Leveraging Last-Mile Analytics for Delivery Success (A Quick Guide)

E-commerce is booming, with a global e-commerce growth rate for 2024 forecast at 9.4%.

But with this growth comes immense pressure on retailers to meet customer expectations for fast, on-time deliveries.

This is where last-mile delivery – the final leg of getting the order from the warehouse to the customers’ doorstep – becomes crucial. However, the last mile is fraught with challenges like driver shortages, traffic delays, and inventory issues. 

So how can you, as an e-commerce retailer, effectively tackle these last-mile issues?

The answer lies in leveraging last-mile analytics. 

Last-mile analytics refers to gathering and analyzing data from each stage of the last-mile delivery process, from first-mile pickup to final delivery. 

By leveraging last-mile analytics, you can gain transparency into each order’s journey and use actionable insights to optimize routes, boost capacity, and exceed customer expectations.

In the blog, let’s discuss this in a bit more detail.

What is Last Mile Analytics?

Last mile analytics refers to the process of gathering, analyzing, and optimizing data related to the final leg of the delivery journey – from the transportation hub to the customer’s doorstep.

The overarching goal of last-mile analytics is to improve operational efficiencies, enhance overall customer experience, and increase cost-effectiveness in the critical final stage or last leg of delivery. 

Last mile analytics typically involves:

• Collecting data points across the last mile delivery process – examples include driver location data, proof of delivery, customer feedback, traffic patterns, weather conditions, etc.
• Analyzing this data to identify bottlenecks, inefficiencies, and gaps. Key metrics tracked include delivery ETAs, driver performance, last-mile delivery route optimization, customer satisfaction, and order accuracy.
• Using optimization algorithms, predictive analytics, and data modeling to improve last-mile planning and execution. This could involve dynamic route planning, ETA forecasting, inventory visibility, and more.
• Creating dashboards to monitor last-mile analytics in real time and help teams make data-driven decisions on the go.

Companies that get last-mile analytics right can achieve substantial competitive advantage in their industries. So, let’s discuss those advantages.

8 Key Benefits of Last Mile Analytics

Last-mile analytics offers several benefits for businesses involved in last-mile logistics, supply chain management, and e-commerce. Here are some key benefits:

1. Improved customer satisfaction

By optimizing last-mile delivery, ensure more accurate ETAs, minimal missed deliveries, and proactive communication with customers. This way, you can deliver that coveted “wow” customer experience. 

According to Dotcom Distribution research, 87% of U.S. online shoppers cited shipment speed as a critical consideration when deciding whether to make another purchase from an e-commerce site.

Additionally, analytics also provide insights into customer preferences for delivery based on factors like demographics, location, etc. This enables more personalized delivery options.

2. Increased delivery efficiency

Optimizing delivery routes and schedules through last-mile analytics leads to increased delivery efficiency. 

You can analyze factors such as traffic patterns, delivery windows, and real-time conditions to minimize travel time and ensure timely deliveries. This not only satisfies customer expectations but also streamlines delivery processes.

All of this will help you deliver more orders in shorter time windows.

3. Enhanced visibility

Last-mile analytics provides you with greater visibility into your delivery operations.

Through data analytics, companies can track and analyze key performance indicators (KPIs) related to delivery times, accuracy, and other metrics. Even real-time tracking and visibility into last-mile movements enable proactive decision-making.  

This last-mile visibility enables you to identify issues like weather delays or traffic congestion, find areas for efficiency enhancement, and allow for continuous improvement.

4. Lower last-mile costs

Efficient route planning and resource utilization contribute to reducing last-mile costs.

You can identify cost-saving opportunities by analyzing data related to fuel consumption, vehicle maintenance, labor costs, and other operational expenses. This cost optimization enhances the overall profitability of last-mile delivery operations.

5. Data-driven decision-making

Instead of making assumptions, it’s best to make decisions based on actual data, and last-mile analytics helps you do so.

By analyzing historical and real-time data, you can identify last-mile delivery trends, anticipate demand, and implement strategies to improve the delivery process. This data-driven approach ensures that decisions are based on insights rather than assumptions.

6. Better utilization of assets/drivers

Analytics helps identify over-utilized and under-utilized assets and optimize workload across fleets.

By analyzing data on delivery performance, you can allocate resources more effectively, ensuring that drivers and vehicles are utilized to their full potential. This way, you can get better ROI from your driver pool and fleet investments.

7. Proactive stock management

Predictive analytics in last-mile operations help businesses proactively manage stock levels. 

By analyzing historical data, demand trends, and upcoming orders, you can ensure that warehouses are stocked with the right products at the right time.  

This minimizes the risk of stockouts, prevents overstock situations, and improves overall inventory management.

8. Competitive advantage

By mastering last-mile analytics, you can provide customers with a delivery experience that sets you apart. 

Offering faster, more reliable, and cost-efficient delivery services enhances brand perception, improves customer loyalty, and attracts more customers. 

As consumers increasingly prioritize quick and reliable deliveries, you can serve them precisely when and how they want them with optimized last-mile operations.

12 Critical Metrics to Track in Last Mile Analytics

To optimize last-mile operations, businesses need to monitor a wide range of performance indicators. Here are some of the most important last-mile delivery metrics to track:

1. On-Time Delivery (OTD)

On-time delivery (OTD) is a critical metric that measures the percentage of deliveries completed within the promised or expected delivery window. 

Simply put, it assesses how well a company adheres to its delivery time commitments. 

As per Verte research, 21% of customers feel that a delivery delay of even one day would not be acceptable, while 27% express a desire to get their order the same day or the following day.

So, high OTD rates directly enhance customer satisfaction, as customers value reliability and punctuality in their delivery experiences. The goal should be to maintain an OTD above 95%.

2. Delivery time variability

Delivery time variability looks at the consistency or variability in delivery times. In other words, it evaluates how predictable a company’s delivery schedule is. 

Consistency is key for customers who prefer knowing when to expect their deliveries. For instance, if some orders take 2 days while some take 4 days, it creates inconsistencies.

According to the Ipsos-Octopia research, delivery time is by far the most crucial aspect for online shoppers.

A high degree of variability can lead to uncertainty and customer frustration, highlighting the importance of a steady and reliable delivery timeline.

Tracking variability helps maintain similar lead times for the majority of deliveries. The lower the variability, the better.

3. Delivery accuracy

Delivery accuracy measures the percentage of deliveries made without errors, ensuring that customers receive the correct items in the right quantities. 

For deliveries with multiple items, accuracy needs to be assessed for each line item. A high accuracy rate of 99%+ is essential for customer satisfaction and minimizing returns.

4. First-time delivery success rate

The first-time delivery success rate assesses the percentage of orders successfully delivered on the first attempt without issues like invalid addresses or customer unavailability.

The higher the rate, the lesser the number of expensive re-deliveries needed. You should strive for over 90% first-time success.

5. Route efficiency

Route efficiency evaluates how well delivery routes are optimized in terms of distance, time, and fuel consumption. 

By comparing planned vs. actual distances covered, route efficiency can be tracked. Poor route planning, traffic delays, and driver deviations can reduce efficiency and affect delivery times. 

Optimized routes contribute to cost savings and faster deliveries. Consider using an efficient route planning software, like Upper, to minimize unnecessary detours. It not only reduces costs but also ensures that packages reach their destinations swiftly.

6. Vehicle utilization

Vehicle utilization assesses the efficiency of using delivery vehicles, considering factors like capacity and mileage. 

Tracking vehicle capacity utilization helps identify available capacity to handle more deliveries and work at full potential. 

Underutilized fleet capacity is wasteful. The target should be optimizing loads to achieve over 80% utilization of available vehicle capacity.

7. Driver productivity

Driver productivity measures the number of deliveries or stops completed per driver within a specific time frame. 

Metrics like deliveries per driver per day, per hour, and total distance covered by each driver provide insights into driver performance. By tracking individual productivity, training needs and staffing gaps can be identified to balance work allocation. 

Higher productivity means that drivers are more efficiently utilizing their time and resources. 

8. Delivery cost per stop

Delivery cost per stop calculates the cost incurred for each delivery made. This KPI measures total delivery cost divided by the number of stops. 

Granular data on cost per stop highlights unprofitable delivery locations and aids decision-making on pricing strategies, vehicle size, etc.

Think of it as determining the cost-effectiveness of delivering each package – reducing this cost per stop contributes directly to the financial health of last-mile operations.

9. Customer satisfaction scores

Customer satisfaction scores are a direct measure of how satisfied customers are with their delivery experiences.

Feedback surveys, NPS, and reviews provide quantifiable data on customer perceptions of the last mile experience. Monitoring satisfaction metrics helps in identifying areas where issues exist and prioritizing improvements that customers value the most.

10. Return rates

Return rates measure the percentage of delivered items that are subsequently returned by customers. 

The average e-commerce return rate is between 20% and 30%.

High return percentages indicate potential damage, inaccuracies, or other issues. Analyzing return data helps uncover root causes – like inadequate packaging or shelf life – to prevent future returns and improve delivery processes. 

11. Peak period performance

Peak period performance evaluates how well delivery operations perform during high-demand periods, such as holidays or promotional events. 

By evaluating service levels during peak vs. non-peak times, companies can identify capacity shortages based on demand fluctuations and seasonality. Ensuring operations can scale effectively during peak times is crucial for meeting customer expectations.

12. Inventory turnover in the last mile

Inventory turnover in the last mile measures the rate at which inventory is utilized or replenished, specifically for last-mile deliveries. Optimizing inventory turnover helps prevent stockouts and overstock situations. 

Consider the importance of maintaining optimal inventory levels to meet customer demand without unnecessary surplus. It contributes to smoother last-mile operations and inventory management.

Industry-Specific Use Cases for Last-Mile Analytics

Here are some common use cases and examples of how last-mile delivery analytics can be applied in various industries:

1. E-Commerce and retail

In the e-commerce and retail sector, last-mile analytics is crucial for managing high order volumes. Businesses use analytics to:

• Optimize delivery routes
• Track real-time driver locations and predict ETAs
• Improve delivery accuracy
• Analyze customer feedback and satisfaction metrics
• Monitor return rates by product category and location

This results in reduced delivery times, lower operational costs, and increased customer satisfaction.

2. Healthcare providers and pharmaceuticals

In healthcare and pharmaceutical industries, last-mile delivery analytics is utilized to

• Ensure on-time delivery of critical clinical supplies like pharmaceuticals, blood products, etc. to hospitals
• Monitor temperature deviations for shipments requiring strict temperature control to avoid spoilage
• Optimize critical equipment and medical supplies stocking at clinics to reduce last-minute emergency shipments
• Analyze late deliveries, delays, and bottlenecks in the delivery of implants, prosthetics, etc. for surgical procedures

3. Logistics and transportation industry

Logistics and transportation companies use last-mile analytics to manage large volumes of shipments efficiently. Analytics helps to:

• Identify underperforming delivery routes and drivers
• Reduce transit times
• Dynamically adjust delivery schedules and routes, factoring in vehicle capacity and staff availability
• Track temporary spikes in delivery volumes during peak seasons
• Analyze inventory levels to optimize last-mile delivery times

This results in cost savings and improved service levels for both businesses and end consumers.

4. Food and grocery delivery

Food and grocery delivery services leverage last-mile delivery analytics to ensure the timely and efficient delivery of perishable goods. Analytics is employed to:

• Track food or grocery temperature and delivery times to ensure food safety compliance
• Optimize delivery fleet routing during peak meal times to meet customer demand
• Analyze meal and grocery delivery data to identify items that travel well vs. those at risk of spoilage
• Improve order accuracy by tracking complaints about incorrect/missing items

This is particularly important in the competitive and time-sensitive nature of the food and grocery delivery industries.

5. Automotive industry (Parts delivery)

Automotive manufacturers and suppliers use last-mile analytics to streamline the delivery of spare parts to dealerships, distribution center, service centers, and end consumers. Analytics helps to

• Enable real-time tracking of high-value auto-part shipments
• Analyze warranty part replacement rates to identify potential delivery damage or mishandling
• Manage demand fluctuations
• Optimize timing and bundling of parts delivery from warehouses to minimize urgent/rush shipments
• Evaluate impacts of expedited vs. standard delivery on customer satisfaction scores

Best Practices for Implementation of Last-Miile Analytics

Here are some best practices for the successful implementation of last-mile delivery analytics:

1. Start small and expand scope over time

Begin the implementation with a pilot program or a limited scope before scaling up. Initially, focus on one or two high-impact areas like delivery costs or customer satisfaction.

This approach allows for testing the effectiveness of last-mile analytics in a controlled environment, identifying challenges, and refining strategies before a broader rollout. 

Once the analytics process is streamlined, expand the scope to cover more metrics and use cases.

2. Focus on critical KPIs first

Avoid getting overwhelmed by identifying 3-5 metrics. Instead, it’s better to prioritize and focus on only the most critical Key Performance Indicators (KPIs) that align with your goals. 

Whether it’s on-time delivery, delivery accuracy, or cost per delivery, concentrating on essential KPIs ensures a targeted and strategic approach.

3. Crafting analysis plans

Develop clear and comprehensive analysis plans to guide the use of last-mile analytics. You need to define:

• Specific metrics to be measured
• Data sources to be utilized
• Methodologies for analysis
• How insights will influence decisions

Having well-defined analysis plans ensures consistency in data interpretation and helps in extracting actionable insights from the analytics.

4. Integrate analytics into existing operations

Seamlessly integrate last-mile delivery analytics into existing operations and technology systems. This integration ensures a smooth transition and allows for a cohesive view of the entire supply chain. 

Compatibility with existing systems, such as Warehouse Management Systems (WMS) and Transportation Management Systems (TMS), enhances overall efficiency and data accuracy.

5. Make sure data is reliable and accurate

Ensure the reliability and accuracy of the data used for analytics. 

Invest in data quality, validation, and error-checking mechanisms to rectify bad data which leads to false insights. You can even establish protocols like machine learning algorithms to detect anomalies.

Reliable data is fundamental for making informed decisions and avoiding the risk of inaccuracies that could impact the success of last-mile operations.

6. Continuously monitor analytics and refine strategies

Keep reviewing progress on a weekly/monthly basis, identify new opportunities and gaps, analyze results, and refine strategies based on insights gained. 

Continuous monitoring allows for proactive adjustments. This ensures that the analytics solution remains aligned with changing business needs and market conditions.

Turn Insights into Outcomes with Upper’s Reports and Analytics

As we have seen, last-mile analytics provides actionable insights to tackle the biggest pain points in last-mile delivery – from inefficient routes to poor customer experience.

However, the key is translating analytics into positive outcomes for your business.

This is where solutions like Upper, a leading route planning and optimization software. Upper offers robust last-mile analytics and reporting capabilities built right into its route optimization platform. Businesses get access to delivery performance, driver tracking data, and intuitive reporting to make smart decisions.

Want to know what all Upper has to offer that can help you with your last-mile analytics?

1. Data analytics and reporting

Upper collects and processes data from delivery history and driver performance, offering three distinct types of reports:

• Summary report: Provides a concise overview of key metrics and delivery performance.
• Detailed report: Offers a granular view, allowing businesses to analyze specific aspects of their last-mile operations.
• Driver live tracking report: Enables real-time monitoring of driver movements, enhancing visibility.

2. Rich dataset

The reports furnish essential data points, including:

• Estimated and actual duration for a nuanced comparison of planned vs. actual delivery times.
• Estimated distance to gauge the distance covered between delivery locations.
• Start and end times of deliveries, facilitating precise timing analysis.
• Delivery status tracking for insights into completed and missed deliveries.
• Delivery notes capture crucial information and special instructions.
• Proof of delivery, offering a digital confirmation of successful deliveries.

3. Performance measurement

Upper’s reports and analytics enable businesses to measure and evaluate delivery performance comprehensively. 

By generating and downloading weekly reports, businesses can analyze each driver’s performance, identify efficiency bottlenecks, and make data-driven decisions for continuous improvement.

As you look to leverage data and analytics to master your last mile, having the right technology infrastructure is crucial. 

Solutions like Upper provide the analytics foundation while also offering other capabilities like route optimization, scheduling, dispatch, and driver apps tightly integrated together. 

So, start a 7 days free trial today and experience the difference firsthand!