You ran a LinkedIn ad, sent a cold email sequence, posted on Reddit, and someone signed up for your trial. Which channel gets the credit? If you can't answer that question, you're guessing with your marketing budget. And guessing is expensive.

This glossary covers the 25 marketing attribution terms that actually matter for a solo founder. No agency jargon. No enterprise fluff. Just the words you need to understand which marketing channels work and which ones are burning your cash.

The fundamentals: what attribution actually means

Attribution The process of identifying which marketing touchpoints caused a customer to convert. Without attribution, you're flying blind. With it, you know whether your Reddit comments, Google Ads, or email sequences actually drive revenue.

Touchpoint Any interaction a potential customer has with your brand before converting. A Google ad click is a touchpoint. Your tweet showing up in their feed is a touchpoint. A referral link from a blog post is a touchpoint. Attribution tracks which ones matter.

Conversion The action you want a visitor to take: a purchase, a trial signup, a demo booking. Attribution answers the question 'which marketing channel drove this conversion?' The definition of conversion changes by business. For a SaaS, it's usually a paid signup. For ecommerce, it's a purchase.

Customer journey The complete path a customer takes from first hearing about your product to buying it. A typical solo founder's customer journey might look like: Reddit comment, Google search, landing page visit, retargeting ad, email sequence, trial signup, paid conversion. Each step is a touchpoint, and attribution tells you which steps actually influenced the final decision.

Impression When your ad, post, or content is shown to someone, regardless of whether they click. Impressions matter for brand awareness but are notoriously difficult to tie to actual sales. Most attribution models focus on clicks because impressions are hard to measure accurately. But ignoring them undervalues awareness campaigns.

Single-touch attribution: the simple models

First-touch attribution Gives 100% credit for a conversion to the very first touchpoint. If someone found you through a Reddit comment, read your blog, clicked a retargeting ad, and then signed up: Reddit gets all the credit. Useful for understanding how people discover you. Biased toward awareness channels and ignores everything that happens after.

Last-touch attribution Gives 100% credit to the final touchpoint before conversion. Same journey: the retargeting ad gets all the credit, even though Reddit started the whole thing. This is the default model in Google Analytics and most ad platforms. It overvalues bottom-funnel channels like branded search and retargeting, which makes them look more effective than they really are.

Last non-direct click A variant of last-touch that ignores direct traffic (someone typing your URL directly). If a customer clicked your email link, then came back later by typing your URL and purchased, last non-direct click gives email the credit instead of direct. This is Google Analytics' default attribution model and is more honest than pure last-touch since direct traffic is often the result of earlier marketing efforts.

Last paid click Credits only the final paid channel interaction, ignoring unpaid touchpoints entirely. If someone read your free newsletter, searched for your brand, then clicked a Google ad and purchased: Google gets the credit. Ad platforms love this model. It makes paid channels look essential by design.

Last non-brand click Credits the final touchpoint that wasn't a branded search term. If someone searched 'best email tool for solo founders' (non-brand), read your comparison, then later searched 'ad-vertly pricing' (brand) and signed up, the non-brand search gets the credit. This model isolates the campaigns that bring in new people, not the ones catching people who already know you.

Multi-touch attribution: the smarter models

Multi-touch attribution (MTA) Attribution that spreads credit across multiple touchpoints instead of giving everything to one interaction. If a customer journey has 5 touchpoints (Reddit, blog, email, Google ad, direct visit), MTA recognizes that all five played a role, just in different proportions. More accurate than single-touch. Also more complex to set up.

Linear attribution Splits credit equally across all touchpoints. Five touchpoints means 20% credit each. Simple to understand, but assumes every interaction is equally valuable. A viral tweet and an 'unsubscribe' confirmation page don't deserve equal credit, but linear attribution treats them the same. Best used as a starting comparison point, not a final model.

Time decay attribution Gives more credit to touchpoints closer to the conversion. A Google ad clicked the day before purchase gets more credit than a blog post read three weeks ago. This model assumes recent interactions have stronger influence, which is often true for short sales cycles. Overvalues retargeting and undervalues content that built trust weeks earlier.

Position-based attribution (U-shaped) Assigns 40% of credit to the first touch, 40% to the last touch, and splits the remaining 20% among middle touchpoints. This model acknowledges that both discovery and conversion are the most important moments in a journey. Popular with B2B SaaS companies because it balances awareness and closing channels without complex modeling.

Data-driven attribution Uses machine learning to analyze thousands of customer journeys and determine each touchpoint's actual contribution based on observed conversion patterns. Google Analytics 4 and Google Ads offer this for free if you have enough data (typically 300+ conversions in 30 days). More accurate than rules-based models but requires significant data volume to work.

Algorithmic attribution A custom machine learning approach where you build your own model to assign credit. Different from data-driven attribution because the algorithms are tailored to your specific business and customer behavior. Requires a data scientist or attribution tool. Not something solo founders need on day one, but good to know the term if you eventually scale your marketing stack.

Money terms you actually need to know

ROAS (return on ad spend) Revenue generated per dollar spent on advertising. If you spend $100 on Meta ads and earn $300 in sales, your ROAS is 3x. Simple formula: revenue from ads divided by ad spend. A 3x ROAS means every dollar returns three dollars. Keep in mind that ROAS ignores non-ad costs like tools, your time, and creative production.

ROI (return on investment) Profit divided by total investment. Unlike ROAS, ROI accounts for all costs: ad spend, software subscriptions, your time, freelancer fees. Formula: (revenue minus total costs) divided by total costs, then multiply by 100 for a percentage. A campaign with 4x ROAS might have negative ROI once you include the $200/month attribution tool and 10 hours of your time setting it up.

CAC (customer acquisition cost) The total marketing and sales cost to acquire one paying customer. Divide total marketing spend by the number of new customers acquired in the same period. If you spent $1,000 on ads and content last month and got 10 new customers, your CAC is $100. Attribution tells you which channels contribute to that $100 cost, so you know where to invest more and where to cut.

Incremental attribution Measures the lift generated by a specific channel or campaign compared to what would have happened without it. The key question: 'did this ad actually cause the sale, or would that customer have bought anyway?' This is different from standard attribution, which just assigns credit to touchpoints that were present. Incremental attribution is the gold standard for proving marketing actually works.

Marketing mix modeling (MMM) A statistical analysis that measures how each marketing channel contributes to overall sales over time. Unlike attribution models that track individual user journeys, MMM looks at aggregate data across months or years. It accounts for external factors like seasonality, competitor activity, and economic conditions. Expensive and data-heavy, typically used by companies spending $500K+ per month on marketing.

Attribution window (lookback window) The time period during which a touchpoint can claim credit for a conversion. Common windows: 7 days, 28 days, or 90 days after the interaction. If your attribution window is 7 days and someone clicks your ad on day one and converts on day eight, the ad gets zero credit. Set your window to match your sales cycle. Short cycles (ecommerce) use 7 days. Longer cycles (SaaS, B2B) use 28 to 90 days.

Tracking and data terms you'll encounter

UTM parameters Small text snippets added to the end of a URL that tell analytics tools where a click came from. Example: ?utm_source=reddit&utm_medium=social&utm_campaign=launch. Without UTMs, all your link traffic from social posts, newsletters, and partner sites shows up as 'direct' in Google Analytics. Every solo founder should use UTMs on every link they share.

Assisted conversions Conversions where a channel appeared somewhere in the customer journey but wasn't the last click. If someone discovered you through a blog post, clicked a retargeting ad, and converted through an email: the blog post and retargeting ad get 'assisted conversion' credit. High assisted conversion numbers mean a channel is doing valuable mid-funnel work even if it never gets last-click credit.

Identity resolution The process of connecting different devices and sessions to the same person. When someone browses your site on their phone, researches on their laptop, and buys on their iPad, identity resolution stitches those three sessions into one customer journey. Difficult to do well without a tool. Most solo founders just don't have this, and that's fine for getting started.

Cross-device tracking Tracking a single user across multiple devices, such as phone, laptop, and tablet. Without it, your analytics treats the same person as three different visitors. Identity resolution is the technical mechanism that makes cross-device tracking possible. Most ad platforms (Google, Meta) do this natively. Independent attribution tools require explicit setup.

First-party data Data you collect directly from your own properties: your website, your app, your email list. Email addresses, purchase history, and site behavior all count. As third-party cookies disappear, first-party data becomes the only reliable foundation for attribution. Start collecting emails and tracking on-site behavior now. You'll need it.

Server-side tracking Sending conversion data from your server to analytics tools instead of relying on browser pixels. More reliable than client-side tracking because it isn't blocked by ad blockers or privacy settings. Also requires technical setup: you need to configure your backend to fire events when conversions happen. Worth doing once you have paying customers and want accurate attribution numbers.

Dark traffic Traffic that shows up as 'direct' in Google Analytics but actually came from somewhere else. Someone clicks your link in a WhatsApp message, Slack channel, or email app that strips referrer data. Analytics can't tell where they came from, so it labels them 'direct.' Dark traffic can be 20 to 50 percent of your reported direct traffic. The fix: use UTM parameters on every link you share anywhere.

Click-through attribution Assigns credit only when someone actually clicks on a link or ad. Different from view-through attribution, which gives credit for simply seeing an impression. Click-through is more conservative and more defendable because you can prove the interaction happened. Most attribution models for solo founders should default to click-through until you have enough data to evaluate view-through impact.

Tracking pixel A tiny piece of code placed on your website that fires when specific actions happen. Facebook Pixel, LinkedIn Insight Tag, and Google Tag are all tracking pixels. They tell ad platforms when someone visits your site, views a product, or completes a purchase. Essential for retargeting campaigns and conversion tracking. One caveat: ad blockers block them, so your numbers will always be undercounted.

Attribution modeling engine Software that applies attribution logic to your data and calculates credit distribution across touchpoints. Google Analytics 4 has a basic built-in engine. Tools like ad-vertly's full-stack marketing agent handle attribution as part of a broader marketing automation workflow, so you don't need a separate attribution tool.

How solo founders should use this glossary

You don't need to memorize all 25 terms. Start with these five: attribution, first-touch, last-touch, ROAS, and UTMs. Those five concepts alone will improve how you evaluate your marketing efforts.

Once you have those down, graduate to multi-touch attribution, data-driven attribution, and assisted conversions. By then, you'll have enough traffic and conversion data to make those models meaningful.

The point of attribution isn't to build a perfect model. It's to stop guessing which marketing channels work. Even a basic first-touch and last-touch comparison tells you more than most founders ever learn about their customer journey. Tag your links, track your conversions, and let the data do the talking. Or use a tool like ad-vertly that automates marketing attribution and campaign execution for you, so you can focus on building your product.