how product discovery happens.

So, how does it choose what to show you? The platform analyzes structured metadata and previous model responses. It will look back on how it handled similar queries before it ever touches new search results. 

The personalization potential is what makes this even more powerful. ChatGPT will be able to tailor your shopping experience by elevating or demoting various factors of your results based on your needs. For example, if you have a shopping budget of $50, ChatGPT can elevate price as a “signal” and only show you appropriate results. OpenAI is doubling down on the modern customer’s need for personalization.

Is ChatGPT Just Another Shopping Assistant?

Not exactly. Yes, it gives you product recommendations like other AI shopping assistants.

However, ChatGPT takes it a step further by allowing you to shop in a way that feels like texting with a smart, well-informed friend.

Here’s what sets it apart: 

  • Conversational search: You don’t have to use exact filters or keywords. You can talk to it naturally and refine your search.
  • Live product data: ChatGPT pulls real-time pricing and availability from partner retailers.
  • Built-in checkout: With select partners, you can complete a purchase directly in the chat.

This changes the experience from “browse and compare” to “ask and buy.”

That kind of frictionless experience makes it especially appealing for time-strapped users, mobile shoppers, and anyone who already uses ChatGPT regularly. It takes online shopping from endless options to making an informed and personalized decision quickly.

How ChatGPT Shopping Will Impact E-Commerce

ChatGPT isn’t just adding shopping features. This will rewrite how people discover and buy products.

Instead of browsing categories or scrolling search results, users now get personalized recommendations just by asking a question. That creates a new funnel, one that starts with natural language. This could be new territory for many e-commerce brands.

Discovery Is Getting More Personal

In traditional search, people type product-focused keywords. With ChatGPT, they might say:

“I need a thoughtful gift under $50 for a coworker.” Or “What are some comfy sneakers for walking in Europe this winter?”

These are context-rich prompts that AI can interpret and respond to with curated product suggestions. Brands with clear, structured product data and natural-language copy will excel in this type of environment.

Product Pages Matter More Than Ever

AI pulls data from your listings, descriptions, and reviews. If your content is outdated or poorly structured, you might not even show up to ChatGPT shoppers.

And with impulse buys likely to spike in this kind of frictionless experience, your clarity and trust signals can make or break a sale.

This is the next frontier of AI in e-commerce. The game is constantly evolving, and now it’s about showing up where customers are asking questions and ensuring your brand is one of the first answers shown.

How To Optimize Your E-Commerce Product Pages for ChatGPT Shopping

If you want your products to show up in ChatGPT’s recommendations, your product pages need more than nice images and a sale price. You need structure, clarity, and language that AI understands.

Here’s how to get there:

1. Use Product Schema Markup

Structured data helps AI understand what’s on your page. Add product schema so ChatGPT (and other tools) can pull in your:

  • Price
  • Availability
  • Reviews
  • Product name and image

This is the foundation. Without it, you’re invisible to most recommendation engines.

2. Write Natural, Benefit-Focused Descriptions

ChatGPT’s main focus here is pulling product info and providing an output that sounds conversational. Rewrite your descriptions to sound like how people talk:

  • Don’t: “Ergonomic, breathable mesh back with tilt-lock feature”
  • Do: “Keeps you cool and comfortable during long workdays”

3. Keep Product Names Clear

Avoid overly clever names. “The Cloudstep LX” might sound cool, but no one’s searching for that. Try: “Men’s Waterproof Running Shoes – Cloudstep LX”.

4. Feature Fresh Reviews and Ratings

Recent social proof helps both users and AI understand what’s worth recommending. Keep reviews visible and up-to-date.

5. Speed Up Your Mobile Site

A slow page kills conversions, especially if someone’s trying to buy right in the moment. Optimize images, reduce scripts, and test your load time on mobile to ensure the best user experience.

FAQs

How do you use ChatGPT for shopping?

To use ChatGPT for shopping, start a conversation with a shopping-related prompt like “Find me wireless earbuds under $100.” If you’re using ChatGPT Plus, you’ll get product recommendations that also include links. Some users may also have access to built-in checkout through select partners.

Conclusion

ChatGPT shopping is a new channel, not just a new feature. One where conversation replaces search bars and product discovery happens through real-time, AI-driven recommendations.

If you’re in e-commerce, now’s the time to adapt. That means optimizing your product pages with proper schema markup and making sure your content speaks the way real people do.

Your potential customers are already chatting. The question is: is your brand ready to be part of that conversation?

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Audience Segmentation in Marketing: Definition, Types & Best Practices

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If your marketing still treats everyone the same, you’re falling behind.

Audience segmentation is what turns generic campaigns into personalized, high-performing ones. Segmented email campaigns can generate a 760 percent increase in revenue compared to non-segmented ones.

That same principle applies across paid ads, social content, product messaging, and just about any other marketing channel you can think of.

Without segmentation, you’re guessing what your audience wants. That leads to wasted ad spend, and low engagement.

Segmentation gives you an edge. It helps you deliver the right message, to the right people, at the right time.

In this guide, you’ll learn what audience segmentation is, how the different types work, and how to apply them to drive better results across your funnel.

Key Takeaways

  • Audience segmentation is the process of dividing your broader audience into smaller, more specific groups.
  • Segmentation helps improve engagement, click-through rates, and conversions across every channel.
  • There are five core types: demographic, geographic, psychographic, behavioral, and firmographic (which is specifically for B2B).
  • Good segmentation starts with real data, not assumptions, and improves over time.
  • The most effective marketing strategies use segmentation to deliver more personalized and relevant messaging.

What Is Audience Segmentation?

Audience segmentation is the process of dividing your broader audience into smaller, more specific groups based on shared characteristics. These characteristics can be demographic, geographic, behavioral, or even psychographic.

The goal is simple: understand your audience better so you can speak to them more effectively.

Think of it like this: you wouldn’t send the same message to a first-time visitor and a loyal customer. And you wouldn’t talk to a 23-year-old in the same way you’d market to a 65-year-old. Segmentation helps you avoid that one-size-fits-none approach.

This isn’t just a tactic for email marketers, either. It’s a core part of building relevant campaigns across paid ads, landing pages, SMS, product marketing, and more.

Here’s what segmentation unlocks:

  • More personalized content and offers
  • Smarter ad targeting
  • Higher engagement rates
  • Better alignment across your marketing funnel

Audience segmentation often gets confused with defining your target audience. But while defining a target audience helps you understand who you’re going after at a high level, segmentation helps you break that audience down into actionable groups for more precise messaging.

Audience segmentation dashboards in action.

Source

Why Audience Segmentation is Essential

Most marketers aren’t struggling with a lack of data. The challenge is turning that data into action.

That’s where customer and audience segmentation creates real value. When you group your audience based on shared traits or behaviors, you can tailor your messaging, timing, and channels to what actually resonates.

Brands that use segmentation typically see:

  • Higher open and click-through rates
  • Increased customer lifetime value
  • Lower cost per acquisition (CPA)
  • More efficient use of ad budgets

65 percent of consumers expect personalization in their customer experience. And it’s not limited to email. Whether you’re running Google Ads, building a product launch campaign, or personalizing a homepage—segmentation improves performance across the board.

An infographic explaining the differences between marketing funnels wiith and without segmentation.

Source

It also allows you to meet customers where they are in their journey. Someone new to your brand might need education. A returning customer may be ready for an upsell. With segmentation, you can deliver the right message at the right moment.

Types of Audience Segmentation

There are several ways to segment your audience. Each type gives you a different lens into what drives your customers’ behavior. The best strategies use a mix of these, depending on your goals, product, and data.

An infographic explaining types of audience segmentation.

Source

Here are the five most common types of audience segmentation:

Demographic Segmentation

This is the most straightforward method. You segment based on traits like:

  • Age
  • Gender
  • Income level
  • Education
  • Marital status

Example: A clothing brand might promote its premium line to high-income professionals while marketing basics to students or entry-level workers.

Geographic Segmentation

Here, you group users by physical location:

  • Country or region
  • Climate
  • City size
  • Urban vs. rural

Example: A food delivery app might market lunch deals to users in busy cities while promoting family meals in suburban areas.

Psychographic Segmentation

This method looks at the “why” behind your customer’s actions:

  • Personality traits
  • Interests and hobbies
  • Lifestyle choices
  • Core values

Example: A fitness brand might market high-performance gear to athletes and eco-friendly materials to sustainability-minded shoppers.

Behavioral Segmentation

Segment based on how people interact with your brand:

  • Purchase history
  • Engagement level
  • Brand loyalty
  • Product usage

Example: A SaaS company might send upgrade offers to heavy users and reactivation emails to inactive accounts.

Firmographic Segmentation (B2B Only)

This is the B2B version of demographic segmentation:

  • Company size
  • Industry
  • Revenue
  • Location
  • Decision-maker role

Example: A software vendor might offer enterprise features to large corporations and budget-friendly plans to startups.

Real-World Segmentation Examples Across Channels

Segmentation works across every channel you’re using. The tactics change, but the principle stays the same: send the right message to the right person.

Email Marketing: New subscribers get your welcome series. Inactive customers (90+ days) get a win-back offer with a discount. Same list, different messages based on engagement level.

An email encouraging a reader to look at an abandoned cart.

Paid Advertising: Cart abandoners see retargeting ads featuring the exact product they left behind. Cold audiences see brand awareness content and educational posts. Match the ad creative to where they are in the funnel.

Content Personalization: SaaS visitors see automation guides and workflow content. E-commerce brands see conversion optimization and retention posts. Your CMS can handle this with simple behavioral tags based on past visits.

Product Rollouts: Power users get early beta access to new features. Light users get the stable release later with more documentation. This reduces your support burden and makes heavy users feel valued.

SMS Marketing: Previous buyers in specific zip codes get flash sale alerts for local stores. First-time visitors get a welcome discount. High intent plus geographic relevance equals higher conversion rates.

An example of SMS marketing.

Source

The channel doesn’t matter. What matters is matching the message to the person and where they are in their journey.

How To Segment Your Audience, Step-By-Step

Getting started with segmentation doesn’t have to be complex. Here’s a simple process you can use to organize your audience into actionable groups.

1. Start With Data You Already Have

Look at what’s in your CRM, email platform, or analytics tool. Useful data often includes location, purchase history, on-site behavior, and sign-up source.

2. Define Your Most Important Attributes

Based on your goals, decide which traits matter most. For an e-commerce brand, it could be past purchase behavior. For a SaaS company, it might be usage level or company size.

3. Build Initial Segments

Group your audience using filters like:

  • “Has purchased in last 30 days”
  • “Visited pricing page but didn’t convert”
  • “Signed up from Facebook campaign”

Start simple. You can get more granular later.

4. Map Each Segment to the Customer Journey

Think about where each group is in their decision-making process. Someone early in the funnel needs education. A returning visitor might need an incentive.

If you haven’t done this yet, use customer journey mapping to connect segments to meaningful actions.

5. Test, Learn, and Refine

Segmentation isn’t one-and-done. Use A/B testing to refine your messaging, offers, and timing by segment. Drop what doesn’t work. Scale what does.

Best Practices for Audience Segmentation (That Actually Work)

Anyone can slice up an email list but effective segmentation goes beyond basic filters. Here are a few proven tips to get better results without overcomplicating your strategy.

Use Real Data, Not Assumptions

Avoid guessing what people care about. Use actual behavior, survey responses, or analytics to guide how you group your audience.

Keep Segments Useful, Not Just Accurate

A perfect audience profile is useless if it’s too small to act on. Prioritize segments that tie directly to your business goals—like conversions, upsells, or retention.

Don’t Over-Personalize

Over-segmentation can create unnecessary complexity. You don’t need 30 different versions of the same email. Focus on meaningful variations that actually move metrics.

Update Your Segments Regularly

Customer behavior changes. Segments should too. Review and refresh your data often to avoid targeting stale or irrelevant groups.

Align Segments With Personas

Your audience groups should reflect the same needs and motivations as your core buyer personas. If you don’t have a clear set, start with this guide to building an accurate customer persona.

Examples of customer personas.

Source

Common Segmentation Mistakes to Avoid

I see the same mistakes over and over. Avoid these pitfalls to get better results from your segmentation strategy.

Segmenting too early. You need data before you can segment effectively. If you’re working with a brand-new list or product, focus on collecting behavioral data first. Premature segmentation based on assumptions will waste time and money.

Creating too many micro-segments. A segment with 47 people isn’t actionable. Keep your segments large enough to matter. If a group is too small to justify custom creative or messaging, fold it into a larger segment.

Using outdated data. Someone who bought six months ago isn’t in the same segment as someone who bought yesterday. Refresh your segments quarterly at minimum. Monthly is better for fast-moving businesses.

Segmenting but not personalizing. Building segments means nothing if you send the same message to everyone. Each segment should get tailored copy, offers, or creative. Otherwise, you’re just organizing your list for no reason.

Ignoring overlap between segments. People can belong to multiple groups. A high-value customer might also be geographically close to your store. Think about how segments intersect and prioritize which message matters most.

Not testing segment performance. Track metrics by segment. If one group consistently underperforms, either refine the segment definition or adjust your messaging. Segmentation without measurement is guesswork.

FAQs

What is audience segmentation?

Audience segmentation is the process of dividing your broader audience into smaller groups based on traits like behavior, interests, demographics, or location. It helps you deliver more targeted and relevant marketing.

What are the types of audience segmentation?

The most common types include demographic, geographic, psychographic, behavioral, and firmographic segmentation. Each one gives you a different way to understand and connect with your audience.

How do you segment your audience effectively?

Start with data you already have—like purchase history or engagement. Then group users based on shared traits, align segments to the customer journey, and continuously refine based on performance.

Conclusion

Audience segmentation isn’t a tactic you add later. It’s where effective marketing starts.

By breaking your audience into meaningful groups, you gain the ability to tailor messages, prioritize the right channels, and improve your results across the board. Whether you’re building email campaigns, running paid ads, or planning content, segmentation keeps your strategy focused and relevant.

Start with the data you already have. Pick one or two segments that align with your goals. Then test, learn, and scale.

The more precise your segmentation, the more personal your marketing will feel and the better it will perform.

Need help building a segmentation strategy that actually drives results? Check out my consulting services for hands-on support.

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SaaS in AI Search: Who’s Ranking (+ How to Steal Their Spot)

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AI chat is the number one source B2B buyers use to shortlist software.

Not review sites. Not vendor websites. Not salespeople. AI chat.

G2’s 2025 survey of 1,000+ decision makers found that 87% say AI tools like ChatGPT, Perplexity, and Gemini are changing how they research software.

Half of SaaS buyers now start in AI chat instead of Google Search.

They’re “one-shotting” their research with prompts like “Give me CRM solutions for a large gym that work on iPads.”

What used to take hours of “Google —> right-click —> open new tab” is condensed to minutes.

If your product doesn’t show up when buyers ask AI to recommend solutions in your category, you’re losing deals before they begin.

This guide shows you exactly how to change that.

I’ll walk you through:

  • How AI visibility works for SaaS
  • Why some brands dominate AI answers
  • What you can do to make sure AI recommends you

Side note: The data in this article comes from Semrush’s AI Visibility Index (August 2025), focusing on the Digital Tech and Software category.

The 3 Types of AI Visibility for SaaS Brands

There are three ways your brand can show up in AI search:

  1. Brand mentions
  2. Citations
  3. Recommendations

Three Types of AI Visibility for SaaS

Type 1: Brand Mentions

Brand mentions mean your brand appears in the AI’s answer.

It’s not always an endorsement. It’s simply the AI recognizing your brand as relevant to the topic.

For example, I asked ChatGPT:

“How can remote teams stay aligned on projects?”

ChatGPT outlined a few tactics and listed several tools, naming specific brands as examples with no extra context about any of them.

ChatGPT – Remote team aligned on projects

At this level, how AI talks about your brand is super important. AKA: brand sentiment.

A positive tone builds early trust while a negative one sets bad expectations.

Let me show you what I mean.

I asked ChatGPT:

“What do marketers on Reddit say about top reporting dashboards.”

ChatGPT summarized Reddit’s discussions, listed a few tools, and included criticisms about some products.

ChatGPT – Summarized Reddit's discussions

If I were evaluating dashboards, the negative details about AgencyAnalytics and Looker Studio would create a subtle bias that would follow me as I continued my research.

That’s no bueno.

So make sure sentiment around your mentions leans positive.

How do you keep an eye on brand sentiment?

Easy. Use Semrush AI Visibility Toolkit.

Just go to “AI Visibility” > “Perception” and you’ll see key sentiment drivers surrounding your brand. The tool will show you Brand Strength Factors (positive influence on sentiment) and Areas for Improvement (negative sentiment factors).

AI Visibility – Perception – Gong – Key Sentiment Drivers

Type 2: Citations

Citations are instances of AI using your content as a source.

It’s a strong signal that the AI trusts your brand and is using your content to build its answer.

In Google AI Mode, citations show up as clickable links on the right-hand side of the response.

AI Mode – Omnisend vs. Mailchimp

In ChatGPT, they appear as footnotes or small inline links.

ChatGPT – Omnisend vs. Mailchimp – Links

Citations come with two complications.

First, they’re not as visible as brand mentions.

The footnote-style links are easy to miss, which means you probably won’t get meaningful traffic from these citations.

Second, citations don’t always create brand awareness.

The AI can use your content, but not mention your brand.

Semrush’s AI Visibility Index report calls this the “Zapier Paradox.”

In the Google AI Mode dataset, Zapier was the most-cited domain in the entire software category. It appeared in around 21% of the analyzed prompts.

The Zapier Paradox – Authority vs. Mentions

Yet it ranked only #44 for brand mentions.

That means the AI trusts Zapier’s content enough to use it constantly.

But that trust hasn’t translated into more visibility for the brand itself.

That doesn’t mean citations are useless. Far from it, since they’re still the only method of sending users directly from AI search to your website.

But if you’re cited for an answer that recommends other brands (like Zapier often is), the citation isn’t super useful for your brand.

That’s why you want the third type of AI visibility.

Type 3: Product Recommendations

Product recommendations are where the AI moves from “here are some options” to “here’s what you should choose.”

To get recommended, your brand typically needs two things working in your favor:

  • Positive sentiment
  • Enough verified facts for the AI to feel confident putting your name forward

For example, when I asked:

“Which CRM is best for small businesses?”

ChatGPT recommended six CRM platforms.

ChatGPT – Top CRM Options for small businesses

Each one came with a breakdown of strengths.

That’s the AI directly influencing my consideration set.

And when the AI wraps up the answer with the top three CRMs, these three brands stay top of mind.

ChatGPT – Recommendation

As the reader, I’m thinking:

“Alrighty. These are the tools I should probably compare.”

That’s the power of SaaS product recommendations in AI search.

The AI isn’t just helping me explore the category. It’s shaping the shortlist I walk away with.

How AI Models Choose Which SaaS Brands to Surface

When AI answers a query, it cross-checks sources.

It compares what you say about your product with its training data. Along with what the rest of the internet says about you.

If the details line up, you’ve got consensus and consistency: two forces that drive visibility in AI search.

Consensus

Consensus happens when many credible places describe your product in the same way.

In practice, the AI is looking for alignment across sources like:

  • Review sites (G2, Capterra, TrustRadius)
  • Industry blogs and SaaS publishers
  • Expert posts on LinkedIn or in public newsletters
  • User communities like Reddit and Quora
  • Your own website and documentation

Basically: anywhere your product is being talked about in a credible context.

Building Authority for Your Ecommerce Brand

Take Asana, for example.

It routinely appears in AI answers about project management tools.

And you can see why when you look at its footprint online.

Across multiple places, you’ll find the same core description repeated from their website to Capterra to Reddit.

Asana – Collage

All of these brand mentions alone help boost Asana’s potential visibility.

But when they also all point to the same story, that’s consensus. This helps AI feel confident surfacing the brand repeatedly.

Consistency

Consistency means the details match everywhere they appear.

When AI scans the web, it’s looking for verifiable facts. If those specifics line up, it trusts them.

But, if those signals don’t match, the model becomes unsure.

(Just like you would if five people gave you five different versions of the same “fact.”)

For example, let’s say:

  • Your pricing page says your Standard plan includes unlimited reports
  • Your help center says Standard users get 50 reports a month
  • Recent reviews say they hit limits after a week

Now you’ve got three conflicting stories.

When the AI sees that, it can’t tell which one is true. It might use the right one, or it might use the wrong one. Or it might not use any of them.

That’s why data hygiene matters in AI search.

The key facts about your brand should be consistent everywhere your brand is described.

Three Pillars of Data Hygiene for Ecommerce

The Content That Dominates SaaS AI Search

Not all content carries the same weight in SaaS AI search.

Some formats show up repeatedly because they help models verify what’s true about a product.

Review Platforms

Review platforms are some of the most heavily cited sources in SaaS AI search.

Google AI Mode – Is Jobber good for plumbers

These sites, including G2, Capterra, and TrustRadius, give AI unfiltered, third-party proof about your product.

The platforms help the model validate:

  • Who you are
  • What your product actually does
  • How reliable it is
  • How users feel about it

In other words, this is where AI goes to separate your claims from real user experience.

And the data shows how influential they are.

According to Semrush’s AI Visibility Index, G2 is the 4th most-cited source for ChatGPT and 6th for Google AI Mode across the entire tech and SaaS category.

Top Sources Digital Technology & Software

That tells us that:

  • Review platforms aren’t just buyer research hubs
  • They’re part of an AI’s verification layer

What people say about you in these places becomes part of the material the AI uses when describing your brand.

Further reading: LLM Seeding: A New Strategy to Get Mentioned and Cited by LLMs

Community & User-Generated Content (UGC)

Community conversations are another major source LLMs lean on in SaaS AI search.

They cite from places like:

  • Reddit
  • Quora
  • Industry forum threads
  • Product community boards
  • Niche groups where users compare experiences

For example, I asked ChatGPT:

“Why do people switch from ActiveCampaign to Klaviyo?”

ChatGPT cited two Reddit threads in its answer.

ChatGPT – Reddit citations

That’s why your presence in these community spaces matters.

Not in a “go spam Reddit” way.

But in a “be part of the conversations that shape how people talk about your product” way.

Because those public, unscripted conversations can become part of your brand’s source of truth inside LLMs.

Further reading: Reddit Marketing in 3 Hours/Week: From 0 Karma to Real Cred

Best-Of Listicles & Tool Roundups

Best-of listicles and tool roundups give LLMs structured, pre-sorted information that they can easily digest.

These articles hand the AI a ready-made map of a category, including:

  • Who the key players are
  • How the tools differ
  • Which products consistently show up together

The AI regularly pulls from a mix of major publishers, niche SaaS blogs, and established industry media.

For example, when I asked for the top AI SEO tools, Google AI Mode’s citations included a bunch of best lists.

Google AI Mode –Top AI SEO Tools

Every roundup, comparison post, or “best tools for X” mention becomes one more anchor AI tools can grab when they’re trying to answer a question about your category.

Pro tip: Don’t ignore your own media. AI models also use company-owned content as reference material. So create your own well-structured roundups and comparison pages in the niches where your product plays.

For example, when I asked ChatGPT whether Omnisend or Mailchimp is better for ecommerce, one of the citations was Omnisend’s own blog post comparing the two tools.

In other words: their own content helped shape the AI’s narrative.

ChatGPT – Omnisend source

Documentation & Product Knowledge Bases

AI also uses your product documentation to understand how your product works: what it does, who it’s for, and what its technical capabilities are.

For example, when I asked Google AI Mode, “Is Semrush good for enterprise?” the model pulled from several Semrush-owned pages:

  • The Enterprise landing page
  • A press release on the enterprise platform
  • A blog on “What Is Enterprise SEO”
  • An enterprise client case study

Google AI Mode – Is Semrush good for enterprise

Together, those pages gave the model context to understand Semrush’s enterprise offering.

One more thing:

Make sure your content is well-structured, clear, and complete.

If it’s vague or lacks key details, the AI might look elsewhere to fill the gaps.

The Semrush study shows this clearly with pricing.

When SaaS brands don’t publish transparent pricing, AI models fill the blanks using community speculation. This speculation is often tied to negative sentiment.

So, how do you structure your content for better AI visibility?

Use:

  • Clear, explicit content using conversational language
  • Clean formatting that makes details easy to extract
  • Tables, charts, and Q&A blocks that package information neatly
  • Headings that signal hierarchy

Non-Sematic & Sematic HTML

Want the full breakdown? Our article on how to rank in AI search walks you through the full process.

Video Content

Text may fuel most AI answers, but video (especially YouTube) has become a meaningful signal, too.

In fact, YouTube is the 10th most-cited source in Google AI Mode for SaaS-related prompts.

Top Referring Domains in Google AI Mode

This means AI isn’t just reading the web. It’s also learning from what people show and say on camera.

For SaaS brands, that’s a real visibility lever.

If your product appears in YouTube reviews, tutorials, comparisons, or walkthroughs, the AI can pull those details straight into its explanations.

For example, when I asked Google AI Mode whether the paid version of HubSpot is worth it, one of the citations was a YouTube review.

Google AI Mode –Is HubSpot worth it

If you don’t have a YouTube presence yet, it’s worth planning for.

Start by getting your product included in other creators’ reviews and tutorials.

Then build out your own YouTube channel to control the narrative long-term.

What This Shift Means for Your SaaS Brand

If you’ve already put in the work on your SaaS SEO basics, you’re already in a good position.

But AI search adds a new layer, and it requires a few more steps to stay visible.

Make AI Visibility a Company-Wide Effort

AI search visibility isn’t something marketing can brute-force on its own since consensus and consistency play such a major part.

Multiple teams should keep their corners of the internet aligned in your brand story.

This means:

  • Marketing keeps claims factual and up to date
  • Product Marketing ensures documentation, changelogs, and feature pages match what’s actually live
  • Customer Success helps maintain accurate review-site profiles
  • PR/Comms monitors media mentions so nothing drifts off-message

AI Search Strategy

To make that doable, create a simple internal “source of truth” every team can follow.

This doesn’t need to be a 100-page brand bible.

Start with:

  • Exact product names, tier names, and feature labels
  • The approved value props and phrasing you want repeated everywhere
  • Performance claims or metrics that should stay consistent across your site, docs, and press
  • Integration names and technical terms written the same way across all surfaces

We do this at Semrush.

And it makes a huge difference in making sure everyone is speaking the same language.

AI Search Playbook

Start With Your Website

Your website is the part of your presence you fully control, so this is where to start making optimizations.

Make sure your content is clear, crawlable, and structured so AI can easily parse it.

Here’s where to focus first:

  • Put all content in HTML: AI reads HTML far more reliably than JavaScript
  • Use clear headings and hierarchy: They help both users and models navigate the page
  • Add schema markup: It gives AI models a structured way to understand your data exactly the way you want

Schema Markup Validator – Testing WordPress plugins

(We don’t know how heavily AI tools lean on schema right now. But given it’s an SEO best practice, it’s still worth doing anyway.)

Next, create content that covers the full customer journey.

The more touchpoints you cover, the more chances you have to show up as users explore your category.

Your goal isn’t just to appear when someone searches for your brand — it’s to appear whenever they search for anything related to your category.

For example, Semrush publishes content for every stage:

  • Top of Funnel (Awareness): Guides like “AI SEO Tips: How to Earn Citations & Mentions in AI Search”
  • Mid-Funnel (Consideration): A full FAQ category answering the most common SEO questions people search before choosing a tool
  • Bottom of Funnel (Decision): A fully crawlable knowledge base explaining product features, workflows, and how the platform actually works

Semrush – Content for every content stage

Expand Beyond Your Site

Once your website is solid, the next step is to build credibility in the external sources AI cross-references.

The same core facts — your features, use cases, pricing signals, customer proof — should show up consistently on sites like:

  • G2, Capterra, TrustRadius (user validation)
  • Niche media and publisher sites (authority)
  • Partner blogs and integrations (ecosystem relevance)
  • Community spaces like Reddit or LinkedIn (real-world use and sentiment)

Social & Technical Layer

When all of these places tell the same story about what you do and who you’re for, you build consensus.

And once that consensus forms, AI can surface and confidently recommend your brand.

Getting your brand into all these places takes time. So, stack your efforts in layers:

  • Lock down key review sites first
  • Join conversations already happening in communities like Reddit
  • Pitch niche SaaS sites, journalists, and publishers

Track the Signals That Show You’re Gaining Ground

It’s not as easy to track AI visibility right now as it is to track SEO visibility.

But there are a few indicators that reveal whether you’re becoming part of the model’s go-to answers.

These are worth checking monthly or quarterly:

  • Share of voice: How often your brand appears in AI-generated results for your category
  • Brand sentiment: The tone of the mentions
  • Citation frequency: How often your domain is used as a source in AI answers

Use Semrush’s AI Visibility Toolkit to track these metrics.

AI Visibility – Brand Performance – Gong – Share of Voice vs. Sentiment

Example of a Brand That’s Winning in AI Search (Slack)

Slack ranks ninth overall in the Digital Technology/Software category for AI visibility.

Top 20 Brands Digital Technology & Software

That visibility isn’t tied to one use case or category, as Slack shows up everywhere for various queries.

From prompts about remote work to team communication and the best tools for small businesses.

Your Performing Topics – Prompts

Here’s what they’re doing that you can steal:

Slack Owns Their Category (Not Just Brand-Specific Prompts)

Slack doesn’t only show up when someone searches for “Slack.”

They show up for everything inside their category, in prompts about:

  • Use cases: “team chat for remote work”
  • Features: “tools with shared channels”
  • Problems: “how to align remote teams”
  • Price: “team communication tools”

Shopping Prompt Patterns

Showing up in these various category prompts builds early recognition.

This then affects what happens next as the user goes deeper into their buying journey.

For example, a user might start an AI conversation with:

“Which is better, Slack or Teams?”

ChatGPT – Which is better Slack or Teams

Slack shows up in the citations because they’ve published content that answers that question.

Now, let’s say the user sees a drawback in the AI’s answer.

ChatGPT – Slack & Teams – Things to watch

The user might follow up with:

“What are Slack’s security concerns?”

And Slack again shows up in the citations, this time through their own blog content.

ChatGPT – Slack's security concerns

Slack is actively shaping the conversation.

As the user moves from comparison to evaluation to decision, Slack’s content keeps appearing in the AI’s reasoning.

In short: Slack gets to influence the story at every step of the buyer journey.

Slack’s Messaging Is Clear

One thing Slack absolutely nails is message consistency.

Everywhere you look — their website, their docs, their review profiles, their blog — you get the same story about what Slack does and who it’s for.

Go to their site and you’ll see pages laying out features, use cases, and integrations. All in plain, straightforward language.

Even their blog posts break down new features in that same accessible tone.

Slack Blog

That clarity matters because it makes it incredibly easy for AI to learn what’s what.

When your content follows a simple structure of “Here’s the feature, here’s what it does, here’s how it works,” the model can easily classify information.

But Slack doesn’t just do this on their site.

Jump over to their review profiles and you’ll find the exact same messaging — the same features, same categories, same positioning.

TrustRadius – Slack reviews

That consistency is a big plus.

When your messaging stays the same across every channel, you give the AI reliable information to work with.

Slack Is Present Everywhere LLMs Go for Answers

Slack has a footprint across every layer that large language models pull from.

The community layer: Reddit threads, Quora discussions, and YouTube reviews:

Reddit – Slack apps

The expert layer: SaaS tutorials, niche SaaS blogs, and trusted industry publishers:

Upscale – Slack – Remote tips

The verification layer: G2, Capterra, and TrustRadius:

G2 – Slack reviews

This breadth matters because it helps LLMs find patterns.

When Slack’s value prop, features, and positioning appear the same way across all three layers, the AI treats that agreement as “high-confidence” information.

This gives the AI zero doubts about what Slack does and what it offers — and therefore what kinds of queries the AI should recommend Slack for.

Help AI Find and Feature Your SaaS Brand

For SaaS AI search, the game is simple:

Show up everywhere the AI looks.

For software companies, that means being intentional about what you publish, how you structure it, and where you show up across the web.

You don’t just need to “write more content.”

You need to create the right content, in the right places, in the right formats that AI models rely on.

It’s a big shift, for sure.

But you can make the whole thing far easier by following our search everywhere optimization guide.

The post SaaS in AI Search: Who’s Ranking (+ How to Steal Their Spot) appeared first on Backlinko.

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Why Running Seasonal Use Acquisition Campaigns Will Boost Your App’s Success

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The holiday season is one of the most lucrative and competitive times of the year for app marketers. With users in the mood to browse, buy, travel, and celebrate, it’s a golden window to capture attention, drive installs, and boost engagement. 

As shoppers embrace gifting, experiences, self-improvement, and more, the period presents the perfect opportunity to connect your app with seasonal behaviors – but success depends on how effectively you plan and execute.

By developing tailored mobile user acquisition strategies and creative campaigns that resonate with the festive mindset, you can strengthen visibility, fuel app installs, and turn short-term peaks into long-term growth.   

In this blog, we’ll explore how to craft high-performing seasonal campaigns that resonate with the festive mindset and keep your app top of mind during the busiest shopping season of the year.

Key Takeaways

  • Adapt creatives and messaging to align with seasonal moods and trends.
  • Use limited-time offers to drive urgency and engagement.
  • Upweight marketing budgets to capitalize on peak seasonal activity.
  • Leverage user-generated content (UGC) to boost authenticity and reach.
  • Optimize Apple Search Ads and Custom Product Pages to maximize visibility.

Upgrade your Creatives to Match the Season

To stay competitive and maximize results, your creative approach must reflect the holiday spirit. Users are actively searching for seasonal inspiration, so aligning your visuals, copy, and value proposition with this period can dramatically increase engagement.

1. Seasonal Visuals

Incorporating festive design elements such as colors, typography, and imagery helps your app feel relevant and timely. Use holiday cues that create an emotional connection, ensuring to stay on brand and balanced. 

Pair this with seasonal messaging that captures attention, whether that’s highlighting limited-time features, discounts, or ways your app enhances the holidays. Done well, these creatives signal that your app is current, relatable, and part of the seasonal excitement.

Examples of seasonal messaging in apps.

2. Themed Messaging

Adapt your tone and messaging to reflect the joy and energy of the season. Phrases like “Get in the Holiday spirit” or “Make gifting easier this year” can help your campaign feel conversational and relevant.  If you’ve added new features or updated your app for the holidays, make sure they are clearly showcased in your ad copy and store listing. This is a great way to let users (new and returning) know that you have fresh and relevant content, products, and deals for the season.

3. Create Value for Users

Ask yourself how your app adds value during the holidays. Whether it helps users manage gift lists, discover deals, or stay organized, communicate that benefit clearly. The goal is to position your app as useful, not just festive.

4. Limited-Time Offers 

Exclusive promotions and time-sensitive deals are powerful conversion drivers. Use clear CTAs like “Limited-time offer” or “Ends soon” to build urgency. In your visuals, spotlight these offers alongside seasonal products or app features.

For instance, Mixbook – an online photo book and personalized gift creation platform – ran a paid acquisition campaign offering 50% off during the holiday season. The combination of festive imagery and a compelling offer helped the brand capture high-intent users when purchase intent was at its peak.

Mixbook's paid acquisition campaign.

Source: Mixbook Facebook Ads

Upweight Your Budgets for Seasonal Campaigns

The holidays aren’t the time for evenly distributed spend. Competition is higher, but so is opportunity, meaning strategic budget allocation is key.

Focus your spend where you can achieve the greatest impact and concentrate on high-performing channels and audiences rather than spreading budgets thinly. A good approach can be to prioritize one or two paid acquisition channels that align closely with your highest-performing segments, to ensure you’re investing where impact will be the greatest. 

For example:

  • Travel apps often see surges in December and again in January, when users plan trips for the new year. Increasing budgets during these moments ensures you capture high-intent users when they’re most likely to convert.
  • Shopping apps should front-load investment in November and early December to align with Black Friday, Cyber Monday, and Christmas activity. Visibility during these periods delivers stronger ROI than a steady year-round spend.

By investing more heavily during high-intent windows, you’re positioning your app to be seen when users are most motivated to act. 

Holiday ads from Jet2Holidays

Source: Jet2holidays Christmas Screenshots

Leverage User-Generated Content (UGC) to Drive Engagement

Seasonal campaigns don’t have to rely solely on paid creatives. User-generated content adds authenticity, builds trust, and stretches your budget further. 

UGC allows users to share real experiences, and during the holidays, these organic stories resonate more than any brand-produced ad. 

Some ways you can harness user-generated content effectively:

  1. Showcase genuine testimonials: Feature authentic reviews in your app store listings and ads. For example, a productivity app could highlight how users managed their holiday planning with ease.
  1. Run holiday-themed contests: Encourage users to share festive photos or stories connected to your app, such as “Best Holiday Recipe” or “Gift Guide Challenge.”.
  1. Create a holiday hashtag campaign: Build a seasonal hashtag to increase visibility and encourage sharing.
  2. Feature user success stories: Share real examples of how users benefited from your app during past holiday seasons to demonstrate real-world value.
  3. Incorporate UGC in Ads: Ads featuring real users often outperform studio-produced creative in engagement and CTR.
User-generated content from Starbucks

Benefits of UGC:

  • Wider reach: User posts expose your app to their personal networks.
  • Increased trust: Audiences are more likely to believe peer recommendations over branded messages.
  • Cost-effectiveness: Repurposing authentic content reduces production costs.
  • Higher engagement: UGC blends naturally into social feeds and typically generates higher engagement on social media.

Use Apple Search Ads to Accelerate Your Seasonal Growth

Apple Search Ads (ASA) are one of the most effective ways to reach high-intent users, people already searching for apps like yours. During the holidays, when search behavior shifts and competition increases, optimising your ASA strategy is essential. 

  1. Seasonal keyword research: Identify seasonal search terms and trends using ASO tools. Keywords like “holiday planner,” “gift ideas,” or “Christmas shopping” can unlock new audiences during this period.
  2. Seasonal Custom Product Pages (CPP): Custom Product Pages allow you to tailor visuals and messaging for specific keywords or campaigns. Update your CPPs with festive creatives, special offers, or limited-time product features to deliver a more relevant user experience.
  3. Plan for Higher Competition: Expect CPCs to rise during peak seasons, so factor that into your forecasts. To maintain ROI, prioritize creative testing – visuals, messaging, and offers that can help you convert at a higher rate when competition is stronger.
Apple Search Ads.

Maximizing Lifetime Value of Seasonal Installers

Seasonal campaigns can generate huge bursts of installs, but the real value lies in retention. Many users acquired during holiday periods are motivated by discounts or limited-time offers – meaning they risk churning once promotions end.

To counter this, segment seasonal installers early and design retention campaigns around their behavior:

  • Offer exclusive post-season promotions or loyalty rewards.
  • Send early-access invitations for future sales or events.
  • Reinforce values through personalized push notifications or in-app messages that highlight ongoing benefits.

By nurturing these users beyond the holiday period, you can turn one-off installs into long-term, high-value customers.

Seasonal Growth Beyond Retail

While shopping and eCommerce apps experience some of the most visible holiday peaks, seasonal user-acquisition opportunities span almost every vertical. The key is to identify the moments that matter most for your audience and align your campaign strategy around them.

Travel & Experiences: December and January are peak planning months. Apps can use “escape the cold” or “plan your next adventure” narratives to capture high-intent travelers and early-year bookings.

Fitness & Wellness: The new year is synonymous with fresh starts. Fitness, nutrition, and mindfulness apps can capitalize on this momentum with “reset” or “new routine” messaging.

Finance & Money Management: After the holiday spending rush, users often turn to budgeting and saving. Finance apps can position themselves as the go-to solution for taking control in January.

Entertainment & Streaming: With people spending more time at home, apps in entertainment, gaming, and streaming can highlight shared experiences, relaxation, or discovery.

Food & Delivery: From festive feasts to New Year get-togethers, delivery and recipe apps can tap into convenience, celebration, and seasonal indulgence.

Productivity & Learning: As goals and resolutions take shape in early Q1, these apps can drive engagement by helping users stay organized, productive, and inspired.

Conclusion

The holiday season presents a unique opportunity for app marketers to connect with users at scale, but seizing that opportunity takes strategy, timing, and creativity. 

 From festive creatives and limited-time offers to smart budget allocation, user-generated content, and Apple Search Ads, every element of your user acquisition strategy should work together to maximize performance.

And remember, seasonality isn’t just about the holidays, it’s about harnessing moments. By aligning your app marketing with user behaviors and mindsets throughout the year, you can build campaigns that not only drive downloads but sustain growth long after the festive season ends.

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