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Diagnostic, Predictive and Prescriptive Analytics

Personalization Product & Service Analytics

Explore various range of Analytics tools for your distinct enterprise needs. The Why, How, When, Where of the data points in an enterprise can be answered and explored by the below techniques

1) Diagnostic Analytics -

Understand from past performance by mining of trends and patterns from historical data to look at the reasons for specific results.
This is usually achieved by drawing out a univariate/bivariate plot and analysis.

Use cases to solve:
  1. Sales trends against factors like product price, promotion etc.
  2. Market trends for seasonality, holiday season etc.
2) Predictive Analytics -

Discover future outcomes and predict possibility of an event. Achieve this by combining the response function matrix along with rules decision matrix with historical data.
There are 6 steps to create a system of predictive analytics:

  1. Data extraction and cleaning
  2. Dimension modeling and segmentation
  3. Segregation of data into Training data set and Predicting Data set
  4. Using Predictive algorithm according to the data object
    • Linear regression and non-linear pool regression model
    • Classification methods like Bayes Classification, Logistic Regression, Decision tree, Random forest
    • Cluster analysis like K-means
  5. Improvisation of the model with business based value chain understanding
  6. Distribution / consumption of prediction into various decision points
Use Cases of Predictive Analytics:

Prediction and improvement of inventory levels for product, services, resource pool and each unit of consumption in a enterprise

Prediction of sales data with marketing levers like cross sell /up sell Demand forecasting for each product offering

Maximization of sales with sales force distribution analytics

Prediction of customer behavior leading to moment of truth

Pricing analytics vis competition sku pricing

Loyalty and customer life time value modeling to predict value of each lead

3) Prescriptive Analytics-

Empower your business with deep learning models and use prescriptive analytics to answer the Why, How, When, Why Not, Which other of each decision. Use prescriptive analytics to classify and identify the real hidden significant decision variables of the last mile granularity.

Identify future opportunity uncover trends in hidden risk pattern to add real value to your business. Prescriptive analytics relies on continues learning by using in new data from feed to continuously improving accuracy.

  1. Data extraction and cleaning
  2. Dimension modeling and segmentation
  3. Segregation of data into Training data set and Predicting Data set
  4. Using business rules + algorithm according to the data object
  5. Feeding of actual results back into the test data
  6. Distribution/consumption of prediction into various decision points
Use Cases of Prescriptive Analytics:

Moment of truth analytics – both pre-buying and post buying

Optimization of right inventory levels to provide for supply chain levels for the right demand at the right time

Business and revenue model optimization to provision optimum user analytics

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