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6 smart deployments enabled by cognitive computing

09 Nov 2015  | Lisa Morgan

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"Some people are using machine learning for virus detection or malware detection. That's a classification problem because you're deciding whether something is good or bad. If it's not detected, then it's undetectable for all your customers," said Masergy chief scientist Mike Stute.

Masergy still uses machine learning to detect what's normal in a network. With a neural network, tuning is a process of modifying a dataset and rescoring it again and again, sometimes randomly. Ultimately, the constant retuning of an anomaly system changes its detection behaviour in an adaptive and continuous way.

Personalise shopping experiences

Personalise shopping experiences

(Image: PublicDomainPictures via Pixabay)

Demographic marketing doesn't cut it in the Digital Age because customer expectations are high and their attention spans are short. Marketing platform provider Retention Science uses machine learning and artificial intelligence to help its customers market more effectively to their customers on a one-to-one basis. Marketers can personalize offers and optimize the timing of offers, such as sending an email within the time-frame in which the customer is most likely to open it.

"A few years ago, it was more about predicting what people are thinking or going to do, but now people want to know what to do with it," said Andrew Wagge, co-founder and CTO of Retention Science. "They want to identify the proper treatment for each one of their customers. We can analyse all the data points and bring signals together to do that. A lot of what we do involves reinforcement learning, so it's about continually updating the predictions, learning from what's going on, learning from what we do, and then trying to take a different course of action based on whatever happens."

Improve investment performance

Improve multichannel marketing

(Image: Geralt via Pixabay)

Improve multichannel marketing

Timely multichannel marketing analysis is often frustrated by information silos, technical challenges, and uncertainty about the best course of action. Omnichannel marketing analytics platform provider Datorama uses machine learning algorithms to provide actionable recommendations across channels. Customers can simply upload their data without worrying about the structure of the dataset or metadata since the platform analyses the structure of the dataset and adds metadata automatically.

The platform can identify the same entity from 200 different datasets. That capacity allows it to automatically normalise the data and create a semantic model. The machine-learning algorithm uses the newly loaded data, as well as what it has learned from every dataset that has ever been loaded into the system. Datorama also combines machine learning with artificial intelligence to find relationships within a given dataset and connections to other datasets. In addition, the company also uses natural-language processing and semantic analysis as part of its smart-data-mapping process.

"The Waterfall implementation of identifying data sources and having technical people implement ETL has a negative effect on marketing BI because of the amount of data sources and the dynamic nature of the data sources," said Efi Cohen, CTO and cofounder of Datorama. "We're providing a way to handle the exact same problem, and the ROI is positive."

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