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Things to Consider

When preparing your data for analysis and integration with OpenGTM, it's essential to be mindful of certain aspects that can significantly impact the quality and usefulness of the insights generated. Here are some critical things to consider:


Understand the minimum requirements to ensure that the analysis provided by OpenGTM is meaningful and based on sufficient data. Keep in mind that more data is always better, but you should start with what you have. This is why the OpenGTM platform was built with small datasets in mind and make experiementation easy.

  • Minimum Wins to Get Persona Discovery: To provide accurate and useful persona discovery, there needs to be a minimum of 3 'win' instances in your data set, but at least 20 are recommended. This ensures that the analysis covers the main use cases of your business.
  • Minimum Wins and Losses to Get Propensity to Buy Scores: For the system to calculate a meaningful propensity to buy score, it needs at least 1 'win' and 1 'loss' data point to understand the contrast and characteristics of each. At least 10 wins and 10 losses are recommended though.
  • Minimum Converted Leads to Get Persona Discovery & Scoring: The limits above apply to converted leads to get personas and scores.