Consumer mapping solutions follow a generalized approach that compromises on accuracy, leaves little room for customization, and comes at a much higher cost.
For businesses where location accuracy is paramount, a ‘One Map Fits All’ approach can be a huge limitation to performance and scalability. These errors become more pronounced as operations scale and teams struggle to optimize platform performance.
The ‘One Map Fits All’ approach doesn’t fit all, as it lacks the ability to support the unique use cases of an organization. Consumer-centric maps are built to cater to a wider audience, rarely making them a good fit for businesses with specific requirements. This gives rise to a variety of challenges, including compromising on ETA and trip time accuracy.
Mapping solutions for businesses need to provide highly accurate travel time predictions before the trip begins. Yet, consumer solutions aren’t business-critical and simply update the ETA dynamically throughout the journey, producing a higher threshold of error.
The implications of these errors are far more serious for a business than consumers, while this might only slightly inconvenience a regular consumer. If inaccuracies spread across ride allocation, the CX, platform efficiency, and revenue can be negatively impacted.
Solutions of a ’One Map Fits All’ approach also do not account for the complete delivery driver journey. For instance, food delivery ETAs need to be calculated from the counter to couch instead of just the on-road travel times. When it comes to ride-hailing, the driver might take a few extra minutes before starting their journey to the customer. In logistics, truck drivers on long-distance trips often need to take breaks for eating and sleeping, which isn’t accounted for on standard maps.
The ‘One Map Fits All’ approach restricts the ability to customize or overlay proprietary Points of Interest (POIs). Generic maps don’t allow you to add custom POIs without a large amount of coding jugglery, which gives rise to challenges such as walking times for food delivery. In addition, this approach produces missing POIs and when requested, it takes mapping providers months before adding them in. Without customization, maps lack contextual POIs that are relevant for specific use cases.
Limitations on scalability are presented when using the ‘One Map Fits All’ approach. To provide near real-time agent locations and a better user experience, frequent API calls are needed. However, a higher frequency of calls increases API costs, while a lower frequency makes tracking feel sudden and jerky.
Specific scenarios need to be more scalable than consumer-centric solutions in order to provide and manage high latencies, API calls, and traffic spikes. For example, mapping providers can rarely support large matrix API calls that need to run during peak hours, while breaking these into smaller units increases costs further. Another high-density requirement consists of running new data model simulations. Finally, running API calls on a large set of older data can range across months and lead to additional costs.
Even for fewer calls, high latency API calls directly impact your customer experience, as any interaction that takes over 100ms is user perceivable, which makes the UI lag. Challenges like sudden large spikes of traffic can cause downtime in the product. However, with a more scalable solution, this could be a valuable opportunity.
Consumer maps rarely provide local nuances and real-world considerations that are vital for business operations. Including serviceability restrictions, regulatory considerations such as HOV lanes, odd or even rules, and vehicle types. Also, real-world considerations such as traffic, road closures, and road accidents. This long list of somewhat daunting challenges can still be overcome, and fairly easily at that.
A custom map stack can help leverage maps in new exciting ways, with the biggest difference being a shift in mindset. The definition of a custom map stack consists of a map stack that generally picks up from the base map data and has layers added onto it. These layers add contextual data that fits specific use cases. A custom map stack puts you in the driver’s seat, giving greater control for solving problems, options for modification based on feedback, and adding customizations for unique needs.
As a company scales, it becomes essential to have maps that learn from existing data and give control over intricate aspects of a mapping platform. This is crucial to implement in order not spend money on the extra time, effort, and the expense of building a custom solution for a company alone.
Learn more: https://nextbillion.ai
Also published on: https://www.gislounge.com/why-the-one-map-fits-all-approach-doesnt-fit-all/