Companies depend on data to respond to evolving needs, improve customer relationships, and reduce the risks that threaten business operations. Through big data analytics, organizations can predict emerging trends and discover valuable insights that help them make strategic decisions.
That said, one of the biggest benefits of the effective use of data for organizations is its ability to help reduce costs. From marketing strategies to customer service, properly leveraging analytics and swaths of data can help organizations glean better insights to cut down on operational costs and increase revenue.
Here are six ways organizations can use big data to drive down costs:
Create Targeted Opportunities for Marketing Campaigns
Data has always been an invaluable component of effective marketing campaigns. Big data has helped businesses move away from mass-marketing campaigns to focus on more targeted and personalized strategies.
Businesses can now capture data from every customer touchpoint, giving them a better understanding of both customer behavior and intent. By evaluating customer behavior, companies can create strategic marketing plans that target a particular customer cohort—for example, by offering personalized recommendations based on previous purchases or social media activity.
In the case of performance marketing, advertising costs are billed when a targeted online user performs a certain action, such as clicking on a paid advertisement. Using data from customers who have taken similar actions, big data analytics can identify the variables that are most likely to influence a customer’s click. In this way, big data analysis results in less wastage, making advertising more relevant and less expensive to carry out.
In fact, one Forrester study found that 37% of marketers waste budget unnecessarily because of poor quality data. Using customer profile data, companies can identify the marketing channels that are more likely to lead customers towards conversions or sales. This allows them to deploy their marketing dollars more strategically by creating and executing better-targeted marketing campaigns.
Digitize Your Supply Chain for Insights and Resilience
According to data compiled by IBM, 84% of chief supply chain officers (CSCOs) report that a lack of supply chain visibility is the biggest challenge they face. Supply chain digitization improves traditional supply chain management systems through the integration of new technology, combining real-time location and business data from across the entire supply chain into a single, central source of information that creates end-to-end visibility. As a result, organizations can improve efficiency, prevent disruptions, and remain competitive in their markets.
Supply chains generate massive amounts of data, including internal historical sales data, supplier performance records, point of sale consumer data, and landed cost data. Through digitization, companies can gather and analyze this data to identify problem patterns, bottlenecks, and other opportunities to reduce costs.
Agility is also vital in supply chain management. Decisions often need to be made quickly and can have a significant financial impact, costing millions of dollars. With a digitized supply chain, organizations can glean valuable insights from real-time status reports, resulting in faster decision-making, a stronger ability to identify service area gaps, and opportunities to boost performance and optimize connections with customers and suppliers.
Identify Fraud to Better Prevent Loss
Fraud can be costly for a business in any industry. Data and analytics can help organizations discover trends that point towards suspicious activity to reduce fraud and thwart criminal efforts.
For example, big data can help retailers build profiles and set thresholds for normal customer behavior regarding the purchase of a specific product over a period of time. With this baseline established, retailers can then identify customers whose behaviors indicate that they may be committing return fraud. Retailers can then blacklist these customers or take other actions to help prevent return fraud.
Enhance Log Analysis to Understand Resource Needs
Log events, audit trail records, and even simple logs can all provide useful insight into the activity that is occurring across various systems. This data can then be used to understand user behavior, improve application or infrastructure performance issues, proactively mitigate risk, and ensure compliance with security policies, audits, and regulations.
Log analysis can also help with resource management, enabling companies to better understand their current resource usage and future resource requirements. Too few resources can cause poor performance and lost sales, while too many available resources can result in increased expenses. By tracking system resource usage, businesses can detect where their systems need extra capacity for better performance in addition to identifying underutilized assets that can be restructured and optimized to improve productivity, efficiency, and performance.
With insights from log data, DevOps and IT teams can react to issues faster, shortening the time needed to detect and resolve production problems. Log analysis can also give insight into issues before they occur, allowing teams to be proactive by identifying issues and their root causes before they cause downtime or other performance issues.
Make Better Customer Service Table Stakes
Poor customer service not only results in a loss of sales, but it also keeps businesses from earning valuable lifetime customers. In fact, research from Gladly found that 92% of consumers would stop purchasing from a company after just three or fewer poor customer service experiences.
Through the effective use of data from CRM systems, companies can create accurate customer profiles that help them quickly get up to speed on customer issues and provide faster service to resolve problems. Data analytics can help businesses identify weak areas in their customer service strategies to enhance the way they interact with customers.
With big data, companies can also identify the activities that keep current customers satisfied. In most cases, it costs more to win new customers than to keep existing customers happy, as attracting new customers can require costly promotional campaigns. Data from surveys, reviews, and other online and offline customer feedback mechanisms can help businesses create post-sales strategies that improve customer retention.
Drive Productivity and Efficiency with Real-time Data
The availability of real-time data can play a vital role in improving productivity and operational efficiency within a company. Analytics software solutions can be used to produce reports that cut through the noise that big data collection generates. Managers, employees, and customer service reps can use these easily digestible reports to pinpoint the exact information they need.
Data can also increase productivity within teams, improve hiring methods to help managers recruit and keep the best talent, and give insights on effective management and training methods that improve employee satisfaction and performance.
As more data becomes available, AI and machine learning algorithms can give better insights that improve operational efficiency, lead to greater efficiency, and help companies deliver better services to their customers—all of which contribute to lower expenses.
Accelerate Big Data Analytics with Pure Storage
More businesses are using big data to cut costs and increase profits. It’s become increasingly clear that big data holds a wealth of information that can help organizations direct financial resources more efficiently to optimize expenses and significantly reduce costs.
Get more value from your data with a modern data infrastructure that can help you accelerate decision making, simplify operations, and power analytics. All-flash data storage solutions like Pure Storage® FlashBlade® can help you leverage big data to make better, faster business decisions.
FlashBlade is the world’s leading unified fast file and object (UFFO) platform. This fast and efficient scale-out flash storage solution is perfectly suited for analytics, machine learning, and artificial intelligence workloads.