For businesses where location accuracy is paramount, a ‘one map fit all’ approach can be a huge limitation to your performance and scalability. Consumer mapping solutions follow a generalized approach that compromises on accuracy, leaves little room for customization and comes at a much higher cost. 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 an organization’s unique use case. Consumer-centric maps are built to cater to a wider audience and are rarely a good fit for businesses with specific requirements. This gives rise to a variety of challenges, including compromising on ETA/trip time accuracy. Mapping solutions for businesses need to provide highly accurate predictions before the trip begins. Yet, consumer solutions aren’t business-critical and simply update the ETA dynamically throughout the journey, they have a higher threshold of error.
While this might slightly inconvenience a regular consumer, the implications for a business are far more serious. If inaccuracies spread across ride allocation, it can impact CX, platform efficiency, and revenue. Also, solutions do not account for the complete 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 may take breaks for eating and sleeping, which isn’t accounted for by standard maps.
The ‘One Map Fits All’ approach also restricts the ability to customize or overlay proprietary POIs. Generic maps don’t allow you to add custom POIs without a lot of coding jugglery. This gives rise to challenges such as walking times for food delivery.. In addition, the approach has absent POIs and mapping providers take months before adding them. Without customization, maps lack contextual POIs that are relevant for specific use cases.
Limitations on scalability are presented if using the ‘One Map Fits All’ approach. To provide near real-time agent locations and for better user experience, frequent API calls are needed. However, a higher frequency of calls increases API costs, while a lower frequency makes the tracking feel sudden and jerky.
Certain scenarios need to be more scalable than consumer-centric solutions provide in order to 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. Again, running the API 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. A custom map stack puts you in the driver’s seat, giving greater control for solving problems, options for modifying based on feedback, and adding customizations based on your unique needs.
About the Author
Ajay Bulusu, is the co-founder of NextBillion AI, an industry-leading startup in mapping platforms providing software-as-a-service (SaaS) for enterprises. Bulusu has spent the last decade working in mobility, maps, digital advertising, and eCommerce across various roles in the US, Singapore, India, Japan, and the UK. Bululu formerly worked at Google and led the Geo team at Grab. After building out the Grab Geo team from 0-400, he found the inspiration to start NextBillion AI. Bulusu handles investor relationships, APAC customer relationships, and manages the largest early-stage customers of Nextbillion AI.