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Sales and marketing teams use machine learning and data science to create and adapt campaigns faster than ever before, and apply predictive analytics to answer questions previously based on a hunch. Our ListenLogic platform ingests customer-interaction data (calls, emails, chats, survey, social media) and uses advanced natural language processing, sentiment detection, and machine learning classifiers to turn unstructured data into valuable and insightful analytics used to prevent customer churn, improve customer satisfaction and contact center efficiency, and increase the effectiveness of sales. An additional major pain point is around legacy vendors who own the interaction data clients need to access for customer experience analytics.

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Top Trending Articles

Article Summary
More results... Marketing needs to produce valuable content but salespeople also need unique content to stand out and engage buyers in the market. The next step in the adventure was to get the band back together in 2012 and build a new product as entrepreneurs we could never afford an agency so we built Grapevine6 to help entrepreneurs connect with their markets. Our AI technology personalizes the content recommendations for each salespersons unique brand and makes sharing 3 rd party thought leadership and brand marketing content simple and efficient. The marketing groups that do the best job of orchestrating the customer journey to make purchasing decisions easier for customers will win and AI for content will have the biggest impact.
Article Summary
Our ListenLogic platform ingests customer-interaction data (calls, emails, chats, survey, social media) and uses advanced natural language processing, sentiment detection, and machine learning classifiers to turn unstructured data into valuable and insightful analytics used to prevent customer churn, improve customer satisfaction and contact center efficiency, and increase the effectiveness of sales. The use of ML and AI is only going to expand further as more and more companies realize the amount of data that can be analyzed and adopt applications to analyze this data and provide a better customer experience . Data quality is currently the largest pain point, as many of the datasets used for measuring customer experience were not initially designed to be used for analytics.
Article Summary
Ive got the opportunity to work on developing our data product, got stuck into some modelling projects and had the pleasure of learning a lot about some new businesses which we will be working with in future. Get involved its a great industry to disrupt with new thinking, certainly now theres a real opportunity to bring new techniques into marketing and media we at TSW have really benefited from this one of our recent hires borrowed techniques for sound engineering and signal processing to overcome a problem of noisy data, previously weve borrowed ideas from physics, the problem is finding and attracting new talent.
Article Summary
Data scientist- Algorithms are for those who are expert in machine learning , passionate about creating business value by infusing data in our product and processes and lastly, Data scientist- Inference is those who are statisticians, economists, and social scientists using statistics to improve our decision making and measure the impact of our work. If you are working as a data scientist but the company has a different track than your comfort zone, you should think for a job change. But when the organisation in which you are working on isnt giving you the flexibility to use the tools you want, that is the time a data scientist can think for a job change.
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Article Summary
In 2018, for example, Gartner projected that open source databases would account for "10% of DBMS spending, reflecting accelerating adoption by enterprise users." As important as open source has been, and as disruptive as non-relational databases like MongoDB remain, there's a far bigger trend in database adoption, and it's all about cloud . Figure A Notice that as important as open source and non-relational databases are, the far bigger trends driving database adoption are the kissing cousins of scale-out architectures and cloud computing. But there's another reason that cloud adoption is off the charts: Cloud computing fulfills many of the promises of open source, while also enabling the promise of non-relational databases.