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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:

Fields Not to Include

Certain types of data should typically be excluded from analytics processes due to privacy concerns or because they simply do not contribute to meaningful insights:

  • IDs: Unique identification numbers, whether for users, transactions, or products, often don't add value to the analysis and pose a risk of identifying individuals.
  • Emails: Email addresses are personally identifiable information and should be excluded to ensure privacy and comply with data protection regulations.
  • Phone Numbers: Similar to emails, phone numbers are sensitive and should be kept out of analytical processes.
  • Others with Unique Values on Every Row or Record: Any field that has a unique value for every entry (like serial numbers or unique transaction IDs) typically doesn't contribute to aggregate analysis and might risk identifying individuals or sensitive business information.

Limits

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.

Conclusion

When preparing your data for analysis on the OpenGTM platform, considering these factors can significantly impact the effectiveness and applicability of the insights generated. Ensuring data privacy, understanding the limits of the system, and aiming for recommended record counts will help in achieving the most accurate and actionable results. Always aim for the best data practices to maximize the value derived from your analytics efforts.