Improved Market Access of HERE and Esri Location Functionality via AWS

It has been no secret that Amazon Web Services (AWS) has been steadily increasing their pervasiveness and utility across all aspects of an enterprise’s digital transformation. AWS’s extensive public cloud service-oriented architecture (SOA) has enabled them to provide flexible pay-as-you-go compute, networking, and storage capabilities to many industry verticals. Indeed, AWS’ Elastic Cloud Compute (EC2), Simple Storage Service (S3), and Direct Connect and CloudFront (CDN) services have been instrumental in helping diverse companies deliver more personalized products/services/content to a wider global audience. Aside from its core services, AWS has also been expanding its portfolio with its recent additions of developer-centric services such as its container orchestration platforms—Amazon Elastic Container Service (ECS), Amazon Elastic Kubernetes Service (EKS)—and its serverless architecture service, AWS Lambda. AWS’ incoming CEO, Adam Selipsky, aims to continue driving growth by expanding cost-efficient service offerings through its continuously expanding global data center footprint (AWS regions, local zones, and outposts). The introduction of Amazon Location Service is another chapter that continues this legacy.

Amazon Location Service allows customers to cost effectively add location data and functionality to their applications without compromising on user privacy or data security. Customers can enrich their applications with location functionality from well-established location-based service (LBS) providers Esri and HERE Technologies. Esri and HERE offers a deep reservoir of maps, points of interests, geocoding, route planning, geofencing, and asset tracking that can augment/develop existing/new products and services. Amazon Location Service is claimed to go as low as 1/10th the cost of the most common LBS providers, and customers pay only for the number of user requests, assets tracked, or devices managed.

Cloud advancements in Artificial Intelligence (AI) and Machine Learning (ML) have enabled enterprises to dynamically synthesize a multitude of data points relating to their customers’ demographics, preferences, and behaviors, allowing them to generate more revenue through the delivery of hyper-personalized products, customer engagement, and pricing. The integration of quality location intelligence data within an enterprise’s omni-channel analysis can further enhance its ability to develop impactful strategies to improve customer experience, revenue generation, or fine-tune operational efficiencies.

The main use cases enabled through location data (e.g., geo-marketing, route-planning for vehicle fleets, supply-chain management, discriminate pricing) are versatile and can offer a sizable impact on the operational and strategic execution of many industry verticals. For example, a brick-and-mortar store can utilize advanced location functionalities to improve customer experience by adding live physical store metadata that shows real-time inventory, availability of service, and operating hours. Additionally, location data can help physical retail stores transition their business models to e-commerce by streamlining operations while also delivering more value to customers. Location intelligence can help optimize the management of in-store pick-ups and home deliveries through route optimization based on inventory availability amongst their respective stores. Location functionalities can also improve e-commerce customer satisfaction through real-time asset tracking.

Real estate companies also stand to gain substantially by embedding location data within their operations. By integrating map interfaces of HERE and ESRI with first-, second-, or third-party data, real-estate companies can develop richer maps that aggregates critical real-estate related data points. Real-time valuations of properties can be driven by data layers depicting recent sales of similar properties within the neighborhood, nearby points-of-interests, future development projects within the area, or even automatic calculations of commute/travel times of a potential customer’s travel to a workplace/school. Financial institutions can utilize location data to enhance their “Know Your Customer” (KYC) protocols and fraud detection systems by having accurate visibility of the locations of loan applications or financial transactions.

As discussed in the previous section, the impact of location functionality is applicable in many different contexts. Companies applying location intelligence in operations that directly align with their core business strategy and product/service delivery will position themselves to fully maximize the value of location technology. Successfully integrating and maximizing location data and functionalities, however, will require companies to exercise vigilance in certain issues.

Accurately measuring the Return on Investment (ROI) from embedding location intelligence within a process is critical for a company to decide if the process is worth adding in the long-term. While Amazon Location Services have lowered the costs in acquiring these functionalities, it is essential for companies to be judicious in allocating their resources to processes that ensure higher revenue projections. Another issue that is worth looking into is how companies, newly emboldened with location functionalities, should manage their influx of location data. Considerations such as cross-border data flows and regulatory frameworks governing Personally Identifiable Information (PII) should be assessed before deploying location-centric services across many localities. Amazon Location Service does ensure data privacy and security by removing customer metadata and account information from queries before they are sent to an LBS provider. Possible complications and risks may arise, however, as Amazon Location Service gives customers complete control of their location data. Proper education and information dissemination on managing data will mitigate any risks pertaining to improper data management.