Product engineering services

Data analytics primarily involves statistical analysis of available data sets, in order to generate business insights using automated systems and advanced applications.
Modern enterprises employ Business analytics as a tool to derive meaningful conclusions and patterns in-support of predictive modeling for harnessing maximal business potential and growth. Data analytics allows the organization to better realize its process flaws so that strategists and executives can eliminate the bottlenecks and promptly improve on their operational efficiency.

At PSI, we apply our expertise in advanced analytics and inference techniques to help enterprises derive productive business insights and patterns through Business Intelligence, Data Ingestion & Warehousing Solutions. Our analytical outcomes are designed to improve client’ business efficiency by optimizing existing processes and work models and explore new avenues towards productive growth.

Industries we serve

  • Management Consulting
  • Banking and Institutions

Domains we serve

  • Marketing analytics: Marketing Analysis is an essential part of data analytics that combines various algorithms to evaluate the impact that it has on the current structure in place. This involves a close assessment of various market metrics and trends to determine impact and translate it into better revenue. PSI’s marketing analytics combines with available datasets and metrics to ensure in-depth analysis of marketing strategies and its performance. Based on the evaluation, the scope of improvement can be defined which enables our clients to derive better business decisions and ROI.
    • Retail analytics: Retail analytics maps with customer insights with existing processes to determine the scope and need for improvement in retail sales and marketing. Feasible data collected through various supply chain movements, consumer demands, sales and other metrics, is used to evaluate market trends and develop a customer-centric approach to power better sales and revenue.
    • Pricing analytics: Pricing Analytics predominantly works at optimizing and improving pricing strategies to gain improved profitability & market share. Data collected from various sources is used to evaluate existing purchase trends and devise better price strategies while balancing price and profit ratio.
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    • Sentiment analysis: Also known as Opinion mining, sentiment analysis is the branch of natural language processing, used to broadly identify and understand mass opinion and emotions expressed over a subject in form of written or a language. Sentiment analysis can be used to fortify marketing analysis, product reviews and customer services by studying unbiased opinions about a product or brand.
    • Consumer insights and analytics: Data abundance in today’s world is largely seen as an asset to evaluate and compute market trends and insights for improved marketing and profit gain. Customer insights and Analytics combines the most appropriate datasets at hand with various technology and algorithmic methods to derive most profitable business decisions and leads.
  • HR analytics: HR analytics is a vital component of corporate analytics employed to vastly improve the HR practices of an organization and overall employee performance graph. Applying analytical computation within HR processes greatly improves decision making and resource utilization, all leading to a surged enterprise ROI.
    • Attrition: Flight risk scoring: Employee retention is a crucial element for every enterprise HR practice and holds utmost significance in delivering on-time projects and crucial services. Analytics powered HR practices enable accurate prediction of attrition risks in an organization, which can then be mitigated with timely countermeasures and corrections in the problem areas. In-depth analysis of past data and patterns greatly improves employee engagement and retention trends.
    • Talent aquisition: In today’s competitive business world, the need for the suitable human resource has advanced to enormous heights. A highly-competitive global landscape has reasonably increased the complexities of talent acquisition and management, promoting the use of data analytics and segmentation for betterment in potential hiring and employee retention rates within an organization.
  • Financial analytics: Financial analytics in corporate environment ensures revenue growth by means of data analytics and predictive modelling using comprehensive analysis of large data sets to evaluate monetary trends and correlations. Financial analytics estimates probable financial scenarios and their impact on a company’s market capital and profitability.
    • Credit risk: Advancements in financial analytics has majorly eased the risk associated with credit allocation and investment. Statistical analysis of large data sets enables financial organizations to effectively compute the underlying risks and creditworthiness of credit seeker. Advanced predictive modeling assesses customer’s back data to derive decisions regarding credit allotment, in a relatively fast and precise manner.

Service we offer

  • Data Engineering
  • Data Visualization
  • Data Sciences