Data-driven attribution

데이터 기반 기여 모델

Use observed paths to distribute conversion credit across touchpoints.

···
html
<div class="dda"><div class="head">PATH <b id="path">A · SEARCH → EMAIL → BUY</b></div><div class="parts"><div><span>SEARCH</span><i id="s"></i><b id="sn">60%</b></div><div><span>EMAIL</span><i id="e"></i><b id="en">40%</b></div></div><div class="footer"><span>OBSERVED PATHS</span><strong id="sample">A + B + C</strong></div></div>
css
.dda{width:min(86%,570px);font:850 clamp(10px,max(2vmin,1vw),15px)/1.2 sans-serif}.head{display:flex;justify-content:space-between;gap:6px;padding-bottom:clamp(8px,2vmin,16px);border-bottom:2px solid var(--fg)}.head b{color:var(--accent);font-size:clamp(10px,max(1.8vmin,1vw),14px);text-align:right}.parts{display:grid;gap:clamp(10px,2.6vmin,20px);margin:clamp(15px,4vmin,32px) 0}.parts>div{display:grid;grid-template-columns:clamp(40px,10vmin,75px) 1fr clamp(32px,7vmin,48px);align-items:center;gap:8px}.parts i{height:clamp(16px,4vmin,29px);background:var(--accent);width:0;transition:width .65s}.parts>div:nth-child(2) i{background:var(--accent-2)}.parts b{text-align:right}.footer{display:flex;justify-content:space-between;border-top:1px solid var(--line);padding-top:9px;color:var(--muted);font-size:clamp(10px,max(1.7vmin,1vw),12px)}.footer strong{color:var(--fg)}
js
let alt=false;function draw(){const s=alt?35:60,e=100-s;document.querySelector('#path').textContent=alt?'B · EMAIL → SEARCH → BUY':'A · SEARCH → EMAIL → BUY';document.querySelector('#s').style.width=s+'%';document.querySelector('#e').style.width=e+'%';document.querySelector('#sn').textContent=s+'%';document.querySelector('#en').textContent=e+'%';document.querySelector('#sample').textContent=alt?'B + C + D':'A + B + C'}draw();setInterval(()=>{alt=!alt;draw()},1800);

Data-driven attribution learns from converting and non-converting paths instead of applying a fixed equal-credit rule. The actual model varies by advertiser and key event.

The demo changes illustrative weights when the path changes. These values explain allocation, not a production model's estimates.

A model can learn only from observable touchpoints. Do not confuse assigned credit with causal lift; validate consequential decisions with experiments.

When to use

Use it to interpret allocated credit in multi-touch conversion reports.

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