Friday, May 10, 2019
Business Intelligence and Analytics 16
1 Machine Learning Will Be the Next Big Thing in Supply Chain Management
https://datafloq.com/read/machine-learning-will-next-big-thing-supply-chain/6355
Supply chain management, or SCM, is becoming a more critical job every year, as more consumers turn to e-commerce and warehouses and distribution centers grow. Technology is catching up to the exponential expansion of this industry, but it’s been a slow process. It’s a lot of work for one person, or even a team, to sort through thousands or millions of orders. Machine learning is on the cusp of becoming the next big thing in SCM. How will machine learning change how companies manage their supply chains?
2 5 steps to move closer to one-to-one personalization
https://marketingland.com/5-steps-to-move-closer-to-one-to-one-personalization-260802
“One-to-one marketing” continues to be one of the biggest buzzwords of 2019. At MarTech West last month, it felt like everyone at the conference discussed how their teams are working to foster personalized connections with customers through technology stacks. It also was refreshing to hear that I’m not alone and no one has achieved truly one-to-one marketing – at least not yet.
3 The AI Boom: Why Trust Will Play a Critical Role
https://knowledge.wharton.upenn.edu/article/coming-ai-breakout-need-rules-road-now/
The exponential pace of technological advancement has made it more challenging than ever before to address its unintended consequences. We have seen it with digital innovation, especially social media, and we are only just beginning to contemplate it with artificial intelligence, which holds the promise of being the most transformational technology of the information era.
4 Wise Practitioner – Predictive Analytics Interview Series: Zeydy Ortiz at DataCrunch Lab
https://www.predictiveanalyticsworld.com/patimes/wise-practitioner-predictive-analytics-interview-series-zeydy-ortiz-at-datacrunch-lab/10381/
DataCrunch Lab, a few questions about their deployment of predictive analytics. Catch a glimpse of her presentation, Will They Stay Or Will They Go? A Customer Lifetime Value Case Study, and see what’s in store at the PAW Financial conference in Las Vegas.
5 How to win customers and influence them so they stick with you
https://www.smartinsights.com/ecommerce/win-customers-influence-them-stick-with-you/
More choice means it not only becomes harder to persuade customers to engage with a particular proposition but also to retain their interest post-transaction.
6 Three steps for using data to crack customer-centricity
https://www.marketingweek.com/2019/05/07/three-steps-data-customer-centricity/
In terms of data availability, marketers have moved from famine to feast in recent years. With information being emitted by washing machine, wrist and everything in between, we’re swimming in our ‘new oil’.
7 How to Help Your Content Team to Have Better Ideas
https://www.searchenginejournal.com/content-team-better-ideas/306601/
Leading or managing a content team can be a highly rewarding experience. However, all too often, it results in a dilemma. You are expected to generate the majority of ideas for your department’s creative output.
8 How Digital Platforms Have Become Double-Edged Swords
https://sloanreview.mit.edu/article/how-digital-platforms-have-become-double-edged-swords/
It’s not difficult to see how digital technology and innovation have rapidly transformed our world over the last three decades. If the industrial revolution was built by the factory system, the changes we see today are organized around digital platforms.
9 How is Data Science Changing the World?
https://towardsdatascience.com/how-is-data-science-changing-the-world-db0c4b3cdb8
In this article, you will go through the role that a Data Scientist plays. There is a veil of mystery surrounding Data Science. While the buzzword of Data Science has been circulating for a while, very few people know about the real purpose of being a Data Scientist. So, let’s explore the purpose of Data Science.
10 11 Classical Time Series Forecasting Methods in Python (Cheat Sheet)
https://machinelearningmastery.com/time-series-forecasting-methods-in-python-cheat-sheet/
Machine learning methods can be used for classification and forecasting on time series problems. Before exploring machine learning methods for time series, it is a good idea to ensure you have exhausted classical linear time series forecasting methods. Classical time series forecasting methods may be focused on linear relationships, nevertheless, they are sophisticated and perform well on a wide range of problems, assuming that your data is suitably prepared and the method is well configured.
