The first model posed a data variation challenge. Due to the explosive growth of Amber, historical data is one of the most important data sources to make future predictions. Amber however is growing so fast that historic data becomes invalid within weeks, making it complex for the model to learn long-term patterns.
In addition, the use case is a typical example of ‘greedy data coverage’. There are that many factors that move a customer to decide to take an Amber (for example: a large meeting at another office location, an interesting conference, train strikes, own car low on fuel), that adding many data sources to the model is simply requisite to make it more accurate. However, the cost of that might be higher than the gain. So careful designing is required.
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