Practical lessons when implementing machine learning in marketing projects

Tenet #1: Predictive modeling should be tailored to your business needs

Our take: Be deliberate in selecting the outcomes you are predicting; just because a system predicts something doesn’t make it useful or actionable.

Tenet #2: Models should be transparent and accessible to the end user

Our take: Avoid pre-built models whenever possible. If you can’t clearly explain to an executive why a model is giving you a particular answer, you shouldn’t be using that model to make important business decisions.

Tenet #3: Empower users to focus on variables and outcomes, not technology

Our take: Don’t force teams to access new tools or go hunting for data. Make machine learning outcomes available where business is being done today.

Tenet #4: Modern analytics requires a modern tech stack

Our take: Advances in technology are fast moving; business adoption is not. Expect to see companies move in to fill the infrastructure gap that plagues so many businesses around building business-centric, production-grade machine learning models.

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Analyzr makes machine learning analytics simple and secure for midmarket and enterprise customers that may not have a full-fledged data science team

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Analyzr: Insights, Achieved

Analyzr: Insights, Achieved

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Analyzr makes machine learning analytics simple and secure for midmarket and enterprise customers that may not have a full-fledged data science team