Modern business management is made up of three pillars. These pillars include data, analytics, and business operations. Businesses generate large volumes of data and often struggle to get value from these data assets. A data analyst in Washington D.C. utilizes experience and know-how in understanding and interpreting your data. Once data is understood it is easier to develop projections, improve operations, and reduce waste.
How Data and Analytics Merge in Business Management
A successful analytics strategy requires reliable data for it to produce actionable insights. The actionable insights are then used to make better decisions on operations. Analytics can be categorized into predictive and descriptive analytics. Descriptive analytics aim to provide descriptions of business processes to help in decision making. By using descriptive analysis we are able to provide metrics and key performance indicators. Often presenting this information via dashboards enables us to quickly identify the overall performance of a business.
For example a sales analyst can use descriptive statistics to understand data from the previous year and use those insights to forecast sales for the next year. Predictive analytics encompasses a broad range of techniques such as data mining and machine learning. With predictive analytics you are able to understand the relationship between many factors. For example predictive analytics can be used to predict which customers are likely to leave a company. In this article we will discuss how using both analytics helps improve business operations and management.
Business management, data, and various analytics offer a clear view of how your business is performing. Areas often viewed when looking at the management of a business include financials, customer management, risk factors, and the quality of operations.
Benefits of Understanding Data for Financial Management
Financial management benefits from data analysis in multiple ways. By analyzing monthly profit and loss data against corporate budgets a business is able to forecast cash needs and profit and loss for up to a year. By integrating and analyzing data from different systems a business is able to understand profitability at an individual product level. These insights help businesses decide on the pricing of products.
Understanding the cost of delivering products and services to customers and factoring profit margins helps businesses eliminate guess work in pricing. The pricing is proven to be effective as well as profitable. Additionally, by mining finance data a business gains insights that can be shared with other departments to inform them where their efforts need to be directed. For example, by analyzing finance data against customer relationship data the finance team is able to understand the sales pipeline. This information offers insights into how to improve sales funnels and strengthen customer retention. Factors based on this data that may require an overhaul include shipping or product placement.
What Analytics Can Reveal About Customer Management
The benefits of understanding analytics and data about your customers is unlimited. Effective customer relationship management can reduce costs by minimizing customer churn. It also improves customer loyalty, and promotes a higher revenue from selling to existing and new customers. These insights and data also enable your business to cultivate stronger branding.
By analyzing transactional, demographic, social media, and data from other sources businesses are able to create customer profiles. Customer profiles give the business a thorough understanding of customers and their context. By understanding the customer you are able to provide personalized offers, recommendations, and services.
Equipped with customer profiles your business is able to send relevant marketing messages at distinct times and places. Making your marketing campaigns successful and improving your return on investment.
Using data and analytics to manage the customer oriented areas of your business reduces cost by only targeting customers who are most likely to respond. For example, your data analyst at dc Analyst could evaluate customer churn. Using predictive analytics to identify customers who are likely to leave, your data analyst can provide insights on how to begin developing custom campaigns to retain those customers. Thus, ROI, response rates, and customer loyalty improves as a result of targeted marketing messages and customized offers.
Managing Risk with Data and Analytics
Breaches in computer networks lead to theft of personal data and intellectual property from government agencies and information or financial service providers. Such breaches are very costly and impact brand reputation. Because of the many vulnerability points an automated process is needed to assist cyber security experts in identifying threats.
For example, by relying on insights from predictive analysis of historical data experts are able to identify suspicious activities that may indicate a breach. This proactive approach helps prevent service attacks, data leaks, cyber espionage, data theft, and website defacement.
Furthermore, a data analyst at a credit oriented company can use predictive analysis to assign credit scores that guide you in making a minimum risk decision quickly. This enables your business to make decisions that maximize profit and minimize loss. Businesses with lending products such as credit cards, loans and mortgages face a risk of loss of money due to default.
Likewise, in financial services industry protection of customer money is the cornerstone of success. Criminals and hackers continue to find innovative ways to steal money and customer information. By using insights from predictive analysis of historical transactions financial service providers are able to detect fraudulent activities.
Other companies that benefit greatly from data analytics and risk management include those operating in insurance sectors. In insurance claim processing is a lengthy and fraud prone exercise. Customers are dissatisfied when they have to wait for long periods to receive payment. Insurance companies are also exposed to risk from fraudulent claims.
By relying on insights from predictive analysis of historical data processing fraudulent claims can be identified and further investigation done. This way claims that are not suspicious are quickly paid out and those found suspicious are investigated and resolved. The waiting time is reduced resulting in customer satisfaction. Loss of money to fraudulent claims is reduced thus reducing risk.
The span and influence of risk management is vast. Incorporating a data analyst in Washington D.C. gives your business an extra layer of protection. Using predictive analysis can help your business reduce loss and protect your customers.
How to Use Data to Improve the Quality of Your Business
Traditionally data analysis and interpretation has been used to monitor and improve the quality in the manufacturing industry for decades. Statistical quality control techniques have begun to benefit various other industries such as service, healthcare, and telecommunications. In manufacturing use of experiments helps in understanding factors that affect processes. Adjustments can then be made to identify optimal manufacturing conditions.
By using optimal conditions wastage is eliminated and the final product is satisfactory to customers. By using a statistical process businesses in manufacturing and service are able to monitor deviations from set quality benchmarks and take corrective action.
Here are a few examples. If the weight of a product is critical to quality filled weights can be monitored and deviations from the set weight corrected. If a bank considers waiting time critical to customer satisfaction the waiting time can be monitored to identify deviations from satisfactory waiting time. Call center operations have a big impact on cost, revenue, and the level of customer satisfaction. Traditional call center quality management was manual but analytics provide a way to automate the process. By using analytics you can identify important conversations and you are able to train your agents to provide a better experience.
Understanding and applying useful predictive and descriptive analytics to your business can reduce waste, improve profitability, and cultivate stronger customer relationships. When using analytics to identify risk and develop strong customer management processes your business is prepared for the next steps of growth.