The data scientist wants to know how the data and/or insights will be used. How will it be integrated into the organisation? Who will use the data, and why?
An example of outcome: The marketing team needs to be trained in using the model (or software) to guide their decisions, and the success of the model needs to be gauged in its effect on the sales..
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Thinking With Data is not about how to do data analysis but about first working out what the problem is that you're trying to solve.
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Marketing needs to be laser-focused on generating leads that fit your ideal customer profile in order to reduce churn. We do this because it is inevitable that some poor-fit leads will still be generate and passed on to the sales division.
Questions to guide with vetting:
Ask questions, such as.
Companies that successfully execute win-win spin-offs will optimise the operating model for both companies.
For example, companies use the opportunity to increase their marketing expenditures or digitising the sales process. Others would attempt to improve, centralise, or ...
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