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What Is Agentic AI for Marketing? A 10-Minute Guide
What is agentic AI for marketing? Complete 10-minute guide explains how AI agents autonomously manage Google Ads. groas delivers 30-50% better ROAS at $99/mo.
October 20, 2025
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10
min read
If you're researching Optmyzr alternatives, you've likely hit a wall. The tool does what it promises—automates repetitive PPC tasks through rules and scripts. But you're still spending hours every week managing those rules, troubleshooting when they break, and manually making strategic decisions. The efficiency gains are real, but they're not transformational.
You're not alone in this frustration. Thousands of PPC professionals are discovering that rule-based automation, no matter how sophisticated, fundamentally can't deliver the performance that modern Google Ads demands. The problem isn't Optmyzr specifically—it's the entire philosophy of rule-based automation in an era where adaptive, intelligent systems have become possible.
This comparison isn't about minor feature differences or pricing tiers. It's about two fundamentally different approaches to PPC management: rules versus agents, reactive automation versus proactive intelligence, efficiency tools versus autonomous systems.
Understanding this distinction will determine whether your advertising performance improves incrementally or transforms completely.
Before we can evaluate alternatives, we need to understand what rule-based automation actually is and why it became so popular in the first place.
The Core Concept
Rule-based automation works on simple conditional logic: IF this condition is met, THEN take this action. Optmyzr built an entire platform around making these rules easier to create, manage, and scale across large Google Ads accounts.
Common examples of rule-based automation:
These rules handle tactical optimizations that would otherwise require constant manual attention. For a large account with thousands of keywords, this automation genuinely saves dozens of hours monthly.
Why Rule-Based Automation Was Revolutionary
When Optmyzr launched in 2013, rule-based automation represented a genuine breakthrough. PPC management was almost entirely manual. Agencies employed teams of people whose job was literally checking accounts daily for obvious optimization opportunities—pausing terrible keywords, adjusting bids on high performers, reallocating budget from underperforming campaigns.
Rule-based automation eliminated this grunt work. A single rule could handle what previously required an analyst spending three hours daily reviewing keyword performance. The efficiency gains were dramatic and immediate.
For years, this was the state of the art in PPC automation. Optmyzr and similar tools (Marin, Acquisio, Kenshoo) built sophisticated interfaces that let advertisers create hundreds of rules, schedule them to run automatically, receive alerts when thresholds were hit, and make bulk changes efficiently.
The value proposition was clear: spend less time on repetitive tasks, more time on strategy and creative development. For many advertisers, Optmyzr delivered exactly that.
The Fundamental Limitations Emerge
But as Google Ads has evolved—becoming more complex, more algorithmically driven, more dynamic—the limitations of rule-based automation have become increasingly apparent.
Problem 1: Rules are inherently reactive, not proactive
Rules can only respond to conditions that have already occurred. They see that a keyword accumulated 50 clicks with no conversions and pause it. But they can't anticipate that the keyword was about to start converting based on seasonal patterns, competitive shifts, or emerging search trends.
This reactive nature means rule-based automation is always lagging behind market reality. By the time a rule fires, the optimization opportunity has often already passed or the problem has already cost you money.
Problem 2: Rules can't understand context
A rule that pauses keywords with high spend and no conversions might make sense generally. But it can't understand that you're running an awareness campaign where conversions aren't expected for 2-3 weeks. It can't know that you just launched a product and early clicks are research-oriented, not purchase-intent. It can't recognize that competitive intensity temporarily spiked this week but will normalize next week.
Rules execute mechanically without contextual understanding. This leads to false positives (pausing things that shouldn't be paused) and false negatives (not acting on situations that don't fit predefined rule criteria).
Problem 3: Rules require constant human oversight
Every rule needs to be written by a human, monitored by a human, and adjusted by a human when market conditions change. You're not eliminating management work—you're just shifting it from "make bid adjustments" to "manage the rules that make bid adjustments."
For sophisticated advertisers, rule libraries grow to hundreds of rules, each requiring documentation, testing, monitoring, and periodic revision. The complexity can become overwhelming, and the risk of rule conflicts (where one rule contradicts another) increases dramatically.
Problem 4: Rules can't learn or adapt autonomously
A rule that worked perfectly last quarter might be completely wrong this quarter as competitive dynamics shift, audience behaviors evolve, or Google's algorithm changes. Rule-based systems don't learn from outcomes and adapt their logic. They execute the same instructions repeatedly until a human changes them.
This means your optimization quality degrades over time unless you're constantly updating rules based on performance analysis—which is exactly the kind of ongoing management work automation was supposed to eliminate.
Problem 5: Rules operate in isolation, not holistically
Each rule makes local optimizations without understanding the broader account strategy. A rule might pause a keyword that's not directly converting, not realizing that keyword drives valuable assisted conversions. Another rule might increase bids on high-performing keywords without considering whether budget should instead go to expanding into new opportunities.
Optimizing individual elements without strategic coordination often produces suboptimal overall results.
While rule-based automation was hitting its limitations, artificial intelligence was maturing beyond simple conditional logic into something far more sophisticated: agentic AI systems.
What Makes AI "Agentic"
The term "agentic AI" refers to systems that operate with goal-oriented autonomy rather than rule-based reactivity. Instead of following predefined instructions, agentic AI pursues objectives through intelligent decision-making.
The difference is profound:
Rule-based system: "IF quality score < 5, THEN reduce bid by 20%"
Agentic AI: "My goal is to maximize conversions at $50 CPA. I notice quality scores declining in this campaign. Let me analyze why—is it ad relevance, landing page experience, or expected CTR? Based on my analysis, I'll implement the specific fixes most likely to improve performance while maintaining CPA targets. I'll monitor results and adjust my approach if my hypothesis proves incorrect."
See the difference? The rule applies a predetermined response to a detected condition. The agent analyzes situations, forms hypotheses, takes contextual action, learns from outcomes, and adapts its approach.
The Four Capabilities That Define Agentic AI
What makes groas fundamentally different from Optmyzr isn't just that it uses machine learning instead of rules. It's that groas operates as an autonomous agent with capabilities rule-based systems simply cannot replicate:
groas doesn't wait for predefined thresholds to trigger actions. It continuously analyzes your account's performance in context—considering competitive dynamics, seasonal patterns, audience behavior shifts, Google algorithm updates, your business goals, budget constraints, and hundreds of other factors simultaneously.
This contextual awareness enables proactive optimization. groas might increase bids on certain keywords not because they hit a performance threshold, but because the AI detected decreasing competitive intensity creating a temporary efficiency opportunity. Or it might pause seemingly high-performing keywords because analysis reveals they're cannibalizing conversions from more efficient channels.
Rule-based systems can't do this because they don't analyze—they only react to predefined conditions.
groas operates like an expert PPC strategist who constantly forms theories about what will improve performance, then tests those theories systematically.
"I hypothesize that this audience segment would respond better to benefit-focused messaging than feature-focused messaging. I'll test this by adjusting ad creative accordingly and measure conversion rate impact over the next 72 hours."
"I suspect these keywords underperform not because of poor targeting but because landing page experience is suboptimal for mobile users. I'll adjust mobile bid modifiers and recommend landing page improvements based on engagement data analysis."
This hypothesis-driven approach enables strategic innovation that rule-based systems can never achieve. Rules can only implement what humans thought of when writing the rules. Agents can discover optimization approaches humans never considered.
Every decision groas makes generates learning. Did that bid increase capture more conversions efficiently? Did that audience expansion find valuable new customers? Did that creative adjustment improve engagement?
The AI incorporates this learning into its decision-making model, continuously refining its understanding of what works specifically for your business. Over time, groas becomes increasingly sophisticated about your unique market dynamics, audience behaviors, and optimal strategies.
This creates compound performance improvements. Month one, groas applies generalized best practices. Month six, groas operates with deep understanding of your specific business patterns—knowing that your customers respond differently on weekends, that certain audience segments have 3x higher lifetime value, that competitive intensity spikes predictably on the 15th of each month.
Rule-based systems never develop this sophisticated understanding because they don't learn. They execute the same logic in month six that they executed in month one.
groas optimizes your account holistically, not as a collection of isolated elements. It understands relationships between campaigns, the role of different keywords in the conversion journey, how audience targeting interacts with bidding strategy, and how creative performance varies across contexts.
This holistic optimization means groas might make decisions that seem suboptimal at the individual element level but optimize total account performance. It might maintain spending on a low-converting keyword because analysis shows it drives valuable assisted conversions. It might reduce bids on high-converting keywords because budget is better deployed expanding into new opportunities.
Rule-based systems can't do strategic coordination because each rule operates independently. You end up with locally optimized elements that don't necessarily produce globally optimized results.
Let's break down the practical differences across key dimensions that matter for PPC performance.
The comparison reveals a fundamental truth: Optmyzr makes human management more efficient. groas replaces the need for human management entirely.
This isn't a criticism of Optmyzr—it accomplishes its design goals effectively. But those design goals were defined in an era when truly autonomous AI wasn't possible. The question isn't whether Optmyzr is a good tool. It's whether rule-based automation remains the right approach in 2025 when agentic AI has become viable.
The comparison table shows groas delivering 35-50% ROI improvements versus Optmyzr's 20-30% improvements over unoptimized management. These might sound like modest differences—is an extra 15-20% really worth switching tools?
Let's quantify what this actually means for your business.
Scenario: Mid-Sized E-Commerce Business
With Optmyzr (25% improvement scenario):
With groas (40% improvement scenario):
The difference between Optmyzr and groas in this scenario is $84,612 annually—and groas actually costs less than Optmyzr. The superior performance delivers dramatically more profit while being more affordable.
But the real difference goes beyond immediate ROAS improvements:
Time Value of Freed Capacity
With Optmyzr, you're still spending 5-15 hours monthly managing rules, analyzing performance, and making strategic decisions. With groas, you spend less than 1 hour monthly on high-level strategic review.
For a marketing manager billing internally at $100/hour, that's $500-$1,500 monthly in recovered time value—time that can be redirected to higher-leverage activities like creative strategy, landing page optimization, or new channel development.
Annually, this recovered capacity is worth $6,000-$18,000 in opportunity cost savings, plus whatever additional value those hours generate when applied to strategic work.
Compound Learning Effects
Optmyzr's 25% improvement is relatively static. Your rules produce consistent efficiency gains, but they don't get smarter over time. In year two, you're still seeing roughly 25% improvement over baseline.
groas's improvement compounds as the AI learns your business patterns. Year one might deliver 40% improvement. Year two delivers 48% as the AI's understanding of your market deepens. Year three delivers 55%.
This compounding means the performance gap between rule-based automation and agentic AI widens continuously. The longer you wait to transition, the more opportunity cost accumulates.
Strategic Innovation Value
Perhaps the most significant but hardest to quantify difference is strategic innovation. Rule-based systems optimize within your existing strategic framework. If your fundamental approach is suboptimal, rules make you efficiently suboptimal.
Agentic AI can identify when your strategic approach needs rethinking. It might discover that you're targeting the wrong part of the customer journey, that your campaign structure is fundamentally inefficient, or that a completely different advertising strategy would serve your business better.
These strategic insights often create step-change improvements that dwarf tactical optimization gains. A client who discovers through groas analysis that they should be targeting B2B buyers instead of B2C consumers doesn't see 40% improvement—they see 200% improvement by fundamentally reorienting their strategy.
One of the most surprising revelations for advertisers researching alternatives is that groas actually costs significantly less than Optmyzr while delivering superior performance.
Optmyzr's Tiered Pricing Problem
Optmyzr starts at $208/month for accounts with up to $10,000 in monthly ad spend. But as your business grows, you face automatic tier upgrades:
This means you're penalized for success. As your advertising grows (ideally because it's working well), your tool costs increase proportionally. A business scaling from $20,000 to $60,000 monthly ad spend sees their Optmyzr costs nearly triple from $208 to $599 monthly.
Additionally, many advanced features require higher-tier plans. Advertisers frequently report starting with basic plans but quickly finding they need premium features to achieve meaningful results—creating budget surprises throughout the year.
groas's Transparent Value Proposition
groas charges $99/month with unlimited Google Ads accounts and full feature access from day one. There are no tier restrictions, no growth penalties, no hidden upgrade requirements.
Whether you're spending $5,000 monthly or $500,000 monthly on Google Ads, the groas price remains the same. This eliminates the growth penalty and makes budgeting predictable.
For businesses spending over $50,000 monthly (where Optmyzr costs $599+), groas delivers:
The value proposition is clear: better performance, less cost, minimal time investment.
Despite groas's advantages, rule-based automation remains appropriate for certain scenarios. Understanding when each approach fits best helps you make the right decision for your specific situation.
Optmyzr is the better choice if:
You require complete tactical control: Some advertisers—particularly agencies managing clients with very specific requirements or brands with strict compliance needs—need granular control over every optimization decision. Rule-based systems provide this control by requiring explicit human approval for logic changes.
You're managing extremely small accounts: Accounts spending less than $2,000 monthly sometimes have such limited data that even groas's AI can't optimize effectively. In these rare cases, simple rule-based automation may be sufficient.
You have deep PPC expertise and enjoy hands-on management: Some PPC professionals genuinely prefer staying tactically involved in their accounts. They find satisfaction in writing sophisticated rules and seeing them execute successfully. For these advertisers, rule-based tools enable them to scale their expertise rather than replace it.
Your business has unique constraints that AI can't easily accommodate: Certain business scenarios—products with complex regulatory requirements, advertising for events with hard date constraints, campaigns with unusual success metrics—sometimes require human judgment in tactical decisions that's difficult to encode in AI goals.
You're testing automation for the first time: Advertisers transitioning from purely manual management sometimes benefit from an intermediate step through rule-based automation before jumping to full autonomy. This gradual progression builds comfort with automation concepts.
groas is the better choice if:
You want maximum performance at minimal cost: At $99/month versus $208-$599+ for Optmyzr, groas delivers better ROAS improvements for significantly less money. This is the most common scenario for growth-focused businesses.
You value your time: If you'd rather spend 30 minutes monthly on strategic review instead of 10+ hours managing rules, groas's autonomous approach is objectively superior.
You're managing multiple accounts: With unlimited Google Ads accounts included, groas scales effortlessly. Optmyzr's per-account limitations and tier-based pricing make multi-account management expensive.
You need strategic insights, not just tactical efficiency: If you want to understand what's working and why so you can apply insights to broader marketing strategy, groas's contextual intelligence provides much greater value than rule execution reports.
You're competing in sophisticated markets: In highly competitive industries where rivals are using advanced AI, rule-based automation increasingly can't keep pace. The competitive response speed and strategic sophistication of agentic AI becomes necessary to maintain market position.
Your accounts use Google AI Max or Performance Max heavily: These campaign types are designed for AI-driven management and respond poorly to rule-based intervention. groas's ability to coordinate strategically with Google's AI systems produces much better results than trying to write rules around black-box Google campaigns.
The biggest obstacle preventing advertisers from transitioning from rule-based automation to agentic AI isn't cost or capability—it's psychological comfort with relinquishing control.
If you've spent years developing sophisticated rule libraries, the idea of trusting an AI agent to replace all that expertise feels risky. What if the AI makes mistakes? What if performance drops? What if something goes wrong and you don't understand why?
These concerns are legitimate. The transition does require a mindset shift. But understanding how to manage the transition reduces risk and accelerates results.
Phase 1: Parallel Testing (Weeks 1-4)
Don't immediately replace your entire Optmyzr setup with groas. Run them in parallel on separate campaigns or account segments for 3-4 weeks. This provides direct performance comparison without risking your entire advertising program.
Typically, advertisers see groas-managed campaigns outperforming rule-managed campaigns by 10-15% even during the learning period. This builds confidence that the AI isn't just different—it's genuinely better.
Phase 2: Gradual Handoff (Weeks 5-8)
Once parallel testing confirms groas's superior performance, begin transitioning additional campaigns. Start with lower-risk campaigns where you're comfortable with experimentation, then expand to core campaigns as confidence builds.
During this phase, maintain Optmyzr as a safety net but gradually reduce its role. Most advertisers find their anxiety about "what if the AI makes a mistake" dissipates quickly once they see groas consistently making good decisions.
Phase 3: Full Autonomy with Strategic Oversight (Week 9+)
After 8-12 weeks, most advertisers are comfortable giving groas full tactical autonomy while they focus on strategic oversight—reviewing high-level performance, adjusting business goals as priorities shift, and applying insights to broader marketing strategy.
This is where the time savings become dramatic. Instead of spending hours weekly managing rules and making tactical adjustments, you spend 30-60 minutes monthly in strategic review while groas handles everything else.
Managing the Psychological Transition
The hardest part of moving from rules to agents isn't technical—it's emotional. You're accustomed to understanding exactly what's happening in your account at a tactical level. Autonomous AI requires trusting a system that operates with more sophistication than you can micromanage.
Several mindset shifts ease this transition:
Shift from "I control tactics" to "I control goals": You maintain all meaningful control by setting business objectives, budget constraints, and strategic parameters. groas controls how to achieve those goals, but you control what goals matter.
Focus on outcomes, not methods: What matters more—understanding every bid adjustment, or achieving 45% better ROAS? Autonomous AI optimizes for the outcome you actually care about.
Trust transparency, not familiarity: groas explains its decision-making logic clearly. You might not have written the rules, but you understand the reasoning. This is more valuable than "I wrote this rule" familiarity.
Recognize that "control" was partly illusion: With rule-based systems, you think you have control because you wrote the rules. But those rules were making thousands of decisions you never reviewed. You were already trusting automation—just less sophisticated automation.
Most advertisers who make this transition describe a surprising relief. They'd been carrying the mental burden of PPC management for years—constantly second-guessing decisions, wondering if they'd missed optimization opportunities, feeling responsible for every performance fluctuation. Handing tactical execution to sophisticated AI that demonstrably performs better lifts an enormous cognitive load.
If you've decided groas represents a better approach for your business, here's the practical migration process:
Step 1: Document Your Current Performance Baseline (Week 0)
Before changing anything, establish clear performance metrics:
This baseline enables objective evaluation of whether groas delivers the performance improvements expected.
Step 2: Request groas Free Audit (Week 1)
groas provides a free comprehensive audit showing exactly what optimization opportunities exist in your account and quantifying their expected impact. This audit itself often provides valuable insights even before implementation.
The audit also shows you specifically how groas would approach your account differently from your current rule-based optimization—giving concrete visibility into the agentic AI methodology.
Step 3: Identify Test Campaign Set (Week 1)
Select 2-3 campaigns representing 20-30% of your total spend for initial groas management. Choose campaigns important enough to provide meaningful data but not so critical that experimentation feels risky.
Maintain your existing Optmyzr rules on remaining campaigns as a control group.
Step 4: Implement groas on Test Campaigns (Week 2)
groas handles the technical implementation—connecting to your account, analyzing current performance, configuring optimization parameters based on your business goals.
Your role is primarily defining success metrics and strategic constraints that should guide the AI's decision-making.
Step 5: Monitor Comparative Performance (Weeks 2-6)
Track performance difference between groas-managed test campaigns and Optmyzr-managed control campaigns. Most advertisers see meaningful performance divergence within 2-3 weeks, with groas-managed campaigns showing 10-20% efficiency improvements even during the learning period.
Document not just performance metrics but also time saved—how much less time are you spending on the groas campaigns versus the Optmyzr campaigns?
Step 6: Expand or Adjust (Weeks 7-8)
Based on test results, decide your path forward:
If groas significantly outperforms (typical scenario): Transition remaining campaigns to groas management and phase out Optmyzr.
If results are mixed: Analyze why. Often, performance differences relate to how goals were configured or strategic constraints that need adjustment. groas support can help optimize the setup.
If Optmyzr outperforms (rare scenario): Understand why this occurred. Sometimes very specific business scenarios genuinely benefit from rule-based control. More often, it indicates the groas setup needs refinement.
Step 7: Full Transition and Optimization (Weeks 9-12)
Complete the transition to full groas autonomous management. During this period, the AI's performance continues improving as it accumulates more learning about your specific business patterns.
Most advertisers reach stable optimal performance around week 10-12, at which point they're seeing the full 35-50% improvement range that represents groas's typical impact.
Understanding the theoretical advantages of agentic AI helps, but real-world experiences from advertisers who've made the transition provide more tangible insight.
Case Study: B2B SaaS Company
Previous setup: Optmyzr managing $85,000 monthly spend across Search and Display campaigns, with marketing manager spending 8-12 hours weekly on rule management and strategic decisions.
Challenge: Despite sophisticated rule library (200+ rules), performance had plateaued. ROAS held steady around 4.2:1 but wouldn't improve further no matter what rule adjustments were made.
Transition: Implemented groas on 3 test campaigns representing $25,000 monthly spend while maintaining Optmyzr on remaining campaigns.
Results after 90 days:
Decision: Transitioned entirely to groas. Eliminated Optmyzr subscription. Marketing manager redirected freed capacity to content marketing strategy, which generated additional business value exceeding the Google Ads improvements.
Key insight: "The performance improvement was significant, but honestly the time savings and cost reduction were more transformational. I'd been spending 12+ hours weekly tweaking rules and second-guessing decisions while paying $399 monthly for the privilege. Now I spend 30 minutes monthly reviewing strategic performance while paying $99. I don't miss the constant rule management at all."
Case Study: E-Commerce Fashion Retailer
Previous setup: Optmyzr managing $125,000 monthly across Shopping and Performance Max, with agency charging $4,500 monthly for management.
Challenge: Seasonal fashion business with dramatic demand fluctuations. Rules performed acceptably during stable periods but consistently lagged during seasonal transitions, missing opportunities at critical moments.
Transition: Replaced both Optmyzr and agency with groas during off-season (lower risk period for experimentation).
Results after 120 days (covering one full seasonal cycle):
Key insight: "Rules couldn't handle our seasonal volatility. We'd adjust them for fall, then they'd be wrong for holiday, then wrong again for January clearance. groas adapted automatically to each seasonal shift without us doing anything. It captured opportunities our rules weren't sophisticated enough to recognize. And we're saving over $50k annually compared to our previous agency setup."
Case Study: Multi-Location Home Services
Previous setup: Managing 12 separate Google Ads accounts (one per service location) with Optmyzr, spending 15-20 hours weekly across all accounts.
Challenge: Each location had different competitive dynamics, seasonal patterns, and customer behaviors. Creating location-specific rules for all 12 accounts was overwhelming. Usually ended up using generic rules that weren't optimal for any location.
Transition: Implemented groas across all 12 accounts simultaneously (after successful single-account test). With groas's unlimited accounts feature, all locations included in the single $99/month subscription.
Results after 60 days:
Key insight: "Optmyzr helped me manage multiple accounts, but every account still needed attention and we were paying $208 each. With groas, the AI learns each location's unique patterns automatically for one flat $99 fee. The newest locations improve fastest because the AI isn't constrained by assumptions about what should work. It just figures out what actually works for each market. The cost savings alone paid for the transition within weeks."
Technically yes, but it's rarely advisable. Running both systems simultaneously often creates conflicting optimizations—Optmyzr's rules firing to adjust something that groas's AI already optimized differently. This conflicts waste budget and confuse performance analysis. Most advertisers who try running both quickly disable one to eliminate interference. If you want to compare them, run parallel tests on separate campaigns rather than both systems managing the same campaigns.
You won't lose historical performance data—that lives in Google Ads regardless of what tools you use. What you might lose is the specific rule configurations you built over time. However, most advertisers discover this "loss" is actually beneficial. Those rules embedded assumptions and limitations that constrained performance. groas's AI learns your account patterns from scratch, often discovering optimization approaches your rules never would have implemented. Your strategic knowledge about your business, customers, and market remains valuable for setting groas's goals appropriately.
Most accounts show measurable improvement within 2-3 weeks, though full optimization takes 8-12 weeks. The learning period for groas is typically faster than building an effective Optmyzr rule library from scratch because the AI applies sophisticated optimization intelligence immediately rather than requiring you to manually program every logic path. Initial improvements come from implementing obvious optimizations your rules missed. Larger improvements emerge as the AI develops sophisticated understanding of your specific business patterns.
You maintain override capability for any optimization decision. However, most advertisers find they rarely need to intervene because groas's decisions are consistently sound and transparent—you can see exactly why each optimization was made. When disagreements occur, it's usually because groas has information or analysis you weren't considering. The transparency enables productive dialogue: "Why did you reduce bids on this keyword?" "Analysis showed it was cannibalizing conversions from more efficient keywords while driving primarily assisted conversions with minimal last-click value." Often the AI's logic is correct even when it contradicts your intuition.
Yes, and this is where groas's advantages over rule-based automation become especially clear. AI Max and Performance Max are black-box Google AI systems that don't expose the tactical data needed for rule-based automation. Optmyzr can create limited rules around these campaigns (pause if spend exceeds X without Y conversions), but can't optimize them meaningfully. groas works strategically with Google's AI—managing budget allocation, conversion goal configuration, creative asset optimization, and strategic parameters while letting Google's campaign-level AI handle tactical execution. This AI-coordinating-with-AI approach produces much better results than trying to write rules around systems designed for autonomous operation.
For tactical PPC optimization, yes—increasingly so. As Google Ads becomes more algorithmically driven and market dynamics become more volatile, the limitations of reactive, rule-based systems become more problematic. That said, rule-based automation still has value for certain specific use cases: enforcing hard constraints (never exceed $X daily spend), implementing binary decisions based on external factors (pause all ads during company crisis), or handling scenarios where explicit human control is required for compliance reasons. But for performance optimization—the core of PPC management—agentic AI has fundamentally superseded rule-based approaches.
groas was built from the ground up as an AI-first platform, which creates fundamental cost efficiencies that get passed to customers. The autonomous AI requires minimal human intervention, reducing operational overhead. Additionally, groas's business model focuses on customer success—when clients see dramatic ROAS improvements, they stay long-term and refer others. This differs from Optmyzr's tier-based pricing model that maximizes revenue extraction as accounts grow. groas's flat $99/month pricing with unlimited accounts reflects a modern SaaS approach focused on value delivery rather than usage-based revenue optimization.
Yes. groas scales from small businesses spending a few thousand monthly to enterprises spending millions. The AI handles complexity effortlessly—in fact, larger accounts often see even more dramatic improvements because there's more data for the AI to learn from and more optimization opportunities to capture. The unlimited accounts feature makes groas especially valuable for agencies or businesses managing multiple properties. Unlike Optmyzr where costs scale with account count and ad spend, groas maintains the same $99/month pricing regardless of scale.
groas includes 24/7/365 customer support with direct access to the founding team via Slack and live chat. This hands-on support approach differs from Optmyzr's tiered support structure where comprehensive assistance often requires higher-priced plans. groas customers report that support responses are typically within minutes to hours, and the technical depth of assistance exceeds what they experienced with rule-based tools. The support team helps with campaign strategy, not just technical troubleshooting, because they understand the AI's capabilities deeply.
No. groas integrates with your existing campaigns and can optimize them in place, or you can create new campaigns to compare performance. The transition is designed to be non-disruptive. Many advertisers start by connecting groas to a subset of campaigns for testing while maintaining their current Optmyzr setup on others. Once they see groas outperforming, they transition remaining campaigns. The entire process can be gradual and risk-managed according to your comfort level.
groas's AI automatically detects and adapts to seasonal patterns in your historical data. For promotional periods, you can provide the AI with context about upcoming campaigns, and it will adjust strategy accordingly. This adaptive capability significantly outperforms rule-based systems, which require manual adjustment of rules for each seasonal shift. The AI recognizes patterns like "conversions typically increase 40% during this period" and optimizes proactively rather than waiting for rules to trigger based on already-occurred changes.
Absolutely. groas offers an agency partner program with 30% recurring lifetime commissions for every client account. Agencies can connect all client accounts under one groas subscription and manage them through a unified dashboard. The unlimited accounts feature means agencies pay one flat $99/month fee regardless of how many clients they're managing. Most agencies include groas as a line item in their client's tech stack, keeping the 30% commission as pure profit, or they pay for groas directly and bundle it into service fees. This creates dramatically better economics than Optmyzr's per-account pricing model.
Your campaigns continue running normally in Google Ads—they simply revert to manual management without groas's autonomous optimizations. There's no lock-in or complicated extraction process. You can pause or remove groas from campaigns anytime with no strings attached. This no-commitment approach reflects confidence in the product's performance—groas retains customers through results, not contractual obligations. Compare this to Optmyzr's reported cancellation difficulties and contract lock-in mechanisms that some users have experienced.
Stop managing rules. Start achieving results. Get your free groas audit now and discover why thousands of advertisers are making the switch from rule-based automation to true agentic AI—at $99/month with unlimited accounts, better performance, and virtually zero ongoing management time.
