FAQ

Frequently Asked Questions

You’ve got a question? We’ve got the answers!

Here’s everything you need to know about the worlds of Speech and Interaction Analytics, Contact Center Operations, AI models, and more.

1. Speech & Interaction Analytics

This section answers foundational questions about how speech and interaction analytics work. Expect simple explanations of transcription, sentiment, languages, use cases, and analysis capabilities.

 

What is speech analytics, and how does it work?

Speech analytics is the process of automatically analysing recorded customer calls to extract valuable insights. It uses technologies like automatic speech recognition (ASR), natural language processing (NLP), machine learning and in the case of Xdroid’s VoiceAnalytics, its proprietary LLM, Xdroid IQ also, to identify keywords, trends, sentiment, and intent in conversations.

Xdroid deploys this combination to identify keywords, sentiment, intent, and conversational patterns; provide real‑time agent assistance (pop‑ups, KPI alerts, compliance coaching); generate call summaries; and deliver proactive recommendations and coaching opportunities, empowering businesses to boost customer experience, optimise operations and fill in the gaps, ensure compliance, and elevate agent performance.

ASR stands for Automatic Speech Recognition (ASR), which is a technology leveraged by Xdroid’s solutions to transcribe speech in real time or offline as a batch.

NLP stands for Natural Language Processing, which is a technology used to understand and study the content, tone, and intent behind conversations, particularly with customers. Xdroid’s solutions utilise this technology for an in-depth analysis of customer interactions.

LLM or Large Language Models and Generative AI, such as Xdroid’s proprietary Xdroid IQ, is an AI system trained to understand and generate human-like language. Xdroid IQ powers advanced features like auto-summarisation, NPS prediction, and other AI insights into the customer journey. It’s trained specifically for contact centres and can be fine-tuned on your data for more accurate, multi-language insigh

Machine learning is a subtype of AI that allows systems to learn from data and improve over time. Xdroid uses machine learning, in Xdroid BI services, to deliver predictive models, to enhance analysis of calls, detect patterns, predict outcomes like NPS, identify churn-risk and more.

Speech analytics focuses only on spoken conversations, while interaction analytics analyses all customer touchpoints, calls, chats, emails, and even automated systems like chatbots and virtual assistants. Xdroid’s Interaction Analytics platform brings everything together, offering a full picture of the customer journey across multiple channels, i.e. offers omnichannel analytics, whereas Xdroid’s VoiceAnalytics focuses on voice/speech conversations with customers. By analysing these interactions, businesses can identify friction points, understand failure cases, and gather actionable feedback to continuously improve.

Xdroid’s VoiceAnalytics transcribes and processes calls offline, i.e. after the call ends. It identifies key themes, call reasons, agent actions, and customer sentiment using AI and Xdroid’s proprietary advanced large language model, Xdroid IQ. The results are shown in easy-to-understand dashboards that help teams take prompt, data-driven actions to address any issues and ensure growth.

While not operating in live mode, VoiceAnalytics can also be used in near real-time by scheduling multiple uploads throughout the day, enabling frequent updates and timely insights, particularly useful for operational monitoring, early issue detection, and intraday coaching.

Xdroid supports omnichannel analytics, voice calls, chat, bot conversations, emails, and more with Interaction Analytics. You can get unified insights across all these touchpoints in one platform. The interactions don’t even need to be strictly human-to-human, Xdroid also supports analysis of human-to-machine/automated conversations, such as those with virtual assistants.

Additionally, custom APIs can be developed to extract and analyse interactions from other systems, such as ticketing tools, extending the analytics beyond standard communication channels.

Yes, Xdroid uses AI to analyse sentiment (positive, neutral, negative, etc.) in customer interactions. This helps companies identify distressed, dissatisfied, or unhappy customers early and take corrective action. However, at the end of the day, these are human sentiments that can be nuanced and unpredictable, and solution attempts to understand them as best as they can.

No, Xdroid DOES NOT analyse emotions through biometrics. Instead, Xdroid focuses on sentiment derived from language, based on what is said and how it is phrased.

Sentiment detection in Xdroid operates on two levels:
  • Automatic analysis: This uses AI models trained to recognise common patterns and expressions of sentiment across conversations.
  • Manual fine-tuning: With this, clients can define or adjust sentiment rules and interpretations based on their specific business context.

Manual Fine-Tuning could be needed because, at the end of the day, Xdroid’s solutions are analysing human interactions that cannot be 100% standardised and measured. Human sentiment is subjective after all. Furthermore, words and phrases can have different meanings in different contexts. For example, the word address means different things when a customer says ‘Please send it to my address…’ and ‘I need you to address this.’

Xdroid offers its clients a hybrid approach with Automated AI and Manual Fine-Tuning, ensuring greater accuracy and flexibility in interpreting customer sentiment, aligned with real-world use cases.

Xdroid’s transcription engine is highly accurate and trained across multiple languages and dialects. However, transcription accuracy depends on several factors such as audio quality, background noise, overlapping speech, and microphone setup. 

To ensure transparency and align expectations, Xdroid can perform a Word Error Rate (WER) analysis on a representative sample of customer audio. This allows for a precise, context-specific evaluation of transcription performance.

It’s important to note that, no, Xdroid DOES NOT use customer data to improve or train its models by default.

In cases where domain-specific training is explicitly required, for example, to adapt to particular dialects, accents, or industry-specific terminology, Xdroid may request access to customer data for training purposes.

In such cases, all sensitive data is anonymised, and conversations are tokenised and segmented into encrypted data packets, ensuring that full conversations cannot be reconstructed. This safeguards privacy and guarantees compliance with data protection standards.

This process is only activated when needed for custom model tuning, and always within the scope of a clearly defined agreement with the customer.

Xdroid supports over 30 languages, including English, Spanish, Dutch, French, Italian, German, and more, with regional accent and dialect recognition to ensure high accuracy.

Xdroid can detect compliance violations in conversations, such as missing disclosures or data protection breaches. It flags risky interactions, provides supervisors with alerts and reports for follow-up and redacts sensitive/confidential customer information. Xdroid’s solutions comply with standard regulations like GDPR and PCI and can be customised to follow regional directives as well.

Common use cases include quality assurance, churn prediction, upsell opportunities, customer complaint tracking, agent training, compliance monitoring, and CX improvement.
By analysing 100% of interactions, Xdroid transforms unstructured data into actionable intelligence, helping teams take faster, more informed decisions that directly impact service quality, customer loyalty, and business performance.

Xdroid’s Interaction Analytics enables full visibility across all channels, voice, chat, email, and virtual assistants, and groups insights to provide business intelligence into three key domains:

  1. Revenue Intelligence: Spot upsell and cross-sell opportunities, detect churn signals early, and optimise sales conversations based on real customer intent.
  2. Operational Intelligence: Monitor agent performance, detect long silences or inefficiencies, and improve productivity through real-time KPIs and process adherence.
  3. Customer Intelligence: Understand sentiment trends, surface recurring complaints, track in-call NPS, and perform root cause analysis to proactively enhance customer satisfaction.

Xdroid also offers pre-configured Interaction Analytics models for these three domains, formulated by experts under Xdroid BI.

Real-time analysis offered by Xdroid’s AgentAssist provides live insights and KPI checks, helping agents and supervisors during the call. Post-call analysis offered by Xdroid’s VoiceAnalytics provides deeper insights, offline after the fact and equips businesses with trends for strategic improvements over time.

2. AI & Predictive Models

Here you’ll find questions that explain AI concepts like machine learning, LLMs, RAG, and predictive models. It also covers how these technologies improve forecasting, automation, and customer insights.

What is predictive analytics in the context of customer service?

Predictive analytics uses historical data to forecast future outcomes. In customer service, it helps anticipate customer behaviour, like churn risk, satisfaction levels or potential issues, so businesses can study them and act proactively in the present and future.
With the pre-configured models under Xdroid BI, we apply predictive models to NPS and churn prediction. Building on the same principles, Xdroid is also expanding into areas like fraud detection and more. We also develop custom predictive models tailored to specific client needs, based on their priorities and data availability.

Xdroid’s proprietary Early Churn Detection Model is an Interaction Analytics model, created and fine-tuned by experts, under the Revenue Intelligence cohort of Xdroid’s consultancy service, Xdroid BI. The model analyses customer interactions to detect early signs of dissatisfaction or disengagement. It highlights specific issues driving customer attrition, allowing companies to intervene before it’s too late.

Yes, by identifying at-risk customers early, businesses can offer targeted, tailored solutions, better support, and significantly improve customer retention and loyalty.

Machine learning in Xdroid is specifically used for the development of predictive models. It is combined with other technologies to build models that take into account all relevant elements emerging during a call. Training data is only used upon explicit client request, and each predictive model is fine-tuned to meet the client’s specific needs and context.

Traditionally, NPS (Net Promoter Score) is determined through surveys filled out by customers after their customer service interaction. These surveys, however, have an extremely low response rate and are marred by negative bias, as people are more likely to fill in the feedback form after a bad experience. As a result, the NPS can be inaccurate and not give the enterprise the full picture.

NPS prediction offered by Xdroid uses AI to estimate a customer’s satisfaction and loyalty score based on what was said during the call. Xdroid’s proprietary in-call NPS model has achieved over 93% accuracy by analysing tone, language, and customer-agent interactions.

Xdroid IQ is Xdroid’s proprietary large language model (LLM) that powers advanced features like chat assistance, automatic summaries, smart scorecards, and more. It helps users interact with the platform through natural language and retrieve insights easily.

RAG enhances language models by pulling in relevant documents or data before generating a response. In Xdroid, RAG improves the accuracy and relevance scorecard from your knowledge base, especially useful for multilingual or high-volume contact centres.

Absolutely. Xdroid’s models can be tailored to different industries like finance, retail, energy, and government/public sector or customers. This ensures that insights and predictions are aligned with your specific business context and historical data.

3. Customer Experience (CX)

These questions focus on how analytics enhances customer satisfaction, identifies pain points, and improves journeys. Expect clear guidance on CX metrics, trends, and practical improvements.

How can interaction analytics improve customer experience?

By analysing every customer interaction, businesses can uncover friction points, understand customer sentiment, and optimise processes. This leads to faster resolution, more personalised service, and happier customers.

Xdroid tracks NPS, sentiment, first contact resolution (FCR), average handling time (AHT), and churn risk, giving a 360-degree view of CX performance.

By identifying frequently repeated issues, negative sentiment, long silences, or escalation patterns in calls, Xdroid highlights where customers are struggling—whether it’s a product issue, a policy problem, or poor agent handling.

Through early churn prediction, sentiment detection, and alerts, companies can take action before customers leave. Analytics also help identify root causes of complaints, enabling long-term improvements.

Automation helps companies respond faster, reduce errors, and maintain consistent quality. Xdroid automates tasks like quality scoring, KPI tracking, and reporting—freeing teams to focus on strategy and empathy.

Yes. Xdroid helps businesses understand customer preferences and behaviour, enabling them to tailor offers, support, and communication style to each individual customer.

Xdroid can detect shifts in customer sentiment and tone, periods of silence, and distress signals in voice and text interactions. It flags at-risk or vulnerable customers, allowing agents to provide extra care and support when it matters most.

4. Quality Management & Agent Performance

This section covers everything about QA automation, agent scorecards, and performance metrics. The questions explain how analytics supports coaching, compliance, and fair, consistent evaluations.

What is Automated Quality Management (AQM)?

Automated Quality Management (AQM) uses AI to automatically evaluate 100% of agent interactions instead of relying on randomised manual sampling. It assesses key performance indicators, compliance, sentiment, and more, providing a consistent, objective, and scalable way to monitor quality. Users can take a hybrid approach to AQM with a combination of AI, rule-based and manual KPIs.

Xdroid analyses every customer interaction to monitor performance metrics such as agent behaviour, script adherence, etc. With Xdroid IQ, one also gets AI insights on every call, which include Agent Action Summary and resolution status for further information on the customer journey.

Manual QA is time-consuming and limited in scope, usually reviewing only 1–5% of calls at random. AQM reviews 100% of interactions, instantly and without bias, providing a much more accurate and comprehensive view of agent performance and overall customer service operations.

Xdroid gives its clients the flexibility to program and focus on KPIs that matter the most to their business needs. They can also opt for pre-configured Interaction Analytics models programmed by Xdroid’s industry experts under Xdroid BI.

Some of the key performance indicators can include:

  • First Contact Resolution (FCR)
  • Average Handling Time (AHT)
  • Customer sentiment and emotion
  • Script adherence
  • Periods of Silence/Crosstalk
  • NPS prediction
  • Compliance metric

Yes. Xdroid can identify specific calls and moments that need coaching and detect training opportunities. Users can ask Xdroid IQ to analyse specific calls and give its recommendations. Supervisors can use real-life examples from evaluated calls to provide targeted feedback and improve agent skills.

Supervisors get dashboards, reports, and alerts that show trends and outliers. They can quickly spot underperforming agents or departments to track improvement over time, and even automate quality monitoring with manual and AI scorecards.

5. Contact Center Operations

Expect questions about operational efficiency, integrations, omnichannel analysis, and KPIs like AHT and FCR. This section helps users understand how analytics strengthens everyday call center management. 

How does Xdroid integrate with existing contact center platforms?

Xdroid integrates with leading platforms like Genesys, Avaya, Ringover, Amazon Connect, and more. It can receive voice and text data via APIs, recording platforms, or cloud connectors, ensuring a seamless and scalable setup.

Yes, Xdroid supports omnichannel analytics across voice interactions, emails, live chat, messaging apps, and bots. All channels are analysed in one unified interface, giving a complete view of customer interactions.

Call categorisation automatically labels each interaction based on the reason for contact, for example, billing, complaints, or cancellations. This helps contact centers reduce manual effort, track trends, allocate resources, and optimise processes.

Xdroid identifies repetitive issues, knowledge gaps, and inefficient call flows. By improving agent scripts and standardising responses, businesses can reduce AHT while maintaining service quality.

By analysing why customers call back or escalate, Xdroid helps teams identify where first-time resolution is failing. This allows for smarter routing, better training, identifies underlying operational issues, helps improve them and leads to faster issue resolution.

Yes. Xdroid offers flexible deployment options with a minimum of 25 agents. It’s quick to deploy, easy to use, flexible, and highly scalable, making ot suitable for contact centers of all sizes, small, mid-sized and even those deployed by Multinational companies. 

6. Data Privacy, Security & Compliance

This section answers questions about GDPR, PCI, data storage, redaction, and security practices. It helps users understand how Xdroid protects sensitive information and meets regulatory requirements.

Is Xdroid GDPR and PCI-DSS compliant?

Yes. Xdroid is fully GDPR-compliant and supports PCI standards. It ensures customer data is handled securely, with strict access controls, encryption, and anonymisation techniques.

Xdroid uses advanced data masking, redaction, and anonymisation features to protect sensitive information. Personal data such as credit card numbers or identity information, can be automatically hidden during analysis from both the transcription and the audio.

Xdroid offers flexible data storage options—on-premise, private cloud, or Xdroid’s cloud environment. Clients can choose based on their internal policies and regulatory requirements.

Yes. Xdroid supports data residency and localisation needs, ensuring that customer data does not leave the region or country as required by local laws and regulations based on where the solution is hosted.

Xdroid’s solution can redact sensitive data like credit card numbers, bank account details, and personal identifiers during call transcription based on the specific rules/data fed into the system. This ensures both compliance and customer trust.

7. Real-Time Support with AgentAssist

These questions explain how real-time guidance works during live calls, from prompts to compliance alerts. Expect insights into how AgentAssist improves accuracy, speed, and customer-handling quality.

What is Xdroid’s AgentAssist?

AgentAssist is Xdroid’s real-time support solution that helps contact center agents during live calls. It listens to the conversation as it happens and provides live suggestions, knowledge base prompts, and alerts, making agents faster, more confident, and more accurate.

AgentAssist uses real-time speech recognition and natural language processing to understand the conversation as it unfolds. Based on what is being said, it triggers smart pop-ups with useful information such as compliance scripts, next best actions, reminders, or troubleshooting steps.

AgentAssist can:

  • Offer dynamic prompts and recommended replies
  • Pull up relevant knowledge base articles.
  • Alert agents about potential compliance violations
  • Suggest upsell or cross-sell opportunities.
  • Detect emotional cues and advise on tone adjustment.
  • Remind agents of mandatory disclosures.

Not at all. AgentAssist is designed to be intuitive and unobtrusive. It works in the background and delivers just-in-time insights in a clean interface that supports, not disrupts, the agent’s flow.

Yes. AgentAssist can be tailored to industry-specific use cases such as financial compliance, technical support, insurance claims, or utility billing. You can define specific trigger phrases and recommended content based on your workflows.

Primarily, yes—it is designed for real-time voice support.

Agents using AgentAssist resolve issues faster, reduce average handling time (AHT), and make fewer errors. Customers benefit from quicker, more accurate support, leading to better satisfaction and higher NPS scores.