These days, consumers are hungrier than ever for brands to cut through the clutter and provide exceptional experiences—and content’s role in brand’s delivering on that experience is growing in importance. Increasingly, consumers will only spend time with the content that earns their attention and is personally relevant to them.
That’s where personalization comes in. Personalization has increased in salience with marketers as consumers increasingly reject their one-to-many strategies in favor of a one-to-one relationship with brands. 45% won’t spend time with branded content if it’s not relevant to their interests, and 42% are less interested in a brand’s products and services if the content the brand provides is not personally relevant. For these reasons and more, many marketing organizations have picked up on the importance of having a personalization strategy.
But first, it’s important to understand the full meaning of the umbrella term “personalization” and to be clear about what you’re trying to accomplish as you select your strategy and technology for personalizing content marketing. Here are the different types of content personalization used today and what they mean:
Classic name checking
What it looks like: Using a contact’s name within the greeting or body of an email. Example: “Hi Bill, …”
This is the most basic form of personalization and has been available to marketers for more than a decade. It has become a standard practice for email marketing. If you have an email marketing program in place, you’re probably already doing this. Over the years, this feature has become more prominent in other channels, such as owned websites.
What it looks like: Delivering the same set of targeted content to a select group of contacts.
This approach has gained popularity and can be effective for bottom-funnel customer acquisition but less so for top- and middle-funnel conversions, as these are the touchpoints where consumers are learning more about your products and services. These prospects may have different levels of awareness about your brand; therefore, they need an experience that’s more closely tailored to their awareness level in order to nurture them. This approach can’t replace truly personalized content to an individual’s needs.
Behavioral, demographic or firmographic data can strengthen your relevance to your targets as you plan and build campaigns to nurture them, but this approach still has a high-labor input from having to predict and configure all the possible matrices of customer journeys. Even modern predictive analytics solutions still fall short of personalizing to infinite segments of one, and instead creating many segments of many.
What it looks like: Retargeting ads that show the exact same product a consumer searched for on Amazon.
This is primarily focused on the bottom of the funnel where purchase intent has been made obvious by the consumer. If you’ve ever been retargeted by ads showing the exact same products from Amazon, then you know what this experience is like for your consumers. This is very effective when the purchase decision has been made and you need to get your customer over the finish line. But if the consumer is not ready to purchase, these ads will be ignored or blocked. Many of these platforms also have some cross-channel capabilities such as behaviorally triggered email sends or dynamic web pages on the main site. However, these only work for folks who are trying to lift eCommerce metrics, not personalize an individual’s content journey with a brand.
What it looks like: Using machine learning to predictively personalize content based on user behavior, context, and lookalike analysis—delivering personalized content experiences that are individually relevant to each person.
A challenge for digital marketers is to map when to serve a piece of content based on where a potential or current customer is in their purchase process. For example, a financial services firm could place top-funnel, educational content, such as a Retirement 101 video, in front of a viewer starting to research retirement planning. Once the viewer has engaged with that content, they could then be remarketed with a downloadable guide from the financial services firm. This approach uses machine learning algorithms to dynamically generate journeys of content for individual members of your audience to repeatedly engage them across channels and help them continue to build a deeper relationship with your brand.
Download our guide: How to Master Individualization to Delight Your Customers for best practices on delivering individualized content experiences across every channel.
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