What is data Analytics
The big challenge is to transform the extracted data in usable format and then load in databases so that business or data scientist could analyze it. The advent of CRM systems (Customer relationship managements) and affordable databases cost has enabled the collection of huge amount of structured data.
Data is captured with aim to solve different business problem or to capture different customer behavior:
- Transactional data, Inventory data, Sales data, Cost data, payroll data, accounting & financial data.
- Sales forecast data, macro economic data etc.
Analytics: Analytics is the process of examining raw data to draw intelligence or conclusion about the information. Analytics is widely used by business organizations to increase their revenue, cutting cost, acquiring customer and optimizing profits etc.
Types of analytics:
Predictive data analytics: Predictive data analytics evolves variety of techniques from statistical analysis, modeling, data mining to analyze current and past data for future prediction. Data scientist use historic data to build the model to predict the future. The output is usually a ordered score for each customer indication the propensity of intended event of that particular customer .Some of the statistical techniques used by Predictive Data Analyst are Linear regression model, Discrete choice models, Logistic regression, Multinomial logistic regression, Probit regression, Logit versus probit, Time series models, Survival or duration analysis, Classification and regression trees, Multivariate adaptive regression splines, Neural network etc.
Descriptive data analytics: Descriptive Data Analytics aims to describe the event or the trend in the provided population . The objective is to identify the trend or pattern in the data so that business or scientist could understand the behavior of the population or the subpopulation of the data. This information helps the business leader to build his business intelligence. Some of the poplar statistical techniques used by Descriptive Data Analyst are Univariate Analysis, Bivariate analysis, clustering and Cross tab.
Industry wise Application of Data Analytics:
- Data analytics for retail banking: Customer acquisition, Cross sell & Up sell , Fraud/Bad Debt Score Card, Collection strategies, Recovery strategies, CRM (Customer Relationship management ), Portfolio Valuation etc.
- Data analytics for financial services: Fraud Detection, Fraud Prevention, Delinquency Scorecard, Portfolio Evaluation, Recovery model etc.
- Data analytics for retail /CPG (Consumer Packaged Goods): SKU optimization, Inventory optimization, Sales analytics, Supply Chain Analytics, Forecasting, Market basket, Up Sell & Cross Sell, Response Modeling , Out of stock analysis , CRM , Customer loyalty , Dashboard Reporting etc.
- Data Analytics for telecom: Lifetime value (LTV), Campaign management, Customer Segmentation, Customer churn, Credit risk management, application fraud model etc.
- Data analytics for pharmaceutical / Health care: Forecasting, sales force excellence, incentive compensation & target planning, sample optimization etc.
- Data analytics for market research: Survey data analytics, Regression, correlation, Conjoint analysis, Cluster and segmentation, MaxDiff , TURF, Cross tabulation , Dashboard reporting & charting etc.
- Data analytics for leisure / travel / hospitality: Sentiment analysis, Customer segmentation, Executive dashboard, Customer Behavior, Campaign management etc.
-Posted by : Ankit Nigotiya