Customer Experience ManagementNo organization can remain competitive without knowing what their customers and employees think – which is why insight technology is essential to every organization.

Insight technology is evolving and companies are finding new ways to compete. Employee insight and customer insights are rapidly changing the competitive landscape across most sectors.  

Listening to what customers say – in text-data and in voice-to-text-data – is, generally, the best way to gain insights about how to become more profitable. Companies are facing an increasing flood of customer feedback data in many different channels and languages.

All companies with many customers, e.g. banking, insurance, mobile, online services, consumer products and brands, are flooded with customer interactions.

These can be used to gain customer insight.

Customer interactions are generated – as language data (voice and text) – through different channels, such as customer service, support tickets, email, call center recordings, complaints, reviews, and social media comments. Customer insights tell companies what they should do improve and how to make more money, by increasing sales and margins.

Customer Experience Management (CEM or CXM) is a process to oversee and track customer interactions. It involves measuring, analyzing, understanding, and managing the customer experience and the customer satisfaction. This can be done with customer journey mapping, covering all so-called touch points.

In customer experience (CX) and customer experience management (CEM), it is relatively easy to analyze structured data, such as transaction data. The deeper and robust insights that can be obtained from unstructured data are generally more difficult to come by.

Text-based feedback is largely an untapped resource. Extracting valuable insights from this data has been costly, slow and inconsistent. Traditional machine learning approaches cannot handle the extreme variability in real world language usage.

Most automated solutions are clunky and brittle and do not produce actionable insights at a granular level. People are extremely productive and innovative and language is in constant flux.

Traditional approaches are based on training and the training data is never up-to-date with real world language usage. As a consequence, traditional approaches will always encounter variations not covered in the training data. This is known as the out-of-vocabulary (“OOV”) problem.  

Truly insights-driven businesses will steal $1.2 trillion annually from their less-informed peers by 2020 and leading Customer Insights practices will be the poster-child for business transformation.

Customer insight will be liberated by Artificial Intelligence and Advanced Insights will spark digital transformation.

Global data volumes have a doubling rate of approximately 24 months. Around 80% of all data is unstructured, and most of it is language data (voice and text).

The latest advances in AI-driven text analytics makes it possible to get statistically robust insights, from large amounts of unstructured language (text and voice-to-text) data.

Insight leaders are constantly listening to their customers and employees in order to continuously improve their business in very short cycles, and have outstanding ROI (Return on Investment) on their insights.

The cutting-edge solutions include continuous and incremental on-the-fly learning from of unigrams and n-grams from vast streams of data, leveraging the Internet as the source for learning.

The latest tools for AI-driven text analytics (and Data Mining) use unsupervised learning without a re-train/re-deploy cycle. This is absolutely necessary to keep up to date with the novelty and extreme variability in real world language usage.

Natural Language Understanding is native. Solutions that tray to leverage a translation layer will not keep capture meaning in a reliable way.

Gavagai lets you analyze customer experience and customer feedback in Azerbaijani, Albanian, Arabic, Bengali, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Farsi, Finnish, French, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Javanese, Korean, Latvian, Lithuanian, Malay, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tagalog, Thai, Turkish, Ukrainian, Urdu, and Vietnamese.

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