Sebastian and Mark worked through a strategy for turning around the Adavacare client relationship. Client contact Kharosh is skeptical of paid ad ROI and demanding proof of ad-to-move-in attribution. The session focused on three things: using existing data to demonstrate value, building a content-led growth plan, and closing the attribution gap with CallRail.
Attendees: Sebastian Gant, Mark Hope
Related client: [1]
Kharosh wants proof that paid ads produce move-ins, not just conversions. Showing him form fills and call recordings hasn't been convincing enough. The underlying issue is a broken attribution chain:
Without dynamic number insertion, any call that doesn't originate from a click-to-call action is invisible.
Mark exported all Gravity Forms entries for the year and ran them through ChatGPT.
Results:
- 138 legitimate inquiries — real prospects asking about assisted living, memory care, Medicaid, availability, tours, costs
- 28 spam — empty, gibberish, or non-contextual (normal rate, not a concern)
- 3 job inquiries
Top inquiry themes (from message content):
- Availability / do you have openings?
- Medicaid acceptance and payment questions
- Cost of care
- Scheduling tours
- Move-in process / wait lists
Location distribution: Pewaukee, Glendale, Heartland, and Oak Creek are receiving the most inquiry interest. St. Francis, Nina, Irish Road/Magnolia, and Wabash are smaller.
Note: Gravity Forms notifications and exports need to be properly configured. It's unclear whether Kharosh is receiving email notifications for every form fill. This should be verified and a Google Sheets export + weekly lead report should be set up. See [1] for action items.
Mark exported the last 12 months of search queries from GSC and analyzed them in ChatGPT.
Key findings:
- ~50% of clicks are brand queries — people who already know Adavacare. Good for brand health, but these aren't net-new customers.
- Non-brand queries have high impression volume (~95,000) but low rankings — Google is willing to show the site, but it's not ranking high enough to capture traffic.
- Top non-brand opportunity areas: "assisted living [city]", "memory care near me", "senior living Wisconsin", "dementia care [location]"
- Near Me queries are a sweet spot — high intent, targetable with local content
Recommended content actions from analysis:
- Dedicated, optimized service pages per location
- City names embedded throughout blog content
- FAQ sections with schema markup on every post
- Stronger internal linking between blog posts
Generate all 10 posts this week using the GSC query data and form fill themes. Publish 2 per week for 5 weeks.
Sample topics identified:
1. How to Choose the Right Memory Care Facility Near You — Wisconsin Guide
2. Assisted Living in Oak Creek: What Families Should Know Before Choosing a Community
3. Assisted Living Costs in Wisconsin: What to Expect in 2026
4. Does [Adavacare] Accept Medicaid? Payment Options Explained
5. Memory Care vs. Assisted Living: Understanding the Difference
6. Warning Signs It's Time for Memory Care
7. How to Get on a Wait List for Assisted Living in Wisconsin
8. Scheduling a Tour: What to Expect When Visiting a Senior Living Community
9. Respite Care Options in Wisconsin
10. Independent Living vs. Assisted Living: Which Is Right for Your Family?
Workflow:
- Use GSC export + form fill themes as input to ChatGPT
- Generate topic brief with target keywords, word count (1,600–2,000), and FAQ prompts
- Write in Surfer for keyword density optimization
- Include FAQ section on every post (for AI snippet capture)
- Include city/location names throughout
Don't publish and wait. For each post:
The goal is to signal to Google that new content is getting traffic from multiple sources (social, email, direct), which accelerates ranking.
Budget ask: $100/week Meta boost budget. This replaces the generic awareness campaign that was previously planned.
Create a downloadable guide: "Assisted Living Costs in Wisconsin: Complete 2026 Guide"
This addresses the #1 barrier to conversion identified in the form fill analysis: cost and payment questions.
Goal: Connect ad clicks → calls → CRM move-in records
How it works:
1. CallRail uses the Google Click ID (GCLID) to attribute each call back to the specific keyword and ad that drove it
2. Dynamic Number Insertion (DNI) swaps the phone number on the page based on traffic source — so a visitor from a Google Ad sees a different number than an organic visitor
3. Call data (including transcripts) gets passed into the CRM with the GCLID, so when a resident moves in, you can trace it back to the original ad
Current gap: Landing pages are on the main site (good — traffic flows through GSC/GA), but no DNI is in place. Calls that don't originate from click-to-call are untracked.
Next step: Schedule a 45–60 minute working session with Mark to configure CallRail, set up DNI, add UTM parameters, and connect to CRM.
See also: [2] (if it exists) and [3]
Use this math in the client presentation to reframe the conversation:
| Assumption | Value |
|---|---|
| Legitimate form inquiries (YTD) | 138 |
| Average monthly revenue per resident | ~$6,000 |
| Annual value per resident | $72,000 |
| Total potential value (138 × $72K) | ~$10M |
| At 10% conversion | ~$1M/year |
Talking point: "We don't need huge numbers. We just need 10% of these 138 people to move in, and that's a million dollars in annual revenue. The question isn't whether the ads are working — it's whether we're capturing everything they're generating."