d2b2 brings data science thinking to paid marketing and revenue operations. We audit your stack, build the systems, teach your team how they work, and hand you the keys when the engagement ends. Services include paid marketing setup, revenue analytics, marketing infrastructure, and answer engine optimization.

What is the d2b2 engagement process?

Every d2b2 engagement follows three phases. The Audit: a structured review of your stack, spend, and data to understand what exists and what needs fixing. The Stack Build: implementing tracking, attribution, AI tools, and automation that work correctly from day one. The Handoff: full documentation so your team can run everything independently after the engagement ends.

Who does d2b2 work with?

d2b2 works with small businesses and growth-stage companies where marketing has historically been reactive and underpowered. All engagements are project-based, not retainer-based. Based in San Diego, CA, serving clients across the US.

How is d2b2 different from a traditional marketing agency?

d2b2 applies data science thinking to marketing rather than relying on intuition or waiting on monthly reports. The goal is to build systems you own and understand, not dependency on an ongoing agency relationship. Every engagement ends with a complete handoff so the client can run the work independently.

Marketing Attribution 101: How to Know What's Actually Driving Revenue

Marketing attribution answers one question: when a customer buys, which touchpoints get credit for the sale?

The answer seems like it should be simple. It isn’t. Most businesses are running attribution models that give them systematically wrong answers, and making budget decisions based on those answers.

Why attribution is hard

Most customers touch your brand multiple times before buying. They might find you through organic search, see a retargeting ad, read a blog post, get an email, and then convert after clicking a branded search ad. Seven interactions, one conversion.

Which one caused the sale? The honest answer is all of them and none of them. But your ad platforms, CRM, and analytics tools each claim credit for the ones they can see, which usually means they’re all overcounting.

The main attribution models

Last-click attribution gives 100% of credit to the last touchpoint before conversion. It’s the default in many systems. It systematically undervalues awareness and mid-funnel activities (organic content, social, display) and overvalues direct traffic and branded search.

First-click attribution gives 100% of credit to the first touchpoint. It overvalues top-of-funnel channels and undervalues the channels that actually close customers.

Linear attribution splits credit evenly across all touchpoints. It’s more accurate than last-click or first-click but treats a brief retargeting impression the same as a long blog post read.

Time-decay attribution gives more credit to touchpoints closer to the conversion. It’s better for short sales cycles but may still undervalue awareness channels.

Data-driven attribution (available in GA4 and Google Ads for accounts with sufficient data) uses statistical modeling to assign credit based on observed patterns across many user journeys. It’s the most accurate model available in standard tools, but requires minimum conversion volume to work and operates as a black box.

Which model to use

For most small and mid-size businesses, the practical recommendation is: use data-driven attribution in GA4 if you have the volume (typically 1,000 or more conversions per month across your property). Otherwise, use linear attribution as the default. It’s not perfect but it doesn’t systematically mislead you the way last-click does.

More importantly, don’t rely on a single attribution model for strategic decisions. Use your ad platform data for within-platform optimization, and make cross-channel budget decisions using blended metrics from your source of truth (CRM, payment processor, order data).

Building a practical attribution framework

Step 1: Define your conversion event clearly. Is a “conversion” a lead form submission? A qualified sales call booked? A closed deal? The answer changes everything downstream.

Step 2: Implement consistent UTM parameters. Every paid link, every email, every social post should have UTM parameters. Without them, you can’t see channel attribution in GA4.

Step 3: Connect GA4 to your CRM. GA4 can tell you where traffic comes from. Your CRM can tell you which leads actually closed. Connecting them, even manually through UTM capture in your forms, lets you see lead-to-close rates by channel. That’s far more useful than conversion rates alone.

Step 4: Run a quarterly attribution review. Look at revenue by channel using your source of truth, compare to ad platform reported metrics, and understand the gaps. This review should take one hour and should directly inform your next quarter’s budget allocation.

What attribution doesn’t tell you

Attribution models describe correlation, not causation. A channel that appears in many customer journeys may be riding the wave of demand rather than creating it. Channels that work well together often can’t be evaluated independently.

The best supplement to attribution modeling is incrementality testing: measuring whether removing a channel actually reduces conversions. This is harder to set up but gives you much more reliable signal.

d2b2 builds attribution frameworks for small businesses and growth-stage companies. If you want to actually understand where your customers are coming from, let’s talk.