Predictive Analytics using Statistical Analysis

Predictive analytics is a kind of advanced analytics that leverage historical as well as new data to forecast activity, behaviour and trends. This includes applying statistical analysis techniques, analytical queries and machine learning algorithms to data sets in order to create predictive models that place numerical values or scores.

In this data-driven and intelligence era, predictive analytics leverage various technologies like big data analytics, IoT, Cloud and AI. Machine learning has made predictive analytics highly efficient by analysing large amounts of data. 
Let’s have a sneak peek of the industries those use predictive analytics.

Newly Incorporated

Data preparation

Bootstrapping

The Cresco Approach

  • Marketing Campaigns

    Predictive analytics is being used to drive data-driven customized marketing campaigns, understanding customer behaviour, customer approach, utilising the right strategy to create future marketing campaigns, measuring key performance indicators and maximizing campaign ROI.

  • Enhancing Operational Efficiency

    Many organizations today leveraging predictive analytics to streamline various business operations such as managing the demand-supply, logistics, inventory, resource, cross-selling etc.

  • Risk Management

    Predictive analytics application for identifying more about the customer’s reluctance to purchasing a product, the various factors which prevent a customer from making the purchase decisions and discovering ways for how to reduce the risks involved.

  • Fraud Detection

    Using various analytical tools to find out more about the pattern discovery of the fraud transaction in financial domains, precluding the criminal actions, applying behavioral analytics to preclude fraud, investigating about zero-day vulnerabilities and eliminating the risks of advanced frauds.

  • Customer Relationship Management (CRM)

    To retain most customers and get them to purchase more from you, regression analysis and clustering techniques are being used in CRM systems which can allow you create customer groups based on their buying pattern, demographics, gender, age etc. This will help you optimize your customer life cycle, enabling you to launch more targeted & effective marketing efforts.

  • Building Recommendation Engines

    Personalized recommendations are being used by various industries such as e-commerce, food tech, online cab and others to boost their user loyalty and engagement. Collaborative filtering is a predictive analytics technique which uses past behaviour to create recommendations.

  • Improving Employee Retention

    HR departments of several Fortune 500 companies are using predictive analytics to improve their hiring and employee management policies. Data from the HR database can be used to optimize the hiring process and identify the best talent from the industry. Performance data and employee personality profiles can be evaluated to identify when an employee is likely to leave so that proactive efforts can be made to retain best talent pools.

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Predictive Analytics is set to grow at an enormous speed as the need for making data-driven decisions are raising. Organizations are now aware of the importance of their data and intend to derive the maximum benefits from it using predictive analytics to achieve a competitive edge as well as business efficiency.