This week, we examine how eBay is using AI to improve language and trade. We learn the secret of digital transformation and share a useful tool that will help you cut through the noise when it comes to AI applications. We consider how machine learning and a new modeling approach may improve attribution. And finally, we discuss how much personalization is too much personalization.
by Kurt Greenbaum, contributing writer for WeForum.org
Machine Learning and artificial intelligence are being adopted by many organizations with the hopes of increasing efficiency. However, until now, researchers haven’t found empirical evidence supporting the promised strides in labor productivity and economic activity. In a new paper, data from online e-commerce site eBay is analyzed and its shown how they have successfully used AI for language translation, which has increased trade between countries by 10.9 percent. Learn more about the findings in this insightful article.
by Jay Dettling, contributor for MarTechAdvisor.com
Digital Transformation, which is the process by which companies leverage digital technologies in everything they do, has become the defining challenge for businesses. From product to sales to operations to marketing, it’s an important aspect of improving business. Often, business leaders focus on the technology platform and data strategy they want to achieve while giving less weight to the critical steps of engaging employees inside the business and the customers who fuel adoption. Find out how digital transformation starts with your employees.
by Steve Herrod, commentary for InformationWeek.com
We’re living in an exciting time. If the hype around artificial intelligence and machine learning is to be believed, algorithms will be able to drive your car, increase your cybersecurity, and make a perfect chicken pot pie. This all sounds great, but it’s hard to separate the hype from what is actually being used. If you’re employing machine learning in a new commercial solution that has the potential to revolutionize a product, process, or even an entire sector, the bottom line is, make sure that your use of ML is more substance than hype. With this in mind, this article shares a framework you can use.
by Jennifer Videtta, contributing writer for MarketingLand.com
Attribution is something that digital marketing teams struggle with. In fact, only one out of every four marketers can confidently attribute revenue to their digital efforts. As your marketing stack continues to grow, attribution accuracy can become a real problem. And being able to confidently track ROI and communicate value to your organization is even more important in this growth. Typically, marketers use single-touch or multi-touch attribution models. But shifting to a chain-based model, with the help of machine learning, may be the approach that will improve the quality of marketing intelligence and eliminate your attribution woes.
by Tyler Bishop, contributing writer for Forbes.com
Brands now have the ability to offer highly personalized experiences to consumers based on an enormous amount of data at our fingertips and AI technologies. But is there a line in which there is too much personalization? And do customers even want this? In this article, the line is examined. Ultimately, understanding what you want to get out of personalization efforts and what it will look like in practice will help you elevate your efforts and truly be on the forefront of defining what digital personalization may actually become.
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