We all know some aspect of AI, how it is changing enterprise business and bring more intelligence in the way companies used to traditionally understand their loyal customers, new leads and market.
Marketers believe its five times more expensive to find new customers than to market existing customers and maintain profit.
The traditional way of marketing will keep scope very restive and there is already huge competition in these traditional ways.
In the world on AI, companies are thinking different ways to turn marketing into sales opportunities-
With all this incredible computer training capability so close at cards, it’s time to make the devices cleverer about how devices are getting smarter. The most influential, life-changing thing that will come along—and you can count on a great many people trying to solve this one—is when the machines get better at building models that they can use to build models. Which means the evolution of model life from the parent model.
Organizations are operating to solve a variety of Machine-Learning evolving riddles. From marketing in AI- Algolytics Sp. z o.o. created ABM (Automated Business Models). You give it customer behavior data and it builds predictive models based on user-provided parameters (what you want to know, what data you want to exclude, what you expect the answers to be). Skytree automatically analyzes and chooses the best model for a given data set. BigML’s WhizzML automates machine learning workflows by making repetitive, time-consuming tasks the work of a mouse click.
With the world of DataRobots, you need to choose your target criteria based on domain level data (sales, service). Then DataRobot produces hundreds of sub-models for you to explore and squeeze. When you find the model that makes the most sense, deploy it in the wild, and feed the results back to DataRobot for further refinement.
Data is the new Business-This is where the power of aggregated data comes in. It gives AI the ability to continuously learn about the environment (sporting goods, fashion, hardware) from a multitude of sources without ever being “finished.” Continuous learning about products and industries can be combined with continuous learning about individuals as well
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