Predictive Analytics

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Predictive analytics means using data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened and provide the best assessment of what will happen in the future. We help you set up and maintain end-to-end predictive data science workflows.

Predictive Analytics Technologies

Regression – Analyzing data for key elements that can be used to predict an outcome. Sometimes, it’s a “Yes-or-No” type of outcome (Will a customer make a purchase?). Other times, it’s more specific (Exactly how much will a customer demand?).

Forecasting – Reviewing historical data and using it to predict the future. These forecasts are the result of sophisticated statistical models that take into account trends, as well as seasonal fluctuations.

Classification – Mathematically grouping similar individuals by common characteristics, also referred to as “cluster analysis”. The results can be surprisingly valuable, especially when highlighting a group that may have been historically overlooked.

Machine Learning – Using data to detect patterns and adjust program actions. Wide array of business applications, from data mining for new insights, to forecasting of time series.

Text Analysis – Offering companies the opportunity to turn petabytes worth of text into predicted sentiment on a range of topics and products, including the ability to leverage social listening via social media outlets.

Example Demand Forecasting

End-to-end workflow for timeseries prediction based on AI 

The Predictive Analytics Process

  1. Define Project : Together, we define the project outcomes, deliverable, scope of the effort, business objectives and identify the data sets that are going to be used.
  2. Data Collection : Data mining for Predictive Analytics prepares data from multiple sources for analysis.
  3. Data Analysis : We inspect, clean and model data with the objective of discovering useful information and arriving at a conclusion.
  4. Statistics : We validate the assumptions and test them using standard statistical models.
  5. Modelling : Predictive modelling gives us the ability to automatically create accurate predictive models about the future. There are also options to choose the best solution with multi-modal evaluation.
  6. Deployment : We deploy predictive models to integrate analytical results into everyday decision-making process to get results, reports and output by automating the decisions based on the modelling.
  7. Model Monitoring : We manage and monitor models to review the model performance to ensure that it is providing the results expected.

Your benefits

Log-hub Predictive Analytics

Higher Forecasting Accuracy

See what the future holds. Statistical Forecasting techniques can take your historical performance and answer those questions.

Better Customer/Product Clustering

Identify different groups of customers/products for targeted analysis, precision marketing, or just better understanding. Clustering and segmentation can make that happen by grouping similar customers/products together.

Better Risk Management

Analyze your data to predict individual or group behavior, and quantify the risk associated with customers or acquisitions. Regression analysis can take key aspects of existing data and return meaningful, valuable, insights.

Detect New Business Opportunities

Employ machine learning algorithms to mine your data for opportunities that you might not know you had, ensuring that you’re aware of every advantage and mitigating disadvantages.

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