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.

But, we must also bear in mind that there is many a slip between the cup and the lip. As many studies such as those from Gartner and the Economist show, the failure to convert data analytics projects to achieve significant growth is quite high. That is because, many approach it like a technology project, investing in best-in-breed solutions but failing to create a strategy or a business case to justify those investments.

Even when creating a business case, rather than having broad, comprehensive goals, it is best to prioritize the investment categories. To experience tangible top- and bottom line benefits, the investment decisions should be based on your insights maturity. Forrester recommends pursuing insights-driven business (IDB) transformation, where enterprise data is transformed into insights that can trigger actions and result in desired business outcomes. For this, along with tactical investments, it recommends making strategic investments into people, process, data, and technology.

Creating the Strategy–The Best Practices

Indium worked with a top Oil & Gas consulting firm that leverages IoT-driven analytics to help

Oil & Gas companies to enhance and optimize their operations. Indium’s expertise in Big Data analytics enabled it to develop a strategy that involved a three-phase implementation. The client was able to experience:

• 50% reduction in time-to-market (TTM) by leveraging the product
• Minimized downtime costs and maximized productivity using predictive maintenance and improved failure prevention

A clear definition of the goal, a clear roadmap for the project, and various other factors contributed to Indium’s success in helping the firm meet its objectives. In our experience, often data analytics projects do not deliver because of:

• Not defining the goal to be achieved
• Not creating a solution or design for the objective
• Not translating data science insights into actions to change the way business functions

Some of the best practices that Indium swears by to overcome these challenges and ensure improvement in bottom line from a well-implemented data analytics strategy include:

1. Look for Quick Wins: Start small and identify critical but short projects to begin with. These projects may bear fruition within a month or two and impact your revenues and efficiency significantly.
2. Identify a Champion: It is important for identifying an owner for these different projects who will ensure the goal is achieved at the local level.
3. Assess Available of Data: Data is a broad term. While there may be organizational data available, it needs to be clean and relevant for the project you have undertaken. Also, enriching with external data will enhance the accuracy of the insights.
4. Establish KPIs: Establishing metrics to measure the impact of the project serves to objectively assess the success of the initiative.
5. Periodic Reviews: This is very important to know if the project is progressing as planned or needs a course correction to achieve the desired outcomes.
6. Create the Right Team: Just as important as the technology and data are the people involved in the project. It should be cross-functional and have a sponsor who drives the project.
7. Involve IT: To scale up from the Proof-of-Concept (POC) stage, it is essential to involve the IT team so that the necessary resources can be made ready. An ideal team should have data scientists, data engineers, architects, visualization experts, and data science storytellers either in-house or outsourced.
8. Getting the Right Tools: Yes, tools do matter. The composition of your team will determine whether to go with visual modeling or to go for coding languages such as Python or a mix of both. Whether to go for multi-cloud, the computational power needed, how much data volume and velocity you need to provision for will also have an impact on the outcome of the project. Use Agile: Creating an Agile Data Science approach can help you implement projects fast, fail fast, and manage risks better to achieve the desired results quickly.
9. Plan for Scale: Not enough provisioning for scaling from POC to scale is one of the primary reasons for the failure of data analytics projects. Allocating sufficient budgets and resources is essential to see the project achieve its full potential. This should also include identifying operational issues when you scale and creating a roadmap right from the start.
10. Trigger Action: Often, the insights drawn from data analytics projects may not get converted to actions. So along with insights, predictive and prescriptive analytics are essential to achieve the end result.

Indium – To Meet Your Data Analytics Objectives

Indium Software has more than 20 years of experience in cutting-edge technologies, data science, Big Data, and data analytics. Our process-oriented approach along with technical expertise and cross-domain experience help us in assessing each project we undertake critically to create bespoke solutions that meet the individual needs of the organization. For data analytics projects too, we first study the data, the objectives, and the technology of our clients before we create a strategy that leverages existing capabilities and recommend complementary solutions to protect their existing investments. The roadmap also ensures that the projects can scale as they fulfill goals in the near term and can be leveraged even as the client grows from strength to strength.

To know more about Indium and our capabilities in helping you achieve your data analytics goals, contact us now.

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According to research and analyst firm Gartner, end-user spending on public cloud services is set to reach USD 304.9 billion in 2021, growing at a compound annual growth rate (CAGR) of 18.4 percent since last year. It is also worth mentioning that a vast majority of organizations using cloud services plan to increase their spending on cloud (further!) to overcome the disruption of the global health crisis.

The rise in cloud spending is likely to be sustained for a few years, Gartner says, with organizations increasing their investments in remote-working technologies, mobility, collaboration and more.

In return, what advantages are organizations aiming to gain from spending on cloud engineering services, which include analytics, business intelligence, databases, networking, software, et cetera? Let us find out.

Centralized data access

When data is siloed across your content management systems, marketing automation, ERPs and numerous other systems, it’s challenging to get a comprehensive view of your business and decision-making is harder too.

Cloud-based data analytics platforms help organizations by integrating all the data into a single point of access for every user to make business decisions.

Among the key advantages of having centralized data access is you can eliminate duplicate entries of data, provide executives and decision-makers with the right data at the right time, reduce the time spent to identify which gathered data is right, and enhance data intelligence capabilities of the organization.

Security and governance

Governance may be challenging but security is built into cloud analytics platforms, helping you configure user permissions to make sure everyone in your organization has access to the data they need while also ensuring accountability and transparency.

With the increasing volume of customer data being collected and used in 2021, organizations must ensure that privacy and governance requirements are met.

Today’s customers want to know what details of them are being collected and where they are stored. It is possible they will lose faith in an organization if their personal details are widely accessible.

Gartner predicts that, until 2025, 90 percent of organizations which fail to control public cloud use will inadvertently share sensitive data, which also means identity and access management (IAM) will be a key challenge for individual and machine users in 2021 and beyond.

Scalability

Instead of purchasing new hardware as data requirements change, cloud analytics platforms provide organizations with the option to turn on or turn off their services as required. For example, you can scale up your services quickly if you have a spike in data and, when the activity is normal, scale back down.

The ability to scale up or scale down cloud resources helps organizations to significantly reduce the risk that comes with rapid growth. Most applications can be managed cost-effectively on the cloud and be easily migrated using lift-and-shift strategies.

This flexibility further helps organizations to provide innovative products and services and not be constrained by infrastructure that does not suit their current requirements.

Data sharing and availability

The best cloud-based analytics platforms and solutions provide business users easy access to data, enabling them to analyze and explore in every possible context.

From laptops to smartphones, cloud analytics services also give users a comprehensive, unified experience irrespective of the device, including being able to analyze and share data and applications anywhere.

Cost savings

According to a McKinsey study, legacy systems account for 74 percent of a company’s IT expenses while affecting agility too.

With cloud services, no upfront costs are involved as the cloud service provider fulfills all infrastructure needs. Less power consumption and the lack of need for in-house expertise for server and software maintenance result in even more cost savings with cloud platforms.

Cloud service providers also have multiple data centers and offer resilience with data replication, which is particularly useful during a system crash or natural disasters such as flooding.

In addition, updates and upgrades happen automatically on cloud-based data analytics platforms. This saves organizations significant costs as they don’t have to worry about ongoing maintenance which is typically part of on-premise servers.

Summary

From providing centralized data access to reducing costs, cloud-based analytics solutions are helping companies get maximum value from their data. As the volume of data grows exponentially, cloud is very much the future of data analytics as it provides agility and fosters company-wide use of analytics for data-driven business decisions.

The post 5 Key Advantages Of Cloud-based Data Analytics Platforms appeared first on Indium.

Today, a dissatisfied customer will not take time to switch loyalties.

It goes without saying that if a business wants to prosper, they must put their act together through data analytics by collecting and managing data at every point of the customer interaction to be able to give best-individualized experience.

In the course of interaction of an advanced customer analytics ensures that data that is unique to the user can be collected at various stages, be it usage of mobile apps, social media interaction and a lot more

The company should be able to use this information through big data analytics tools to give the best user experience, something that will go a long way to sustain clientele and to beat the competition.

Is Big Data Analytics Different from the past?

Earlier we were following the ETL model of data handling. ETL stands for Extract, Transform and Load.

The data was captured by systems primarily to obtain reports from past actions. Different methods are used to load variety of files into a huge database that generates desired actionable reports.

But this was instead a rigid system, which was unable to take care of multiple data streams especially in large volumes.

The systems were also not able to change the input info on a real-time basis and were more suited to respond to fixed queries.

But Big Data analytics has changed it all. Now by using advanced analytics, companies can decide better.

Data Analytics has enabled improved reporting interface structures, better data extraction capabilities, automatic file handling protocols, the creation of highly indexed data streams and above all, the capability of cloud hosting and blockchain technology to run and control a large system on a real-time basis.

All this boils down to better business decisions thereby improving sales and marketing effectiveness and at the same time reducing costs and eliminating wastages for all digital businesses.

The companies can derive significant benefits from data analytics services to get positive outcomes for the business and customers while ensuring a high level of data protection. The most important advantages of advanced analytics are:

1. Proactive Approach

According to a report by Compliance, Governance and Oversight Counsel, 60% of data carries little or no value. Most data lose their value if not contextualized in real-time, so it’s essential for businesses to make data available in real-time with the lowest latency possible and scalable capacity.

When sharing info, the only expectation that the customers have is to understand their requirement correctly and to give seamless interaction at all stages of interaction by doing so companies can improve the customer experience, a sure shot way to forge lasting relations.

2. Offering Right Product

Efficient data analytics and collection helps businesses to understand the changing technological demands of the market to be able to position the right product or services at the right time.

3. Customized Service

Today customer behaviors are incredibly volatile and unpredictable coupled with lots of options available for them to choose.

The only way to gain them is to offer personalized response and make them feel personally valued.

Using the Big Data analytics, it is possible to create interactions best suited to the customer type and personality.

For this, the system should be able to capture the customer attitudes, their location and preferences to offer a customized response in a multi-channel environment.

4. Improving Operation Efficiency

Data analytics helps in understanding, designing and optimizing the products at highest efficiency, that is capable of meeting customer needs and expectations.

The Big Data solutions help in improving field operations, enhancing productivity and thereby minimizing costs and improving margins.

The powerful real-time data analytics creates following operational advantages:

• It makes it possible to capture errors and failures instantly. It helps companies to react immediately before damage is done and the customer decides to shift to the competition. It is always easier to retain business and difficult to bring them back once they are lost.
• It helps to understand competition and changing barriers instantly. Companies can use this info to stay ahead of the competition by timely capturing the actions and changing strategies of the other market players. For example, if your competitor reduces the rate, you must know it instantly before the business is lost. With data analytics, it is all possible.
• It assists in improving deliverables to have better conversions rates and increasing revenues. By monitoring the products that customers are buying, companies can respond pro-actively to any issues in product performance. For example, a motorbike company can deploy real-time sensors to inform the user to attend to any maintenance issue in advance to avoid downtime.
• It helps in detecting frauds before they are done to take the advance corrective actions. It is instrumental in the financial world. Financial companies can deploy Big Data analytics tools to instantly capture any attempt by criminal elements to hack the system before reputations are lost.
• Security systems and fraud analysis through advanced analytics help to protect all the financial and physical assets against internal and external misuse and damage. Proper data management coupled with analytics and timely reporting of frauds creates a safer operational environment for business and its users. The data across various business verticals and platforms can be combined in real time to ensure a uniform organizational view and tolerance to activities not matching with the business values and ethics.
• It helps in reducing cost by improving the efficiency of operations. A well-designed data analytics environment allows the business leaders and decision makers to capture and monitor the important variables in advance so that the corrective actions can be taken on a real-time basis. No longer waiting for reports and then doing dissection in history.
• It helps to capture real-time sales data about; What is selling? At which location is selling? How much is selling? Who is selling more? Etc.
• It helps companies to align to changing customer preferences on a real-time basis. It is done by mapping competitors’ activities and capturing the results of the sale promotion activities

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