Data Science for Customer Feedback
The only way to keep up with user feedback lies within Data Science.
It is nearly impossible for a known brand to parse through all customer feedback on the Web. Millions of comments come through e-commerce reviews, customer support, social media, forums… There is not enough time for a human team to sift through these messages and find relevant content to create actionable insights.
The future of customer support must rely on Data Science techniques to help Customer Support teams find those insights in time. Companies are slowly turning to automated tools to bridge the communication gap between them and their clients, like chatbots and social listening tools.
Data Science is an essential tool to create meaningful interactions with our clients here at Birdie. We use Active Learning techniques to improve our Dashboard in real-time using customer feedback to customize their experiences.
Using millions of consumer reviews collected from e-commerces all around the world, we deliver specific insights over aspects of products: terms that represent a feature of the product or from the services attached to it.
In order to create that Dashboard, we need an accurate aspect taxonomy. It is not enough to identify, extract, and display product attributes if we cannot group them in useful ways. We have a team that creates those taxonomies and strategies to generate those at scale, but sometimes clients want to use their own information schematics. This is done by direct feedback in our Dashboard and used to recreate taxonomies tailored to their needs, giving our clients a great degree of personalization and helping us create even better models.
To Birdie, Consumer Success is not only between us and our clients: it is the whole product. We need to use everything we learned from the multitudes of collected consumer feedbacks to build an adaptable application that will be used by other Consumer teams around the world.
