Business Objectives

  • Businesses aim to achieve certain business outcomes as part of their strategy.
  • The actual outcome of their business processes may or may not be a desirable business outcome
  • Managers wish to know
    1) The prospects of their business achieving desirable outcomes.
    2) Reasons leading to specific outcomes.
    3) What actions they must take to ensure success in their business.
  • Unsupervised Learning models can be employed on business data to do segmentation and clustering analysis, which can help identify parameters influencing specific business outcomes.
  • Identified parameters can help identify specific actions which managers & businesses must take to improve their business performance.
  • Further, using the data corresponding to the identified influencing parameters, Supervised Learning models can be trained which can predict business outcomes.


Data Preparation

Extract relevant fields from Input Data.

Bring in additional external data.

Data Cleansing, Transformation & Join.

Customer’s internal data is studied along with external data sources. Data is cleansed, transformed and joined to create data which can be consumed for data modeling.


Build Unsupervised Learning models to

find segments & clusters.

Identify key influencing parameters.

Unsupervised learning algorithms are used on prepared data to find segments and clusters of data depicting different business outcomes and factors leading to those outcomes

Predictive Modeling

Train Supervised Learning Models.

Observe results on train:test split.

Validate results on blinded data.

Supervised learning algorithms are trained to predict future business outcomes. These models are tested on train:test data split and then further validated on blinded data inputs


  • Segmentation and clustering analysis results including factors influencing different business outcomes. Results are visualized using Tableau, PowerBI, Qlik Sense or MATPLOTLIB
  • Supervised Learning trained models capable of predicting business outcomes for new data inputs. Models are wrapped into RESTful APIs and hosted on in-premise or cloud servers.

Case Studies

  • Learn about neurIOT’s research in predicting outcomes of In Vitro Fertilization (IVF) treatment using Machine Learning and Data Science techniques.
  • Learn how neurIOT applied Unsupervised Learning models to help leading B2B online marketplace improve their online sales.
  • Learn how neurIOT helps leading Japanese consumer electronics company to identify consumer financing fraud using Anomaly Detection.