Contributions
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How can behavioral data analysis predict future consumer trends?
If you are going to combine your segmentation models with predictive models then behavioural data should be at the heart of the segmentation. Behavioural data (based on previous behaviours) is likely to be more predictive of future behaviour (though beware applying it to completely different contexts) than attitudinal or demographic data (Which help add the 'why' to the behavioural data 'what' so are still important). For example, consider pre-election polls (attitudinal) versus exit polls (which are based on reported behaviour and therefore tend to be a lot more accurate).
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How can behavioral data analysis predict future consumer trends?
One of the best things about scalable and on demand cloud based data analytics platforms is that you may need a lot less compute power for deployment than you did for training. It's important to ensure that your architecture is optimised to minimise costs - enabling you to reinvest that saved money in other model development, or (unless automated) driving adoption of the deployed model in your organisation. For non-automated models, the value of any model (from deterministic to predictive), no matter how accurate, is a function of adoption - so make sure you get the right balance between investment in new/better models and stakeholder engagement (e.g. knowledge sharing and education etc.) to drive adoption and increase the value delivered.
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How can behavioral data analysis predict future consumer trends?
Data quality is very important. In an ideal world you would utilise all data sets that contain consumer behaviours...but it is important to remember that one of the goals is to try to link all the behaviours of the same individual. If these are spread across multiple data sets and there is no key (or inferential way of linking behaviours) you may have to exclude some data if it adds more noise than signal. Also, if a particular data set contains multiple records for the same customer, it's important to clean up that data before you start using it for descriptive outputs like segmentation or predictive models. If you can't clean it you need to carefully evaluate its impact on model accuracy and long term impact on decision making.
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