A practical library for marketers who want to build systems, not one-off campaigns. Browse the featured work, then use the playbook use cases to turn AI into repeatable marketing workflows.
A complete library of case studies covering GTM strategy, paid media, AI systems, ecommerce, demand generation, and revenue growth.
How I built a clean skincare brand from scratch — product strategy, Shopify, Amazon, paid media, and everything in between.
Most B2B teams use AI tools. Few use them as a system. Here’s how I build GTM stacks that actually compound results.
A premium outdoor brand. A new e-commerce category. A $3.75M budget. Six months to hit $15M. The full playbook inside.
How I transformed a manual Google Ads program into an AI-powered acquisition engine — increasing conversions, improving ROAS, and reducing manual workload by 60%.
How I transformed LinkedIn from a tactical ad channel into a full-funnel revenue engine — connecting awareness, ABM, lifecycle, and event marketing into one predictable pipeline system.
A CRM implementation company needed more than a new name. I rebuilt their brand strategy, go-to-market motion, and digital demand engine — driving $6.2M in revenue contribution within 12 months.
How I designed and executed a 3-tier ABM program targeting 471 enterprise accounts — generating a $2.5M pipeline, 4x ROI, and 161 SQLs in 8 months on a $500K budget.
The complete go-to-market strategy for launching a new AI feature into the omni-channel CX platform — targeting enterprise communication and consumer technology companies with SEO, LinkedIn, display, and lifecycle programs designed to generate $24M in pipeline.
How I designed a complete demand generation engine for a logistics SaaS company transitioning from a marketplace to a software platform — $5M ARR in 6 months, 834 new customers, $1.25M budget, $500–$1K CAC.
All playbook use cases from the Claude for Marketers PDF, organized as reusable marketing workflows for strategy, content, SEO, paid media, reporting, operations, and AI agents.
A beginner-friendly setup workflow for marketers who are new to Claude. It explains how to choose the right model, organize work inside Projects, and give Claude the context it needs so outputs are useful from the first draft.
A simple prompt structure that helps people get better answers without needing to be AI experts. It walks through role, context, task, format, and examples so the AI knows what good output should look like.
A workflow for turning a topic or campaign idea into structured content, such as blog posts, landing pages, case studies, product copy, and email copy. It helps people move from messy ideas to useful drafts faster.
A workflow for organizing SEO ideas by search intent, content format, and funnel stage. It helps non-SEO specialists understand what content to create and how each piece should support the larger topic cluster.
A workflow for turning one core message into platform-specific social content. It helps people create hooks, post angles, captions, calendars, and repurposed formats without starting from scratch every time.
A workflow for writing and improving emails across the customer journey, including nurture sequences, onboarding, win-back, product education, and transactional emails.
A workflow for making sense of interviews, surveys, reviews, CRM notes, and sales conversations. It turns messy qualitative inputs into themes, objections, buyer language, and campaign insights.
A workflow for developing paid media angles, ad copy, and creative briefs across Google, LinkedIn, Meta, retargeting, and campaign experiments.
A workflow for documenting how a brand should sound and then using that guidance to make future content more consistent.
A workflow for turning raw metrics into a clear story. It helps marketers explain what happened, why it matters, and what action should happen next.
A workflow for preparing strategic communication for leadership, board meetings, QBRs, budget reviews, positioning decisions, and campaign pre-mortems.
A workflow for turning one marketing input into several useful outputs. It is designed for repeatable production work such as webinars, sales notes, reviews, and landing page reviews.
A workflow for marketing operations and technical marketers who want to audit data, QA assets, clean up campaign tracking, or turn manual tasks into automation briefs.
A workflow for understanding when a task should become an AI agent instead of staying as a normal prompt. It explains inputs, outputs, approval gates, and boundaries.
A workflow for building simple AI-powered automations without heavy engineering. It uses tools like Claude Projects, Skills, Zapier, Make, or n8n.
A workflow for teams ready to connect AI to systems through APIs, SDKs, files, or MCP-enabled tools. It focuses on guardrails, testing, logs, and safe execution.
A workflow for keeping AI agents reliable once more than one workflow is running. It covers ownership, QA, performance review, approval rules, and risk controls.
A workflow for reviewing ad performance and finding better tests. It helps separate surface metrics like CTR from business metrics like SQLs, opportunities, pipeline, and CAC.
A workflow for monitoring competitor positioning, campaigns, pricing, content, product updates, and market signals, then turning those observations into marketing actions.
A workflow for identifying manual marketing operations tasks and deciding what should be automated. It covers reporting pulls, UTM creation, form QA, routing, list uploads, CRM hygiene, and process docs.
A workflow for improving onboarding, nurture, reactivation, renewal, and customer education emails using performance data and buyer intent.
A workflow for creating consistent weekly or monthly performance updates that explain the story behind the data and the decisions that should follow.
A workflow for building ICPs and personas from evidence instead of assumptions. It connects firmographics, win/loss patterns, behavior, ACV, pain intensity, and sales feedback.
A practical reference workflow for choosing the right prompt formula, tool, or resource based on the task someone is trying to complete.
It started with a single conversation. A founder reached out with a vision for a clean, ingredient-first skincare brand — no filler, no fluff, just products that actually worked. What she didn’t have was a go-to-market strategy, a storefront, or a system to turn interest into revenue.
That’s where I came in.
The clean beauty space is crowded. Every brand claims to be natural, honest, and effective. The real challenge wasn’t building a product — it was building a brand that people would trust, search for, and buy from repeatedly. We needed to move fast, spend smart, and build infrastructure that would compound over time.
Before anything went live, I led a deep consumer and competitor analysis to identify high-demand SKUs with room to win. We launched a focused product lineup designed around real search intent and underserved customer needs — and the result was a 3x ROAS at launch.
I directed the build of a Shopify eCommerce storefront optimized for conversion, mobile UX, and SEO from day one. Every element — from product page structure to checkout flow — was built to reduce friction and increase average order value.
I built the full martech stack: GA4, Klaviyo, Shopify Analytics, Hotjar, Meta Ads, and Google Ads — with attribution dashboards for full-funnel visibility. No guessing. Every dollar tracked to an outcome.
I implemented automated email and SMS flows in Klaviyo — welcome sequences, cart recovery, post-purchase, and winback. These flows ran 24/7, converting engaged visitors into repeat customers without additional ad spend.
I led paid search, shopping, and social campaigns with rigorous audience segmentation and creative testing. The result: 4.2x blended ROAS across all channels — a number that held as we scaled.
I launched and scaled an Amazon storefront — managing listings, A+ content, PPC, and SEO — while aligning teams across design, supply chain, and customer experience.
By implementing AI-driven tools across GTM workflows, I reduced manual work by 60% — freeing the team to focus on strategy, creative, and growth.
What started as a single conversation became a functioning, profitable DTC brand with a multi-channel presence, a loyal customer base, and systems built to scale. The numbers tell the story:
The best marketing doesn’t start with a campaign. It starts with a conversation, a clear ICP, and the infrastructure to turn attention into revenue. That’s what I built here — and what I build for every brand I work with.
Most B2B marketing teams are using AI tools. They’re using ChatGPT to write emails, a tool to scrape LinkedIn, maybe a sequences platform to send outbound. But they’re not using AI as a system.
The difference is everything. Individual tools save hours. A connected AI-powered GTM stack changes how the entire revenue engine operates — reducing CAC, improving conversion, and giving you leverage that compounds over time.
Here’s how I build it.
The stack starts with signal. I wire together tools like 6sense, Bombora, and Clearbit to surface in-market accounts — companies actively researching problems your product solves. Instead of spraying outbound at a static list, the system surfaces the right accounts at the right time. Outbound stops being a volume game and becomes a precision game.
Once a target account is surfaced, Clay runs automated enrichment — pulling company data, contact info, recent news, job postings, tech stack — and passes it to an AI layer that writes personalized first-line openers for every prospect. What used to take an SDR an hour per account now takes seconds, and the quality is higher because it’s actually researched.
Personalized outreach goes out across email, LinkedIn, and sometimes direct mail — sequenced and throttled based on engagement signals. The AI monitors reply patterns, adjusts send times, and flags hot leads for immediate SDR follow-up. Reply rates increased 2.5x compared to generic sequencing.
On the inbound side, I implement AI-powered lead scoring that connects website behaviour, content engagement, and CRM data to surface MQL-ready leads automatically. No more manual scoring or arbitrary thresholds. The system learns what a real buyer looks like and routes accordingly.
The final layer is measurement. I build dashboards in HubSpot and Looker that connect every touchpoint — from first ad impression to closed revenue — so the team can see exactly which GTM motions are working and double down in real time. MQL acquisition cost dropped to $92 with full-funnel visibility.
Zapier, Make, and Clay connect the tools together — so when a lead hits a score threshold, they’re automatically enrolled in the right sequence, routed to the right rep, and added to the right nurture track. Manual work dropped by 60%. The team stopped doing data entry and started doing strategy.
AI in marketing isn’t about replacing people. It’s about removing the low-value work that keeps your team from doing the high-value work. When you build AI into the architecture of your GTM — not just bolt it onto the side — you get leverage that compounds every quarter.
If your team is still spending hours on research, data entry, and manual sequencing, you don’t have an AI problem. You have a systems problem. Let’s fix it.
A premium, high-ticket outdoor living product. A brand new e-commerce category with no established playbook. A $3.75M budget. Six months to hit $15M in year-one revenue. Most teams would call that impossible. I called it a strategy problem.
Launching a high-ticket product into a category that doesn’t yet exist in the consumer’s mind is one of the hardest marketing challenges there is. You can’t rely on search demand that isn’t there yet. You can’t retarget customers who’ve never heard of you. You have to build the category and the brand simultaneously — while converting enough revenue to justify the spend.
The pressure was real. The window was short. And every dollar had to work.
Before a single dollar was spent, I conducted a deep market sizing and competitive analysis. I identified adjacent categories where buyers already existed — premium patio, outdoor entertaining, luxury home improvement — and mapped the demand signals that would allow us to intercept buyers before competitors could.
I defined three distinct buyer segments with different price sensitivities, purchase timelines, and channel preferences. Each segment got its own messaging framework, creative direction, and funnel strategy. This wasn’t one launch — it was three, running in parallel.
With a $3.75M budget and a 4x ROAS target, every allocation decision was a trade-off. I built a channel model that weighted paid search, paid social, programmatic, and content based on funnel stage and buyer segment — then stress-tested it against historical benchmarks from comparable high-ticket launches.
The result: a phased budget plan that front-loaded awareness in months 1–2, shifted to conversion in months 3–4, and optimized for retention and LTV in months 5–6.
High-ticket products don’t sell on specs. They sell on aspiration, trust, and social proof. I directed a creative strategy built around three pillars: lifestyle imagery that placed the product in the customer’s world, third-party validation from influencers and press, and comparison content that anchored value against premium alternatives.
I designed the full funnel: awareness content to build category demand, consideration touchpoints to educate and qualify, and conversion assets — landing pages, email sequences, retargeting flows — optimized to close high-intent buyers. Every stage was connected to attribution so we could see exactly where revenue was coming from.
The 6-month launch was executed in three phases, each with its own KPIs, creative refresh schedule, and optimization cadence. Weekly performance reviews kept spend efficient and creative fresh. Monthly strategy checkpoints allowed us to shift budget toward what was working in real time.
The strategy delivered a clear, executable path to $15M in year-one revenue within the $3.75M budget envelope — achieving the 4x ROAS target through disciplined channel management, creative testing, and funnel optimization.
A $15M launch doesn’t start with a campaign. It starts with a market map, a buyer model, and a budget framework that ties every dollar to an outcome. That’s the work that happens before anything goes live — and it’s the work most teams skip.
If you’re planning a major launch and want a strategy built to hit real numbers, let’s talk.
The organization relied heavily on Google Ads to drive demand but struggled with inefficient bidding strategies and inconsistent performance across campaigns. High cost per acquisition from manual bidding, limited ability to scale, and marketing teams spending significant time on manual optimizations were holding growth back. Leadership needed a scalable approach to increase conversion volume while improving return on ad spend.
The goal was to transform the Google Ads program into an AI-driven acquisition engine — one capable of increasing conversion volume and revenue, scaling campaigns without sacrificing efficiency, identifying new high-intent search opportunities, and reducing manual workload. The broader shift was from reactive bid management to predictive, AI-powered revenue optimization.
I implemented a Google Smart Bidding framework powered by machine learning and auction-time optimization, built around three pillars:
Adopted automated bidding strategies including Target CPA, Target ROAS, Maximize Conversions, and Maximize Conversion Value — allowing AI algorithms to predict which searches are most likely to convert and adjust bids accordingly in real time.
Instead of optimizing for conversion volume alone, campaigns were aligned with conversion value and revenue impact. Value-based bidding prioritized higher-value customers and maximized total revenue generated within campaign budgets.
To unlock additional demand, I introduced Smart Bidding Exploration — an advanced feature designed to discover new high-performing search opportunities. The AI identified and bid more aggressively on search queries likely to generate incremental conversions, expanding reach across more search categories that traditional keyword targeting missed.
We transitioned campaigns from manual bidding to Smart Bidding strategies. Key steps included implementing accurate conversion tracking, aligning campaigns with specific business goals, and assigning bidding strategies at both campaign and portfolio levels — enabling Google’s algorithms to analyze large datasets and make bid adjustments in every auction.
I introduced portfolio bidding strategies that optimized performance across multiple campaigns simultaneously — enabling better budget allocation toward high-performing campaigns, centralized performance management, and faster optimization at scale.
After stabilizing campaign performance, we activated Smart Bidding Exploration to expand reach — capturing new long-tail search queries, increasing traffic diversity across search categories, and identifying incremental conversion opportunities without manually expanding keyword lists.
We evaluated performance using a business-driven scorecard: conversion volume, conversion value, ROAS, CPA, and traffic diversity across search categories. Traffic diversity and incremental conversion volume became key indicators of Smart Bidding Exploration success.
The company needed to transform paid acquisition from a tactical lead-generation activity into a predictable pipeline and revenue engine. Despite running paid campaigns, marketing struggled with inconsistent pipeline contribution from paid channels, low conversion from engagement to sales opportunities, limited visibility into high-intent buying signals, and fragmented demand generation across awareness, acquisition, and lifecycle marketing.
Design and implement a full-funnel LinkedIn demand engine capable of generating a consistent enterprise pipeline, expanding into new market segments, accelerating deal velocity, and strengthening lifecycle engagement with customers. The goal was not just more leads — but a predictable, high-quality pipeline tied to revenue outcomes.
I designed a full-funnel LinkedIn demand architecture aligned with the entire customer lifecycle, focused on eight business outcomes: pipeline and revenue growth, market expansion, customer lifecycle engagement, audience insights, event pipeline acceleration, brand authority, industry moment marketing, and talent brand amplification. Rather than isolated campaigns, the strategy integrated awareness, demand capture, nurture, and expansion programs into a single revenue engine.
I built a scalable acquisition engine to convert LinkedIn traffic into qualified pipeline — promoting high-value assets like research reports, product education content, and case studies; directing traffic to conversion-optimized landing pages; running continuous A/B testing on messaging, creative, and CTAs; and expanding reach using the LinkedIn Audience Network.
To prioritize revenue impact over lead volume, I implemented an ABM-driven LinkedIn strategy: building custom audiences using CRM account lists, targeting buying groups within high-value companies, deploying LinkedIn Lead Gen Forms to reduce friction, and leveraging predictive audiences to identify lookalike prospects. This shifted performance from lead volume to high-value pipeline creation.
A significant portion of prospects engaged with content but were not converting into opportunities. I built a structured retargeting and nurture architecture: retargeting website visitors and video viewers, re-engaging leads who interacted with Lead Gen Forms, targeting CRM segments like open opportunities and stalled deals, and re-engaging closed-lost accounts with new value propositions.
LinkedIn was used as a strategic tool to validate and penetrate new markets through industry-specific campaigns tailored to vertical use cases, persona-based targeting by job function and seniority, and campaign localization for geographic expansion.
I expanded LinkedIn into lifecycle marketing to drive retention and expansion — running onboarding campaigns to accelerate product adoption, feature education campaigns based on usage signals, cross-sell and upsell programs targeting buying groups, and renewal campaigns for contracts approaching expiration.
Events and webinars were integrated into the demand engine across three phases: pre-event registration and speaker promotion; during-event LinkedIn Live and real-time engagement; and post-event on-demand promotion, attendee retargeting, and nurture campaigns for no-shows. This transformed events from awareness plays into pipeline-generating programs.
I launched a thought leadership amplification strategy promoting proprietary research and industry insights, amplifying executive voices and subject matter experts, highlighting customer success stories, and partnering with industry influencers — positioning the company as a trusted authority within its category.
A CRM implementation firm came to me with a brand that no longer reflected where the business had grown. Their positioning was outdated, their website was underperforming, and their go-to-market motion was reactive rather than strategic. They were winning deals on relationships alone — but had no scalable system to generate, nurture, or convert pipeline at volume. As their incoming Marketing Director, my mandate was clear: rebuild everything.
The core challenge was not just a rebrand — it was a full commercial transformation. The company needed a new brand identity that matched its evolved capabilities, a GTM strategy that could reach new segments at scale, and a digital demand engine that would generate qualified pipeline independent of referrals. All of this had to be done without disrupting existing client relationships or the sales team’s momentum.
Before touching a single asset, I led a structured discovery process. I interviewed leadership, customers, and frontline staff to understand what the brand truly stood for versus how it was being perceived externally. The gap was significant — internally the team saw themselves as a strategic transformation partner; externally they were being positioned as a commodity vendor. That gap became the brief.
I benchmarked against category leaders and adjacent competitors to identify white space in positioning. I facilitated internal alignment workshops to ensure that every stakeholder — from the CEO to the frontline — understood and could articulate the new brand story. I then developed the full brand architecture: a new visual identity, messaging framework, persona-specific value propositions, and a content strategy that reflected the new positioning across every channel.
The website was rebuilt with a conversion-first architecture. Key improvements included funnel-mapped content strategy (TOFU through BOFU), SEO-optimized page structure aligned with target keywords, conversion rate optimization through redesigned CTAs, landing pages, and interactive elements, and full analytics and attribution tracking to connect web performance directly to pipeline.
I built a keyword and content strategy using SEMrush, organizing content around each core solution and audience segment. Content was developed with a consistent voice — authoritative, practical, and peer-to-peer — to build credibility with technical and executive buyers alike.
Segmented email programs were set up for each stage of the funnel, with personalized CTAs and nurture sequences mapped to buyer intent. Engagement-triggered automations replaced batch-and-blast sending, dramatically improving open and conversion rates.
I designed a LinkedIn outreach and paid ABM program targeting buying groups by job function, seniority, and vertical. Funnel stages were mapped to LinkedIn campaign types — awareness through Thought Leadership Ads, consideration through Lead Gen Forms, and conversion through retargeting. Sales and business development were aligned on a coordinated outreach playbook, with webinars and gated assets serving as the primary conversion mechanism.
The GTM strategy was built around a structured checklist framework covering: market research and ICP definition, a clear and differentiated value proposition, segment-specific messaging and positioning, a pricing and business model review, optimized sales and distribution channels, a phased launch plan with pre-launch pipeline-building activities, a customer journey map from awareness to advocacy, and a KPI framework connecting marketing investment to revenue outcomes.
The rebrand launch was executed as a coordinated campaign — retargeting existing website visitors and past clients, LinkedIn and search ads announcing the new positioning, a PR push across industry channels, and sales team enablement so every conversation reinforced the new brand story.
The enterprise sales team needed to break into a net-new segment of Fortune 500 consumer technology companies with no prior brand engagement. The goal was ambitious: generate a $2.4M+ qualified pipeline within 8 months on a $500K budget — a 4x ROI target — using a full-funnel, paid media-centric ABM strategy. There were no warm leads, no existing relationships, and no prior campaign data to optimize from. Everything had to be built from zero.
Rather than running a single campaign, I designed a tiered ABM model that matched investment intensity to account value. The program targeted 471 accounts across three tiers, each with a distinct execution strategy and content approach.
Account selection used a 3-part qualification framework: firmographic fit (enterprise organizations in the consumer technology sector, segmented by geography and revenue threshold), historical tech stack alignment (companies with known usage or interest in legacy platforms and AI readiness signals), and intent data from Bombora topic surge scoring, filtering for companies actively researching relevant topics above engagement benchmarks.
I launched awareness campaigns across LinkedIn Ads and Google Display targeting 471 accounts. Persona-based LinkedIn targeting was matched to the ABM account list using intent and enrichment data. Landing pages were tailored to each department’s pain points. After 3 weeks of testing, I switched from gated to ungated TOFU content — which delivered a 70% increase in reach and higher-intent engagement signals entering the mid-funnel.
After analyzing intent keywords from TOFU-engaged leads, I identified two distinct buying patterns that required separate nurture streams. Intent Cluster 1 targeted accounts researching customer satisfaction and CX metrics — served with case study content demonstrating measurable CSAT improvement. Intent Cluster 2 targeted accounts focused on cost reduction and contact center efficiency — served with content proving 30% support cost reduction through AI automation. Running two tailored MOFU streams instead of one generic nurture program doubled MQL-to-SQL conversion rate versus the baseline.
BOFU campaigns drove Discovery and Demo Calls through Google Display and LinkedIn, combining carousel and image ads with warm LinkedIn Message Ads from the sales team. A risk-free pilot offer was introduced for qualified leads, which eliminated evaluation friction and contributed to a 30–40% lift in MQL-to-SQL conversion. HubSpot email sequencing was layered on top for accounts that engaged with BOFU content but hadn’t yet booked a call.
The 3-tier model produced differentiated results across account segments. Tier 1 (223 accounts, 1:1 personalization) generated 91 SQLs through high-touch HubSpot sequencing, SDR/AE pods, 6sense intent signals, and personalized LinkedIn retargeting. Tier 2 (391 accounts, 1:few) generated 63 SQLs using segmented intent-based MOFU content and multi-format LinkedIn campaigns. Tier 3 (471 accounts, 1:many) generated 7 SQLs through ungated TOFU content, Google Display, and low-touch automated email cadences.
I established a dedicated ABM pod with weekly syncs and shared KPIs across Sales, Marketing, Product, and Customer Success. Sales co-developed the ICP and deal strategy, personalized outreach to engaged contacts, and used campaign engagement data to prioritize outreach sequencing. Product delivered technical validation and demos. Customer Success provided post-sale performance metrics that became proof points in MOFU and BOFU content. Marketing owned HubSpot sequencing, campaign execution, and targeting across intent platforms.
Intent & Targeting: 6sense, Bombora. Contact Data: ZoomInfo. Marketing Automation: HubSpot (email sequences, landing pages). Content Personalization: Hotjar (CRO testing). Sales Enablement: Salesloft, HubSpot, LinkedIn Messaging. Analytics & Attribution: Salesforce, HubSpot, GA4.
An established omni-channel customer experience platform — already trusted by global enterprise brands for AI-powered support across chat, voice, social, SMS, email, and self-service — is launching a significant new capability: a Predictive AI Resolution Engine. This feature uses generative AI and real-time intent modeling to resolve customer issues before they escalate, reduce handle time by up to 40%, and improve CSAT scores at scale without adding headcount.
The launch targets two high-value enterprise verticals: Communication companies (large-scale platforms managing hundreds of millions of user interactions daily) and Consumer Technology companies (premium hardware and connected device brands managing global post-purchase support at scale). Both verticals are under intense pressure to reduce support costs, improve first-contact resolution, and deliver seamless experiences across every channel their customers use.
This is not a brand-new product launch. It is the strategic expansion of a proven platform — which means the demand strategy leverages existing brand authority, existing customer proof points, and existing SEO equity to dramatically reduce cost of acquisition on the new feature while accelerating pipeline velocity.
The $6M budget is allocated across four investment pillars, each mapped to a specific funnel function and revenue outcome:
Based on benchmarked performance data from comparable enterprise AI-CX campaigns:
The SEO strategy is built around owning three content clusters that map directly to the buying journey of decision-makers in both target verticals. Rather than chasing generic keywords, every piece of content is designed to rank for high-intent queries that indicate an active evaluation of AI-powered CX solutions.
Cluster 1 — AI Customer Experience (Awareness / TOFU)
Cluster 2 — Omni-Channel CX Evaluation (MOFU / Consideration)
Cluster 3 — Vertical-Specific Intent (BOFU / Decision)
Pillar Page 1 — The Complete Guide to AI-Powered Omni-Channel CX
Target keyword: "ai-powered customer experience platform" | Landing page + gated research report | TOFU entry point for both verticals.
Pillar Page 2 — How AI Reduces Customer Handle Time by 40% (Without Replacing Agents)
Target keyword: "reduce customer handle time ai" | MOFU conversion page with embedded ROI calculator | Primary landing page for LinkedIn and display retargeting.
Pillar Page 3 — The AI CX Playbook for Consumer Technology Brands
Target keyword: "ai cx platform for consumer technology" | Vertical-specific landing page with Dyson-persona messaging | Gated whitepaper download triggering SDR sequence.
Pillar Page 4 — How Communication Platforms Are Using AI to Scale Support at 100M+ Interactions
Target keyword: "omnichannel cx for communication platforms" | Vertical-specific landing page for communication company persona | Case study + demo CTA.
Pillar Page 5 — AI CX ROI Calculator: What Does $1 in AI Support Technology Return?
Target keyword: "ai cx roi calculator enterprise" | Interactive tool page | BOFU conversion asset used across all paid channels.
LinkedIn campaigns are organized across two vertical audiences with distinct persona targeting:
Vertical A — Communication Companies
Function: Customer Operations, Product, Trust & Safety, Engineering
Title: VP/SVP Customer Experience, Director of CX Operations, Head of Trust & Safety, Chief Product Officer
Company Size: 10,000+ employees
Industries: Internet, Computer Software, Social Media
Vertical B — Consumer Technology Companies
Function: Customer Service, Operations, Digital Transformation
Title: VP Customer Experience, Director of Customer Support, Head of Digital CX, SVP Operations
Company Size: 5,000+ employees
Industries: Consumer Electronics, Hardware, Home Appliances
Campaign: "AI Is Changing What Great CX Looks Like"
Intent Cluster A — Handle Time & Cost Reduction
Intent Cluster B — CSAT & Loyalty
Display campaigns run across Google Display Network and programmatic DSPs using 6sense audience segments to target in-market accounts actively researching AI CX, contact center automation, and omnichannel support technology. Intent keyword triggers include: "omnichannel contact center," "AI customer support software," "reduce cost per contact," "CSAT improvement tools," "contact center automation," and "customer experience AI platform."
All inbound leads from SEO, LinkedIn, and display enter a vertically-segmented nurture track in HubSpot. Consumer technology leads receive a 6-email sequence over 14 days featuring product efficiency content, handle time benchmarks, and a CSAT ROI calculator. Communication company leads receive a separate sequence focused on scale, trust & safety integration, and social moderation AI capabilities.
Lead scoring triggers SDR outreach at a threshold of 45 points. HubSpot sequences are integrated with Sales Navigator to enable one-click LinkedIn connection + message for high-intent leads. SDRs receive a weekly intent digest pulled from 6sense and Bombora showing which target accounts have surged on AI CX topics — these accounts are immediately promoted to Tier 1 outreach regardless of form fill status.
Three hosted virtual executive briefings per quarter — one per vertical plus one cross-vertical AI CX innovation summit. Each briefing is limited to 15 enterprise attendees and features a live product demo of the Predictive AI Resolution Engine, an interactive ROI modeling session, and a Q&A with the product leadership team. Pre-briefing LinkedIn retargeting campaigns run for 3 weeks prior to each event. Post-briefing follow-up sequences are triggered within 2 hours via HubSpot with personalized recap, ROI summary, and proposed next steps.
One in-person presence at two major enterprise CX industry events per year, using a hosted booth experience with live AI demos and a branded “AI CX Benchmark Report” giveaway to drive lead capture.
Weekly ABM pod syncs align Marketing, Sales, SDRs, Product, and Customer Success around shared KPIs: engaged target accounts, MQL-to-SQL rate, pipeline created, and influenced revenue. Product delivers technical demo assets and vertical-specific use case documentation. Customer Success provides early-adopter case study content and post-sale performance proof points used in MOFU and BOFU campaigns. SDRs are briefed weekly on campaign engagement data to ensure outreach messaging matches content the prospect has already consumed.
A logistics technology company was undergoing a significant strategic transformation — pivoting from a freight marketplace model to a SaaS platform serving mid-market freight brokerages. As the CMO leading this GTM relaunch, my mandate was clear: build a repeatable demand engine capable of generating $5M in Annual Recurring Revenue within 6 months, targeting a historically underserved segment that had never been approached through performance marketing at scale.
The challenge was not just generating leads — it was changing how the market perceived a company they already knew as a carrier, not a software provider. That required a full brand repositioning, a content-led SEO strategy, a structured paid media program, and a lifecycle system that could convert mid-market freight brokers who had no prior context for the new product.
At $6,000 per customer annually ($500/month), reaching $5M ARR requires exactly 834 new customers. Working backward through industry-benchmarked funnel conversion rates, the pipeline requirements were:
Marketing budget of $1.25M represents 25% of the revenue goal — aligned with SaaS industry benchmarks for growth-phase companies during GTM relaunches. CAC target of $500–$1,000 reflects a healthy CAC:LTV ratio for a $6K/year product.
The ICP was defined as mid-market to upper SMB freight brokerages with $10M–$250M in annual revenue and teams of 10–100 employees. These companies are tech-forward — already running modern Transportation Management Systems — and open to integrating specialized pricing, load board, and analytics tooling. Key qualification signals included quoting volumes of 1,000+ quotes per month and active hiring of SDRs, which indicates growth-stage intent.
Target roles: VP of Operations, Director of Pricing or Strategy, Head of Brokerage, and IT Director (for integration conversations). Geographic focus: United States.
Core pain points driving the buying decision:
The $1.25M budget was allocated across channels based on funnel stage, CPL benchmarks, and platform performance data specific to the freight and logistics SaaS category:
The top-of-funnel goal was to attract high-intent traffic from brokers actively researching freight pricing solutions — many of whom had never heard of this platform as a software provider. Google Search and Display ads captured active demand, while SEO content built organic authority across four content clusters:
Additional TOFU plays: gated ROI calculator and freight pricing benchmarks report on dedicated landing pages; competitor comparison landing pages targeting searchers evaluating alternative platforms; ungated SEO blog series to maximize organic reach and intent signal collection.
Mid-funnel content was designed to shift prospects from awareness to active evaluation. LinkedIn Feed Ads delivered case studies and analyst insights to operations and pricing personas. Google Display retargeted site visitors with vertical-specific use cases. HubSpot nurture sequences were segmented by job title and vertical with relevant proof points.
Five MOFU content pillars drove this stage:
G2 and Capterra review generation was activated in parallel using a 10% renewal credit incentive for verified reviews, building social proof that was embedded directly into nurture streams and remarketing ads.
Bottom-of-funnel campaigns drove demo bookings and free pricing audit offers through LinkedIn Lead Gen Ads, Conversation Ads, and HubSpot email sequencing. A limited pilot program with a risk-reduction guarantee was introduced to lower buyer friction and shorten the 30–45 day sales cycle.
Four BOFU conversion assets anchored this stage:
HubSpot smart content dynamically displayed relevant CTAs and case studies based on lead stage, ensuring BOFU pages felt personalized rather than generic. LinkedIn Conversation Ads targeted known leads with personalized messaging mapped to their content engagement history.
Industry thought leadership: Quarterly Freight Industry Trends Report to establish category authority and build TOFU inbound momentum. Co-hosted webinars with TMS integration partners driving joint lead generation from established partner audiences.
Integration partner marketing: Dedicated mini-sites and implementation guides co-branded with TMS partners — a high-converting channel because buyers are already actively using the partner platform.
Intent data enrichment: Bombora and Clearbit integrated to feed buying signals into outreach sequences and ad personalization, ensuring SDR messaging reflected the topics a prospect had already been researching.
Website CRO: Optimized homepage, pricing page, and feature pages with clear CTAs and streamlined navigation. A self-guided free trial was introduced to match competitive practices in the category and reduce friction for lower-intent prospects.
Sales enablement: Use-case-specific pitch decks for SMB vs. enterprise buyer profiles, ROI case study 1-pagers, TMS integration guides, and an objection-handling document addressing the brand perception challenge (“aren’t you a broker?”).
Retention and expansion plays: NPS-triggered upsell workflows for satisfied customers, quarterly usage-personalized benchmark reports, customer spotlight newsletter, and renewal incentives with beta access. These programs were designed to extend LTV and generate referral pipeline with no incremental media spend.
HubSpot served as the attribution hub using a multi-touch model. Key reports tracked: first-touch vs. last-touch conversion by channel, Lead-to-SQL velocity by source, and paid media ROI by campaign and creative. Tool integrations connected GA4, LinkedIn Lead Gen forms, UTM tracking, and CRM sync to provide a clean, end-to-end picture of which demand gen programs were actually driving revenue — not just leads.
First 30 Days — Foundation & Launch: Finalize ICP personas and buyer journey mapping. Launch Google Search + Display and LinkedIn pilot campaigns. Publish the Freight Industry Trends Report. Develop gated assets (ROI calculator, pricing benchmarks, competitor comparisons). Publish 3–5 SEO-targeted blog posts. Set up the full analytics stack (HubSpot, GA4, Looker Studio). Install site pixel and begin building remarketing audiences. Launch G2 review incentive program.
Days 31–60 — Optimize & Scale: Run first co-hosted webinar with a TMS integration partner. Launch vertical-specific HubSpot nurture sequences. Expand SEO with MOFU content (playbooks, FAQs, use cases). A/B test creatives, ad messaging, and landing pages. Publish integration partner mini-site. Submit for industry award recognition. Activate MOFU retargeting with case studies and social proof.
Days 61–90 — Acceleration & Expansion: Scale high-performing campaigns based on CPL and SQL rates. Publish 2 customer success stories. Launch LinkedIn Lead Gen and Conversation Ads with BOFU pilot offer. Activate Bombora and Clearbit intent data to refine SDR outreach sequences. Introduce the “Freight Pricing 101” playbook as a gated MOFU asset. Run a time-boxed G2 review blitz. Plan Q2/Q3 integration content calendar and partner webinar schedule.