Home The Sell Sider The 10/90 Rule: The Media Industry’s Future Business Model

The 10/90 Rule: The Media Industry’s Future Business Model

SHARE:

“The Sell Sider” is a column written for the sell side of the digital media community.

Today’s column is written by Mark Grether, CEO at Sizmek.

We’ve all heard the saying, “Every company is becoming a tech company,” but it seems we’re moving past that notion, with every company now becoming a media company.

It’s clear that the content consumed by audiences must be relevant, and it’s up to brands to ensure it’s rigorously curated with purpose. The more insights available when delivering content to a specific consumer, the more value both parties receive.

With this in mind, organizations across industries are voraciously searching for the next best strategy to enhance interactions with their respective customers. Some companies are taking steps to restructure their business model, making first-party data creation, aggregation and analysis critical components.

It’s a critical strategy that will help companies maximize the impact of first- and third-party content through a modern business model.

The 10/90 Rule

The future business model for digital media companies will line up with what I call the 10/90 rule. Here, organizations leverage content explicitly to garner data, with 10% of content delivered to consumers generated by the organization, and the other 90% from other less expensive sources to enhance monetization.

This approach runs contrary to the standard monetization methods publishers use. Typically, content is produced and then it needs to be profitable. For example, the subscription fee or ad dollars on the piece of content need to be greater than the cost of producing it. In the 10/90 model, a piece of content doesn’t need to be monetized in the traditional sense. Instead, it’s produced to collect data that is monetized somewhere else.

The particular set of content used for data generation plays a critically important role. Insights   gathered from this effort provide strategic, real-time feedback into how a company can make the biggest impact possible with its larger body of content. Put simply, the data gathered from the small subset can be applied to all other existing and new content, ensuring digital media companies attract and personally interact with their most coveted audiences in the right way.

The 10/90 Rule In Practice

Yahoo implemented a similar 10/90 model with its email business. In the early days, the company had editors produce content for various sections or channels, such as the finance section. This accounted for 10% of its total page impressions. Yahoo leveraged user data, based on what content particular people enjoyed reading, to discern what specific audiences liked, and leveraged its web-based email service to show relevant advertisements when users checked email. Its email advertisements resulted in 90% of the company’s impressions.

Another prime example is Google. The company gleans insights from data collected around user searches, which represent the 10% of content generated by the consumer using company-owned offerings. The user data is then leveraged to form 90% of the company’s monetization efforts, but these returns aren’t necessarily associated with google.com – the other areas are their operated properties such as AdWords and YouTube.

However, the data alone isn’t valuable; only when it’s combined with media does it generate value. To maximize returns, the larger body of content needs to be developed at the lowest price possible.

The clearest and most sure-fire way to aggregate massive amounts of content at a low cost is to have a user base develop it. The data gleaned from the smaller subset of company-generated content then guides brand creative and distribution strategies for future user-generated content. This ensures the maximum amount of return is received through intelligent audience interactions.

The most profitable businesses in the world apply data and technology to better optimize their media assets. As we’ve passed the initial stages of data gathering, organizations need to find new and streamlined ways to have that data and content work for them. It’ll be interesting to see how the industry continues to leverage data not only for enhancing user experience, but for pushing the boundaries of business efficiency, too.

Follow Sizmek (@sizmek) and AdExchanger (@adexchanger) on Twitter.

Tagged in:

Must Read

Who Will Stand Up For The Open Web?

The open web is done, stick a fork in it. Banner blindness is near universal, search traffic has run dry and publishers are struggling for oxygen. But what if that’s … not true?

A comic showing lab techs as stand-ins for legislators experimenting with provisions for US state privacy laws, including restrictions on collecting sensitive data.

What Publishers Don't Know About New Jersey’s Data Broker Law Could Cost Them

Attention, publishers: Although you might not think of yourself as a data broker, in the great state of New Jersey, that’s not really your call anymore.

Predict Bowl Icon. Magician Element, Forecasting Symbol – Vector.

Why This Marketing Measurement Company Just Open-Sourced Its Forecasting Engine

MMM can tell marketers what worked, but Lifesight’s open-sourced forecasting tool aims to tell them what to do next.

Privacy! Commerce! Connected TV! Read all about it. Subscribe to AdExchanger Newsletters

Podcasts Are Becoming More Programmatic. But Now Advertisers Have To Keep The Ad Load In Check

As programmatic buying becomes more common in audio, marketers and platforms fight the temptation to cram in as many placements as possible.

PubMatic Jumps On The Show-Level CTV Targeting Bandwagon

Connected TV advertisers are still pining after show-level control. And PubMatic announced contextual targeting at the episode level is available to media buyers accessing CTV inventory through its platform.

SQREEM Touts The Large Behavioral Model – Not The LLM – As The Winning Predictive Engine

Rather than relying on machine learning, SQREEM uses a mathematical AI model to track how systems change over time and predict audience behavior.