Multi Truck Group — #1 Local Pack
in 11 Canadian Cities
Client’s Challenge
Multi Truck Group is a Canadian freight and logistics company operating nationwide. Despite quality service and years of experience, their online reputation didn’t match reality. A 3.2★ rating with several unaddressed negative reviews made them invisible in Google’s Local Pack.
Competitors with inferior service held TOP-3 positions simply by having more reviews. Multi Truck Group was losing potential clients at the Google Maps stage — people weren’t even calling.
- 3.2★ average rating — need 4.5+ for Local Pack visibility
- Inconsistent NAP data across 50+ business directories
- Zero GMB posts in the last 6 months
- Only 3 photos in profiles (need 20+ for engagement)
- Negative reviews left unanswered — reputation damage
- 12 cities of operation but only 1 GMB profile
How We Solved It
GMB Audit & Setup
Created optimized GMB profiles for all 12 operating cities. Standardized NAP data, added 25+ photos per profile, set up GMB Posts schedule.
Citation Building
85 business directories synchronized with consistent NAP data. Canadian-specific directories plus industry verticals (freight, logistics).
Review Generation System
Automated SMS review requests sent 24h after successful delivery. Personalized messages with driver name and delivery details. Direct Google review link.
Reputation Management
AI-powered response system: all reviews answered within 2 hours. Positive reviews thanked with specifics; negative reviews addressed with resolution offers.
Local Landing Pages
SEO-optimized landing pages for each city: Toronto, Vancouver, Calgary, Montreal, Ottawa, Edmonton, Winnipeg, Halifax, Victoria, Saskatoon, Hamilton.
Our Implementation
review_automation.py
# WebCoreLab — Multi Truck Review Generation System
import anthropic
from twilio.rest import Clie
t as TwilioClie
t
class ReviewSystem:
asy
c def send_review_request(self, delivery: dict):
# Ge
erate perso
alized SMS 24h after delivery
msg = await self.claude.messages.create(
model="claude-haiku-4-5",
max_toke
s=200,
messages=[{"role": "user",
"co
te
t": f"Write a brief SMS aski
g for a Google review. Driver: {delivery['driver']}. Route: {delivery['origin']} → {delivery['dest']}. U
der 160 chars."
}]
)
self.twilio.messages.create(
body=msg.co
te
t[0].text + f"
{GMB_REVIEW_URL}",
from_=TWILIO_NUMBER,
to=delivery['customer_phone']
)
asy
c def respo
d_to_review(self, review: dict):
# Auto-respo
d withi
2 hours
resp = await self.claude.messages.create(
model="claude-so
et-4-5",
max_toke
s=200,
messages=[{"role": "user",
"co
te
t": f"Review: "{review['text']}" Rati
g: {review['rati
g']}/5. Write professio
al response."
}]
)
return resp.co
te
t[0].text
# New reviews: 94 (avg 4.7★) i
3 mo
ths
# Rati
g: 3.2★ → 4.8★
# Respo
se rate: 100% withi
2h
Measurable Impact
Measured 3 months after campaign launch
“We went from being invisible on Google Maps to dominating the Local Pack in 11 cities. The automated review system was a game-changer — clients actually appreciate the personalized follow-up, and our rating went from 3.2 to 4.8 in just three months.”
— I.M., Operations Director, Freight Company (NDA)