ConvoZen – Simplifying Customer Communication Across Channels
Managing customer conversations across different channels can become complex as businesses scale. ConvoZen provides a unified platform that brings voice, chat, email, and messaging into one connected system, making it easier to handle interactions without losing context or consistency.
Instead of switching between multiple tools, teams can manage all conversations in a single environment. This helps improve response time, reduce manual effort, and maintain a consistent communication flow across every touchpoint. By organizing interactions in a structured way, ConvoZen allows businesses to keep track of customer journeys and ensure that no query is missed.
Another key advantage is the ability to handle growing volumes of conversations without adding operational complexity. As customer demand increases, ConvoZen supports teams in maintaining quality while scaling communication efforts efficiently.
By centralizing communication and simplifying workflows, ConvoZen helps businesses stay responsive, organized, and better equipped to manage modern customer expectations.
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Data Analysis is an integral part of every business.There are various data analysis technique which can be applied with all kinds of data in order to find patterns, discover insights and making data-driven decisions. The following gives a brief idea of the process and the techniques undertaken during data analysis:
Process followed in Data Analysis
Define your goals clearly
Collect the required data
Data Cleaning by removing unnecessary rows and columns and redundant data
Analyze data using various tools
Visualizing Data
Drawing an inference and conclusion
Methods of Data Analysis (for both Quantitative and Qualitative Data)
Sentimental Analysis
Regression Analysis
Time Series Analysis
Cluster Analysis
Predictive Analysis
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Recurrent Neural Network(RNN's) model manages Sentiment analysis here in python code, learn the application of Recurrent neural network and difference between RNN and CNN.
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Learning Recurrent Neural Network, applications, and its role for Sentiment Analysis.
As everyone knows that Sentimental analysis plays an important role in these days because many start ups have started with user driven content 1 . Only finding the voice is not be the real time scenario so finding the Sentiment analysis of agent and customer separately is an important research area in natural language processing. Natural language processing has a wide range of applications like voice recognition, machine translation, product review, aspect oriented product analysis, sentiment analysis and text classification etc 2 . This process will improve the business by analyze the emotions of the conversation with respect to the customer voice separately and also agent voice separately. In this project author going to perform speaker identification and analyze the sentiment of the customer and agent separately using Amazon Comprehend. Amazon Comprehend is a natural language processing NLP service that uses machine learning to extract the content of the voice. By using the speaker identification author can extract the unstructured data like images, voice etc separately so it is easy to analyze the business performance. Thus, will identify the emotions of the conversation and give the output whether the customer conversation is Positive, Negative, Neutral, or Mixed. To perform this author going to use some services from Aws due to some advantages like scaling the resources is easy compare to the normal process like doing physically such as support vector machine SVM . AWS services like s3 is a object data store, Transcribe which generate the audio to text in raw format, Aws Glue is a ETL Service which will extract transform and load the data from the S3, Aws Comprehend is a NLP service used for finding sentiment of audio, Lambda is a server less where author can write a code, Aws Athena is a analyzing tools which will make complex queries in less time and last there is quick sight is a business intelligent tool where author can visualize the data of customers and also agents.
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In today’s world of online reviews, email surveys, chat-bots, mystery shoppers and call centers there is never any shortage of customer feedback coming in for customer experience focused companies. The problem we face now, is how do we make sense of all the feedback (data) that is coming in? Below are 6 steps to follow …
Social Media, Online Review Sites, Email Surveys. With so many ways for people to interact with your attraction, how do you make sense of it all? Or more importantly, how can you capitalize on it? Chapter 1 – Content is the Prince; the Guest is King If you’re in the marketing world then you may …
