Expert performance marketing workflows for PLG companies

Finds where extra spend stops paying off for every ChatGPT campaign and sets budgets where the next dollar works hardest.

Finds where extra spend stops paying off for every Google Display campaign and sets budgets where the next dollar works hardest.

Finds where extra spend stops paying off for every Google Search campaign and sets budgets where the next dollar works hardest.

Finds where extra spend stops paying off for every LinkedIn campaign and sets budgets where the next dollar works hardest.

Finds where extra spend stops paying off for every Meta (Facebook & Instagram) campaign and sets budgets where the next dollar works hardest.

Finds where extra spend stops paying off for every Microsoft (Bing) campaign and sets budgets where the next dollar works hardest.

Finds where extra spend stops paying off for every Reddit campaign and sets budgets where the next dollar works hardest.

Finds where extra spend stops paying off for every Snapchat campaign and sets budgets where the next dollar works hardest.

Finds where extra spend stops paying off for every TikTok campaign and sets budgets where the next dollar works hardest.

Finds where extra spend stops paying off for every X campaign and sets budgets where the next dollar works hardest.

Finds where extra spend stops paying off for every YouTube campaign and sets budgets where the next dollar works hardest.

Finds native automated rules, scripts and third-party tools that conflict with Roadway agents (both changing the same budgets or bids).

Uses an LLM to classify where your ads appeared on Google Display and YouTube (placements and videos), and finds brand-unsafe adjacency that keyword lists miss.

Audits whether every channel has server-side conversion coverage (CAPI, enhanced conversions, offline imports) with good match quality and deduplication.

Moves Google Display from optimizing to lead volume to optimizing to predicted pipeline or revenue value, using CRM and order data.

Moves Google Search from optimizing to lead volume to optimizing to predicted pipeline or revenue value, using CRM and order data.

Sends CRM pipeline and revenue values back to LinkedIn so results are measured on value, and recommends the switch to value-based bidding for a person to make in Campaign Manager once there is enough valued data.

Moves Meta (Facebook & Instagram) from optimizing to lead volume to optimizing to predicted pipeline or revenue value, using CRM and order data.

Moves Microsoft (Bing) from optimizing to lead volume to optimizing to predicted pipeline or revenue value, using CRM and order data.

Moves Snapchat from optimizing to lead volume to optimizing to predicted pipeline or revenue value, using CRM and order data.

Moves TikTok from optimizing to lead volume to optimizing to predicted pipeline or revenue value, using CRM and order data.

Moves YouTube from optimizing to lead volume to optimizing to predicted pipeline or revenue value, using CRM and order data.

Reads product events from PostHog to find the behaviors that predict upgrades, then uses the audiences you already sync from PostHog as targets, lookalike seeds and exclusions. Needs the PostHog MCP connected in Roadway.

For SaaS, sets channel spend limits from how fast each channel pays back its cost rather than a flat budget: channels that pay back fast earn more room.

Reconciles what each ad platform claims against CRM or order data, and produces a trust factor per channel for budgeting.

Watches competitor news for acquisitions, shutdowns and product sunsets, and launches switch campaigns on Search, Bing and LinkedIn for customers who suddenly have a reason to look.

Finds over-fragmented campaign structures that starve automated bidding of data, and proposes consolidated structures channel by channel.

Alerts when a platform's reported conversions start drifting away from CRM or backend truth, which usually means a tracking or modeling change.

Detects sudden auction pressure (CPC jumps, new competitors, overlap spikes) on search, and separates it from internal causes.

Detects when platform automation (broad match, Advantage+, optimized targeting, Audience Expansion) quietly shifts spend toward retargeting, existing customers or low-quality inventory.

Forecasts a launch's paid demand by channel, and sets the launch budget and its cap from comparable past launches.

Forecasts month-end and next-month results at several budget levels, so cap decisions are made with expected outcomes rather than gut feel.

A test-and-learn loop for ChatGPT ads: writes new titles and one-line copy from the winning angles, tests each against the current best with a fixed split, and retires the losers on cost per goal once there is enough data.

Measures how many Google Display conversions your ads truly drive and recalibrates targets with the result.

Measures how many Google Search conversions your ads truly drive and recalibrates targets with the result.

Measures how many LinkedIn conversions your ads truly drive, when the account has enough conversion volume for a matched-market test, and recalibrates targets with the result.

Measures how many Snapchat conversions your ads truly drive and recalibrates targets with the result.

Measures how many TikTok conversions your ads truly drive and recalibrates targets with the result.

Measures how many YouTube conversions your ads truly drive and recalibrates targets with the result.

Tags every ad across channels by angle, hook, format and offer, then finds which creative attributes drive performance across channels.

The performance marketing agent's weekly budget rebalance across every connected channel, based on where the next dollar works hardest and pipeline quality.

Restates every channel's cost per goal and return using impact-test results, so budget decisions are based on real results, not attributed ones.

Connects launch campaigns to feature adoption, activation and pipeline in product and CRM data, so launch spend is judged on business impact.

When a competitor raises prices, removes a plan or changes packaging, updates Google Search conquesting campaigns and copy to capture switchers.

When a competitor raises prices, removes a plan or changes packaging, updates LinkedIn conquesting campaigns and copy to capture switchers.

When a competitor raises prices, removes a plan or changes packaging, updates Meta (Facebook & Instagram) conquesting campaigns and copy to capture switchers.

When a competitor raises prices, removes a plan or changes packaging, updates Microsoft (Bing) conquesting campaigns and copy to capture switchers.

Checks every approved change against its measured impact a week later, and rolls back the ones that hurt, so bad changes never become the baseline.

Spins up paid campaigns on the queries behind your best organic pages, using rankings from the Semrush or Ahrefs MCP, so winners get more than their organic share. Add the Peec AI MCP to include pages that AI answers cite. Needs the Semrush or Ahrefs MCP connected in Roadway.

Uses what converts in ChatGPT ads (situations and questions) to find new search keywords. If the Semrush or Ahrefs MCP is connected, it also checks which situations have no page ranking yet and writes SEO briefs for them. Without it, it skips the briefs.

Takes proven Google Search keywords, themes and ad copy and rebuilds them as ChatGPT ads campaigns, targets and creative.

Watches LinkedIn for posts by employees, customers and creators that mention you, and drafts Thought Leader Ad campaigns around the best ones, ready to approve.

Reviews what every coworker did, what it changed and what happened (Task History plus action-impact memory), and proposes the next workflows worth automating, or the ones to retire.

Uses native split tests to prove whether a different Google Display bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different Google Search bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different LinkedIn bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different Meta (Facebook & Instagram) bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different Microsoft (Bing) bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different Snapchat bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different TikTok bid strategy or target actually beats the current one.

Uses native split tests to prove whether a different YouTube bid strategy or target actually beats the current one.

Decides how much to spend on brand search by testing whether brand ads are incremental when you already rank first organically and competitors are absent.

Spots keywords, ads and audiences that break out or stall, and proposes the next targeting, creative or landing page test, with drafts built in Figma, Canva or Runway, or your page builder. Needs a page builder MCP such as Webflow, Framer, Lovable or Ploy, and an image or video MCP such as Figma, Canva, Runway or Google Veo, connected in Roadway.

An opt-in workflow that watches competitor status pages and, during a major outage, proposes a surge of ChatGPT spend on switch-intent audiences with pre-approved copy. Every surge waits for your approval, even if auto-approve is on.

An opt-in workflow that watches competitor status pages and, during a major outage, proposes a surge of Google Search spend on switch-intent audiences with pre-approved copy. Every surge waits for your approval, even if auto-approve is on.

An opt-in workflow that watches competitor status pages and, during a major outage, proposes a surge of Microsoft (Bing) spend on switch-intent audiences with pre-approved copy. Every surge waits for your approval, even if auto-approve is on.

Monitors competitors in the ad libraries (Google Ads Transparency Center, Meta, LinkedIn, X) and through web search, and drafts counter-positioning copy for every channel when their messaging shifts.