Extraprise CRM: Extending Systems Outside the Four Walls

Thanks to the Internet of Things (IoT), modern CRM systems have advanced to the point where companies can now leverage the tremendous amounts of data available not only from their own internal systems but also from suppliers, business partners, customers, and other resources, and they can push information out to those parties as well, in real time. This new type of CRM, called extraprise CRM, has huge potential for changing how companies do business forever.

Extraprise CRM is a new artificial intelligence-based CRM that enables companies to go outside the boundaries of their own four walls and integrate with suppliers, business partners, and customers via digital twins, explains Jim Dickie, a research fellow at Sales Mastery, a research firm that specializes in how companies are leveraging technology to transform sales.

Digital twins are virtual models of actual processes, products, or services, acting as a bridge between the physical and digital worlds to help companies more quickly and efficiently analyze data and monitor systems to prevent problems before they occur, improve products, streamline processes, and even plan for the future. They largely rely on IoT sensors to gather data about the real-time status or location of physical assets, product performance under various conditions, customer traffic patterns within retail stores,

how customers use products, and a host of other useful information. That data is then transferred to cloud-based systems that process and analyze it within the proper business context.

The underlying principle behind extraprise CRM is to uncover, decipher, and act on more data than has ever been available before. While the data that companies can collect from their own resources can supply details about shopping patterns and preferred types of purchases, for example, that is only a small fraction of what is available through extraprise CRM. Companies can receive much more detailed information and better insight by incorporating data from other stakeholders, such as suppliers and business partners, via extraprise CRM, Dickie explains.

For example, IoT sensors in the plant where GE builds its jet engines can supply ongoing and historical information about the manufacturing process, confirming that components meet specs before being placed into assembly. All of that detail can then be shared with the purchaser—an airplane manufacturer like Boeing, for example—who can then confirm that the engine meets its unique specs before the engine is mounted under the wing.

In its own test of the aircraft before sale, Boeing can monitor how the engine and other components perform under different altitudes, speeds, and weather conditions and share that data with the buyer—an airline like Delta, for example—and with GE.

Similarly, once Delta buys the finished plane, it could combine Boeing’s and GE’s test information with its own actual in-flight performance data, all of which can be shared internally and back through the supply chain to maximize the performance of its fleet. Theoretically, the data could show the airline that flying at different times of day, altitudes, speeds, and so on maximize fuel economy, and it could adjust schedules and flight patterns accordingly. Another partner could augment the sensor data with weather information, such as temperature, wind speed and direction, barometric pressure, etc., to help Delta refine flight plans even further. Customers can even supply feedback, letting Delta know that the newer engines are a bit noisier than previous models, which the airline can then share back through its supply chain.

Within this scenario, each individual part of the supply chain avoids having to go through the expense or time of conducting batteries of tests individually to determine whether theoretical changes in design or flight patterns will produce better results. Doing that in the physical world would take way too much time and effort.

The concept of digital twins is not a new one. The technology was first proposed as early as 2002, but it has really only been made possible within the past few years with the birth of the IoT. And Gartner has included digital twins in its Top 10 Strategic Technology Trends for the past three years, predicting that by 2022 as many as a billion things could be represented by digital twins.

GE is a staunch proponent of digital twins, going so far as to say that they don’t just need to act independently. “Sometimes a group of digital twins or an aggregate can also be beneficial,” the company said in a recent blog post. “If your organization is monitoring multiple systems of the same type of assets, you can start to learn from all of them as a cohort and find similar patterns or trends, and that analysis can lead to refining models for higher fidelity in the future.”