Montgomery & Company (MoCo) is a weekly podcast and radio show hosted by two-time WNBA Champion and Co-Owner/Vice President of the Atlanta Dream, Renee Montgomery, in partnership with WABE Atlanta. Both insightful and compelling, MoCo features interviews with some of the world’s top athletes, entertainers, and innovators as well as roundtable discussions with Renee’s colleagues, friends, and family, about sports, culture and building generational wealth. Montgomery & Company: Sports, Cultu ...
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Using Data to Work Smarter, Not Harder: Lessons from Collective[i] CEO Tad Martin
Manage episode 207263840 series 1877580
内容由Deciding by Data提供。所有播客内容(包括剧集、图形和播客描述)均由 Deciding by Data 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal。
When Tad Martin first joined Overstock.com in 2000, he entered a company with a lot of valuable data waiting to be used, but lacking the technology to use it. As COO, Tad led a lengthy and pricey big data overhaul to get the company up to speed. After he left Overstock, Tad wanted to empower other organizations to harness the power of big data in an easier way. He started Collective[i] for a profession that he thought was underserved in big data solutions: sales professionals. Using predictive analytics based on their network of sales professionals, Collective[i] makes recommendations to its users on things like which buyers to target and when. In this episode, we explore: -How sales professionals can make more money while doing less work using predictive analytics. -What it means to create network effects with data. -The strides big data technology has taken over the past two decades. -And the things sales professionals get wrong when they don't rely on the data. Deciding by Data is hosted by Jeremy Levy, the co-founder and CEO of Indicative. This episode was produced and edited by Lauren Feiner. Our music is by Chris Zabriskie and BoxCat Games. This podcast is sponsored by Indicative, the leading behavioral analytics platform. Go to www.get.indicative.com to learn more. Be the first to know when a new episode is released and stay up to date on the latest data news by signing up for our newsletter at decidingbydata.com/newsletter Read more about the podcast and Tad Martin at decidingbydata.com. Follow us on Twitter @decidingbydata.
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14集单集
Manage episode 207263840 series 1877580
内容由Deciding by Data提供。所有播客内容(包括剧集、图形和播客描述)均由 Deciding by Data 或其播客平台合作伙伴直接上传和提供。如果您认为有人在未经您许可的情况下使用您的受版权保护的作品,您可以按照此处概述的流程进行操作https://zh.player.fm/legal。
When Tad Martin first joined Overstock.com in 2000, he entered a company with a lot of valuable data waiting to be used, but lacking the technology to use it. As COO, Tad led a lengthy and pricey big data overhaul to get the company up to speed. After he left Overstock, Tad wanted to empower other organizations to harness the power of big data in an easier way. He started Collective[i] for a profession that he thought was underserved in big data solutions: sales professionals. Using predictive analytics based on their network of sales professionals, Collective[i] makes recommendations to its users on things like which buyers to target and when. In this episode, we explore: -How sales professionals can make more money while doing less work using predictive analytics. -What it means to create network effects with data. -The strides big data technology has taken over the past two decades. -And the things sales professionals get wrong when they don't rely on the data. Deciding by Data is hosted by Jeremy Levy, the co-founder and CEO of Indicative. This episode was produced and edited by Lauren Feiner. Our music is by Chris Zabriskie and BoxCat Games. This podcast is sponsored by Indicative, the leading behavioral analytics platform. Go to www.get.indicative.com to learn more. Be the first to know when a new episode is released and stay up to date on the latest data news by signing up for our newsletter at decidingbydata.com/newsletter Read more about the podcast and Tad Martin at decidingbydata.com. Follow us on Twitter @decidingbydata.
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14集单集
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