How can near-real-time communication modes offer context?

The nature of communication is changing, as the collaboration space becomes more aligned with the broader trend of digital transformation. Real-time communication modes -- such as voice or video -- have the highest value for users, since the intended meaning of the interaction is clear. If the spoken word lacks clarity, the missing pieces can usually be deduced from the nuance of speech or the speaker's body language.

However, near-real-time applications -- such as chat-based messaging -- are gaining massive adoption, but they have their tradeoffs. The shorter the form of communication, the less information is conveyed. This is where near-real-time communication is lacking, compared with real-time communication modes.

In essence, near-real-time communication modes lack context. If taken at face value, these interactions often require augmentation to the original message until the intended meaning is clear.

While text-based communication modes can be highly efficient, the benefit can be offset by the extra effort required to provide the missing context. This is problematic in the workplace and poses a major challenge in the contact center, as well, especially as customers now rely more on text-based channels to chat with agents.

This brings us to contextual communications. When near-real-time communication modes are used in isolation, there is no context. Collaboration platforms and unified communications help address the need for contextual communications.

By integrating messaging with other communication modes and business-based platforms, such as customer relationship management, context can be provided in real time. This is invaluable in the contact center, since agents cannot put the customer on hold to hunt down pertinent information. Artificial intelligence, in this case, is the real driver and very much part of the digital transformation trend.

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