As a location data company, we know how valuable geocoding can be in unlocking the potential of location data. Address geocoding, in particular, is an essential tool for businesses seeking to map and analyze data by location. In this guide, we will provide a complete overview of address geocoding, including what it is, how it […]
Lead prioritization is not a new concept. Businesses use CRM and other platforms to manage and prioritize their leads for faster conversions and better deal size. What has worked until now is to prioritize leads based on ICP (Ideal Customer Profile) and Degree of Intent, which is essential but insufficient. We take it a step
Quick Service Restaurants thrive on walk-ins. Even though these businesses have partnered with food delivery services for online orders, the overheads and order ticket sizes are the two challenges that come along with it. With hyperlocal targeting, brands in this model can identify specific catchments around their facility with a higher density of their target
Risk assessment and credit profiling for new to credit customers is tricky. Along with it, there is great scope for improving the accuracy of risk prediction for credit-served customers too. For NAVI, we discovered location attributes that indicate the risk appetite of prospect users. Next, we augmented their user database with these location variables/ attributes
In this study, we will discuss how retail major Lenskart has benefitted from its partnership with GeoIQ, a location-based intelligence provider. Having access to location-based data is immensely helping businesses that are running their decision-making engines on data. It has been established fairly well that data is the new fuel for businesses to improve their
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
