Using Data to Make Decisions

In the global digital economy, companies are facing a growing challenge in managing the vast amounts of data they now gather to create greater value for their business and their customers without crossing the line into unethical, unlawful or unwanted use. For organisations to sustain this growth, they must not only reassess their data strategies but also incorporate governance measures.

Proactivity and anticipation of business needs

Businesses need to understand customers’ needs in order to improve customer satisfaction and retention. Customers want companies to understand their needs and establish meaningful interactions and deliver well evaluated insights after giving their data and permitting to assess it. Firms need to use both digital as well as conventional methods to keep track of their data such as email, phone number, frequency of purchasing product/services, their buying habits, etc.

Improved Operational Efficiency

Inefficiency in operations is the one of the key factors which affects profit, customer satisfaction, and brand image of a business in a negative manner. Therefore, it is important that enterprises constantly focus on improving their operations. Data Analytics can be used to improve operational efficiency as well as customer satisfaction. Advanced analytics can also be used to increase productivity and optimize a company’s personnel based on business needs and consumer demand.

Risk Mitigation and Fraud

Analytical data not only improves your operational efficiencies but also protects your firm any fraudulent behaviour and is also efficient in quickly anticipating future actions. For predictive fraud propensity models leading to warnings, statistical, network, path, and big data techniques will provide fast reactions triggered by detecting threats in real-time, as well as automatic warnings and mitigation. Moreover, enterprise-wide data integration ad correlation can provide a single perspective of fraud across multiple organizations, products and transactions.

Informed Business Decision Making

Companies can use analytical data to minimize financial loss, improve business decisions and use predictive analytics in order to find the most optimum decision-making solutions and most efficient response to situations. For example, companies can use data analytics tools to assess the success of the modifications and display the results to help decision-makers decide whether to roll out the changes across the organization.

Personalized Customer Experience

Customer behaviour is one of the crucial aspects that any business needs to cover, e-commerce, retail, social media, etc. can be used to collect relevant data to understand customer behaviour. The data collected can be analyzed to predict customer response toward a product or service. The data can also be used to provide a more personalized experience to the customers. Using e-commerce transaction data, an enterprise may build a predictive model and decide which products to promote during checkout in order to increase sales. It can be used to make a business proactive, and anticipative and help them understand customer behavior.

Relevant Product Delivery

When changes or new technology is demanded, effective data collection paired with analytics helps businesses to stay competitive. Furthermore, it helps businesses understand the market demand in order to provide the most relevant and requested products. Nihilent creates differentiated customer satisfaction and greater business value by turning information into intelligence insights.

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