Get an honest read on which channels drove pipeline.
“Which channels actually drove pipeline, and how much should I trust the answer?”
Picks first, last, or multi-touch to fit the question, names each model's blind spots, and produces a caveated channel read.
What this skill does
Helps you choose an attribution model that answers the question you actually have, then builds it from your touch data. First touch to see what starts deals, last touch to see what closes them, multi-touch to spread credit across the journey. Whichever you pick, it names the model's blind spots up front and turns the result into a channel-contribution read that says where pipeline comes from without pretending to a precision the data does not support.
Inputs
- Works today with: a CSV or paste of deals with their touches, each touch carrying channel or source, timestamp, and the deal it maps to, plus outcome and amount.
- More powerful connected to a CRM: it reads campaign membership, lead source, and opportunity touches automatically across the whole funnel.
- Sharper with a data warehouse: it can stitch web, ad, and CRM touches into one journey and run true multi-touch instead of a single-field proxy.
- Sharper with a BI tool: pushes the channel-contribution read into a dashboard the whole team reads the same way.
How it works
- 1Model selection
State the question, then pick the model, then name what it will hide. First touch over-credits the top and ignores everything that closed the deal. Last touch does the reverse. Multi-touch spreads credit but leans on complete journey data you may not fully have. Choose deliberately and print the tradeoff, so nobody reads the output as the whole truth.
- 2Credit assignment
Assign credit by the chosen model. First and last touch give one channel the whole deal. Linear multi-touch splits it evenly across touches in the window. Always state the credit basis, pipeline created or closed revenue, because a channel that sources cheap pipeline and a channel that sources deals that close are not the same channel.
- 3Blind-spot ledger
Every model ships with its blind spots listed beside the result. Single-touch models cannot see assists. Multi-touch cannot tell a decisive touch from a passing one. Offline and word-of-mouth touches are invisible to all of them. Naming the blind spots is what makes the read trustworthy instead of a story.
- 4Channel-contribution read
Roll credit up to the channel taxonomy and report each channel's share, alongside its cost where you have it. The honest output is not "this channel drives X percent of revenue" full stop. It is "under last touch, credited on closed revenue, this channel takes X percent, and here is what that model cannot see."
- 5Cross-check
Where you can, run the same deals under a second model and show where the two disagree. A channel that looks huge on first touch and tiny on last touch is doing top-of-funnel work. The gap between models is itself a finding.
Output format
ATTRIBUTION · last touch · credited on closed-won revenue · trailing 2Q
Channel Credited revenue Share Note
Outbound $1.20M 38% closes deals, first-touch share is lower
Paid search $0.70M 22%
Events $0.55M 17% under-credited here, strong on first touch
Referral $0.48M 15%
Organic $0.26M 8%
Blind spots of this model: assists get no credit, offline touches invisible, one touch takes the whole deal.
Cross-check: under first touch, Events jumps to 26%. Events start deals that outbound closes.
Illustrative figures. Your run reports your real touches.
Tips
- The model is named, and its blind spots are printed beside every result.
- Credit basis (pipeline vs closed revenue) is stated. No ambiguous "contribution."
- Offline and untracked touches are acknowledged as invisible, never silently zeroed into someone else's credit.
- No single model is presented as the truth. The read carries its own caveats.
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