This is how big data analyzes can reduce costs

This is how big data analyzes can reduce costs

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Big data analysis is designed to provide companies with actionable insights. Here are six ways organizations can leverage the volume and speed of data to increase operational efficiencies and 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 big data analysis, companies can predict emerging trends and gain valuable insights that help them make strategic decisions. However, one of the biggest benefits of using data effectively for businesses is the opportunity to reduce costs. From marketing strategies to customer service, by leveraging analytics and datasets, businesses can gain better insights to reduce operational costs and increase revenue.

Below, Pure Storage discusses six ways companies can use big data to reduce costs.

1. 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 activities.

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 actions, big 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.

In a study, Forrester found that 37 percent of marketers wasted budgets due to poor quality data. 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.

2. 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 can have significant financial implications that can cost millions of euros. With a digitized supply chain, companies can gain valuable insights from real-time status reports. This results in faster decision making, better identification of service gaps, and opportunities to improve performance and improve relationships with customers and suppliers.

3. Fraud detection to prevent losses

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

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

4. Improved log analysis to understand resource needs

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

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