4.6 • 216 Ratings
🗓️ 13 October 2022
⏱️ 25 minutes
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0:00.0 | If you had a tool, any kind of tool, but in this case, data science, but it could be anything, |
0:08.8 | that improved your decision quality by half a percent every day for 365 days, you'd be looking |
0:18.5 | at roughly 2,000% improvement annualized. |
0:23.5 | We said to ourselves, having really done it, we felt like that as business leaders, we knew |
0:31.6 | more about what had to happen next than perhaps very many people anywhere. |
0:38.9 | I'm Mark Stuce, the CEO of Proof Analytics. |
0:47.5 | This is Code Story, the podcast bringing you interviews with tech visionaries who share in the critical moments of what it takes to change in industry and build and lead, a team that has your back. |
1:02.4 | I'm your host, Noah Laparte, and today how Mark Stuce built the platform to attribute revenue and provide the analytics to really move the needle. |
1:13.5 | All this and more. |
1:15.1 | On Code Story. |
1:19.7 | Mark Stuce was a CCO and CMO in large companies like Honeywell Airspace. |
1:24.7 | He claims that he is not a data scientist, but can play one on TV and can speak to the |
1:29.6 | needs of a data scientist. If he wasn't pursuing his current venture, he would likely be a history |
1:34.1 | professor, as he is seriously passionate about 15th century innovation in the pre-renaissance era. |
1:40.5 | He's also a parent of two teenage boys, so outside of tech and history, he spends time doing what they want to do. |
1:46.8 | As a communications and marketing leader, Mark was frustrated with the lack of recognition for his marketing teams to the bottom line. |
1:54.1 | In addition, he solved major analytics problems at his prior companies, but had massive budgets to do so. |
2:00.7 | He saw that software and automation was the way forward. |
2:06.2 | This is the creation story of proof analytics. |
2:13.6 | How well you leverage analytics into daily decision making to make even slightly better decisions day in and day out is what it's all about. |
2:24.1 | The problem has been historically that data science has been very slow, very complicated, very expensive, and very difficult for so-called normal people, meaning |
2:36.2 | not people who are not trained data scientists, to understand it. |
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