VoiceAnalytics

Xdroid’s Voice Analytics

Insights. Actions. Results.

Our Artificial Intelligence solution will drive your company

to get actionable results.

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Artificial Intelligence & Machine Learning

for the Contact Center

Xdroid has reinvented Emotion Analytics for Contact Centers

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Xdroid’s Voice Analytics uses Machine Learning and Artificial Intelligence to identify and measure speech style based emotions, keyword, and expression-based sentiment, and speech characteristics such as speech rate, intonation, articulation, etc.

Xdroid’s AI-based software engines perform two types of analysis.

On the one hand, the engines provide transcripts of conversations for many different languages and speaker diarization by assigning each speech segment of the conversation to either the agent or the customer.

On the other hand, the engines provide an indication of speech characteristics for every speech segment. An extensively trained deep neural network classifies speech segments into three main categories (low, medium, high) for characteristics such as speech style (happy, disappointed, displeased or uncertain), speech rate, volume, pitch, intonation & articulation.

Combining categorizations with probabilities yields a score for all characteristics on every speech segment and the overall conversation.

Those metrics together with the transcription are then used by VoiceAnalytics’ comprehensive, flexible, and very user-friendly query capabilities to filter out those conversations that need further attention from all, 100%, of the conversations. The results of those filters are presented to the user in a clean, clear, and intuitive dashboard with charts that allow Contact Center Managers and Quality Managers to spot areas of attention in the blink of an eye. This will enable them to obtain all the relevant detailed data within just a few mouse clicks.

Voice Analytics evolved

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Since its foundation in 2008, Xdroid has continuously taken customer feedback and needs to the heart. This resulted in the launch of its solution’s 3rd major release, the so-called VoiceAnalytics V3 in Q2, 2020

The V3 GUI, which is jam-packed with action-oriented screens, will catch the user’s eye right away. V3 provides numerous intuitive dashboards, graphs, and heatmaps that immediately give an overview of how the contact center or a group of agents performs, how that performance has been evolving and how a specific customer case has been handled.

It also shows which Key Performance Indicators and other Quality Management metrics need immediate attention. Besides its excellent action-oriented interface, VoiceAnalytics V3 contains lots of powerful functionalities, too many to list here.

To highlight just one, it provides a proprietary scripting language developed from the ground up to facilitate more intelligent and advanced searches in the huge database of analytics results produced by the state-of-the-art AI-based.

Insight Learning

helps you Predict your customer’s behavior

Xdroids Insight Learning module, based on Artificial Intelligence, can perform predictive analysis across all conversations to identify trends and patterns in the analytics data.

This is especially important for conversations that ended with a customer’s starting legal actions or fraud attempts related to conversations and successful outbound sales calls.
Xdroid’s Insight Learning helps you to recognize the factors that played a crucial role in these conversations. Xdroid Insight Learning module takes CRM data and speech characteristics into account and complex relationships that, without Machine Learning, would be left uncovered.

Our system will automatically generate rules that can be applied to new conversations and alerts that can be set for conversations that meet these criteria. The right people can be notified in time and can take appropriate action.

With this approach, new conversations that show a particular trend can be identified far more efficiently.

For instance, when on average 5% of the customers have canceled their contract within a week after calling for support, one can feed the associated 5% of calls with a label “canceled” and the remaining 95% of calls with a label “not canceled” into the Insight Learning module. That then will automatically identify the differences in patterns between both types of calls.

The Machine Learning module will analyze all conversations and show that specific subsets of the “negative” calls meets certain specific criteria like the length of a call exceeding a specific value and/or speech styles and speech characteristics exceeding specific values and/or specific positive or negative keywords being used, etc.

Filtering out the calls that meet those criteria will yield calls for which the likelihood of being a “negative” call is multiple times higher than the general average of 5%. As a result, calls that need further attention can be effectively filtered out so that appropriate follow-up actions can be taken to keep customers from canceling their contracts.

By combining multiple rules, machine learning can achieve even higher efficiency, so predicting the customer’s behavior is even more accurate.

The platform’s alerts function can be assigned to these rules to notify the right people in time to take appropriate action.

Contact centers can easily and quickly understand what factors play a key role in certain behavior or events, like contract cancellation or Net Promoter Score surveys or Successful Sales calls, etc.