Multi-Location OBGYN Practice

A multi-location OBGYN practice operating across Michigan, Illinois, Alabama, and Georgia needed to grow new patient volume without proportionally increasing ad spend. The practice offered obstetrics, gynecology, prenatal services, mammography, and women’s health imaging across 15 locations, but their Google Ads program was generating patients at $549 each with no clear visibility into which campaigns were actually driving new patient bookings versus existing patient interactions. DeltaV Digital rebuilt their paid search program from the data layer up, implementing CRM-based attribution, smart bidding algorithms, and a shared learning strategy across all locations that transformed cost efficiency within 12 months.

The Highlights

• New patient bookings grew from 234 to 1,268 per year (442% increase) with only a 19% increase in total ad spend, a 23x return on incremental investment.
• Cost per acquisition dropped from $549 to $129 (77% reduction) by shifting optimization from general traffic to verified new patient conversions.
• New patient phone bookings surged 900% after implementing call tracking that distinguished new patients from existing patient calls for the first time.

The Challenge

The practice was spending $137,000 per year on Google Ads and acquiring 234 new patients at $549 each. That number was unsustainable as the network expanded to new markets.

The root problem was measurement. The campaigns were optimizing for all conversions, including calls from existing patients checking appointment times, website visits from people already in the system, and form submissions that never turned into booked appointments. Without a way to distinguish new patient acquisition from general website activity, every optimization decision was based on contaminated data.

Keyword targeting compounded the issue. Campaigns were limited to exact and phrase match terms, missing the natural language queries that real patients use when searching for care. And each location’s campaign operated independently, meaning a new clinic in Alabama had to start from scratch even though the Michigan clinics had years of conversion data that could inform what works.

The practice needed three things: accurate new patient tracking, smarter budget allocation across 15 locations, and a way to accelerate performance for new location launches without months of ramp-up time.

The Solution

We rebuilt the paid search program in four layers, each one dependent on the one before it.

Layer 1: CRM Integration for True Patient Attribution

Before touching a single campaign, we implemented LIINE as the practice’s CRM platform and integrated it directly with Google Ads conversion tracking. This allowed us to distinguish between new patient booked calls, new patient online bookings, existing patient interactions, and first-time inquiries that had not yet booked.

This distinction was the foundation. Without it, smart bidding algorithms optimize for the wrong signals. With it, every dollar the algorithm allocated was pointed at genuine new patient acquisition.

Layer 2: Smart Bidding on Clean Data

We transitioned from manual CPC bidding to Google’s smart bidding algorithms, specifically configured to optimize for new patient acquisition conversions from the LIINE integration. The algorithm analyzed historical conversion patterns, identified high-intent keywords, adjusted bids in real time based on conversion likelihood, and allocated budget across campaigns to maximize patient volume.

The key: smart bidding only works when the conversion data it learns from is accurate. The CRM integration in Layer 1 is what made Layer 2 effective. Most practices that try smart bidding without clean attribution data see worse results, not better.

Layer 3: Broad Match Keyword Expansion

With smart bidding controlling efficiency, we expanded the keyword strategy to include broad match terms alongside existing exact and phrase match keywords. This unlocked search queries the practice had never appeared for: natural language medical queries, long-tail variations, and symptom-based searches that patients actually type.

Broad match without smart bidding wastes budget. Broad match with conversion-focused smart bidding becomes a discovery engine that finds high-value patients the manual approach would never reach.

Layer 4: Shared Bidding Strategy Across All Locations

We unified all 15 location campaigns under a portfolio bidding strategy. This meant every location’s conversion data fed the same learning algorithm. When the Michigan clinics discovered that certain keywords or audience signals predicted new patient bookings, that intelligence was immediately available to campaigns in Alabama and Georgia.

For new locations, this eliminated the typical learning phase. Instead of spending weeks or months accumulating enough data to optimize, a new clinic’s campaigns launched with the predictive intelligence of 14 other locations behind them. Ramp-up time compressed from months to weeks.

The Results

The transformation happened within 12 months. With only a 19% increase in ad spend (from $137,000 to $163,000), new patient bookings grew from 234 to 1,268 per year, a 442% increase. That is a 23x return on the incremental $26,000 invested.

Patient acquisition cost dropped from $549 to $129, a 77% reduction that held even as volume scaled. The practice was acquiring five times more patients for roughly the same budget.

The conversion breakdown revealed where the growth came from:

• New patient online bookings: 954 (up 372% year over year)
• New patient booked calls: 330 (up 900% year over year)
• First-time patient interactions: 3,547 (up 1,149% year over year)

The 900% increase in booked calls was particularly significant. These were phone conversions that the previous tracking system could not distinguish from existing patient calls. They were always happening. The practice just could not see them, measure them, or optimize for them.

Average cost per click dropped 23% (from $2.84 to $2.20), meaning the campaigns were not just converting better but also paying less for each visitor. The combination of lower CPCs and dramatically higher conversion rates is what produced the efficiency gain, supported by landing page optimization that ensured visitors reached pages designed to convert.

As of February 2026, the campaigns continue to maintain acquisition cost at approximately $129, well below the $150 target. The optimization framework is evergreen: it continuously learns and improves as new conversion data accumulates. Each new location that launches makes the entire system smarter.

Key Takeaways

19% more spend, 442% more patients. The efficiency was not about spending less. It was about spending on the right signals. When the algorithm knows what a new patient looks like, it stops paying for everything else.

Fix the data before you fix the campaigns. The CRM integration was the single most important decision. Smart bidding on bad data produces bad results faster. Smart bidding on clean, new-patient-specific data produced a 77% CAC reduction.

Shared learning across locations is a compounding advantage. Portfolio bidding meant every location made every other location smarter. New clinics launched with months of intelligence they did not have to earn themselves.

DeltaV Digital is an integrated digital marketing agency connecting SEO, paid media, and web development into a unified growth system. If you are running paid search for a multi-location healthcare practice and want to see what clean attribution and smart bidding can do for your patient acquisition cost, request a free assessment.