Ad Tech Podcasts

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This week on the podcast Victor Lopez returns to discuss the current trends in programmatic media buying and ad tech. As the industry changes, client needs are also constantly evolving and the media plans that best server brands need to change with them .

Victor has extensive experience running programmatic media buying, and is the Programmatic Media Director at Affiperf, which is a subsidiary of Havas. He has recently been promoted to take over buying across all channels of online media.

One of the interesting trends that we talked about in this episode is that more dollars flowing into programmatic media and coming from all other channels, not just TV or traditional print. Programmatic spending is increasing at the expense of all other media. Clients initially took some time to warm up to the concept of programmatic, but now are adopting it in larger numbers.

Programmatic Media Buying, DMP Data, and Branding Dollars - Return of Victor Lopez

What exactly is a Data Management Platform, and why is it so important? Miles Pritchard, Global director of marketer and agency solutions at Lotame, will explain what a DMP is and how they are being used by marketers and agencies. We'll also look ahead at some of the challenges that advertisers are facing, and how being able to collate data from multiple different sources can help deliver more targeted and effective advertising. DMPs are becoming a critical part of every marketer's technology stack, so tune in and listen to Miles explain the industry best practice and how to get started.

15. What is a Data Management Platform? Miles Pritchard from Lotame

In this explorative conversation with we dig a bit deeper into the current state of Programmatic Marketing with the Chief Marketing Officer of MediaMath. Joanna O’Connell’s in-depth experience have given her a unique perspective into the current state of programmatic, and what the impact of technology has had and will have on the industry. Joanna’s valuable insights help paint a more complete picture.

For full show notes, visit programmaticuntangled.com

The State of Programmatic with Joanna O'Connell - Ep. 2

Join Matt Nash, Managing Partner UK at Neo @ Ogilvy as we discuss how ad agencies are harnessing the power of data to produce powerful and effective advertising. We discuss how the future of advertising lies in being able to predict what a user is going to do, and how we'll be able to tell stories in a sequence of ads across devices. Whilst data is important, we'll also hear about the importance of creativity and context in creating ads that connect with audiences.

17. Harnessing data, creativity and context to produce powerful ads - Matt Nash, Neo@Ogilvy

In this episode I join Tom Goodwin, EVP, Head of Innovation at Zenith Media to take a look at the state of advertising and ask, does advertising have a future? Along the way we discuss everything from online banks, data privacy, personalisation, tracking and how technology should be making the industry more creative.

20. Why advertising is broken and how technology will unleash our creativity with Tom Goodwin

This week's episodes is for data scientists, sure, but also for data science managers and executives at companies with data science teams. These folks all think very differently about the same question: what should a data science team be working on? And how should that decision be made? That's the subject of a talk that I (Katie) gave at Strata Data in early March, about how my co-department head and I select projects for our team to work on.

We have several goals in data science project selection at Civis Analytics (where I work), which can be summarized under "balance the best attributes of bottom-up and top-down decision-making." We achieve this balance, or at least get pretty close, using a process we've come to call the Idea Factory (after a great book about Bell Labs). This talk is about that process, how it works in the real world of a data science company and how we see it working in the data science programs of other companies.

Relevant links: https://conferences.oreilly.com/strata/strata-ca/public/schedule/detail/63905

How to pick projects for a professional data science team

If you're a data scientist at a firm that does a lot of software building, chances are good that you've seen or heard engineers sometimes talking about "agile software development." If you don't work at a software firm, agile practices might be newer to you. In either case, we wanted to go through a great series of blog posts about some of the practices from agile that are relevant for how data scientists work, in hopes of inspiring some transfer learning from software development to data science.

Relevant links:
https://www.locallyoptimistic.com/post/agile-analytics-p1/
https://www.locallyoptimistic.com/post/agile-analytics-p2/
https://www.locallyoptimistic.com/post/agile-analytics-p3/

Agile Development for Data Scientists, Part 1: The Good

There's just too much interesting stuff at the intersection of agile software development and data science for us to be able to cover it all in one episode, so this week we're picking up where we left off last time. We'll give a quick overview of agile for those who missed last week or still have some questions, and then cover some of the aspects of agile that don't work well out-of-the-box when applied to data analytics. Fortunately, though, there are some straightforward modifications to agile that make it work really nicely for data analytics!

Relevant links:
https://www.agilealliance.org/agile101/12-principles-behind-the-agile-manifesto/
https://www.locallyoptimistic.com/post/agile-analytics-p1/
https://www.locallyoptimistic.com/post/agile-analytics-p2/
https://www.locallyoptimistic.com/post/agile-analytics-p3/

Agile Development for Data Scientists, Part 2: Where Modifications Help

If you've been in data science for more than a year or two, chances are you've noticed changes in the field as it's grown and matured. And if you're newer to the field, you may feel like there's a disconnect between lots of different stories about what data scientists should know, or do, or expect from their job. This week, we cover two thought pieces, one that arose from interviews with 35(!) data scientists speaking about what their jobs actually are (and aren't), and one from the head of data science at AirBnb organizing core data science work into three main specialties.

Relevant links:
https://hbr.org/2018/08/what-data-scientists-really-do-according-to-35-data-scientists
https://www.linkedin.com/pulse/one-data-science-job-doesnt-fit-all-elena-grewal

The Three Types of Data Scientists, and What They Actually Do

The ClickZ Digital Marketing Podcast, presented and produced by Tim Flagg, published by ClickZ.
ClickZ, the original digital business intelligence company founded in 1997, provides best practice advice, trends and insight from leading analysts and practitioners to a global community of more than 100,000 digital marketing and e-commerce professionals. 