Setting up multi touch attribution sounds great on paper—track every touchpoint, get the full story, optimize everything. But in the real world, it gets messy fast. If you’re a marketer, analyst, or technically-minded founder who wants to actually use multi touch attribution in Hf, this guide is for you.
We’ll skip the “why attribution matters” lecture. Instead, here’s how to set it up, what to watch out for, and how to avoid common traps that waste your time or muddy your data.
1. Get Your Foundations Right
Before you even touch Hf, make sure your basics are in order. Multi touch attribution relies on tracking user journeys across channels and sessions. Garbage in, garbage out.
Checklist:
- Clean, consistent UTM tagging: If your campaigns use random naming conventions, you’ll never get clean attribution. Standardize UTMs across your team.
- Universal user IDs: Do you have a way to recognize the same person across devices and sessions? If not, attribution will be fuzzy. (Yes, this is hard. Do your best.)
- All key channels tracked: Make sure you’re capturing data from all the platforms you care about—ads, organic, email, etc.
- Data privacy is sorted: Respect user consent. If you’re in the EU or California, don’t skimp on this step.
Pro tip: Don’t try to attribute everything. Focus on the main conversion events that actually matter to your business.
2. Map Out Your Attribution Model
Hf gives you flexibility with attribution models—first touch, last touch, linear, time decay, custom. But more options can mean more ways to get confused.
What actually works:
- If you have a long sales cycle, linear or time decay models are usually more honest than last-click.
- If you run lots of awareness campaigns, first-touch tells you what’s driving new people in.
- Custom models sound cool, but unless you’re a data scientist and have lots of clean data, they rarely add real value.
What to ignore: Fancy models that promise “AI-powered attribution” without showing their math. More complexity doesn’t mean more accuracy.
How to choose:
- Sketch out your typical customer journey. Where do people first hear about you? What actually nudges them to convert?
- Pick the model that matches your real-world process, not the one that looks most sophisticated.
3. Set Up Tracking in Hf
Now you’re ready to jump into Hf itself. The setup isn’t rocket science, but attention to detail matters.
a. Import Your Data
- Connect the sources you use most—Google Ads, Facebook, CRM, website analytics.
- Check for duplicate or missing data. It happens more than you think.
- Import historical data if you want a baseline, but don’t obsess over going back years. Most businesses care about the last 3-12 months.
b. Define Conversion Events
- Decide what counts as a conversion. Purchases? Demo requests? Newsletter signups?
- Set up these events in Hf with clear, unambiguous definitions.
- If you have multiple conversion types, make sure you track them separately. Don’t lump everything into one bucket.
c. Verify Attribution Paths
- Use Hf’s path analysis tools to check if journeys make sense. Are you seeing wild, impossible sequences? That’s a red flag for broken tracking.
- Spot-check a few known users (if privacy allows) to see if their journeys look right.
Pro tip: The first week, expect weird data. Give it time and keep an eye out for obvious mistakes.
4. Clean Up and Normalize Your Data
Messy source data will make your attribution reports useless. Spend time on this; it pays off later.
- Standardize channel names: Google, google.com, Google Ads, GAds—these should all map to “Google Ads” (or whatever naming convention you pick).
- Merge duplicate users: If you have both email and cookie-based IDs, try to reconcile them for the cleanest possible picture.
- Remove junk traffic: Bots, internal users, test accounts—filter them out ruthlessly.
Hf-specific tip: Use the platform’s data mapping features. Don’t be afraid to create rules that fix recurring data quirks.
5. Test, QA, and Iterate
You’re not done after the initial setup—far from it. Attribution is only as good as your ongoing maintenance.
- QA your attribution regularly: Look for sudden spikes or drops in channel performance. If something looks too good (or bad) to be true, it probably is.
- Get feedback from real users: Sometimes the path you think leads to a conversion isn’t what’s actually happening.
- Run sanity checks: Compare Hf’s output to what you see in other analytics tools. The numbers won’t match exactly, but big gaps mean something’s broken.
Pro tip: Don’t chase perfect attribution. You’ll never fully “close the loop.” Good enough is usually good enough.
6. Use Attribution to Make Actual Decisions
Attribution isn’t just a reporting toy. The whole point is to make better calls about where to spend time and money.
- Look for clear patterns. If a channel consistently drives assists (not just last-clicks), don’t cut its budget just because it doesn’t “convert” directly.
- Ignore tiny sample sizes. Don’t make big decisions based on a handful of conversions.
- Share results with people who can act on them—your ad buyers, content team, whoever.
What not to do: Don’t use attribution to “prove” what you already want to believe. Let the data challenge your assumptions.
7. Common Pitfalls to Avoid
- Overcomplicating your setup: The more custom rules and edge cases you build in, the more likely something will break.
- Ignoring cross-device journeys: If someone clicks an ad on mobile and buys on desktop, you’ll miss that without good user stitching.
- Chasing attribution perfection: There will always be some dark traffic, privacy gaps, and tracking blind spots. Accept it.
Honest take: Most teams get more value out of a simple, well-maintained setup than a fancy, brittle one.
8. Keep It Simple and Iterate
Multi touch attribution in Hf can give you real insight—but only if you stay focused on what matters. Don’t get lost in the weeds or fooled by shiny features.
- Start simple. Get the basics working.
- Check your data for weirdness, often.
- Use the results to make practical decisions.
- Tweak as you learn more. Don’t be afraid to change your model if your business shifts.
Remember: your goal isn’t to win a data science prize. It’s to understand what’s actually working, so you can do more of it. That’s it.