Month: July 2022

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It’s true: You really do need to spend money to make money

It’s true: You really do need to spend money to make money

Advertising is a commitment. It can also be expensive. And while we know that brands are prioritizing their brand awareness efforts in the coming year, there isn’t a marketer on the planet who’s not focused on the tangible returns that their spend delivers. And given that focus, it’s not uncommon for brands to pull back when the returns aren’t there. Somewhat counterintuitively, however, that’s usually not a good strategy. 

The knee-jerk reaction to reign in spending when returns are lackluster is logical. Why continue—or even increase—spending if it’s not generating positive results? As odd as it might sound, the answer is because you’re likely not spending enough to get the returns you want. In fact, there’s a spending threshold to generate the best returns, and if you don’t hit that, the returns will likely be underwhelming. And if you pull back, the problem could worsen.

If you’re not spending enough on advertising, you’re not going to get the returns you’re looking for

In a recent deep dive into an array of cross-channel media plans, we found that 50% of marketers’ media investments are actually too low to drive maximum payback. And in terms of amount, they’re 50% below what they should be to generate the best possible results. When marketers embrace the premise of spending more to earn more—by committing to the ideal amount—they could boost their return on investment (ROI) by as much as 50%.

Armed with an understanding that maximum ROI depends on specific spending levels, marketers can dive into determining what the right spending amount is. Said differently, in order to get the best ROI, brands need to know how much they need to spend to break through.

Here’s an example: In a recent analysis, we found that when a brand spent too little, the vast majority of the audience (87%) were exposed to the campaign less than three times. This group accounted for 68% of the delivered impressions. That means that nearly 70% of the impressions might not have been as effective as they could be.

In a separate example where a brand spent a medium amount, approximately 40% of the audience was exposed at least three times, and only a small portion of the audience (8%), saw the ad eight or more times in a week, which suggests potential ad waste. In the example where ad spending gets very large, 75% of the impressions are attributed to the audience members who see that ad more than eight times, but even in this example,32% of the campaign audience saw the ad only once or twice.

In addition to looking at a few specific cases, we wanted to better understand—at a global level—how frequently brands underspend and in which channels. Through our analysis of ROI observations, we focused on three key questions to understand what spending and ROI looks like—as well as what opportunity is being left on the table:

  • How much spending does it take to be competitive?
  • How does this vary by geography?
  • How do brands’ planned spend levels compare to the optimal spend levels for the media channel?

Based on our analysis, we found that the average brand invests 3.8% of its revenue on advertising1. To stay competitive, we believe a brand needs to spend between 1% and 9% of its revenue on advertising. In our study, we found that most brands spent between 1.4% and 9.2%. Within this range, one-fourth spend less than 3.8% and another quarter spends more than 3.8%.

It’s also worth noting that to compete, a newcomer will need to spend proportionally more than an established player. Conversely, an established brand can trend toward the lower end of the range to stay competitive.

Given the correlation between spend and ROI, modeling is critical for advertisers and agencies interested in finding the right balance to achieve maximum returns. While there are pitfalls to both spending too much and not enough, underspending is notably more problematic.

Across a study of media plans that clients of all sizes provided to Nielsen, we found that 25% of channel-level investments were too high to maximize ROI. Within this group, the spend was 32% too high. Reducing spend would improve channel ROI, but only by 4%. That, however, would result in significantly reduced sales volume, since reducing spend will also reduce ad-driven sales.

The solve here isn’t to slash the budget. Rather, brands should optimize their channel mix. Finding the right balance ensures that spending is properly allocated for reach, efficiency and frequency. For example, an auto manufacturer recently increased its reach by 26% and its impressions by more than 39% by simply optimizing its media allocation. In this example, the brand reduced its allocation across linear TV, digital and CTV to accommodate for the inclusion of radio without adjusting its budget. 

Spending too little poses a greater challenge. On average, brands underspend by 52%. That’s likely too big a gap for many brands to close in a single planning cycle. But for those that can, the upside is significant: ROI improvement of 50.3%.

Globally, underspending is rampant. While most brands allocate most of their budgets to TV, there are many instances where the allocations are still too low to drive maximum ROI. And outside of TV spending, more than half of the media plans Nielsen reviewed showed under investment across display and video.

ROI is just one of the many factors that advertisers and agencies consider when they’re planning their media budgets. The budget, however, is what drives campaign effectiveness. And right now, 50% of global media investments are too low, which means a significant amount of ROI is being left on the table.

For additional insight, download our recent ROI report. 

Note

  1. Nielsen Compass Database 2020-2021
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Advanced analytics: Unlocking data’s full potential

Advanced analytics: Unlocking data’s full potential

Almost 15 million years. Approximately 5 billion days. Or 1.3 x 1011 hours. 

That’s the quantity of entertainment programming Americans collectively watched on streaming services in 2021, according to Nielsen. Driven by broad interest in drama, reality and kid’s programming, streaming viewing hours extended to the 11th power during our last trip around the sun.

What’s behind this mind-boggling number? For one, an ongoing shift in how people regularly access programming. During the final week of 2021, consumers spent 33% of total TV viewing minutes on streaming platforms. Per Nielsen measurement, this was the highest share of viewing to date captured by streaming compared to broadcast and cable TV. And quite possibly, the tipping point at which on-demand streaming started to overtake linear TV viewing as consumers’ preferred entertainment consumption method. In February 2022 alone, for example, Americans streamed 169.4 billion minutes of video content.

The massive time spent viewing streaming content has been fueled by the ongoing proliferation of new streaming services. New entrants from one-stop entertainment content storefronts to more niche-driven players launched to compete with incumbent streaming stalwarts. As the number of available services has expanded, individual players are finding themselves in an ongoing quest to deliver compelling content to attract new viewers while retaining existing ones. 

Adding, pausing or dropping a streaming service is a relatively frictionless experience for the user, and the increasing number of service options is now overwhelming for audiences, which makes content a key differentiator. What programming to develop, whom to cast, where to place it and how to make it available to viewers all become top questions that studios and distributors need to answer. As streaming-first strategies become the norm, content metrics and the advanced analytics they fuel become critical.

To help inform content strategies and tactics, enterprises throughout the media and entertainment ecosystem are leveraging a wealth of data from different sources to achieve success in the face of competition. This holds especially true for content creators and distributors who are trying to move past reliance on tentpole content releases in favor of engaging in continuous optimization designed to bring resonant programming to market on a regular basis. For these players, data-driven content analytics play a critical role in the iterative process behind smart programming creation and distribution. 

Truth can be found in content data and analytics. But a number of factors tend to blur the picture. For one, data available to the entertainment ecosystem can run the gamut in terms of sources and nature. What’s more, it’s often inconsistent in terms of coverage and scale. Data can run from first-party owned to third-party licensed metadata, from very raw to highly normalized, from editorially curated to AI-created.

The abundance and variety of data raises an important question for the industry: Are media and entertainment enterprises considering the best data? 

Players need a lens of clarity and consistency on the content ecosystem, the programming landscape, distribution patterns and content popularity. Normalized and editorially curated program metadata is the primary component providing executives and analysts visibility into everything from macro-level industry trends down to micro-level characteristics of individual programs. Audience data provides another critical component, adding an important signal on content consumption that helps to illuminate how broadly programming is capturing viewers and who they actually are.

By looking through a content analytics lens, creators and owners can make informed decisions about what to create and where to place it to maximize monetization through licensing. On the distribution side, streaming services, networks and platforms can make data-informed decisions on how to put that programming in front of the right audiences in order to optimize engagement and loyalty for their services. 

Gracenote, the content solutions pillar of Nielsen, is uniquely positioned to provide the lens to the industry’s content analytics. Based on the breadth and depth of Gracenote metadata on entertainment content and the connectivity between programming assets provided by the Gracenote ID, the company already helps the biggest and most innovative TV providers deliver advanced content search and discovery capabilities. By combining industry-standard metadata and IDs with trusted Nielsen audience measurement data and long-running data expertise, Gracenote is powering a new generation of content analytics offerings.

Because Gracenote already tracks programming distribution across all platforms, both streaming and linear, the company has unparalleled insight into the content marketplace and the 26,847 unique TV shows and 620,896 individual episodes available now to U.S. viewers across all major streaming services. Analysis of the data reveals that the predominant drop strategy used by these services last year was releasing all of the shows episodes on the same day as opposed to rolling out individual episodes on a weekly basis. This same day release approach was used for 9 times more episodes than the weekly release approach. 

Looking even closer, the average streaming program engaged viewers for 1.85 episodes per viewing day. In comparison, among the top 10% of binge-worthy shows, viewers were engaged for 2.45 episodes. Honing in on the top 5%, the number went up to 2.89 episodes, one additional episode per day compared to the average program. 

So, what’s the key take-away here? For equivalent hour-long programs, a show in the top 5% in terms of binge-worthiness can bring an incremental hour of watch time per day to a streaming platform compared to a show that’s more average in terms of its binge-ability. In the heated competition to maximize engagement and time spent, this is invaluable insight that can actually move the needle on a critical business KPI.

With content analytics, a creator can identify where to focus content acquisition and awareness in large, siloed catalogs. They can see what opportunities there are to promote or recommend content to certain audiences. Programmers can determine the likelihood of viewers to binge watch a certain show to help decide on the optimal drop strategy. And they can look ahead into the next six months to see what the competitive content pipeline looks like to help drive release planning. 

The media and entertainment ecosystem has undoubtedly been working in hyperdrive of late. It all starts with creating compelling programming that captures the attention of viewers and reflects their diverse identities and interests. Efforts extend to developing the user experiences that present that programming to the audiences who crave it. All of this is in service of maximizing return on investment for developing and delivering consistently outstanding entertainment.

Data and analytics are key to success for the M&E industry players engaged in the ongoing process of assessing the marketplace, identifying opportunities, making decisions and analyzing results. And then doing it all over again. Based on the trusted nature of its data and the powerful analytics capabilities this data enables, Gracenote is uniquely positioned to deliver the solutions that enable content creators, owners and distributors to maximize viewer engagement – and push total streaming hours up to yet a higher power.

This article originally appeared in M&E Journal.

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