CX platform provider SandSIV, has added “one of the world’s largest airlines” to its customer base.
The as-yet unnamed carrier joins other business-to-consumer brands in using SandSIV technology as a global hub for Voice of Customer (VoC) intelligence.
Airlines have access to large amounts of internal and external passenger data, much of it unstructured. Big data techniques are now making it possible for them to use this to better understand how loyalty is driven by the experiences they provide.
Those which have embraced this are able to focus on improvements in the areas that matter, cultivate customer loyalty and create competitive advantage.
SandSIV’s enterprise SaaS platform was built from the ground up to holistically manage Customer Experience. Going beyond simple surveying, the cloud-based solution enables enterprises to put the VoC at the heart of their organisation – no matter the language, format, or source of the feedback – and to act in real-time on relevant insights throughout the entire organisation.
Federico Cesconi, SandSIV CEO, said: “In a world where the customer has the power of choice, it can be tough for airlines to provide a differentiated experience and appeal to the next-generation passenger.
Their main challenges relate to the proliferation of feedback sources, tools and channels, the variety of advanced customer analytics techniques that need to be consistently applied to turn raw data into meaningful insights and, of course, the security required to comply with stringent data privacy regulations.”
SandSIV addresses these challenges by delivering a fully integrated Voice of Customer solution that applies second generation deep machine learning (i.e. Google Brain’s TensorFlow) and advanced Natural Language Processing (i.e. Facebook AI Research’s fastText).
It allows the airline to gather data throughout its entire feedback ecosystem including social media, web/mobile, internal systems, and external tools, and to perform in real-time unsupervised topic detection, automated categorisation, and classification as well as language agnostic sentiment analysis.