Deep context

Deep context is a combination of quantitative machine learning and qualitative analytical processes that include a wide-spectrum of datasets, network analysis, sentiment and empathy based analysis . Added to these we factor the event proximity to populations in need, the utilisation of local knowledge and the understanding of transnational threats and regional dynamics, which is especially vital in mitigating against the risk 

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customer experience. Context is a make or break proposition to any customer experience program. insights that are specific to your domain are essential to providing input and driving decision-making and strategy execution throughout an organization:

  • Gain insight into intent themes that provide insight into the exact aspects of the conversation you want to understand
  • Gain insight into how your brand perception across industry specific KPIs.
  • Context for each industry . Industries include retail, telecommunications, healthy living, fashion, restaurant, automotive, finance and more
  • Grow your business by understanding the loyalty concerns your target customers have, and how to address those concerns for customer retention

Compare Algorithm Outputs to Reveal Underlying DataSet Structure

Problem: Data has noise. Data sets must be cleansed, normalized and curated. But you never really can tell for sure which algorithms to apply unless you compare results.


  1. Plan to generate different visualizations  of your data set,
  2. Plan to create and various transforms on your data set
  3. Run through a number of typical algorithms on each view of your dataset.

Purpose:  Clarify which data transformations will reveal the structure of the dataset that underlies your problem.