8 Ways to Use Data Analytics to Support Cost Optimization Across a Call Center

There is no denying data is gold in the 21st C.

It can be “information overflow” or” data streaming” – businesses at this point NEED data to function. Analysing this data, hence by extension, is the very core about which businesses function.

Hence, if you want to gain a competitive edge, data analytics is the key.

Cutting organizational costs? Again, data analytics is the key.

If you’re on the lookout for a way to make the most of data analytics for cost optimization, we’ve got you covered with eight different ways.

In this blog post, we cover the 8 ways data analytics can help any organization in cost cutting.

Read on…

1. Focus On Efficiency and Performance

Of course, performance had to be at the top of the list.

As your organization gathers an ever-increasing amount of data about your customers, the volume of information exceeds the capabilities of traditional ERP systems.

Hence, a new market has emerged that connects big data with ERP systems, facilitating advanced data analysis and generating valuable insights to automate business processes.

A simple example is SAP HANA, which has become the industry standard for in-memory data processing systems. By using big data tools, SAP HANA analyses large volumes of data, resulting in a reduction in operational costs.

With big data analytics in business, the reliability, maintenance, and productivity of organizations improve manifold.

Let us simplify this with an example – In the oil and gas industry, a data-driven approach has been shown to reduce unplanned downtime by up to 36%. This predictive approach not only minimizes delays but also helps reduce costs.

2. Use Data Analytics in Marketing Campaigns

In the past, companies relied on manual processes to study customer behavior for marketing strategies. However, with the growth of information, it has become impractical to survive with this approach.

Fintech companies in India specifically use data analytics for marketing campaigns. Firms like Karboncard, Razorpay, and Kodo, offering corporate credit cards, and forex services are setting the right example for the use of big data.

In today’s business landscape, the logic is simple – Marketers who embrace the integration of big data technologies can make it big at a fraction of the cost.

3. Target Targeted Ads

Expanding on the previous point, the integration of big data in marketing has reduced costs associated with targeted advertising campaigns.

Big data has revolutionized businesses’ marketing strategies, shifting from mass marketing to targeted approaches. By capturing data from various customer touchpoints, you need to utilize what you gain toward customer behavior and intent.

As a result, advertising efforts become more focused and relevant, leading to reduced wastage and lower expenses. A recent study revealed that 37% of marketers waste their budget due to poor-quality data.

Leverage big data into your marketing efforts, and reduce costs associated with ineffective advertising.

Talk about saving big money with small strategies.

4. Leverage Data Towards Supply Chain Management

Data analytics have found extensive applications in optimizing supply chains, enabling companies to enhance inventory management.

The best example would be Amazon, the largest e-commerce company today.

You can use data analytics in optimizing warehouse and fulfillment processes. Amazon gives credit to predictive analytics for the “anticipatory shipping” of its products. This approach reduces shipping costs and delivery time.

Furthermore, factors such as seasons, economic conditions, and weather patterns are taken into consideration when analyzing the acquired data.

The integration of predictive analytics and sophisticated data analysis techniques enables companies to make data-driven decisions, optimize inventory levels, and streamline their fulfillment processes, ultimately resulting in a competitive edge in the market. Supplier performance records need to be looked into to make informed decisions regarding supplier selection and collaboration.

Point of sale consumer data offers valuable information about customer preferences, purchasing behaviors, and market trends. By analyzing this data, companies can tailor their product offerings, marketing strategies, and supply chain operations to better meet customer demands.

Furthermore, landed cost data analysis should be used to identify cost-saving opportunities in transportation, tariffs, customs, etc.

According to research by IBM, a challenge faced by 84% of chief supply chain officers (CSCOs) is the lack of supply chain visibility.

Focus on real-time data that allows for quicker and more informed decision-making.

By staying agile and responsive, organizations can proactively address area gaps and bottlenecks.

5. Slash Down Costs in Logistics

One of the significant advantages that big data offers to e-commerce businesses is its potential to reduce product return costs. On average, the expenses associated with product returns amount to 1.5 times the cost of shipping. Implementing advanced analytics through logistics management software can enhance decision-making processes, helping businesses identify patterns and reasons for returns, ultimately minimizing return rates and optimizing overall supply chain efficiency.

Data analytics tools can be used to identify the products that have a higher probability of being returned. This allows companies to implement preventive measures such as improving product quality, addressing size-related issues, and ensuring compliance with standards.

By integrating big data technologies, businesses can also gain insights into the cities or regions with higher product return rates and identify customers who frequently exchange goods.

6. Enhance Customer Experience

Fraudulent orders pose a significant risk, leading to financial losses for companies. In the e-commerce industry, understanding customers who sometimes place orders and choose cash on delivery (COD) as the payment method, only to cancel the order at the last minute.

Is important. There are also instances where customers do not receive the products they purchased. Analyze customer purchasing and ordering patterns, and then make predictions about the likelihood of a sale.

7. Fraud Detection

Use data analytics for fraud detection.

Leveraging big data can assist organizations in identifying trends indicative of suspicious behavior, thus reducing criminal efforts.

Focus on unusual transaction patterns, unauthorized access attempts, abnormal customer behavior, or discrepancies in data records. By recognizing these red flags, organizations can take proactive measures to prevent fraud and protect their assets.

Overall, the utilization of big data in fraud detection and prevention empowers businesses to safeguard their operations, financial resources, and reputation.

By analyzing large volumes of data, including transaction histories, return records, and customer behavior patterns, retailers can identify irregularities or suspicious trends that deviate from the established baseline.

8. Utilise Real-Time Data

Learn to generate concise reports that cut through the overwhelming volume of data collected. These reports serve as valuable tools for managers, employees, and customer service representatives, enabling them to quickly access the precise information they need to make informed decisions.

Also, know that data-driven insights can boost productivity within teams. So make sure to use it to refine hiring methods, allowing or retaining top talent more effectively.

9. Use Log Analytics

Log events, audit trail records, and other system logs offer valuable insights into the activities.

Use them.

Through this analysis, companies can acquire profound insights into user behavior, helping them identify performance issues promptly.
Additionally, this data-driven approach allows businesses to proactively manage risks, ensuring compliance with security policies, audits, and regulations.

Analyze these logs, to detect patterns, anomalies, and trends that help you understand how users are engaging with your systems.

Final Word

Leveraging big data can have a substantial impact on enterprise costs, leading to cost optimization. By adopting in-memory computing platforms like SAP S/4HANA, organizations can transform their business processes.

Integrating advanced big data tools into your platform can yield significant savings, making it crucial to seek the expertise of reputable companies in this field.

To achieve optimal cost savings through big data, it is essential to partner with reputable companies.

Their expertise can help you make it or break it in the business world.

Picture of Ramitha Ramesh

Ramitha Ramesh

Ramitha Ramesh is the editor at Karbon Business, renowned for her expertise in SEO content marketing strategy for a diverse clientele across the US, UK, and India. With a penchant for exploring the fast-paced realm of finance, business, and marketing, her blog is a treasure trove of insights that offer unique perspectives on the latest trends and developments in the industry. Finding nirvana in food, fun, and travel, she is on a mission to counter the infodemic amidst digital chaos.

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