Revenue
engineered
from the data up.
Most inefficiencies are hiding in the data. d2b2 finds them, builds the integrations and automation to fix them, and hands over clean reporting so your team stays proactive long after the engagement ends. Paid marketing is one branch of the work.
What d2b2 does
AI Solutions Engineering
End-to-end builds using AI tools, Python, and custom integrations — API connections, data pipelines, automation, and reporting systems that make your stack work as one. We don't spec it out for a dev team. We build it.
Paid Marketing Setup & Optimization
Full-funnel paid search and social built on real attribution data, not platform numbers. Account builds, campaign architecture, bidding strategy, and proactive optimization driven by what's actually converting.
Revenue Operations
Pipeline strategy, lead scoring, and sales-marketing handoff automation that close the loop between marketing spend and closed revenue. The strategic layer that makes the data and the stack actually work together.
Marketing Infrastructure
Tracking implementation, CRM tool setup, and automation pipelines that wire your stack together and reduce manual overhead. Built to scale, documented so your team can run it.
SEO & Answer Engine Optimization
Content strategy and optimization built for how people actually find answers, including AI search engines. Organic visibility that compounds long after the engagement ends.
Revenue Analytics & Reporting
SQL-driven analysis and custom reporting that give executives a clear view of what drives revenue and what wastes budget. Clean data and honest answers, not seasonal guesses.
How the work actually looks
Automated lead filtering that tags and routes without anyone in the loop
Lead routing and follow-up automation inside the CRM depended on tags that were being managed by hand. As underlying data changed, the tags drifted. Agents were working off stale segments and the automation rules that were supposed to save them time were firing on bad information.
Built a pipeline that pulls filtered lead data from the warehouse on a daily schedule, evaluates each record against the business logic, checks whether the relevant tag already exists on that contact in the CRM, sets it if the condition is met, and removes it when it's no longer true. The CRM's own rules engine then fires assignment, routing, and follow-up sequences off those tags automatically.
Agent buy rates increased 10% across the board. Better routing and timely follow-up, driven by accurate segmentation, added $15K in monthly revenue.
14 conversion events recovered across a two-domain funnel
Ad platforms had zero visibility into actual conversions. The offer funnel ran on a different domain than the main site, making it completely invisible to Google Ads and GA4. On top of that, the component framework in use was silently dropping click events — so even on-domain conversion signals were not firing. Budget allocation and bid optimization were running completely blind.
Rebuilt the GTM container with cross-domain linker configuration. Rewrote click triggers to use CSS selector matching instead of element IDs, which the framework regenerates on each render. Extended unified tag coverage across Google Ads, Microsoft UET, and GA4 on both domains.
14 conversion event types confirmed firing across both domains. Full attribution from first ad click to offer accepted, enabling accurate ROAS measurement and proper bid optimization for the first time.
True ROAS came in at 0.56. The platform said it was fine.
Paid search performance looked acceptable inside Google Ads. There was no system connecting ad spend to the internal sales database. Platform-modeled conversions and actual closed revenue were two different numbers, and nobody had a way to reconcile them.
Python pipeline connecting the Google Ads API to an internal MySQL database. Monthly reconciliation layer that mapped campaign spend to actual transactions, verified against internal revenue reporting rather than platform conversion values.
True ROAS on the key campaign measured at 0.56. Lead-to-buy rate of 5%+ confirmed strong targeting — the problem was cost per acquisition, not campaign quality. Informed a campaign restructure and bid strategy change.
Monitoring system for brand presence across five AI search engines
AI search is answering questions that used to send traffic to websites. No tool existed to track how the brand appeared in answers from ChatGPT, Perplexity, Gemini, AI Overviews, and Copilot. Traditional rank tracking was completely blind to it.
Direct integration with an early-access answer engine API. A five-surface dashboard comparing citation rates, response quality, and content gaps across engines, with weekly automated reporting built into the existing reporting stack.
Identified which content formats were being cited by AI engines and which were being ignored. Content strategy adjusted based on actual AI response behavior, not assumptions.
A pitch tool that pulled live market data for every dealer meeting
A field sales team was pitching dealers with static slides. No way to show a specific dealer what their local market actually looked like — and no reliable internet connection in dealer meetings to pull it dynamically.
Python pipeline against a production MySQL database, geographic modeling using haversine radius and DMA crosswalk to scope market data to each dealer's footprint, and a service-worker microsite that cached the data for full offline use on an iPad.
Sales team could demonstrate live, location-specific market data in a dealer meeting with no internet required. Data updated daily from the production database.
How every engagement works
The Audit
Before building anything, understand what exists. What's working, what's wasting budget, and where the data is broken. Every engagement starts with a structured review of your stack, spend, and reporting so nothing gets rebuilt that didn't need to be.
The Stack Build
Tracking, attribution, AI tools, and automation built right the first time. Infrastructure that doesn't break six months later, paid campaigns grounded in verified data, and systems documented as they're built.
The Handoff
Everything built gets documented. You leave knowing how it works, how to manage it, and how to extend it. The goal isn't a retainer. It's a business that runs its own marketing stack after the engagement ends.
Most marketing decisions still happen on gut feel and monthly reports that arrive too late to act on. Danny takes a different approach, applying data science thinking to paid growth and revenue operations: reproducible systems, clean attribution, and analysis that gives executives clear answers instead of seasonal guesses.
Every engagement follows the same model. Audit what exists, build what's missing, teach your team how it works, and hand over the keys. The goal isn't a long retainer. It's a business that can run its own marketing stack after the work is done.
The name is a play on the work: D. Byers, databases, data-driven B2B. The focus is on companies where marketing has been reactive and underpowered. The opportunity is making it precise.
Every project gets owned, not managed. If it's blocking and we can fix it — we fix it. We don't route it to a vendor or wait for someone else's sprint cycle.
No overengineered solutions. Clean processes, clear documentation, and nothing built that creates another problem to solve.
- Managed and optimized $1M+ in Google Ads and Meta spend against true revenue attribution, not platform-reported ROAS
- Designed GTM and GA4 cross-domain tracking architectures connecting multiple web properties into a single clean data stream
- Built PMAX campaign structures with custom asset groups and bid optimization grounded in Ad Rank signal analysis
- Developed AEO content pipelines targeting AI search engines including Perplexity, ChatGPT, and Google SGE
- Built HubSpot CRM workflows, lead scoring, and automation that eliminate manual handoffs between marketing and sales
- SQL and Tableau analysis surfacing revenue patterns across ad platforms, CRM, and web analytics for executive reporting
- Custom API integrations and Python data pipelines connecting Google Ads, Meta, HubSpot, and internal databases into unified reporting and automation workflows
- Google Ads
- Meta Ads
- SQL & Data Analysis
- GA4 / GTM
- HubSpot
- Revenue Operations
- API Integrations
- Python
- AI Tool Setup
- Process Automation
- AEO / GEO
- Marketing Automation
- Tableau
Latest thinking
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RevOps aligns marketing, sales, and customer success around shared data and processes. Here's what it actually means in practice and whether it applies to your business.
GA4 Setup for Small Business: The Minimum Viable Configuration
GA4 has a lot of features. Most small businesses need about 20% of them. Here's what to configure first to get reliable data without over-engineering your analytics.