---
slug: logistics-last-mile-delivery-coordination
title: "Last-Mile Delivery Startup Reduces Failed Deliveries 62% with AI Customer Calls"
industry: Logistics & Supply Chain
useCase: Last-Mile Delivery
companySize: 120 employees (60 drivers)
location: Metro area, US
metaTitle: "Last-Mile Delivery Coordination Case Study | QuickVoice"
metaDescription: "A last-mile delivery startup used QuickVoice AI customer calls to reduce failed deliveries 62%, increase daily capacity 16%, and save $312K/year."
canonical: https://quickvoice.co/case-studies/logistics-last-mile-delivery-coordination
ogImage: ""
heroStat: "-62%"
heroStatLabel: "Failed delivery reduction"
tags: [Logistics, Last-Mile, Delivery, Onfleet, Customer Experience]
---

# Last-Mile Delivery Startup Reduces Failed Deliveries 62% with AI Customer Calls

A last-mile delivery startup handling 500 daily deliveries for grocery, pharmacy, and retail clients was bleeding margin on an 11% failed delivery rate. Drivers could not reach customers -- gated communities locked them out, apartments had no answer, and addresses were incomplete. Each failure cost $30 in wasted time, re-delivery expense, and customer churn risk. By deploying QuickVoice AI to call every customer before delivery -- confirming availability, collecting gate codes, and verifying instructions -- the startup cut failed deliveries by 62%, increased daily capacity by 16%, and saved $312,000 in its first year.

---

## 1. Company Profile

This case study examines a last-mile delivery startup operating in a major US metropolitan area. Founded four years ago, the company provides white-label delivery services for grocery chains, pharmacy networks, and retail brands that need same-day and next-day delivery but do not operate their own delivery fleets. The startup has grown rapidly, fueled by the post-pandemic surge in consumer demand for home delivery.

| Attribute | Detail |
|---|---|
| **Company type** | Last-mile delivery (white-label) |
| **Service area** | Major metro area, 1,800 sq mi coverage |
| **Employees** | 120 total |
| **Drivers** | 60 (mix of W-2 and 1099) |
| **Daily deliveries** | ~500 |
| **Client verticals** | Grocery (45%), pharmacy (30%), retail (25%) |
| **Delivery management platform** | Onfleet |
| **Average delivery window** | 2-4 hours |
| **Average order value** | $85 |

The startup had differentiated itself from gig-economy competitors by offering dedicated driver teams, branded delivery uniforms, and guaranteed delivery windows. This premium positioning allowed it to charge 20-30% more than on-demand platforms but came with a correspondingly high bar for service quality. Failed deliveries were not just a cost problem -- they were an existential threat to the business model.

---

## 2. The Challenge

The startup's 11% failed delivery rate was the single largest drag on its financial performance and client retention.

**55 out of every 500 daily deliveries failed on first attempt.** Failed deliveries fell into four categories, each with distinct root causes:

| Failure Category | % of Failures | Root Cause |
|---|---|---|
| **Customer not home / not available** | 41% | Customer forgot about delivery, stepped out, or was unreachable |
| **Access denied -- gated community / locked building** | 28% | Driver lacked gate code, building access code, or buzzer number |
| **Incorrect or incomplete address** | 18% | Missing apartment number, wrong zip code, business vs. residential confusion |
| **Customer refused delivery** | 13% | Wrong items, changed mind, duplicate order |

**Each failed delivery cost approximately $30.** The cost breakdown included $18 in wasted driver time (average 22 minutes lost per failed stop, including travel, waiting, calling the customer, and returning the package to the vehicle) plus $12 in re-delivery costs (re-routing the package, second delivery attempt, or return to the fulfillment center). For perishable grocery and pharmacy items, failed deliveries often resulted in complete product loss -- an additional $40-60 absorbed by the startup or its client.

**Drivers were spending 8 minutes per delivery on non-delivery activity.** Beyond the 55 daily failures, even successful deliveries were being slowed by access issues. Drivers routinely spent 3-5 minutes at gated communities waiting for another vehicle to open the gate, calling the customer to get buzzed in, or walking a building to find the correct entrance. Across 500 daily deliveries, this non-delivery idle time consumed 67 hours of driver capacity every day -- the equivalent of 8 full-time drivers producing zero deliveries.

**Customer NPS was stuck at +31.** While not terrible, this score was well below the +50 threshold that the startup's enterprise clients expected from a premium delivery partner. Post-delivery surveys revealed that even customers who received their orders on time were dissatisfied with the lack of communication -- they wanted to know when the driver was coming, not just that the package arrived.

**Access code management was a daily crisis.** The startup's service area included 340+ gated communities and secured apartment buildings. Drivers maintained personal notebooks of gate codes, which became outdated as communities changed codes. On any given day, 45 deliveries were delayed or failed specifically because of access code issues.

> "Our drivers are fantastic -- they hustle, they're professional, they care. But we were setting them up to fail. We'd send them to a gated community with no code, to an apartment with no buzzer number, to a house where nobody was home. They'd burn 20 minutes trying to figure it out, fall behind on their route, and the whole day cascaded." -- Head of Operations

---

## 3. Why QuickVoice

The startup evaluated three approaches to reducing failed deliveries.

**An SMS notification system** was already in place through Onfleet. Every customer received an automated text with a delivery window and a tracking link. However, the SMS had a fundamental limitation: it was one-way. Customers received the information but had no easy way to respond with gate codes, special instructions, or schedule changes. The text open rate was 71%, but the click-through rate on the tracking link was only 19%, and the number of customers who proactively called to share access information was negligible.

**A dedicated dispatch coordinator** was hired to make pre-delivery calls. After three months, the results were clear: one person could make approximately 80 calls per day, covering only 16% of the delivery volume. The coordinator reduced failed deliveries for the customers she reached, but the role was not scalable. Hiring five coordinators to cover all 500 daily deliveries would have cost $250,000 per year.

**QuickVoice AI voice agents** offered the scale to call every customer before every delivery, the conversational ability to collect gate codes, verify addresses, and confirm availability, real-time integration with Onfleet to update delivery instructions and driver notes, and multilingual support for the startup's diverse metro customer base (English, Spanish, Mandarin).

The startup chose QuickVoice because the cost per call was a fraction of the cost of a failed delivery, and the system could reach all 500 customers daily without adding headcount.

> "The dispatch coordinator experiment proved the concept. When we called customers before delivery, failures dropped. The problem was that one person could only call 80 customers a day. QuickVoice could call 500 -- simultaneously, in three languages, at 6 AM." -- CEO

---

## 4. The Solution

QuickVoice deployed a three-stage customer communication system integrated with Onfleet and the startup's client order management platforms.

### Stage 1: Pre-Delivery Confirmation Call (2 Hours Before Window)

Two hours before the delivery window opens, QuickVoice calls every customer with a personalized message:

1. **Delivery confirmation:** The agent confirms the order contents (without reading every item -- just the category and count, e.g., "your grocery order with 12 items from FreshMart"), the delivery window, and the delivery address.
2. **Availability check:** The agent asks whether the customer will be available to receive the delivery. If not, the agent offers options: leave with a neighbor, leave at a safe location, reschedule, or provide specific instructions.
3. **Access information collection:** For gated communities and secured buildings, the agent asks for the gate code, apartment buzzer number, and any entry instructions. This information is written directly to the Onfleet driver notes for the delivery.
4. **Special instructions:** The agent asks whether there are any special instructions -- ring doorbell vs. knock, leave at side door, call upon arrival, etc.
5. **Address verification:** The agent reads back the delivery address and asks for confirmation or correction. If the customer provides a correction, the Onfleet address is updated in real time.

### Stage 2: Real-Time ETA Call (15 Minutes Out)

When the driver is 15 minutes from the delivery location -- triggered by Onfleet's GPS-based ETA calculation -- QuickVoice makes a brief call to the customer:

1. **ETA notification:** "Your delivery from FreshMart will arrive in approximately 15 minutes."
2. **Final confirmation:** "Will you be available to receive it?"
3. **Last-minute instructions:** An opportunity for the customer to share anything that has changed since the pre-delivery call.

This call serves as a "heads-up" that gets the customer to the door, clears a parking spot, or opens the gate -- eliminating the last-minute access and availability issues that cause failures even when the pre-delivery call was successful.

### Stage 3: Post-Delivery Feedback Call (30 Minutes After)

Thirty minutes after delivery confirmation in Onfleet, QuickVoice calls the customer to collect feedback:

1. **Delivery satisfaction:** "How was your delivery experience today?" (1-5 rating)
2. **Issue identification:** If the rating is below 4, the agent asks what went wrong and logs the issue for follow-up.
3. **Product quality check:** For grocery and pharmacy deliveries, the agent asks whether all items arrived in good condition.
4. **Future preference capture:** "Is there anything we should do differently for your next delivery?" Responses are saved to the customer profile for future routes.

| Call Stage | Timing | Duration | Purpose |
|---|---|---|---|
| **Pre-delivery confirmation** | 2 hours before window | 90-120 sec | Confirm availability, collect access info, verify address |
| **Real-time ETA** | 15 min before arrival | 30-45 sec | Heads-up notification, final confirmation |
| **Post-delivery feedback** | 30 min after delivery | 45-60 sec | NPS collection, issue identification, preference capture |

### Access Code Database

A critical byproduct of the pre-delivery calls was the creation of a living access code database. Every gate code, buzzer number, and entry instruction collected during a QuickVoice call was stored in a structured database linked to the delivery address. For repeat deliveries to the same location, the driver received the access information automatically without requiring a new customer call. After six months, the database contained verified access information for 2,100 locations -- covering 78% of the startup's gated/secured delivery addresses.

---

## 5. Implementation

The deployment was fast by design -- the startup could not afford weeks of integration work while losing $1,500 per day to failed deliveries.

| Phase | Timeline | Scope |
|---|---|---|
| **Phase 1: Onfleet Integration** | Day 1-3 | API connection, delivery schedule sync, driver notes write-back, GPS ETA trigger configuration |
| **Phase 2: Script Development** | Day 3-5 | Pre-delivery, ETA, and post-delivery scripts; client-specific branding (scripts use each client's brand name, not the startup's); multilingual versions (English, Spanish, Mandarin) |
| **Phase 3: Pilot -- Grocery Only** | Day 6-12 | Pre-delivery calls for grocery deliveries only (225/day), all three stages |
| **Phase 4: Full Vertical Rollout** | Day 13-18 | Pharmacy and retail deliveries added, all 500 daily deliveries covered |
| **Phase 5: Access Code DB Launch** | Day 19-25 | Structured storage of access information, automatic driver notes population for repeat addresses |

**The grocery pilot showed dramatic results within one week.** Failed deliveries for grocery orders dropped from 11.3% to 5.1% in the first seven days. More strikingly, driver idle time at delivery locations dropped from an average of 8 minutes to 3.5 minutes. Drivers reported that customers were "waiting at the door" because the 15-minute ETA call gave them a precise heads-up.

**Multilingual support was a differentiator.** The startup's metro service area had significant Spanish-speaking and Mandarin-speaking populations. Previously, the SMS notifications were English-only. QuickVoice calls in the customer's preferred language (detected from order data or selected during the first call) generated measurably higher engagement -- 94% answer rate for calls in the customer's primary language versus 76% for English-only calls to non-native speakers.

**Client reaction was overwhelmingly positive.** The startup's grocery, pharmacy, and retail clients were briefed on the QuickVoice deployment before launch. Two clients had initial concerns about AI calling their end customers. After listening to sample calls and reviewing the pilot data, both clients became enthusiastic advocates. One grocery client requested that QuickVoice calls include a brief promotional message about their loyalty program -- a feature that was added in month two.

**Driver adoption was immediate.** Drivers did not need to change their workflow. The access codes and special instructions simply appeared in their Onfleet driver notes, as if a human dispatcher had added them. The only visible change was that customers were more frequently ready and waiting when drivers arrived, which drivers noticed and appreciated immediately.

> "I delivered to this gated apartment complex on Tuesday -- the one that used to take me 15 minutes every time because I'd sit there buzzing random apartments until someone let me in. This time, the gate code was right there in my notes. I punched it in, drove straight to the building, and the customer was holding the door open. The whole stop took four minutes. I almost didn't believe it." -- Delivery Driver

---

## 6. Results

Performance was measured over a 12-month period following full deployment, compared against the same 12-month period prior.

### Key Performance Metrics

| Metric | Before QuickVoice | After QuickVoice | Change |
|---|---|---|---|
| **Failed delivery rate** | 11% | 4.2% | -62% |
| **Driver idle time per delivery** | 8 min | 3 min | -63% |
| **Access code issues per day** | 45 | 8 | -82% |
| **Customer NPS** | +31 | +52 | +68% |
| **Daily delivery capacity** | 500 | 580 | +16% |
| **Annual cost savings** | -- | -- | **$312,000** |

### Financial Impact Breakdown

| Savings / Revenue Category | Annual Amount | Calculation |
|---|---|---|
| **Failed delivery cost elimination** | $178,200 | 34 fewer failures/day x $30/failure x 297 operating days |
| **Increased capacity revenue** | $85,800 | 80 additional deliveries/day x $5.40 avg margin x 198 incremental operating days (ramped) |
| **Perishable product loss reduction** | $48,000 | 14 fewer grocery/pharmacy failures per day x $9.40 avg product loss x 365 days |
| **Total financial impact** | **$312,000** | -- |

### Operational Impact

The 16% increase in daily delivery capacity -- from 500 to 580 deliveries -- was achieved without adding drivers. The 63% reduction in driver idle time per delivery freed up enough aggregate capacity to handle 80 more stops per day with the same 60-driver fleet. This translated directly into revenue growth, as the startup was able to accept additional volume from existing clients without increasing its driver base.

| Operational Metric | Before | After | Impact |
|---|---|---|---|
| **Average stops per driver per day** | 8.3 | 9.7 | +17% driver productivity |
| **Average time per successful delivery** | 14.2 min | 10.8 min | 3.4 min saved per stop |
| **Route completion rate** | 88% | 97% | Fewer routes with leftover undelivered packages |
| **On-time delivery rate** | 82% | 93% | Major improvement in SLA compliance |

### Customer Experience Transformation

The NPS improvement from +31 to +52 was driven by multiple factors captured in post-delivery surveys:

- 93% of customers said they appreciated receiving the pre-delivery call
- 88% rated the 15-minute ETA call as "very helpful" in preparing for their delivery
- 79% said the proactive communication made them more likely to reorder
- 71% said it improved their perception of the retail brand (not just the delivery service)
- Only 6% expressed a preference for text-only notifications over phone calls

The post-delivery feedback calls generated actionable data that had never been available before. The startup identified recurring issues at specific addresses (a building with a broken buzzer, a neighborhood with confusing street numbers), route-level patterns (deliveries in a particular zone consistently late due to traffic), and client-level trends (one pharmacy's orders had a 3x higher rate of customer complaints about missing items).

> "The NPS jump from +31 to +52 did more for our business than any sales pitch we've ever made. When we showed that number to prospective clients, three of them signed contracts within the month. Premium delivery requires premium communication, and QuickVoice gave us both." -- Head of Business Development

---

## 7. What's Next

The startup has identified four expansion areas for its QuickVoice deployment:

- **Dynamic delivery rescheduling.** When a customer is not available during the pre-delivery call, QuickVoice currently offers reschedule options from a fixed menu. The next phase will integrate with Onfleet's route optimization engine to offer real-time alternative windows based on actual driver availability and proximity. The customer will be able to say "Can you come after 6 PM?" and the AI will check feasibility and confirm or offer the nearest available alternative.
- **Proactive delay notifications.** When Onfleet's route engine predicts that a delivery will miss its window (due to traffic, earlier stops running long, or a driver reassignment), QuickVoice will proactively call the affected customers with an updated ETA before they even know there is a problem. The target is to notify customers of delays at least 30 minutes before the original window closes.
- **Return pickup coordination.** The startup is adding return logistics for its retail clients. QuickVoice will call customers to schedule return pickups, confirm package readiness, and provide the driver with return-specific instructions. The target is same-day return pickup with a 95% first-attempt success rate.
- **Expansion to two additional metro areas.** The startup is launching in two new cities in the coming quarter. QuickVoice will be deployed from day one in both markets, eliminating the failed delivery problem before it starts. The access code database architecture will be replicated, and the startup expects to reach 70% address coverage within 90 days in each new market based on delivery density projections.

The CEO has also begun conversations with the startup's largest grocery client about a co-branded QuickVoice experience -- where the pre-delivery call includes personalized messages based on the customer's order history, such as recipe suggestions for the groceries being delivered or refill reminders for pharmacy orders. This represents a potential new revenue stream for the startup: selling premium customer communication as a value-added service to its clients.

> "We started QuickVoice to fix a cost problem -- failed deliveries were killing our margins. What we got was a customer experience platform that became our biggest competitive advantage. Our clients' customers now expect the call. When we onboard a new client and their customers start getting calls, the feedback is immediate and overwhelmingly positive. It's become part of what we sell." -- CEO

---

## 8. Key Takeaways

**Failed last-mile deliveries are a communication problem, not a logistics problem.** The startup's 62% reduction in failures was achieved without changing routes, adding drivers, or upgrading vehicles. The only change was calling the customer before the driver arrived. The vast majority of delivery failures can be prevented with a single proactive phone call.

**Access code management is the unglamorous key to last-mile efficiency.** Nearly a third of the startup's delivery failures were caused by access issues at gated and secured locations. Building a living database of access codes -- populated through AI-driven customer calls -- eliminated 82% of these failures and created a durable asset that improved with every delivery.

**Driver idle time is the largest hidden cost in last-mile delivery.** The startup's drivers were spending 8 minutes per delivery on non-delivery activity -- waiting for access, calling customers, searching for the right entrance. Reducing this to 3 minutes freed up enough capacity to handle 80 additional deliveries per day with the same fleet. In last-mile delivery, the most impactful optimization is not faster driving -- it is less waiting.

**NPS is a revenue driver, not just a vanity metric.** The 21-point NPS improvement directly influenced client acquisition and retention. Enterprise clients selecting delivery partners care deeply about the end-customer experience, and a measurable NPS improvement is among the most compelling proof points a delivery startup can offer. The startup attributed three new client contracts directly to the NPS data.

**Multilingual support is a last-mile necessity.** In diverse metro areas, reaching customers in their preferred language is not optional. The startup's data showed a 94% answer rate for calls in the customer's primary language versus 76% for English-only calls. In last-mile delivery, where every unanswered call is a potential failed delivery, an 18-point difference in answer rate translates directly to fewer failures and higher margins.

---

*This case study reflects aggregated, anonymized results from a QuickVoice last-mile delivery deployment. Individual results may vary based on market conditions, delivery density, and implementation scope.*