Man on a journey

Understanding the Intelligent Content Journey

5 minute read
Haresh Kumar avatar
Machine learning can't replace marketing savvy, but it can help marketers spend more time building customer relationships.

Machine learning can’t replace marketing savvy or skill, but it’s invaluable in augmenting what marketers are capable of doing. Today, marketers are burdened with executing arduous tasks related to managing technology, as opposed to building customer relationships. The next major disruption for marketers will be in the form of embedded machine learning capabilities that augment and automate the content journey — making content more intelligent.

Let’s walk through each phase of the content journey and try to understand how content intelligence can augment marketing execution. We’ll use Sarah, a marketer at a leading athletic retailer, as an example. Sarah is constantly challenged with posting fresh content on the various channels she manages — editorial content, multichannel marketing campaigns, new products and channel-centric assets, to name a few. Machine learning can help her with the following tasks: content ideation and search, content creation, content management, content personalization, content delivery and content performance.

Content Ideation and Search

When marketers identify content strategy and themes.

It’s critical for Sarah to connect with the shoe enthusiast community. To date, she has had difficulty tracking relevant cultural moments and topics and then quickly composing interesting content for her channels. With machine learning capabilities, Sarah can analyze many external sources, such as social media posts, search trends and customer reviews, to get a view of trending topics. She can then run topical and sentiment analysis to extract key insights.

For example, Sarah might learn that avid runners are showing greater interest in the sole technology of shoes and are increasingly making purchase decisions based on cushioning and support. It just so happens that Sarah’s company has a line of shoes that meets those requirements. With the appropriate tagging structure in place, Sarah can efficiently search through her content repository and pull out relevant assets she would like to use. With the help of machine learning, she can quickly turn topics into insights and build meaningful connections with her customers.

Content Creation

When marketers generate or assemble new content.

Once Sarah has identified the topic and gathered content from her repository, she gets to work on crafting content for her company’s channels. By leveraging machine learning capabilities like natural language generation, Sarah can get a head start and autonomously generate or assemble content. This involves working with a few basic inputs and relevant data sets, such as related listings, that can then be automatically transformed into entire paragraphs of written text. Sarah can focus on reviewing content as opposed to creating it from scratch, saving her time and allowing her to focus on strategic efforts.

Content Management

When marketers use assets that are intelligently structured, organized and used across the organization.

Through the use of existing image recognition technology and manual entry, Sarah spends time carefully tagging the assets in the repository (e.g., shoe and color of the shoe). These are key to content search, reuse and governance.

With machine learning technologies, these core features can tremendously augment Sarah’s execution. For example, machine learning algorithms can be trained to identify company logos or custom objects within an image that may be relevant only to the brand. Further, machine learning technologies can start to interpret and tag content based on context, like “athletic wear,” “excited runner in a desert terrain” or “casual shoes in rain.” For Sarah, this enhanced level of contextual tagging allows her and her colleagues to quickly find the right creative for the project and campaign that that needs to be orchestrated.

Content Personalization

When marketers tailor content to the needs and behaviors of a segment or individuals.

Learning Opportunities

Advanced systems are helping brands bring content personalization to life by enabling rules-based offers and experience management. As Sarah manages the experiences through the customer journey, she relies on these rules to deliver offers.

Machine learning can enable Sarah to scale and create highly personal experiences and offers. It can combine information collected across first-, second- and third-party data sources to create a comprehensive customer profile. She can then optimize the next best offer and personalize the experience for her customers to drive higher conversions and engagement. For example, an offer can automatically be promoted to customers searching on a specific sole technology. Furthermore, she would be able to scale specialized offers to each individual, enabling her to target at a much more granular level and reach a specific customer with the right offer at the right time.

Content Delivery

When marketers adapt or reconfigure content for the appropriate channels.

A large amount of time is spent reformatting and testing content and experiences to fit into the various sizes of devices that people use. Sarah’s challenge is to deliver the right experience to the new connected devices that running enthusiasts are adopting.

Machine learning enables Sarah to contextually optimize the content for all devices, regardless of how the content was authored. This allows Sarah to focus on content creation and lets the system optimize for screen size, connectivity bandwidth, channel, location and various other inputs.

Content Performance

When marketers measure content and use it to optimize future iterations.

Measuring content and its effectiveness at the asset level can unlock greater potential for content marketers. Machine learning could enable Sarah to automate marketing campaigns by auto-selecting individual assets or a combination of the best-performing assets for a particular channel. The system could continuously optimize the content based on customer interactions without Sarah having to step in and make adjustments, unless she feels it necessary to do so. This type of automation not only frees up Sarah’s time, but also ensures that the system is adapting to the changes in consumer behavior and adoption patterns.

Machine learning will revolutionize the way marketers connect with customers, and it will augment their ability to create and deliver amazing experiences.

About the author

Haresh Kumar

Haresh Kumar is Director of Strategy and Product Marketing, Mobile and Connected Experiences at Adobe. He has more than 17 years of experience in marketing, product and strategy for large enterprise brands.