Data analytics has proved to be an invaluable tool for improving security, preventing fraud, and increasing operational efficiency in the BFSI sector by analyzing raw data to uncover trends and insights.

We will examine the top 6 data analytics use cases in this article that are propelling the BFSI sector’s digital transformation.

1. Fraud Detection and Prevention Using Data Analytics in BFSI

Fraud is a constant threat in the quick-paced world of financial services and can cost banks, insurance companies, and other financial institutions a lot of money. It should come as no surprise that cybercriminals frequently target the BFSI sector, searching for vulnerability to exploit, given the amount of money at risk.

BFSI institutions can, however, turn the tables on these fraudsters thanks to the power of data analytics. Financial institutions can identify and stop fraud before it even starts by utilizing advanced analytics techniques like predictive modelling, machine learning, and data mining.

The secret to success is searching through the massive amounts of data produced by BFSI institutions for patterns and behaviors that could point to fraudulent activity. Financial institutions can identify potentially fraudulent activities and act before they cause significant financial harm by developing predictive models based on historical data.

Data analytics is a way to stay ahead of the competition as well as a tool for preventing fraud. BFSI institutions can spot new opportunities and maintain a competitive edge by utilizing the insights gained from data analytics.

BFSI institutions can protect their customers’ funds and open up new doors for growth and success by using the right analytics techniques and a commitment to constant vigilance.

Few Examples

To improve fraud detection and prevention, the BFSI sector can use data analytics in a number of ways. These strategies include, as some examples:

Money laundering

Fraudulent activity may involve moving money through multiple accounts to conceal the source of money that was obtained illegally. Using data analytics tools to identify anomalous patterns in transactional data, which can then be reported to the bank, it is possible to identify potential money laundering activities. While an investigation is being conducted, the bank may take the necessary action, such as alerting the appropriate parties or freezing the affected accounts.

Insurance Fraud

Making a false claim for financial gain constitutes filing a fraudulent insurance claim. Data analytics tools can be used to examine claims data and look for patterns and discrepancies with legitimate claims to find these fraudulent activities. Insurance companies are able to identify and stop the payment of fraudulent claims in this way.

False Credit Card Transactions

Data analytics solutions can identify possible fraudulent activities by examining credit card transaction data, including purchase history, transaction amounts, and location information. This enables banks to recognize such transactions and stop them from being approved, ultimately preventing fraud.

2. Personalized Customer Experience Through Data Analytics in BFSI

Ingenious business models that personalize customer journeys and advance financial inclusion have been developed by BFSI organizations thanks to the advanced capabilities of data analytics technologies like machine learning and Artificial Intelligence (AI). With the help of data analytics, BFSI institutions can use bots to communicate with customers in a variety of languages and dialects, offering individualized and practical branch-like services.

Furthermore, based on customer activity, big data and AI-driven data analytics can analyze customer profiles, behaviors, and needs, enabling institutions to suggest suitable financial services and products. Data analytics solutions have sophisticated natural language processing and machine learning capabilities that allow for accurate understanding of customer intent, facilitating contextual engagement and raising customer satisfaction.

Customer data analytics, for instance, can enable chatbots and voice assistants to give customers wise investment and savings advice. AI-enabled voice assistants can also assess a customer’s loan eligibility, facilitate disbursement, and keep track of equated monthly installments thanks to data analytics (EMIs).

Also Read: Testing a bank application: A Success Story

3. Risk Management Through Data Analytics in BFSI

The BFSI industry is exposed to a variety of risks, including credit, operational, regulatory, liquidity, and market risks, all of which have the potential to endanger their operations. BFSI institutions use data analytics tools to effectively identify and manage these risks to reduce their impact.

Businesses in the financial services industry (BFSI) can learn more about various facets of their operations and spot potential risks by analyzing data. These insights can be used to evaluate risks and create individual mitigation plans for each one. Data analytics, for instance, can be used to analyze customer behavior, spot fraud, keep an eye on market trends, and assess the creditworthiness of customers. Due to their ability to manage risks in real time and make informed decisions, BFSI companies can avert potential problems before they become serious.

4. Predictive Analytics for Investment Decisions in BFSI

Predictive analytics is an essential tool for BFSI companies to use when making informed investment decisions. BFSI companies are constantly looking for investment prospects. These businesses can analyze historical data and statistical models to gain insights into upcoming market trends by utilizing predictive analytics, allowing them to recognize and seize potential investment opportunities.

Here are a few ways that BFSI uses predictive analytics to make investment decisions.

Portfolio management and assessment

Using historical data, predictive analytics can assess the returns & risks related to a specific investment. The predictive analytics model can assist BFSI firms in identifying trends and patterns that may indicate an investment’s likelihood of success or failure, enabling them to decide whether to pursue the investment opportunity or not.

Financial advisor assessment

Firms can analyze the performance of an advisor (internal and independent) by using data analytic techniques. It is possible to predict which advisor is expected to bring in higher revenues by assessing their past performance. Firms can in turn keep these set of advisors highly motivated thus enabling them to beat their past performance and generate higher revenue.

Customer Segmentation

To classify customers based on their investment preferences and behavior, BFSI companies use predictive analytics. Predictive analytics models can identify patterns and trends in customer data through customer analysis, allowing BFSI companies to tailor their investment products to the specific requirements of various customer segments.

5. Regulatory Compliance Through Data Analytics in BFSI

BFSI institutions operate in an environment that is highly regulated, and failure to comply with regulatory requirements can result in costly fines, negative legal consequences, and a damaged reputation.

As a result, these companies must devise creative strategies to guarantee that all legal requirements are met. One such solution that can assist BFSI companies in complying with regulations is data analytics.

Here are some strategies for using data analytics to help BFSI firms comply with regulations:

Reporting

BFSI organizations, as previously mentioned, heavily rely on data. However, it can be difficult to manually generate reports that show compliance with regulatory requirements. In situations like these, data analytics is essential. BFSI companies can use data analytics tools to analyze all data pertaining to compliance activities and produce reports that show regulatory bodies that the company complies with its compliance obligations.

Monitoring Compliance

By examining vast amounts of data related to compliance, BFSI companies can use data analytics to track their adherence to regulatory requirements. This makes it possible for them to spot potential compliance problems and take appropriate action to stop them from developing into serious issues.

Audit Management

By giving auditors the knowledge, they need to assess compliance and pinpoint areas for improvement, BFSI companies can use data analytics to support the auditing process. This reduces the possibility of regulatory fines and helps organizations avoid costly compliance mistakes. Data analytics can offer insights that help auditors in their evaluation process by analyzing data pertaining to compliance activities, such as identifying potential risks and areas of non-compliance. BFSI companies can avoid compliance problems and guarantee that they are successfully adhering to regulatory requirements by doing this.

Read our Success Story on : Real-time collaborative Fraud Analytic solution to combat Identity Theft

6. Cybersecurity Using Data Analytics in BFSI

Cyberattacks and fraud are very common in this sector. An isolated security lapse can result in sizable monetary losses and harm to a company’s reputation. Because of this, data analytics are essential to identifying and preventing cyber threats. Cybersecurity is therefore of the utmost importance in this industry. The BFSI industry can promote cybersecurity through data analytics in the following ways.

Threat Detection

Data analytics can identify potential cyber threats by examining trends and patterns in network traffic or other data sources within BFSI systems. The BFSI company can take appropriate action to eliminate the threat as soon as an abnormal activity is discovered, preventing further damage.

Incident Response

Data analytics helps incident response by supplying real-time data and statistics on cyber threats and security incidents. This allows BFSI organizations to quickly respond to potential security incidents in order to stop them from escalating.

Risk Assessment

Data analytics can be used by BFSI companies to assess the risks of cyberattacks. They can identify areas of risk and create risk mitigation strategies to protect their data from unauthorized access by analyzing data on potential vulnerabilities and cyber threats.

Compliance Management

To make sure that BFSI companies adhere to the various cybersecurity standards and regulations governing their operations, data analytics tools can be used. With the aid of these tools, the company can identify compliance gaps in cybersecurity-related activities and take the necessary corrective action to be following legal requirements and industry best practices.

Wrapping Up

The BFSI sector has always relied heavily on data, but data analytics is pushing that dependence to new heights. BFSI companies can use data analytics to drive digital transformation and open new opportunities for growth by leveraging their data.

BFSI companies can reduce fraudulent activity, personalize customer experiences, increase operational effectiveness, and guarantee regulatory compliance by using data analytics. Furthermore, data analytics can aid in the detection and prevention of cyberthreats, protecting sensitive data from unauthorized access.

BFSI businesses must embrace digital transformation and use data analytics tools in order to stay ahead of the competition. They can accomplish operational excellence by doing this, giving them a competitive advantage in the market.

Our team is here to support BFSI organizations integrate data analytics into their processes as they lead the way in digital transformation. Get in touch with us right away to find out more about how we can support your digital transformation efforts by assisting you in maximizing the power of data analytics. Click here for more details…

The post How Data Analytics Is Transforming the BFSI Sector appeared first on Indium.

But ensuring this can be overwhelming given the volume of data and the large client base. The company needed a real-time analytic solution that could quickly help identify issues to enable timely response for high availability and customer delight. The network was already using Amazon solutions for computing and storage and opted for the highly scalable Amazon Kinesis Data Streams to process multiple terabytes of daily log data and identify events within seconds of their happening. Kinesis also helped to improve efficiency, lower costs, and enhance resiliency.

Amazon Kinesis is a cloud-based, managed, scalable service that allows real-time processing of streaming large volumes of data per second. Meant for real-time applications, it allows developers to consume large volumes of data from different sources and scale up and down as needed while running on EC2 instances.

A Serverless Solution

Amazon Kinesis Data Streams is a serverless solution that provides timely insights by collecting, processing, and analyzing streaming data in real-time without the need for provisioning or managing the capacity needed to run the applications. It can ingest data in any format, including videos, audio, application logs, website clickstreams, and IoT telemetry data in real-time and use it for analytics, machine learning, and other applications

It is a cost-effective solution allowing users to pay per use, thereby reducing ownership costs and improving RoI. It comes with built-in integrations with other AWS services to enable the creation of serverless, analytics, and application integration solutions on AWS fast. It captures gigabytes of data every second from several thousands of sources including website clickstreams, social media feeds, IT logs, database event streams, financial transactions, and location-tracking events, and makes it available quickly for real-time analytics. Some of the key uses include generating real-time dashboards, performing real-time anomaly detection, enabling dynamic pricing, and so on.

It uses Apache Flink, an open-source framework and engine, to process data streams to simplify the building, integrating, and managing of Apache Flink applications with other AWS services

Click Here : Predictive Data Analytics & Data Visualization in the FinTech Industry

AWS Kinesis Data Streams Use Cases

Some of the common use cases of AWS Kinesis Data Streams include:

Real-time Data Analytics: Kinesis Data Streams allow parallel processing power adding value to the access to real-time data.

Real-Time Metrics and Reporting: Kinesis Data Streams allows the data collected or gathered to be used for reporting and simple data analysis in real-time.Complex Stream Processing: By developing Directed Acyclic Graphs (DAGs) from data streams and Kinesis Data Stream Applications, downstream processing through various Kinesis Data Streams Applications is possible. Some of the other use cases include

(i). building video analytics applications for video playback, face detection, security monitoring, machine learning, and other analytics.

(ii). Perform real-time analytics on data where batch processing was being used

(iii). Build real-time applications for various purposes, including application monitoring, live leaderboards, fraud detection, and so on.

(iv). Analyze streaming data from IoT device

Benefits of Amazon Kinesis Data Analytics

Using Amazon Kinesis Data Analytics solutions, users can experience the following benefits:

Powerful Real-Time Processing: The built-in functions in Amazon Kinesis Data Analytics facilitate filtering, aggregating, and transforming streaming data for advanced analytics. The streaming data is processed with sub-second latencies, allowing incoming data and events to be analyzed and responded to in real-time.

Prevent Data Loss: It prevents data loss through synchronous replication of the streaming data across 3 Availability Zones in an AWS Region. This data can be stored for up to a year and provided with multiple layers of protection against data loss.

Security: KDS aids in regulatory compliance by encrypting sensitive data and enabling access only with proper authorization. Server-side encryption and AWS KMS master keys provide data security at rest.

Easy to Use: The AWS SDK, the Kinesis Client Library (KCL), connectors, and agents help with quickly building streaming applications. Data processing is made easy with built-in integrations to AWS Lambda, Amazon Kinesis Data Firehose, and AWS Glue Schema Registry.

Elasticity: It allows the dynamic scaling of applications from megabytes to terabytes in an hour and from thousands to millions of PUT records within seconds. The throughput of the data stream can be adjusted at any time based on the input data volume.

Indium Approach to AWS Kinesis Data Streams

Indium’s data engineering team has experience in providing analytics solutions using streaming data for real-time insights and actions. Further, we are an AWS partner and have the expertise to create custom solutions using AWS Kinesis Data Stream to help businesses improve performance and customer delight. We leverage AWS’s complete set of pre-fabricated toolsets to spur businesses on the path of innovation and growth by making them agile, collaborative, and customer-focused. We can help businesses align the business’s strategic imperatives with cloud platform capabilities and leverage tools such as AWS Kinesis Data Streams to gain actionable insights and timely responses.

The post Data Analytics on Streaming Data using Amazon Kinesis Data Analytics  appeared first on Indium.

Technology is one of the key driving factors in the market for predictive analytics. Newer cloud-native solutions are being continually worked on leaving legacy data analytics solutions behind. This is done so that businesses can derive qualitative and faster intelligence by shifting to cloud-native data solutions.

Discussed below are some best practices that businesses must follow while they leverage their data and analytics resources for better business insights:

Best Practices while Leveraging Data & Analytics Resources

• Source Data with an Ample Strategy: Many companies refrain from adopting the right analytics processes as they believe the quality of the resources are not up to the mark. Data can be sourced or purchased through free open-source resources and other data providers. An organization should balance the cost of acquisition for the resources with what value the data brings to the analytics effort.

• Transition from Analytics Projects to Products: Analytics projects more often than not are to be planned for the get-go and have a defined scope. There needs to be a strategy formed before-hand. Instead, if businesses focus on analytics products, they can generate a considerably higher amount of return on investment (ROI) along with obtaining business insights, thereby improving the overall business performance.

• Maintain a Close Communication Channel with Stakeholders: Engagement and support can be facilitated by enlisting stakeholders onto the initial stages of the analytics processes. The best way to build questions is to clarify assumptions and to get the stakeholder to organically put across their requirements. Simply asking what the stakeholder wants will not suffice, as additional context will have to be provided. This helps to ensure that the key performance indicators (KPIs) and business goals are being met on a regular basis.

• Build High-Performance Teams with Compliance as the Focus: The collection of data needs to be done with compliance being the main focus. Productive teams make for more efficient teams, as they work to integrating analytics into the company’s daily workflow. There needs to be a specific importance given to how compliance affects different factors such as internal business rules, industry standards, and government regulations.

• New Infrastructure Technology with Advanced Analytics: There is a need to consider building an ecosystem that can host different technology types. These technologies can include in-memory computing for highly repetitive analytics. Companies that are measuring the best value for business are gravitating towards the use of advanced analytics. Predictive analytics is one step into the world of advanced analytics that makes use of machine learning and AI to predict future growth and success rates amongst other things.

• Use Governance and Insights to Refine the Analytics Process: Dealing with increased amounts of data and team members accounts for governance to become a significant part of the analytics process. There needs to exist a formal procedure that helps to make certain the data that is captured is consistently of high-quality. There also needs to be a common understanding of the data’s nature across the entire organization.

Relevant Read: How a Well-Implemented Data Analytics Strategy Will Directly Impact Your Bottom Line

There are many forms of intelligence that can be used by a business to derive insights from. Let’s look at how a business can improve their customer service using trend analytics in social media using the Internet of Things (IoT)

Improving Customer Service Trend Analytics in social media

Digital marketing success can be sought out by using business analytics when working with new use cases:

• The Internet of Things (IoT) opens up the possibility of intelligence to be distributed and consequently replenished in an automated fashion. This will surely change the essence of the overall supply chain and calls for companies to add new services that are relevant and of the right fit.

• The usage of chatbots on a global level has been rising in recent years, as the data that is recorded from these conversations can highly enhance future communications. Chat automation powered by past insights can help in improving overall customer service and analyse trends.

• Most companies are trying to leverage their presence and growth on social media to create a better brand image. Social media has an abundance of different types of data that can help an organization with customer service. The most important application of data from social media is analysing the public’s perception of a company’s product or services through reviews and feedback from customers. Social media data analysis also helps in determining the best time frames for company project and products to launch.

• Online commerce and digital marketing are at the forefront of business. It is important to understand different customers, and how each new tool and technology can aid in the same.

• When the marketplace is uneven and uncertain, customers inevitably end up paying more for solutions. There needs to be a certain maturity in the industry in question as the competition increases and the differentiators between businesses get more apparent.

Improve Insights from Business

Most of business cases requires business users to design, interpret, and deliver data that is produced by multiple applications to build technical and business analysis skills. The increasing complexity of the technological ecosystem, coupled with increasing number of data sources is rapidly changing what is considered cost-effective and practical to achieve.

Business intelligence and dashboards for analytics need to be created by business leaders while providing tactical requirements and constant inputs. It is difficult to find this exact combination of skills to make sure that the organization’s maturity is improved along with building competent capabilities to lead up to greater business needs. If you want to leverage the power of data and analytics for your business, you can consult our data engineering and data analytics experts now!

The post How to Leverage your Data and Analytics Resources for Innovation appeared first on Indium.

It can help e-marketplaces with product classification. It can help to improve product design, devise focused marketing strategies, increase operational efficiency, and much more.

Recommended: What Text Analytics Tells Us about a Customer’s e-commerce Shopping Experience

While the list of possibilities is long, the success rate is not as high. According to a Gartner study, 80% of AI projects will be unable to scale in 2020 and by 2022, only 20% will deliver business outcomes.

One of the key reasons for this failure could be not selecting the right text analytics tool that can meet business goals and scale up.

A Buyer’s Guide for choosing a Text Analytics Solution

When scouting the market for the right text analytics solution, the 5 points to keep in mind include:

1. Customization: Each organization has a different business goal and a different set of data mix to work with. Most advanced platforms use a variety of methods such as machine learning, natural language processing, business rules and topic identification to analyze data. However, these tools tend to have a fixed, black-box model approach which may generate results fast but may be ineffective in working with small data sets. Being able to see the combination of algorithms and modify them to suit your specific and unique needs is necessary for you to benefit from the tool. 
2. Accuracy of Sentiment Analysis: Human beings communicate in complex ways. Words in themselves may sometimes mean the exact opposite in a particular context. Sarcasm is a tool that conveys much but requires reading between the lines to understand it. Emojis and exclamations contribute to the meaning. So tools that merely group words to identify the sentiment as positive or negative can be widely off the mark and be misleading. Training the tool with enough data sets to be able to accurately assess the tone becomes very important for the tool to be successful. A solution such as teX.ai from Indium Software is built on a strong foundation of semantics where the tone and the other components of communication are also factored in to arrive at the meaning accurately.
3. Use of Metadata: Aiding the semantics capabilities of any good tool like teX.ai is metadata that is often ignored by many tools. This can enhance the understanding of the sentiment better and get clarity in the face of ambiguity.
4. Multi-Lingual Support: This is the age of globalization where businesses can reach out to international markets. The Internet supports people to express themselves in the language they are most comfortable with and this makes it essential for businesses to be able to tap into chatter in those languages. Text analytics focused on English alone is no longer enough and the tool should be just as proficient in the semantics of that language to be able to unearth hidden meanings.
5. Dashboards and Visualization: Are you getting only basic charts or does the tool empower you to customize reports for a better understanding of the results is a clincher. Tools with better analytics models and enhanced dashboards along with multiple visualization options can help you get a better view from your slicking and dicing of data.

Indium Software’s teX.ai is a comprehensive tool with several visualization options, an intuitive user interface, customizable algorithms providing semantics-based sentiment analysis with multi-linguistic support that can fit the text analytics needs of organizations of any size.

Relevant read: Text Analytics of Social Media Comments Using Sentiment Analysis

Are You Ready for Text Analytics?

While the tool capabilities are very important for the success of the text analytics project, it is also essential to assess your internal preparedness to derive greater success from your text analytics initiative.

• Having the Right Data sets: Your organization must have enough documentation with textual data and of the right kind to get meaningful insights.
• The Right Team: While the text analytics software can help with analytics, your team should have the capability to benefit from the data to gather actionable insights.
• Company-wide Buy-In: Any analytics initiative can provide holistic insights only with an enterprise-wide commitment to implement the changes needed to enhance customer delight.
• Speed of Implementation: The speed of transformation as well as implementing the changes based on insights will have an impact on the success of the project.

Indium – End-to-End Solution Provider

teX.ai is a SaaS product solution from Indium Software, a technology solutions company. Incepted in 1999, Indium is a ISO 27001 certified company with 1000+ team members, servicing 350+ clients across several domains. It provides customer-centric, high quality technology solutions that deliver business value for Fortune 500 and Global Enterprises.

The teX.ai solution and the experienced team with cross-domain expertise can empower you to leverage your unstructured data for insights that can accelerate growth.  teX.ai helps produce structured data, metadata & insights by extracting data from text, summarizing information and classifying content.

If you wish to implement a scalable text analytics project to transform your business, contact us now.

The post Picking the Right Text Analytics Product: A 5-Step Guide appeared first on Indium.

It is referred to as text mining. One of the prominent reasons owing to which people use it is for the extraction of additional data from the unstructured data sources with an eye to enriching the master data of the customers with an eye to production the new customer insight.

It is also useful for the determination of sentiments and different types of products and services.

Results of the survey, tweets, online reviews, emails and different kinds of written feedback consist of insight into the customers.

The recorded interactions have a bunch of information that can be transformed into the text without any hassles.

According to Markets and Markets, the global text analytics market value is forecast to reach USD 8.8 billion by 2022 with a compound annual growth rate (CAGR) of 17.2 percent. Social media analytics and the growing need for predictive analytics for businesses are among the major factors driving the growth of the text analytics market.

With the aid of text analytics, you will be capable of uncovering a wide array of themes and patterns.

Thus, you will have an information about the thoughts of your customers. With it, you will be able to gain an understanding of their requirements and needs.

Top Tools for Text analytics

Text analytics with Hadoop

Analyzing text with the aid of Hadoop happens to be an amazing option when the full volume of the source files is huge and the Hadoop Cluster has prerequisite sources.

Thus, the analysis of the text happens more quickly in Hadoop. The text analysis is known for the extraction of entities from the unstructured text.

It is helpful for the transformation of the unstructured data into the structured data. This is crucial for running any sort of analysis with the aid of the data in text resources.

You just require the text sources as the input for the analysis of the text.

Later on, you can remove the text sources theoretically, as they will not be required for the process of analysis.

Text analytics with HANA

With the aid of SAP HANA, it is possible to extract real insight from the unstructured data.

This platform stands out of the ordinary in offering text analysis, search and text mining functionality from the unstructured text sources.

Statistical algorithms can be applied by which you can detect the patterns in the large document collections, which is inclusive of key term identification as well as document.

Almost 80 percent of the relevant information of the enterprise is derived from the unstructured data.

With the aid of SAP HANA, you can get access to the greater volume of data that is inclusive of unstructured text data from a wide array of sources. SAP HANA allows people to do the full-text analysis.

Text analytics with R

Here is the list of the leading four options that are used in the Big Data Services industry with an eye to accomplishing text analysis in R:

Keyword Match Algorithm

It is considered to be the most powerful tool for performing text analysis. It stands second to none in the extraction of keywords from the not so well separated keywords.

It comes with the option to assign priority to the algorithm. You, however, require a pre-defined list of keywords from where you require searching.

At times, it has been seen to capture a few types of misclassified cases.

Word match algorithm

This is known to be the fix for the min-classified cases that are found in the last algorithm.

Here, words are matched in lieu of the keywords. It functions in a perfect manner to find the well-separated words.

For example, with the aid of this algorithm, it is possible to extract the word Ramesh from Ramesh Shastri.

It enables the priority order as well. For example, in case you intend to give higher priority to Ramesh than Shastri in the above-mentioned tag, it can be executed at ease.

General Expressions

This process requires extensive research from the sentence structures. In order to begin with it, you do not require any sort of list.

The percentage of accuracy is really high if you gain success in finding the stronger and regular expression.

An in-depth research is required for the creation of regular expression. In case the data is not structured well, this process lets you tag a smaller number of the observations.

This algorithm can be used if you are not aware of the language of the text. It functions as the feedback to the other algorithm.

If parameters are optimized in a perfect manner, it can be predicted with ease. You do not need any dictionary.

It is also used for providing feedback to the other algorithms. At times, it is not that precise in the name of the subject.

It has a tendency for capturing the trends that do not indicate anything significant.

Text analytics with Excel

Excel is recognized to be an effective and convenient solution for accomplishing your requirements for text analysis.

You can go for an analysis of several customer reviews for gaining an insight into the product.

The Excel add-in functions on ParallelDots AI APIs that are used by the enterprises and developers for empowering the analytics for the past two years.

You can conduct keyword analysis on a bunch of negative and positive sentences with an eye to understanding why people are disliking or liking the product.

This analysis let you get an insight into the key phrases that contribute to the sentiment about the product.

For instance, a phone manufacturer can conduct the analysis of reviews from the social media, eCommerce sites, and tech review blogs.

After that, keywords can be extracted for the negative and positive sentiment sentences for finding the features, disliked or liked by the users regarding about a specific phone model.

In addition to this, you can go to a higher level by the analysis of the product reviews and then categorize the same with an eye to identifying if the review is a query, feedback, spam or opinion.

This is useful to filter the essential reviews and then act on them quickly.

You can correlate the analyzed data such as intent, keywords, and sentiment with the internal business metrics like sales data, marketing spends for getting actionable insights.

Text analytics with Python

Here are some of the applications in which python stays at the forefront that enable the use of a wide assortment of advanced libraries, specifically the natural language processing toolkit.

It comprises of a series of libraries and advanced functions for the performance of specific operation present in the text for pre-processing it for using the same for the derivation of information from the same.

Chatbots

Though customer service is present for most of the products, it is not available always effective as most of the people want their complaints should be solved during the usual working hours, thereby resulting in a rush. Chatbots are considered the perfect solution to the same.

Sentimental analysis

During online shopping, most of the customers provide feedback. This feedback is classified into the categories like negative, positive and neutral, thereby letting the customers make a better-purchased decision about the product.

It also helps the company in filtering out the flaws from the negative reviews for the improvement of the product.

Conclusion

The text analytics is known for conferring the early warning of the trouble as it showcases the points, your clients are not satisfied with.

With the aid of the text analytics tool, you will gain success in extracting valuable details from the data that cannot be quantified in the other ways at ease.

It is useful in turning the unstructured thoughts of customers into the unstructured data at ease that you can use for your business.

The post 5 Widely used Tools and Techniques for Text Analytics appeared first on Indium.