The 5-Step Process To Setting Crystal Clear PPC Goals via @sejournal, @MenachemAni

Many agencies and marketers believe that success in paid media is primarily down to the quality of your ads or the specificity of your landing pages.

While those elements are important, they’re meaningless unless they sit on a foundation of alignment with client needs.

The cleanest account structure and flawless creatives may hit every platform benchmark, but any success will be short-lived if you’re not clued into what’s actually important to your clients.

Higher revenues, more profit, better lead quality, shorter sales cycles – this is what typically matters to the people paying the bills.

At JXT Group, we make sure that the foundation is laid before building a single campaign by gathering a clear picture of how our clients make money, who their ideal customers are, and what a proper conversion looks like.

Here are the five phases we use to engineer that experience.

1. Understand The Business Model

Financially, most Google Ads clients can be split into one of two business models: those that sell products at face value and those that want leads who convert at a later date, typically through an offline interaction.

Verticals like ecommerce and info products sell their goods (physical or otherwise) at face value, allowing you to see revenue figures inside of Google Ads.

Verticals like local services and SaaS rely on capturing interest in the form of phone calls, form fills, and chat sessions. These leads may or may not turn into actual sales later.

Anyone dealing with physical products also has to factor cash flow, procurement costs, shipping fees, and return rates into both how much they can spend as well as how much return they need on their ad spend.

This means that the same 4x return on ad spend (ROAS) can be great for one brand with low expenses, but put another underwater.

It’s why you cannot use platform metrics like ROAS while ignoring what actually results in net profit after fulfillment.

And leads need to be both high in quality and catered to promptly; otherwise, brands run the risk of low final conversion rates.

As marketers, we want to drive the right type of leads at a cost that matches a client’s close rates and order values, resulting in longer feedback loops and tighter customer relationship management (CRM) integration so we can optimize to actual revenue.

2. Match Goals To Client Priorities

Simply put, not every client is chasing the same outcome.

Some want to scale aggressively and are comfortable with a higher cost-per-acquisition (CPA), while others are laser-focused on efficiency and won’t move unless the numbers are dialed in.

I’ve worked with brands whose main goal was a clean presence, ensuring their ads show only on high-quality placements and live up to their internal values.

There are other niche goals, like outbidding a certain competitor or positioning themselves with a certain audience. All of these are valid, but they require different approaches.

Obviously, you can’t do anything until you figure out what matters most to the client. It might sound obvious, but too many agencies make assumptions based on platform key performance indicators (KPIs).

Just because Google says a campaign is performing “well” doesn’t mean it’s aligned with your client’s goals.

We start by asking the right questions, such as:

  • What would success look like six to 12 months from now?
  • Is your first priority profitability, growth, market share, or brand presence?
  • Would you rather trade volume for efficiency or efficiency for volume?

Once that’s established, we structure everything else around it:

  • How much budget is required.
  • Which campaign types to run and how to structure them.
  • What bid strategies we use.
  • How broad or narrow our targeting needs to be.
  • Messaging on ads and landing pages.
  • Negative keyword lists.
  • Targets for impression share, ROAS/CPA, and other KPIs.

Without these first foundational layers, everything else you do is just guesswork.

3. Set Comprehensive And Specific Goals

Once we understand the client’s business model and goals, it’s time to layer in our expertise. This part involves setting realistic goals that balance client desires with what we know is possible.

We’ll typically call on our vertical knowledge, experiences with past clients, and our understanding of unit economics and fulfillment to paint a complete picture.

There’s no room for mistakes like setting an arbitrary ROAS goal without asking what that revenue actually does for the business. After all, a 3x ROAS doesn’t mean much if the margins are thin or there are hidden costs later on.

With lead generation, the conversion doesn’t end with our intake form. In fact, it’s only the first step. The real value happens offline, when the lead turns into a paying customer, and Google has no visibility.

That gap is where the greatest insights and opportunities lie, and it’s vital that we account for it.

Here’s how to goal-set so that media performance ties back to real-world business needs.

Ecommerce

1. Look at the numbers behind the numbers.

This means breaking down the client’s cost structure.

What’s the cost of goods sold? How much does shipping cost per order? Are there fulfillment fees, returns, or seasonal procurement issues? How many other vendors get paid whose fees need to be accounted for in the ROAS target?

These offline costs directly impact ad sustainability.

2. Understand margins at the SKU or category level.

Not every product has the same margin, so some items can scale at a lower ROAS while others need to stay profitable at first touch.

We try to segment products by margin so we can set different targets where it makes sense.

3. Factor in blended performance.

A customer might enter the funnel through Google Ads but convert through another channel, like email.

We’ll study how Google fits into the entire ecosystem rather than trust a narrow window of last-click attribution, so that we can temper expectations based on how it all fits together.

4. Set realistic ROAS targets.

Once we understand the financials, it’s time to work backwards.

What’s the minimum ROAS needed to break even? What target ROAS will let the brand hit profitability goals?

This becomes our baseline and gives us a platform from which to build situational variance for things like seasonal demand, new product launches, and what competitors are doing.

5. Clarify the business objective behind the spend.

Not all brands spend on ads for the same reason. Some want to acquire new customers, others want to clear out inventory, and others still are launching a new product or range.

Each of these goals needs its own approach to bidding, creative, and measurement.

Lead Generation

1. Map the full conversion journey.

What happens after a lead submits a form or makes a call? Who follows up, how quickly, and what’s the typical close rate?

There is a full post-click sales flow that exists after someone registers their interest. If we don’t understand it, we’re optimizing in the dark.

2. Quantify the value of a lead.

Different leads have different values, and Google is not privy to any of this unless you share that data back as offline conversions.

For lead gen clients, we look at historical data on how many leads turn into sales and how quickly, what the average deal size is, and what the margin looks like.

Then, we set up integrations between Google Ads and their CRM to feed this data back and optimize against it.

3. Use the funnel to set a target CPA.

Once we know things like typical deal value and close rate, we can reverse engineer our way to a CPA that leaves enough margin on the plate.

For example, needing 30 leads to close one deal worth $1,000 gives us very limited margins and runs the risk of blowing through the market.

A client that closes 1 in 10 leads with a $5,000 average sale gives us a much higher ceiling on what they can pay per lead while staying profitable.

4. Control anything we can post-click.

Lead gen gives us a greater opportunity to influence conversions after they click. This means landing page user experience and messaging, form length and format, automated email follow-ups, and CRM workflows.

Small changes here can have an outsized impact on close rates and lead quality.

4. Employ Active Listening During Conversations

Meeting with a new client is a bit like hanging out with someone new for the first time. They might not be willing to dive deep or share as openly as we’d like, but it’s our job to make them feel comfortable enough to do so.

Surface-level answers will only take us so far. To set a truly solid strategy, we want to listen to what’s in the spaces between their words.

What are they really trying to solve? Are they really after more profit or market share, or do they just want cleaner reporting now that they have investors to answer to?

A client might say they want “more leads” when what they really need are better leads that their sales team can actually close, but you’ll never see that light if you take everything they say at face value.

Active listening shows up in the details:

  • Picking up on how the client talks about their sales process, not just the form submission.
  • Hearing concerns about inventory issues before pushing hard on a best-seller.
  • Noticing when a CEO cares more about market visibility than ROAS.

It’s a skill that takes time to develop, but it’s also the only way to avoid misalignment and really build trust.

Get this right, and your client will feel like you’re there to make them look great and are willing to run through brick walls for them.

5. Ask Probing, Leading Questions To Reveal The Full Picture

Potential clients who put up walls need you to cut through the noise.

These questions will help you get to the real motivation behind their desire to spend on paid search, as well as allow you to spot red flags that might indicate a difficult client.

Business Direction

  • What would success look like to you in the next six to 12 months? This helps them move beyond “more leads” or “better ROAS” and focus on outcomes.
  • If Google Ads disappeared tomorrow, what would break in your business? This reveals how critical paid media is to their revenue engine.
  • Is this about profitability, growth, or positioning? Few clients won’t say “all three,” but keep pressing, and they’ll tell you what they’d sacrifice first.
  • Are you looking to maintain, grow, or exit? You should know if they’re scaling to sell, which changes everything about risk tolerance and KPIs.

Finance & Economics

  • What’s your average profit margin after all costs, e.g., ads, fulfillment, labor? If they don’t have this information ready and can’t/won’t source it, that should be a red flag about their openness.
  • What do you pay to acquire a customer? What’s the most you can afford to pay? See if they’re thinking in terms of lifetime value or just looking at front-end performance.
  • Do we need to factor in any fixed costs that most media buyers wouldn’t know about? It opens the door to discussions about warehousing, returns, sales commissions, etc.

Lead Quality & Sales Process

  • What do you consider to be a “qualified” lead? This forces them to define quality, which is far superior to treating all leads the same or leaving the definition vague.
  • What happens after a lead comes through? You want to know how long it usually takes to close a deal and what their team does to facilitate that. The answer will show you how strong or weak their internal follow-up process is.
  • How often do you listen to sales calls or review what’s happening post-click? If the answer is never, it tells you the magnitude of the support they’ll need to improve close rates. This might not be something you can control.

Bottlenecks & Internal Dynamics

  • Who has the final say on marketing and business decisions? You’ll avoid many headaches and painful back-and-forth by establishing this upfront.
  • What have you tried in the past that didn’t work, and why not? Ask this to get insight into previous agency relationships, internal friction, or unrealistic expectations.
  • If we start today and in six months you’re unhappy, what will have gone wrong? This one is gold as it can expose fears, past traumas, and give you a roadmap on how to hit alignment.

But, even if you get all these answers and follow all the advice in this article, communication with your clients is the key to establishing a relationship where you’re trusted and given space to operate.

Without proactive and consistent two-way communication, their perceptions may not align with what you’re doing.

Remember: You’re The Expert, But You’re Not In Charge

One thing many agencies and marketers tend to forget as they manage thousands and millions of dollars in ad spend is that we build on leased land. These are not our accounts and campaigns, and we don’t pay the advertising bills.

So, even though it’s important for clients to defer to our expertise, ultimately, they’re the ones who call the shots when it comes to direction and strategy.

The other angle to this is that it’s not our job to make ourselves look good or even to get a solid case study out of an engagement; those are bonuses.

Our job is to service client needs, maximize results within the spend allocated to us, and make our clients look phenomenal in front of the people they answer to.

More Resources:


Featured Image: ugguggu/Shutterstock

How To Leverage AI To Modernize B2B Go-To-Market via @sejournal, @alexanderkesler

In a post “growth-at-all-costs” era, B2B go-to-market (GTM) teams face a dual mandate: operate with greater efficiency while driving measurable business outcomes.

Many organizations see AI as the definitive means of achieving this efficiency.

The reality is that AI is no longer a speculative investment. It has emerged as a strategic enabler to unify data, align siloed teams, and adapt to complex buyer behaviors in real time.

According to an SAP study, 48% of executives use generative AI tools daily, while 15% use AI multiple times per day.

The opportunity for modern Go-to-Market (GTM) leaders is not just to accelerate legacy tactics with AI, but to reimagine the architecture of their GTM strategy altogether.

This shift represents an inflection point. AI has the potential to power seamless and adaptive GTM systems: measurable, scalable, and deeply aligned with buyer needs.

In this article, I will share a practical framework to modernize B2B GTM using AI, from aligning internal teams and architecting modular workflows to measuring what truly drives revenue.

The Role Of AI In Modern GTM Strategies

For GTM leaders and practitioners, AI represents an opportunity to achieve efficiency without compromising performance.

Many organizations leverage new technology to automate repetitive, time-intensive tasks, such as prospect scoring and routing, sales forecasting, content personalization, and account prioritization.

But its true impact lies in transforming how GTM systems operate: consolidating data, coordinating actions, extracting insights, and enabling intelligent engagement across every stage of the buyer’s journey.

Where previous technologies offered automation, AI introduces sophisticated real-time orchestration.

Rather than layering AI onto existing workflows, AI can be used to enable previously unscalable capabilities such as:

  • Surfacing and aligning intent signals from disconnected platforms.
  • Predicting buyer stage and engagement timing.
  • Providing full pipeline visibility across sales, marketing, client success, and operations.
  • Standardizing inputs across teams and systems.
  • Enabling cross-functional collaboration in real time.
  • Forecasting potential revenue from campaigns.

With AI-powered data orchestration, GTM teams can align on what matters, act faster, and deliver more revenue with fewer resources.

AI is not merely an efficiency lever. It is a path to capabilities that were previously out of reach.

Framework: Building An AI-Native GTM Engine

Creating a modern GTM engine powered by AI demands a re-architecture of how teams align, how data is managed, and how decisions are executed at every level.

Below is a five-part framework that explains how to centralize data, build modular workflows, and train your model:

1. Develop Centralized, Clean Data

AI performance is only as strong as the data it receives. Yet, in many organizations, data lives in disconnected silos.

Centralizing structured, validated, and accessible data across all departments at your organization is foundational.

AI needs clean, labeled, and timely inputs to make precise micro-decisions. These decisions, when chained together, power reliable macro-actions such as intelligent routing, content sequencing, and revenue forecasting.

In short, better data enables smarter orchestration and more consistent outcomes.

Luckily, AI can be used to break down these silos across marketing, sales, client success, and operations by leveraging a customer data platform (CDP), which integrates data from your customer relationship management (CRM), marketing automation (MAP), and customer success (CS) platforms.

The steps are as follows:

  • Appoint a data steward who owns data hygiene and access policies.
  • Select a CDP that pulls records from your CRM, MAP, and other tools with client data.
  • Configure deduplication and enrichment routines, and tag fields consistently.
  • Establish a shared, organization-wide dashboard so every team works from the same definitions.

Recommended starting point: Schedule a workshop with operations, analytics, and IT to map current data sources and choose one system of record for account identifiers.

2. Build An AI-Native Operating Model

Instead of layering AI onto legacy systems, organizations will be better suited to architect their GTM strategies from the ground up to be AI-native.

This requires designing adaptive workflows that rely on machine input and positioning AI as the operating core, not just a support layer.

AI can deliver the most value when it unifies previously fragmented processes.

Rather than simply accelerating isolated tasks like prospect scoring or email generation, AI should orchestrate entire GTM motions, seamlessly adapting messaging, channels, and timing based on buyer intent and journey stage.

Achieving this transformation demands new roles within the GTM organization, such as AI strategists, workflow architects, and data stewards.

In other words, experts focused on building and maintaining intelligent systems rather than executing manual processes.

AI-enabled GTM is not about automation alone; it’s about synchronization, intelligence, and scalability at every touchpoint.

Once you have committed to building an AI-native GTM model, the next step is to implement it through modular, data-driven workflows.

Recommended starting point: Assemble a cross-functional strike team and map one buyer journey end-to-end, highlighting every manual hand-off that could be streamlined by AI.

3. Break Down GTM Into Modular AI Workflows

A major reason AI initiatives fail is when organizations do too much at once. This is why large, monolithic projects often stall.

Success comes from deconstructing large GTM tasks into a series of focused, modular AI workflows.

Each workflow should perform a specific, deterministic task, such as:

  • Assessing prospect quality on certain clear, predefined inputs.
  • Prioritizing outreach.
  • Forecasting revenue contribution.

If we take the first workflow, which assesses prospect quality, this would entail integrating or implementing a lead scoring AI tool with your model and then feeding in data such as website activity, engagement, and CRM data. You can then instruct your model to automatically route top-scoring prospects to sales representatives, for example.

Similarly, for your forecasting workflow, connect forecasting tools to your model and train it on historical win/loss data, pipeline stages, and buyer activity logs.

To sum up:

  • Integrate only the data required.
  • Define clear success criteria.
  • Establish a feedback loop that compares model output with real outcomes.
  • Once the first workflow proves reliable, replicate the pattern for additional use cases.

When AI is trained on historical data with clearly defined criteria, its decisions become predictable, explainable, and scalable.

Recommended starting point: Draft a simple flow diagram with seven or fewer steps, identify one automation platform to orchestrate them, and assign service-level targets for speed and accuracy.

4. Continuously Test And Train AI Models

An AI-powered GTM engine is not static. It must be monitored, tested, and retrained continuously.

As markets, products, and buyer behaviors shift, these changing realities affect the accuracy and efficiency of your model.

Plus, according to OpenAI itself, one of the latest iterations of its large language model (LLM) can hallucinate up to 48% of the time, emphasizing the importance of embedding rigorous validation processes, first-party data inputs, and ongoing human oversight to safeguard decision-making and maintain trust in predictive outputs.

Maintaining AI model efficiency requires three steps:

  1. Set clear validation checkpoints and build feedback loops that surface errors or inefficiencies.
  2. Establish thresholds for when AI should hand off to human teams and ensure that every automated decision is verified. Ongoing iteration is key to performance and trust.
  3. Set a regular cadence for evaluation. At a minimum, conduct performance audits monthly and retrain models quarterly based on new data or shifting GTM priorities.

During these maintenance cycles, use the following criteria to test the AI model:

  • Ensure accuracy: Regularly validate AI outputs against real-world outcomes to confirm predictions are reliable.
  • Maintain relevance: Continuously update models with fresh data to reflect changes in buyer behavior, market trends, and messaging strategies
  • Optimize for efficiency: Monitor key performance indicators (KPIs) like time-to-action, conversion rates, and resource utilization to ensure AI is driving measurable gains.
  • Prioritize explainability: Choose models and workflows that offer transparent decision logic so GTM teams can interpret results, trust outputs, and make manual adjustments as needed.

By combining cadence, accountability, and testing rigor, you create an AI engine for GTM that not only scales but improves continuously.

Recommended starting point: Put a recurring calendar invite on the books titled “AI Model Health Review” and attach an agenda covering validation metrics and required updates.

5. Focus On Outcomes, Not Features

Success is not defined by AI adoption, but by outcomes.

Benchmark AI performance against real business metrics such as:

  • Pipeline velocity.
  • Conversion rates.
  • Client acquisition cost (CAC).
  • Marketing-influenced revenue.

Focus on use cases that unlock new insights, streamline decision-making, or drive action that was previously impossible.

When a workflow stops improving its target metric, refine or retire it.

Recommended starting point: Demonstrate value to stakeholders in the AI model by exhibiting its impact on pipeline opportunity or revenue generation.

Common Pitfalls To Avoid

1. Over-Reliance On Vanity Metrics

Too often, GTM teams focus AI efforts on optimizing for surface-level KPIs, like marketing qualified lead (MQL) volume or click-through rates, without tying them to revenue outcomes.

AI that increases prospect quantity without improving prospect quality only accelerates inefficiency.

The true test of value is pipeline contribution: Is AI helping to identify, engage, and convert buying groups that close and drive revenue? If not, it is time to rethink how you measure its efficiency.

2. Treating AI As A Tool, Not A Transformation

Many teams introduce AI as a plug-in to existing workflows rather than as a catalyst for reinventing them. This results in fragmented implementations that underdeliver and confuse stakeholders.

AI is not just another tool in the tech stack or a silver bullet. It is a strategic enabler that requires changes in roles, processes, and even how success is defined.

Organizations that treat AI as a transformation initiative will gain exponential advantages over those who treat it as a checkbox.

A recommended approach for testing workflows is to build a lightweight AI system with APIs to connect fragmented systems without needing complicated development.

3. Ignoring Internal Alignment

AI cannot solve misalignment; it amplifies it.

When sales, marketing, and operations are not working from the same data, definitions, or goals, AI will surface inconsistencies rather than fix them.

A successful AI-driven GTM engine depends on tight internal alignment. This includes unified data sources, shared KPIs, and collaborative workflows.

Without this foundation, AI can easily become another point of friction rather than a force multiplier.

A Framework For The C-Level

AI is redefining what high-performance GTM leadership looks like.

For C-level executives, the mandate is clear: Lead with a vision that embraces transformation, executes with precision, and measures what drives value.

Below is a framework grounded in the core pillars modern GTM leaders must uphold:

Vision: Shift From Transactional Tactics To Value-Centric Growth

The future of GTM belongs to those who see beyond prospect quotas and focus on building lasting value across the entire buyer journey.

When narratives resonate with how decisions are really made (complex, collaborative, and cautious), they unlock deeper engagement.

GTM teams thrive when positioned as strategic allies. The power of AI lies not in volume, but in relevance: enhancing personalization, strengthening trust, and earning buyer attention.

This is a moment to lean into meaningful progress, not just for pipeline, but for the people behind every buying decision.

Execution: Invest In Buyer Intelligence, Not Just Outreach Volume

AI makes it easier than ever to scale outreach, but quantity alone no longer wins.

Today’s B2B buyers are defensive, independent, and value-driven.

Leadership teams that prioritize technology and strategic market imperative will enable their organizations to better understand buying signals, account context, and journey stage.

This intelligence-driven execution ensures resources are spent on the right accounts, at the right time, with the right message.

Measurement: Focus On Impact Metrics

Surface-level metrics no longer tell the full story.

Modern GTM demands a deeper, outcome-based lens – one that tracks what truly moves the business, such as pipeline velocity, deal conversion, CAC efficiency, and the impact of marketing across the entire revenue journey.

But the real promise of AI is meaningful connection. When early intent signals are tied to late-stage outcomes, GTM leaders gain the clarity to steer strategy with precision.

Executive dashboards should reflect the full funnel because that is where real growth and real accountability live.

Enablement: Equip Teams With Tools, Training, And Clarity

Transformation does not succeed without people. Leaders must ensure their teams are not only equipped with AI-powered tools but also trained to use them effectively.

Equally important is clarity around strategy, data definitions, and success criteria.

AI will not replace talent, but it will dramatically increase the gap between enabled teams and everyone else.

Key Takeaways

  • Redefine success metrics: Move beyond vanity KPIs like MQLs and focus on impact metrics: pipeline velocity, deal conversion, and CAC efficiency.
  • Build AI-native workflows: Treat AI as a foundational layer in your GTM architecture, not a bolt-on feature to existing processes.
  • Align around the buyer: Use AI to unify siloed data and teams, delivering synchronized, context-rich engagement throughout the buyer journey.
  • Lead with purposeful change: C-level executives must shift from transactional growth to value-led transformation by investing in buyer intelligence, team enablement, and outcome-driven execution.

More Resources:


Featured Image: BestForBest/Shutterstock

Non-Profit Organization Announces Free Domain Names via @sejournal, @martinibuster

A non-profit organization that is supported by Cloudflare, GitHub, and other organizations has open-sourced domain names, making them available with no catches or hidden fees. The sponsor of the free domain names explains that their purpose is not to replace commercial domain names but to offer an open-source alternative for developers, students, and people who want to create a hobby site for free.

The goal is to encourage making the Internet a free and open space so that everyone can publish and express themselves online without financial barriers.

DigitalPlat

The open source domains are offered by DigitalPlat, a non-profit organization that’s sponsored by 1Password, The Hack Club (The Hack Foundation), twilio, GitHub and Cloudflare.

The Hack Foundation is a certified non-profit organization of high school students that receive support from hundreds of supporters including Google.org and Elon Musk. The organization was founded in 2016.

According to their website:

“In 2018, The Hack Foundation expanded to act as a nonprofit fiscal sponsor for Hack Clubs, hackathons, community organizations, and other for-good projects.

Today, hundreds of diverse groups ranging from a small town newspaper in Vermont to the largest high-school hackathon in Pennsylvania are fiscally sponsored by The Hack Foundation.”

A notice posted on The Hack Foundation donation web page explains their connection to DigitalPlat:

“The DigitalPlat Foundation is a global non-profit organization that supports open-source and community development while exploring innovative projects. All funds are supervised and managed by The Hack Foundation, and are strictly regulated in compliance with US IRS guidance and legal requirements under section 501(c)(3). “

DigitalPlat FreeDomain

The free domain names can be registered via DigitalPlat and the free domains project is open source, licensed under AGPL-3.0.

An announcement was made by the GitHubs Projects Community on X with a link to a GitHub page for the free domains where the following domain extensions are listed as choices:

  • .DPDNS.ORG
  • .US.KG
  • .QZZ.IO
  • .XX.KG

Technically, those are subdomains. But so are .uk.com domains.

The official GitHub page for the domains recommends using Cloudflare, FreeDNS by Afraid.org, or Hostry for managing the DNS for zero cost.

The .KG domain is from the country code of Kyrgyzstan. DPDNS.ORG is the domain name of DigitalPlat FreeDomain. .US.KG is operated by the DigitalPlat Foundation, a non-profit charitable organization that’s sponsored by The Hack Foundation.

The Open-Source Projects page for the free domains explains the purpose and goals of the free domain offers:

“The project is open source (licensed under AGPL-3.0), transparent, and backed by The Hack Foundation, a U.S. 501(c)(3) nonprofit. This isn’t a trial or a limited-time offer—it’s a sustainable effort to increase accessibility on the web.”

Full directions for registering a free domain name can be found here.

Featured Image by Shutterstock/TenPixels

GEO Tools for SMBs

AI-powered search is a new way for shoppers to discover products. ChatGPT, Perplexity, Claude, Gemini, and even AI Overviews answer shopping-related questions directly — no additional clicks required.

For brands, that’s a double-edged sword. The good news is the potential for additional exposure. The challenge is replacing organic search traffic (see the Semrush study) and surfacing the company and its products in those AI-generated answers.

A growing set of generative engine optimization (GEO) tools promises to fix this problem by measuring and improving how products and brands appear in the responses.

Few GEO platforms offer SKU-level capability — tracking and optimizing individual products in AI answers. Most focus on page-level optimization and citations, making it difficult to bulk update products with optimized content.

Nonetheless, I recently evaluated over a dozen of these GEO platforms to see which are viable for small and mid-sized businesses. Below are three recommendations with use cases, overviews, and limitations.

GEO Tools for SMBs

Writesonic

Writesonic

Writesonic focuses on product page optimization. It lets merchants rewrite and optimize (for genAI) individual product pages or articles, to then publish directly to Shopify, BigCommerce, or WordPress.

Here’s the workflow:

  1. Identify target pages. Manually select SKUs with poor organic search traffic, using Search Console, Shopify analytics, or other SEO tools.
  2. Analyze in Writesonic. Paste product page content into Writesonic or connect via API.
  3. Optimize with content metric. Edit the pages in real time with Writesonic’s Content Score metric.
  4. Update product pages. Export and publish optimized content, including metadata and formatting, to the ecommerce platform, keeping metadata and formatting intact.

Overview

  • Pricing: Tiered plans start at $49 per month.
  • Ease of use: Self-service, minimal learning curve.
  • Integrations: Direct with WordPress; export for Shopify and BigCommerce.
  • Content optimization: Strong, with rewrites of product pages and articles.

Limitations

  • Does not surface underperforming SKUs on its own.
  • No historical performance tracking.
  • No SKU-level competitive benchmarking.

Peec AI

Peec AI

Peec AI provides competitive benchmarking, showing merchants where their products and brands appear in AI-generated answers and how they compare to competitors. Peec AI doesn’t (yet) create or publish content, but its SKU-level gap analysis can guide optimization.

To use:

  • Identify visibility gaps. Track which prompts cite your brand and products, and those of competitors.
  • Analyze competitors. Monitor competitor product visibility at the SKU level for missed opportunities.
  • Export data. Pull CSV files (or link via API) to feed into your search engine, content, or analytics tools.
  • Refine on-page content. Update product pages in Shopify, BigCommerce, or other platforms, closing identified gaps.

Overview

  • Pricing: Tiered plans start at €89 per month ($103)
  • Ease of use: Simple dashboards; quick start.
  • Integrations: No direct cart integrations.
  • Content optimization: Monitoring only; no optimization tools.

Limitations

  • Does not optimize or publish product content.

Profound

Profound

Profound is primarily a measurement platform, monitoring how brands appear across AI-powered search engines. It doesn’t optimize or publish content, but it offers deep discovery and measurement capabilities that can inform SKU-level strategy.

To use:

  • Identify visibility gaps. Use Profound’s dashboards to track your products, categories, or brand in AI answers.
  • Analyze competitors. Benchmark against competitors to pinpoint missed opportunities and find high-impact prompts to target.
  • Surface related prompts. Filter by geography, category, or topic to find prompts that align with your products for potential conversions.
  • Use insights to optimize content. Export reports or integrate with analytics and SEO tools to guide on-site optimization.

Overview

  • Pricing: $499 per month with custom plans available.
  • Ease of use: Training required to interpret fully.
  • Integrations: No direct ecommerce cart integrations.
  • Content optimization: None. Focus is on measurement.

Limitations

  • Does not optimize or publish product content.

Getting Started

Merchants do not require expensive tools to improve genAI visibility. To start:

  • Audit your presence. Use free trials or affordable tools such as Peec AI to see how your products appear in AI answers.
  • Identify high-intent prompts. Ask the genAI platforms, “Identify the most common customer questions about [product/category] by analyzing Reddit, Quora, product reviews, support tickets, and forums.”
  • Start small. Pick a half-dozen products and categories to track monthly. Adjust and expand over time.

AI may produce first-time customers, but loyalty programs, email marketing, and standout service will bring them back.