Since the beginning of modern retailing, retailers have used geographic information to determine the trading area around their stores (Christensen and Tedlow 2000). Subsequently elaborated spatial marketing techniques have emerged and sophisticated models have been developed to address location based marketing efforts (Huff 1964, 1966; Applebaum 1966; Nakanishi, Cooper and Kassarjian 1974; Huff and Batsell 1977; Gautschi 1981; Malhotra 1983; Cliquet 1995, 2006; Baray and Cliquet 2006; Post and Kagan 2012). Today, the emergence of m-commerce and location based services (LBS) represent a new marketing challenge as well as opportunities for retailers. The ability to track customers’ moves through their smartphone Geographic Positioning System (GPS), allows retailers to refine their trading area and deliver timely and relevant promotional strategies to customers in their immediate vicinity through geofencing. This paper explores approaches for the effective integration of several focal areas of advanced analytics on currently achievable big data platforms to enable timely, and maximally effective, geofence-triggered interventions (push marketing) that leverages viral or exponential returns. The viral or exponential growth behavior of modern social media-based interactions has garnered much attention in both public and private circles. The potential for harnessing and controlling these epidemic-like dynamics of spread or diffusion represent a significant and, as yet, underdeveloped marketing approach. This is especially true in the context of geo-fencing strategies and designs. Despite the potential for highly-leveraged returns for location-based services (LBS) several barriers remain. Due to privacy concerns, legal issues, the immaturity of big analytics, and constraints presented by physical-level communications and geo-tracking enabling technology, LBS has remained underdeveloped and under-actualized. This reality is rapidly changing.