Power to the People: How to Leverage Individualization to Embrace People-Based Content Marketing

people-based-marketing

People are complex in their individuality, and savvy shoppers reject being reduced down to theoretical profiles or segments. When marketers attempt to make assumptions about massive audiences, loosely connected by third-party cookies and tracking pixels littered across the web, they fail to deliver on the actual preferences, habits and priorities of real people in real time.

The ability to individualize the brand experience used to be a marketer’s dream. But today this has become a reality with artificial intelligence (AI), and more specifically machine learning.

Innovative brands are starting to offer hyper-personalized experiences to their customers, and in doing so they collect valuable data that captures telling shifts in consumer behavior, attitudes and trends. Machine learning is advancing personalization to the point where every visitor interacting with a brand has their own unique content experience, a practice we call individualization. Individualization paves the way for new levels of accuracy and speed while creating a fluid, always-on feedback loop into editorial and digital strategy.

So now the question is, what can brands do to take full advantage of this feedback loop? How can marketers tap into insights gleaned from individualization? What can marketers do with this data and how can brands activate these insights across an organization? Content optimization is one of the biggest untapped areas when it comes to leveraging data. Data enabled by individualization finally makes it possible to create relevant content and product experiences for each and every consumer. These AI-driven insights allow brands to truly know their consumers and then serve up the creative experiences and messaging that’s relevant. What’s more, brands can use this data to figure out what works to then optimize their strategy accordingly.

Brands that are on the bleeding edge of adopting machine learning technology are already leveraging individualized consumer data to enhance content optimization in two major ways:

Improving Editorial and SEO Strategy

Individualization driven by machine learning gives you a whole new way to understand your content portfolio at a granular level, enabling you to track individual behavior by content type, topics, keywords and more. Brands use these insights to let their audience tell them what to write about next, and inform their editorial calendar, content production and keyword strategy to drive organic traffic.

This is particularly important for CPG brands, many of which are still adapting to deliver content that speaks to the needs of the digital shopper as opposed to the in-store consumer. One leading food brand, for example, recently embraced machine learning as a way to deliver hyper-relevant experiences to each unique site visitor. Individualization insights helped marketers identify significant gaps in SEO strategy as well as strategies to more deeply engage consumers across all owned media.

As a result, the brand’s content marketing team shifted their strategy to develop articles, videos, and recipes that contained keywords that were more frequently searched by the millennial, digital consumer. Over time this brand noticed a steady increase in visitors who consumed three pieces of content or more on the site. They found that these consumers were 6X more likely to click “buy now” from the recipe page on their site.

Looking at Deep Engagement Metrics

What if you could understand precisely how visitors engage with a piece of content in order to identify new opportunities to build relationships with your consumers? Uncovering the specific content characteristics that lead to high-value actions, such as “subscribe” or “buy now,” remove the guesswork from future editorial strategy and planning.

Engagement metrics, like engaged time with content and engaged page views, across all content topics and categories, are giving marketers a clear understanding of what content they should create to get the biggest impact. Other metrics like recirculation and multi-page sessions go beyond standard analytics offerings. These insights are uncovering content blind spots and unlocking trapped value within massive repositories of content, and the data often surprises brands using machine learning in these nascent ways.

One of the most recognizable beauty brands uncovered valuable lessons once they were able to match granular, user behavior with topical content analysis. With over 4,000 articles, videos, blog posts and tutorials across hundreds of topics, this category leader had no way of auditing their entire portfolio to ensure the most popular assets remained up-to-date with fresh imagery and current product information. Shortly after the company embraced an individualized content strategy, the editorial team leveraged performance insights and learned that the most in-demand topics didn’t always align with product launches. They quickly re-allocated resources to update and elevate older pieces of content on topics that continued to engage visitors while associating new products with the categorical themes now known to be most relevant to consumers.

The Bottom Line

Tracking this level of consumer data matched with content performance can tell you where customers are gravitating to on your site and around what topics. By tying real-time actions and behavioral patterns back to content performance, marketers can build a more strategic content development roadmap that more accurately reflects the interests of each individual interacting with their brand.

OneSpot’s individualization platform brings people closer to the things they love by curating the most relevant pieces of content for each individual through machine learning. Learn more about our content individualization platform to optimize your cross-channel content experience.

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