With Voxco’s data analysis solutions, gather data and analyze it on a single platform to generate actionable insights that help you:
✓ Predict future outcomes
✓ Make insightful decisions
✓ Become cost-efficient
Foster a culture of data-driven decisions across your organization
Arm everyone across all levels of your organization with insights derived from visual dashboards
Voxco’s Text Analysis dashboard extracts all the survey data and arranges it in different charts- Sentiment Performance, Word Cloud, Topic, and Open-End Answers.
✔ Analyze open-ended feedback
✔ Understand the emotion behind survey responses
✔ Learn how your customers feel & why
✔ Identify recurring themes & patterns
Voxco’s NPS® dashboard pulls all the data from your survey and it in different charts- NPS® score chart, Distribution chart, NPS® Trend chart, and Sentiment Analysis chart.
✔ Understand the ‘why’ behind your NPS®
✔ Identify the key drivers of customer satisfaction
✔ Discover areas of improvement
✔ Reduce customer churn and boost loyalty
Easily build end-to-end data pipelines and meet diverse use cases
Integrate all your data sources to get a 360-degree view of your customers that is accessible to everyone in your organization.
Measure customer satisfaction across customer lifecycle using NPS® dashboards and text analytics. Take preventive actions in real-time and boost customer retention.
Identify at-risk customers by enabling ML models & analyzing customer behavior with predictive analytics.
Enable automated triggers based on customers’ actions and initiate the next best actions on auto-pilot. For example, cross-sell & up-sell.
Build a dedicated customer base for your company. Add new touchpoints, systems, and features to streamline unlimited amounts of data.
Uncover actionable customer insights from every relevant data source and continuously personalize customers’ offline and online experiences.
Achieve product-market fit by analyzing customer feedback, predicting future customer needs & preferences, and measuring satisfaction.
Spot small errors and anomalies across your business with machine learning models. Combine omnichannel feedback with text analytics and risk modeling to reduce risk and losses.
Data analysis is the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. Data analysis is often used in various fields, such as business, healthcare, education, and scientific research.
The process of data analysis involves various steps, including:
1. Data collection: Gathering relevant data from various sources, such as surveys, experiments, or databases.
2. Data cleaning: This involves removing irrelevant data, correcting errors, and dealing with missing or inconsistent values.
3. Data transformation: Transforming data into a suitable format for analysis, such as converting data types, aggregating data, or creating new variables.
4. Data modeling: This involves applying statistical or machine learning techniques to identify patterns, relationships, and trends in the data.
5. Data visualization: Creating charts, graphs, and other visual representations of the data to help communicate insights and findings.
6. Interpretation: Drawing conclusions and making informed decisions based on the results of the analysis.
Overall, data analysis is a critical process for gaining insights and making informed decisions in a wide range of fields.
A data analysis platform is a software tool or suite of tools designed to facilitate the process of data analysis. These platforms typically provide a set of integrated tools for data collection, cleaning, transformation, modeling, visualization, and interpretation.
A data analysis platform can help streamline and automate many of the tasks involved in data analysis, making it easier and more efficient to work with large, complex datasets.
Some common features of data analysis platforms include:
1. Data integration: The ability to collect and combine data from multiple sources, such as databases, spreadsheets, or APIs.
2. Data cleansing: Tools for cleaning and standardizing data, identifying and removing errors or duplicates, and handling missing or incomplete data.
3. Data transformation: The ability to transform data into a suitable format for analysis, such as by aggregating or summarizing data or creating new variables.
4. Modeling and analysis: Tools for statistical analysis, machine learning, and data mining to identify patterns, relationships, and trends in the data.
5. Data visualization: The ability to create charts, graphs, and other visualizations to help communicate insights and findings.
6. Collaboration and sharing: The ability to collaborate with others, share data and analysis results, and publish reports or dashboards.
Some examples of popular data analysis platforms include Tableau, Power BI, Google Analytics, and SAS. These platforms can be used in a wide range of industries, from business and finance to healthcare and science, to help analysts and decision-makers make sense of complex data.
Choosing the best platform for data analysis can be a complex process and will depend on a variety of factors specific to your organization’s needs and resources. Here are some key considerations to keep in mind when evaluating different data analysis platforms:
1. Data types and sources: Consider the types of data you will be analyzing and where it comes from. Some platforms may be better suited for structured data (such as in a database), while others may be better for unstructured data (such as text or social media data). Make sure the platform you choose can handle the types of data you work with.
2. User interface and ease of use: Look for a platform with a user-friendly interface and intuitive workflows that align with your team’s skills and expertise. Consider whether the platform offers training or support to help your team get up to speed.
3. Scalability and performance: Consider the size and complexity of your datasets and whether the platform can handle them efficiently. Look for a platform that can scale up or down as needed and that can handle real-time or near-real-time data analysis.
4. Data security and compliance: Consider whether the platform meets your organization’s security and compliance requirements, especially if you are working with sensitive or confidential data.
5. Integration and interoperability: Consider whether the platform can integrate with your existing data infrastructure, such as databases, APIs, or other tools you use for data collection or management. Look for a platform that supports open standards and can work with other tools you may need for data analysis.
6. Cost and Licensing: Consider the cost of the platform and any licensing requirements and whether it aligns with your organization’s budget and resources.
Overall, the best platform for data analysis will depend on a variety of factors unique to your organization. It’s important to evaluate different options carefully and thoroughly and to involve stakeholders from across your organization in the decision-making process.