Michel Ballings is a featured contributor to Predictive Analytics Deconstructed: A Super-Simple Recipe for Marketing Success report.
Here's the complete contribution:
Predictive analytics for marketing applications lies at the intersection of three fields:
- computing and IT
- machine learning and statistics
- marketing (applications and data)
To be able to answer how predictive analytics technology in marketing will evolve one needs to look at these three fields holistically.
First, computing power will continue to increase considerably by breakthroughs in both hardware and software. For example, the CUDA programming model allows algorithms to exploit GPUs for massively parallel computations. A typical GPU has 3500 cores whereas a typical CPU has 8 cores.
Second, these breakthroughs will lead to even faster progress in the development of machine learning methods. For example, deep learning algorithms require many parameters to be estimated, and being able to estimate these parameters at a faster rate means that algorithms can be tweaked and improved at a faster rate. Trial and error will be faster.
Third, more and more data becomes available about consumers (e.g., social media) and this will expand the application domain in marketing substantially. For example, with the aforementioned advances, it now becomes possible to mine every photo a consumer uploads to a public social media profile. It will be possible to classify these photos using deep learning, and include that information in the predictive analytics pipeline.
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