Multi-Touch Attribution for Home Service Companies

A homeowner’s AC dies in July. They search “emergency AC repair near me,” click your Google Ad, and book a same-day appointment. Your CRM shows the lead source as “Google Ads.”

But here’s what the CRM doesn’t show: that homeowner drove past your truck in their neighbor’s driveway three months ago. They saw your yard sign. They noticed your company name when they were researching maintenance plans in April. When their AC failed, they didn’t search for any AC company. They searched for you specifically.

Google Ads gets the credit. The yard sign, the truck wrap, and the April website visit contributed nothing, according to your tracking.

This is the attribution problem. And it’s causing home service contractors to misallocate millions in marketing spend every year.

Why last-click attribution lies

Most CRMs and analytics platforms use last-click attribution by default. The last touchpoint before conversion gets 100% of the credit.

For home services, this model is broken. The average homeowner interacts with 7-12 touchpoints before booking a contractor. They see ads, read reviews, ask neighbors, visit websites, compare options, and research for weeks or months before they have an urgent need.

Last-click attribution ignores everything that happened before the final interaction. It overvalues Google Ads (often the last click before booking) by 40-60%. It undervalues brand awareness channels like truck wraps, yard signs, and local sponsorships that planted the seed months earlier.

A roofing company running this analysis found that their “worst performing” channel according to last-click data, local radio ads, was actually the first touchpoint for 35% of their highest-value jobs. When they cut radio based on the bad data, lead quality dropped for six months before they connected the dots.

What multi-touch attribution reveals

Multi-touch attribution distributes credit across every touchpoint in the customer journey. Instead of 100% to the last click, it might assign 30% to the first touchpoint, 20% to the middle interactions, and 50% to the final conversion action.

There are several common models:

Linear attribution splits credit equally across all touchpoints. If a customer had 5 interactions before booking, each one gets 20%.

Time-decay attribution gives more credit to touchpoints closer to the conversion. The final interaction matters most, but earlier touches still count.

Position-based attribution assigns fixed percentages to first and last touch (often 40% each) with the remaining 20% split among middle interactions.

Data-driven attribution uses machine learning to analyze your specific conversion paths and assign credit based on what actually correlates with bookings.

For most home service companies, position-based attribution works best. The first touch (how they discovered you) and the last touch (what prompted them to call) are the most actionable. The middle touches often turn out to be less important than marketers assume.

The home service customer journey

A typical path to booking looks something like this:

Day 1: Homeowner drives past your truck at a neighbor’s house. They notice the logo.

Day 15: Same homeowner sees your Google Business Profile while searching for maintenance tips. They don’t click.

Day 45: A friend mentions they used a good HVAC company. The homeowner checks your reviews.

Day 60: The homeowner’s system starts making noise. They search “HVAC inspection near me” and see your Google Ad. They visit your website but don’t convert.

Day 61: They return to your site directly by typing your company name. They submit a form.

Day 62: Your team calls. They book an inspection.

In this example, last-click attribution credits the direct website visit. But the truck wrap, the Google Business Profile impression, the reviews, and the Google Ad all contributed.

Understanding this journey changes how you allocate budget. The truck wrap doesn’t generate “leads” that show up in your CRM. But it might be the reason people search for you by name instead of searching generically.

Setting up multi-touch tracking

Most home service companies track lead sources poorly. A lead source dropdown with 5 options doesn’t capture a 7-touchpoint journey.

Start with these building blocks:

UTM parameters on every link. Every ad, every email, every social post should have tracking parameters. This tells you which campaigns drove which website visits.

Read more about UTM parameters and how to use them.

Google Analytics 4 configured correctly. GA4 has built-in attribution modeling that most contractors never use. Set up conversion events for form submissions and phone calls. Review the Model Comparison report to see how different attribution models change the credit assignment.

CRM integration with website behavior. Your CRM should capture more than lead source. It should capture which pages the lead viewed, how many times they visited, and how long they spent on your site.

Phone call tracking. Different phone numbers for different campaigns let you attribute calls correctly. Dynamic number insertion shows which website session drove each call.

First-touch tracking. Most systems only capture the last interaction. Add a hidden field to your forms that records the original source, not just the converting source.

What the data usually shows

Home service companies that implement multi-touch attribution consistently find patterns:

Google Ads is overvalued. It captures demand but rarely creates it. When you cut Google Ads, some of those customers still find you through other channels. When you cut the channels that create demand, Google Ads performance drops.

Brand awareness works. Truck wraps, yard signs, neighborhood door hangers, and local sponsorships don’t generate trackable leads. They generate branded searches and direct traffic. If your direct traffic is high and growing, brand awareness is working.

Reviews matter more than you think. Most attribution models ignore reviews because there’s no click to track. But customers check reviews on every journey. Companies with strong review velocity see higher conversion rates from every channel.

Email nurture contributes. The email someone received three weeks ago influences whether they convert today. Time-decay attribution captures this. Last-click ignores it.

Retargeting gets too much credit. Retargeting ads often convert people who were going to convert anyway. The ad didn’t cause the conversion, it just intercepted it.

Reallocating based on attribution data

Attribution data without action is just interesting. The point is making better budget decisions.

A typical reallocation after implementing multi-touch attribution:

Cut low-value middle touches. Many contractors discover that certain campaigns show up as middle touches but rarely as first or last touch. These campaigns are touching people who would convert anyway. Cut them and the results often stay flat.

Invest more in first-touch channels. Whatever is introducing new customers to your brand deserves more budget. This is often local SEO, neighbor marketing, and brand awareness tactics.

Protect last-touch converters. Don’t cut Google Ads entirely just because it gets too much credit. It’s still converting people who are ready to book. The insight is that other channels deserve more credit, not that Google Ads deserves none.

Test brand awareness incrementally. Add truck wrap to one territory. Increase yard sign placement in specific neighborhoods. Measure whether branded search increases in those areas. This is incrementality testing, not perfect attribution, but it validates whether brand spend works.

Read more about marketing attribution for home services and how to avoid common measurement mistakes.

The limits of attribution

Attribution models are imperfect. They can’t capture everything.

Word of mouth doesn’t create trackable touchpoints. A neighbor recommendation influences the decision but never shows up in analytics. Surveys asking “how did you hear about us” help but rely on customer memory.

Offline touchpoints are hard to track. Truck wraps, uniforms, and local sponsorships don’t generate clicks. The best proxy is branded search volume. If people are searching your company name, something is driving that awareness.

Cross-device behavior breaks tracking. Someone researches on their phone and converts on their laptop. Most analytics platforms can’t connect these sessions reliably.

Privacy changes limit data. Cookie restrictions and iOS privacy features reduce what third-party platforms can track. First-party data becomes more valuable.

Given these limits, the goal isn’t perfect attribution. The goal is better attribution than last-click default. Directionally correct beats precisely wrong.

What to actually do

Start with what you have. If you’re using Google Analytics and a CRM, you already have multi-touch data. You’re just not looking at it.

In GA4, navigate to Advertising > Model comparison. Compare last-click to data-driven attribution. Note which channels gain or lose credit. This takes 5 minutes and reveals how distorted your current view is.

Add UTM parameters to everything. Build a consistent naming convention and use it. This is the foundation for any attribution analysis.

Implement first-touch tracking. Add a hidden form field that captures the original traffic source, not just the converting session source. Compare first-touch to last-touch. The differences tell you which channels build awareness versus which channels capture demand.

Survey customers. “How did you first hear about us?” isn’t perfect data but it captures offline touchpoints that analytics misses.

Track branded search volume. If it’s growing, your brand awareness is working. If it’s flat while you’re spending on brand, reconsider the spend.

The contractors who figure out attribution don’t necessarily spend less on marketing. They spend better. They understand which dollars create new demand and which dollars capture existing demand. When budgets get tight, they know what to cut and what to protect.

Most home service marketing isn’t wasted on bad channels. It’s wasted on giving credit to the wrong ones, then optimizing toward a lie.