AI Customer Insights: Real-Time Consumer Intelligence Platform

Why Traditional Customer Analytics Isn’t Enough Anymore

A customer insight analysis based only on surveys gathers input from customers who are willing to complete a survey, receives it days after the interaction which influenced their opinion, and tends to include only those customers who were either extremely delighted or extremely furious enough to respond; all the others remain unheard.

Contact centres suffer from the contrary issue: they have an excess of unstructured data and no feasible method for manually processing it. When people manually sample calls, they cover only about one to five per cent of the total volume of interactions, meaning any company-wide story must be inferred from a negligible amount. A pricing complaint that is on the rise in the first week may not be identified until it appears in a retention report in the twelfth week. Automated customer insights eliminate this delay by evaluating the data as it is produced, rather than afterwards.

What is an AI customer insight?

AI Customer Insights refers to a type of AI system used for analysing customer feedback which makes use of natural language processing and speech analytics on the entire volume of conversations in order to identify intent, the sentiment trajectory, recurring topics, objections, and outcomes. Instead of a QA analyst preparing summaries for a small number of calls, an AI-powered customer insights platform treats each and every conversation in the same manner.

The technical difference lies between transcription and understanding. All tools are capable of converting speech into text; what sets real customer insights AI apart from a simple transcription service is semantic modelling, which enables it to recognise that “your app keeps logging me out” and “I have to sign in again every ten minutes” refer to the same problem even though the two statements share almost no words.

AI Customer Insights vs. Conversation Intelligence: What’s the Difference

Although the two categories share the same underlying technology, they are addressing different business questions, and it is a common cause of call centre customer insights projects failing.

Convozen uses the same transcription and semantic layer. A call that has been scored for agent coaching as part of Conversation Intelligence is also the same call that feeds the aggregate trend models used in Customer Insights, thus keeping the two products aligned.

How Convozen Turns Conversations into Customer Insights

1. Capture. There is no need for a separate recording system since all voice calls, chat threads, and messages from the existing channels are collected.
2. Transcribe. Text is created from speech by using language models that have been tuned to the specific customer base of the business, taking into account regional accents and speech that mixes languages. 
3. Extract. Each conversation is analysed to determine its intent, sentiment, topic, and outcome.
4. Aggregate. The signals are combined from thousands of conversations to identify patterns that would not be apparent in any single transcript.
5. Deliver the findings into the dashboards, since the CRM or BI tools teams currently review them.

Core Capabilities of Convozen Customer Insights

• The intent and topic are detected in each conversation, with common complaints being identified as they arise, not only when a pattern has been spotted in a spreadsheet.
• Tracking the sentiment trajectory, for example, enables you to tell the difference between a customer who began by being frustrated and ended up satisfied and one who started off calm and finished angry.
• Signals of churn and escalation risk, extracted from language patterns which tend to occur before cancellations or supervisor escalations.
• The collection of what customers say spontaneously regarding pricing, alternative options, and missing features.
• The CRM and BI systems are integrated as standard, so the program does not need a separate standalone tool.

Where the Data Comes From: Every Channel, One Intelligence Layer

Most companies carry out their conversations using a number of separate systems, one for calls, another for chat, and a third for email. Convozen has introduced a single layer that covers all of these: voice calls, live chat, in-app messaging, email threads, and WhatsApp. If a complaint is made through chat on Monday and another related one is made over the phone on Thursday, both are regarded as instances of the same fundamental issue.

Real-World Results from Convozen Customers

The platform used by Convozen, which is owned by NoBroker, deals with about 10,000 hours of call recordings each day, and it is expected that AI agents will one day be responsible for 25 to 40 per cent of the call volume as the service expands, according to Google Cloud’s report on the deployment.

Ankit Singh, who is the Vice President of Business Development at Zell Education, has said that he has moved from auditing only a single-digit percentage of calls to now monitoring virtually every interaction in real time, and attributes the change to better results in the areas of conversions, compliance, and customer experience. 

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Built for Indian Languages: Speech Accuracy at Scale

Since the accuracy of the transcription is essential to any downstream analysis, a model which mishears a regional accent or stumbles over a sentence when switching from Hindi to English within a single thought will result in an incorrect sentiment score and faulty intent classification, with the error carrying through all subsequent reports. The speech models used by Convozen, including its Akshara ASR engine, were developed and tested from the beginning using Indian language varieties and code-switching, rather than being added onto an existing English-focused model, something which is directly relevant to a business that has a high volume of calls involving both Hindi, English, and regional languages in the same conversation.

How AI Customer Insights Work for Various Industries

The method used for extraction is the same in all industries, but the area that a company looks at first varies: a BFSI team focuses first on compliance risk, while an e-commerce team looks first at the product feedback loop. As to which signals are sent to which team, that is determined during the setup process.

How to Get Started with AI Customer Insights

1. Connect your current call, chat, and messaging channels. You do not need to replace your infrastructure.
2. Specify the KPIs and topics that are important to the business, rather than using a general template designed for another industry.
3. Look over the initial set of insights and send them to the teams that are responsible for the problems they identify.
4. After the rollout has been proven out, expand the coverage to include further channels and languages.

Arrange a demo to see how AI Customer Insights performs when compared with a business’s own call data, rather than a generic sample.