Data Science

REDUCE COSTS WITH BIG DATA ANALYSIS

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Extensive data analysis is designed to provide companies with actionable insights. Pure Storage highlights six ways companies can increase operational efficiencies and reduce costs with faster data.

Big data opens up new possibilities. Pure Storage explains how companies can use big data to reduce costs. Businesses depend on data to respond to changing demands, improve customer relationships and mitigate risks that jeopardize business operations. With the help of extensive data analysis, companies can predict emerging trends and gain valuable insights that help them make strategic decisions.

However, one of the most significant benefits of using data effectively for businesses is the opportunity to reduce costs. From marketing strategies to customer service, companies can gain better insights to reduce operational costs and increase revenue by leveraging analytics and datasets.

Create targeted opportunities for marketing campaigns

Data has always been a valuable part of effective marketing campaigns. Big data has helped companies move away from mass marketing campaigns and focus on more targeted and personalized strategies. Businesses can now collect data from every customer touchpoint, giving them a better understanding of customer behavior and intent. By evaluating customer behavior, strategic marketing plans can be created that target a specific customer group, for example, by offering personalized recommendations based on previous purchases or social media activity.

In performance marketing, advertising costs are charged when a targeted online user takes a specific action, e.g., B. Clicks on a paid ad. Using data from customers who have taken similar measures, extensive data analysis can identify the variables most likely to influence a customer’s click. In this way, Big Data analysis leads to less wastage, making advertising more relevant and cheaper.

Forrester found that 37 percent of marketers wasted budgets due to poor quality data in a study. Using customer profile data, companies can identify the marketing channels that are more likely to result in conversions or sales. This allows them to use their marketing dollars more strategically by creating and executing more targeted marketing campaigns.

Digitalization of the supply chain for more transparency and resilience

According to IBM, 84 percent of Chief Supply Chain Officers (CSCOs) say a lack of supply chain visibility is their top challenge. Digitization of the supply chain improves traditional supply chain management systems by integrating new technologies. These combine real-time location and business data from the entire supply chain into a single, central source of information, creating end-to-end transparency. In this way, companies can increase efficiency, prevent disruption and remain competitive in their markets.

Supply chains generate vast amounts of data, including internal historical sales data, supplier performance data, point-of-sale customer data, and onboarding cost data. Digitization enables companies to collect and analyze this data to identify problem patterns, bottlenecks, and other cost-cutting opportunities.

Agility is also crucial in supply chain management. Decisions often need to be made quickly and have significant financial implications that can cost millions of euros. Companies can gain valuable insights from real-time status reports with a digitized supply chain. This results in faster decision-making, better identification of service gaps, and opportunities to improve performance and relationships with customers and suppliers.

Fraud detection to prevent losses

Fraud can be costly to a business in any industry. Data and analytics help identify suspicious activity trends to curb fraud and thwart criminal efforts.

For example, big data can help retailers profile and set thresholds for normal customer behavior when purchasing a specific product over a given period. On this basis, customers can then be identified whose behavior indicates that they may be committing returns fraud. Retailers can then blocklist those customers or take other actions to prevent returns fraud.

Improved log analysis to understand resource requirements

Log events, audit trail records, and even simple logs can provide helpful insight into the activities across different systems. This data is useful in understanding user behavior, improving application or infrastructure performance, proactively mitigating risk, and ensuring compliance with security policies, audits, and regulations.

Log analysis can also aid in resource management, giving organizations a better understanding of current resource usage and future resource needs. Too few resources can result in poor performance and lost revenue, and too many available resources can lead to increased costs. By tracking resource usage, you can see where systems need additional capacity to perform better. Likewise, organizations can identify underutilized resources that can be reorganized and optimized to improve productivity, efficiency, and performance.

With insights from log data, DevOps and IT teams can respond to issues faster and reduce the time to identify and resolve production issues. Log analytics also provide insight into problems before they occur, enabling teams to take proactive action by identifying issues and their root causes before downtime or other performance issues arise.

Provide better customer service

Poor customer service leads to lost sales and prevents companies from acquiring valuable lifetime customers. A Gladly study found that 92 percent of consumers would stop buying from a company after three or fewer horrible customer service experiences.

By effectively using data from CRM systems, companies can create accurate customer profiles, enabling them to learn about customer issues and provide faster problem-solving service quickly. Data analysis can help companies identify weaknesses in their customer service strategies and improve how they interact with customers.

Big data can also help companies identify the activities that satisfy existing customers. In most cases, attracting new customers is more expensive than keeping existing customers happy, as attracting new customers requires costly advertising campaigns. Data from surveys, ratings and other online and offline customer feedback mechanisms help companies create post-sales strategies that improve customer retention.

Increased productivity and efficiency with real-time data

The availability of real-time data plays a crucial role in improving productivity and operational efficiency in a company. Analysis software solutions can be used to create reports that break through the background noise of extensive data collection. These easy-to-understand reports are helpful for managers, employees, and customer service representatives to find precisely the information they need.

Data can also increase productivity within teams improve hiring methods to help managers recruit and retain the best talent. Data provides insights into effective management and training methods to improve employee satisfaction and performance. The more data available, the better AI and machine learning algorithms can provide insights that will enhance operational efficiencies, leading to greater efficiencies and helping companies provide better customer service. All this contributes to lower costs

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