The Machine-to-Machine (M2M) field is increasingly becoming a more important source of connections for global mobile operators. The global M2M industry was valued around $45 billion in 2013, and is forecast to reach over $200 billion by 2022. As this is no longer a field test and has proven to be a legitimate growth opportunity, many companies are adapting their businesses and models to serve a range of vertical industries with M2M solutions. Cross-vertical “horizontal enablers” are increasingly developing as well to achieve economies of scale and an overall improvement in efficiency in M2M service delivery.
As of January 2014, there were 428 mobile operators offering M2M services in 187 different countries; that is nearly 40% of all mobile operators worldwide. The fastest growth happened between 2010 and 2013, when global M2M connections went from 75 million to 195 million connections, accounting for 2.8% of total mobile connections in 2013. Ten countries make up for 70% of all M2M connections: China, US, Japan, Brazil, France, Italy, UK, Russia, Germany and South Africa. China and the US jointly account for 44% of all global M2M connections.
M2M as a share of total connections is an indicator of M2M market maturity. The top 4 markets by this measure are Sweden (23%), Norway (15%), New Zealand (14%) and Finland (11%). The rise of M2M in some of these countries was largely driven by regulatory initiatives, especially in the smart metering sector. For example, Sweden passed legislation that requires each household to accurately monitor monthly electricity consumption, which triggered the rollout of smart meters. China also identified M2M as one of its key projects in its 12th Five-Year Development Plan (China’s social and economics initiative series), intending to heavily invest in its Internet of Things market. As the M2M market gains momentum and is increasingly deployed rather than tested, the global market will become a more fluid, connected vessel that allows for information transfer to be more efficient and accurate.