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Reduce Return To Origin: Scale Address Validation

Introduction: Return to Origin (RTO) is a significant problem for businesses that rely on accurate and timely delivery of goods to their customers. When a delivery attempt fails due to an incorrect address or the recipient being unavailable, the courier returns the package to the sender or the warehouse, resulting in additional costs, delays, and

Location Data and Intelligence Trends in 2023

In today’s customer-centric world, businesses and organizations are increasingly harnessing different technologies to optimize operations, improve customer experiences, and drive growth. Location data is particularly valuable in this rapidly changing world, where consumer behavior and market trends can shift rapidly. The geospatial data not only tells us where people are, but also provides insights into

Boosting Debt Recovery and Collections with Address Intelligence

Debt collection is a delicate undertaking for lenders as they tend to dabble between debt recovery and retaining customers. Perhaps the most challenging function in the lending ecosystem, debt collection has immense scope for improving its processes. The current challenges with debt collection are: Incomplete or old address information of the debtors Reachability issues due

GeoIQ’s Solution to Customer Segmentation

Proper customer segmentation is important because it allows businesses to focus their marketing efforts on specific target markets more likely to purchase their products or services. As there is always a scenario where you’ll find a group of a target audience that you have been not able to cater to for your products/services. So, segmenting

Underwriting Credit in 2023: How location intelligence benefits modern BFSI

TL;DR: The information banks and NBFCs have access to is restricted, and does not substantially mitigate risk. It is because the lending industry relies on credit score analysis as a standard tool of risk assessment. Inherent nuances within those systems create ambiguities that, in turn, leave margin for errors, omissions, and confusion — leaving a

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