Introducing: Nearmap for Post-catastrophe Response
Post-catastrophe imagery and AI-derived property damage and condition data unite to help insurers process customer claims more efficiently.
Post-catastrophe imagery and AI-derived property damage and condition data unite to help insurers process customer claims more efficiently.
How often do you ask, “Am I in the right place?”
It’s a good question to consider as the accuracy of location data is crucial for services we use daily—Google Maps, Uber and Lyft, GrubHub, DoorDash, Instacart, UPS, FedEx, and more. Surprisingly, 5% of all last-mile deliveries fail due to inaccurate location data.
While 5% might seem minor, the reality is stark: address inaccuracies result in an average cost of $17.78 per failed delivery. Multiply that by a million errors, and your costs surge to nearly $18 million. Additionally, last-mile deliveries are not just a logistical challenge; they represent over half (53%) of total shipping costs. The last-mile experience profoundly impacts customers, drivers, and ultimately, a business’s bottom line. As service areas continue to grow and expand, the precision of location data becomes increasingly important.
Imagine a system where destinations could be validated within seconds, offering precise location data with unparalleled accuracy. Such a solution—destination validation—exists.
To bring this solution to life, a key ingredient is imagery capture that is consistent, recent, and high-resolution. This includes various types of aerial imagery, such as vertical aerial, oblique, and panoramic views. However, you may ask, “Why would I need that imagery when I can find it elsewhere?” There are two problems with this.
First, imagery sources like Google or Satellite are inconsistent, often outdated, and lack the high-resolution needed to catch the necessary details to finish a delivery. That’s particularly important as homes, buildings, and infrastructures change over time. Second, jumping from app to app or service to service is an inefficient use of time. Plus, you may be forced to check multiple sources to verify which source is the most accurate. That leads to the next piece of the puzzle.
Housing the imagery and location data in an intuitive user interface (UI) would deliver the accurate information you need to go the last mile in deliveries. Ground sampling distance (GSD) resolutions as sharp as 2.2" to 3" would allow businesses to visualize their final destinations with unprecedented clarity. Additionally, AI advancements like computer vision and machine learning could analyze and process the imagery for even more accurate destination plotting; this could include areas for drone delivery, poles and wires, or possible construction activity. Multiple industries would benefit from this technology—let’s consider a few examples.
Ensuring timely and accurate deliveries is important for online customers to maintain customer satisfaction and remain competitive. Acquiring a new customer can be five times more expensive than retaining an existing one. This fact underscores the financial impact of every failed delivery. Destination validation can enable businesses to sharpen their delivery accuracy, dramatically reducing the chance of costly reshipments and returns. Integrating such a system doesn't just keep customers content—it builds long-term loyalty. By exceeding delivery expectations with unmatched precision, businesses cater to the 75% of consumers who favor brands that excel in last-mile delivery, reinforcing their market position and driving profitability.
In logistics, the chain is only as strong as its weakest link. A single failed delivery can result in increased warehouse storage, inventory mismanagement, and lost sales. Destination validation can become the linchpin for operational efficiency, seamlessly integrating with Warehouse Management Systems (WMS) and Transportation Management Systems (TMS) to enhance real-time decision-making. It's not just about reducing errors. Rather, it's about preemptively creating a streamlined path from warehouse to doorstep. Moreover, as companies face pressure to reduce their environmental impact, route optimization facilitated by destination validation can lead to a significant decrease in carbon emissions, solidifying a company’s commitment to sustainability while saving costs.
Ride-share companies operate at the intersection of safety and precision. With destination validation, drivers could receive the most accurate data for optimal pickup and drop-off locations, which is especially critical in high-density urban areas or complex residential layouts. Safety would also improve as the likelihood of drivers and riders finding themselves in potentially unsafe situations is reduced. More importantly, accurate data is essential for cutting costs—consider the average trucking cost per mile in the U.S. is $2.90. Even a modest route optimization that reduces miles driven by 10-30% could translate to significant savings across the fleet. Destination validation technology can solve this and enhance the ride-share and transportation experience for everyone involved.
As businesses continue to adapt to the evolving demands of the marketplace, they should consider bringing destination validation to life. A tool leveraging consistent, frequent, and reliable imagery in an intuitive UI, presents the next major opportunity for success and innovation. Destination validation can empower businesses to navigate the last mile with confidence and efficiency, saving time and resources for the future.
While we make every effort to ensure the accuracy of the data and analysis in blog articles, this information is not to be relied on as professional advice. No endorsement or approval of any third parties or their advice, opinions, information, products or services is expressed or implied by any information in the blog. Should you seek to rely in any way whatsoever upon this content, you do so at your own risk.
