Furious's Blog

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As the media landscape expands and the technical needs of organizations grow, partnering with highly specialized experts is increasingly necessary to succeed. The amount asked of individuals today is so much greater than it was in the past that having true domain expertise in every facet of your role is nearly impossible. But how do you ensure you’re working with the right people? With so much on the line, what steps can you take to set yourself up for success?

Below is a list of questions to ask yourself while engaging with outside resources to set your team up for success.

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Anyone who lived through the boom of digital video is sure to have opinions, memories, and potentially some nightmares of the creation, growth and eventual adoption of viewability. It was a necessary step but one that was wrought with loosely defined and adopted standards, competing “definitions” and, ultimately, heavy lift for operators and publishers.

And still, to this day, as Open Measurement comes to fruition with an ever-expanding set of delivery standards, there will be challenges ahead.

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The most important asset to any sales organization is talented salespeople. What’s their most important asset? Time.

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We all know that TV has gotten more complex—or, from my perspective, ripe with more opportunity. There are more commercial and distribution models than ever for TV (hello content!), which, in other industries, would spell opportunity to most incumbents with a strong portfolio of assets. 

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In today’s media organizations, core business decisions are often made using Excel as the habitual tool of choice, overlooking the power of modern data science to improve outcomes. Big data can help you and your team forecast inventory more accurately to reduce makegoods, create data-backed rate cards that maximize value for each placement, and unearth insights from new, proactive reporting.

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Our recent white paper, “Spinning Data Into Gold with Data Science” lays out how media sales organizations, including broadcasters, operators and cable networks , can turn their data into useful and actionable insights, resulting in higher earnings per share (EPS). Companies that aren’t effectively harnessing their data risk lower performance through missed revenue opportunities and inflated costs.

Here are a few examples of areas that might be leading to “revenue leakage” and cost inefficiency. Gaps that may be closed by effective use of current data management techniques and data science.

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Now that summer has come to an end, it's time to revisit our goals for 2019. To help you attack the rest of the year, and set your media sales organization up for success in 2020, we are pleased to announce the release of our new white paper, “Spinning Data Into Gold: How Data Science Improves Earnings per Share for Media Sellers.”

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Across the finance industry, the great Excel debate is alive and well, with ardent supporters on both sides. Why should those of us in media care what the finance industry thinks of Excel? Considering that many would say our businesses are duct-taped together with Excel, I think it’s worth some examination.

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As any media planner will tell you, forecasting views for TV can be overwhelming—and the consequences for mistakes can be huge. Underestimating the number of views means lost opportunity to sell inventory; overestimating results in a failure to deliver advertisers the guaranteed impressions promised and exposes a seller to liability risk.

We need to predict viewership days, weeks, sometimes months in advance, to carry out sales deals. Since views for linear TV can change drastically—even within the same series from week to week—what is a planner to do?

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Now that we’ve established that maximizing yield is crucial for video advertising businesses, let’s break down what actually impacts yield—and what solutions exist to help media sellers maximize it. Yield has two levers: price and inventory allocation. Media yield management—and ultimately successful improvement in yield—requires a strong foundation in two key areas: forecasting and optimization.