Digital Marketers Struggle with Defining Value for Content, AI Solutions

September 18, 2018 Jon Williams

Digital marketers understand the overall value that relevant, engaging content brings to their organization.

However, they aren’t as versed at how to define the exact cost and ROI for every piece of content they’ve used—or plan to promote to their customers and prospects.

The struggle with creating a nirvana strategy for understanding content monetization across all channels was one of the biggest discussion topics at last week’s DMEXCO 2018 conference in Cologne, Germany. The event brought more than 40,000 global digital marketing and media industry attendees together for two days to discuss trends and technologies affecting the space.

Many DMEXCO attendees eagerly discussed content at the conference, but also admitted they found it complicated to measure the values and costs of the various parts of the content lifecycle including:

  • Creation
  • Licensing
  • Storage
  • Delivery
  • Optimization

They also indicated they didn’t have a strong handle on how to measure the ROI of their organization’s content on each of the increasing number of channels on which they distribute that content.

But having discernable methods to measure content usage for such metrics as new opportunities, MQLs, SQLs, conversions, and new customers, is essential for marketers to determine if they’re creating the content their ideal buyer personas want to read wherever they are in their customer journey. If they don’t have a way to measure this kind of content value holistically, they risk missing the opportunity to properly engage prospects and could end up wasting the time and money they put into content creation.

For example, if a piece of content is underperforming on one channel, but customers are more active on another, marketers need to be aware of the ROI on both of these channels before they alter or remove that valuable content.

Utilizing a platform solution as a single source of truth to measure content value can help marketers connect the content they use across channels—even if it’s been altered for each touchpoint. Technologies such as Marketing Resource Management (MRM) and Digital Asset Management (DAM) can help blend that with other performance data based on the different stages of the buyer’s journey to help marketers gain that holistic view of their content value. They also can gain insights into content costs to help marketers streamline workflows so they can produce better content in a reduced timeframe.

These solutions, however, aren’t turnkey. Marketers must first evaluate where their largest struggles are, then utilize specific solutions that can help them quickly overcome those roadblocks so they can get closer to that nirvana strategy for measuring content value and ROI.

The value of AI

While DMEXCO attendees were eager to learn about solutions for deciphering content value, they were a bit more skeptical about the value that Artificial Intelligence (AI) technologies can provide for their marketing.

Because many AI opportunities for marketing are still in their infancy, they may seem a little far-fetched to some organizations, causing them to neglect exploring some applications that actually might be a good fit.

For example, quick-hit wins for AI, such as automated asset tagging, image recognition, and speech-to-text translation in DAM solutions often provide a quick time-to-value, which can help organizations better understand the merit of such intelligence functionalities. Such automation intelligence is easy to utilize, saves resources, and can enable quicker time to market. Or other MRM solutions that use AI to evaluate and recommend new workflow or resource changes can help streamline such processes to gain similar values.

However, while more extensive AI applications for marketing are continually becoming more accurate, not all of them are mature enough for all organizations to find valuable.

For example, a few select companies are testing the boundaries of using machine learning technologies to “read” customer faces and recommend content based on their findings. Without proper monitoring, such bleeding edge applications run the risk of creating stereotypes of customers—and recommending content that not only doesn’t resonate but in fact upsets a customer—which would negate their value.

Marketers must be able to trust that their AI solutions are recommending decisions that are in the best interest of their organization—and their customers.

AI solutions for marketing are definitely on the horizon and getting better all the time. But organizations still must strategically monitor and evaluate their value before they begin relying on this emerging technology to make business critical decisions.

About the Author

Jon Williams

Jon Williams is Senior Vice President leading Aprimo’s International division. Jon has spent the past two decades helping organizations by unlocking the power of technology to aid the development and management of their marketing organization. Previously, Jon ran the Teradata Marketing Applications division in the United Kingdom, and prior to that, he was a long-term member of the Aprimo business as Director of Application Consulting. Before Aprimo, Jon spent seven years as Principal Consultant at Intelligent Marketing Software provider, smartFOCUS. Jon has a first-class honors degree in Business Decision Analysis—the science and art of using statistical models to solve every day business issues.

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