Karl Wirth — July 22, 2017 — July 22, 2017
Content creation is nothing new in the marketing world. Yet it has traditionally been seen as more of a B2B demand generation tactic — a key component of both an inbound and account-based marketing strategy. But B2C marketers are directing more budget toward content marketing as well. This year, they’re allocating 32% of their total marketing budget, on average, to content marketing (vs. 25% last year).
Content is one way for retailers to differentiate themselves in a crowded market. Relying on discounts and sales may create additional revenue but quickly cuts into your margins and doesn’t enhance your relationship with your customers. Content allows you to express your brand in engaging ways, add thought leadership and value to your visitors, and it is a solid top-of-the-funnel strategy to get people to your site and learn more.
Yet many sites keep content assets and product pages completely separate. How can they be blended together to create a better experience for each visitor? Here are some tips.
One system that understands both
First and foremost, you can’t do anything to blend your content and products together on your site if you don’t have a system that understands both. You need a platform that can recognize the brands, colors, categories, prices, etc. reflected on each product page as well as the topics, keywords, authors, formats, etc. of each of the pieces of content on your site.
Too often, I see marketers keep content and products in silos in their backend systems, even if they are both promoted on the same website. Typically, your content is stored in your CMS, and it has a built-in search engine that indexes and searches your content. Any content recommendations on the site leverage this data. Meanwhile, your e-commerce platform stores your product catalog and inventory. It also has a built-in search engine that indexes and searches your products and it is where all the data that fuels your product recommendations is stored.
If you can’t bring these two sources together, you cannot recommend both relevant products and content to a visitor at the optimal moment.
Draw insights from engagement with both
When you have a system that can understand both content and products, you can draw insights from each visitor’s engagement with both types of information to help you understand their preferences and intent in the moment. Any action a person takes on your site can be a clue to what her interests are and what she is looking for right now, no matter if she is browsing products or content. So rather than use a person’s engagement with content to inform your understanding of her content preferences, and her views of product pages to inform your product recommendations, you should use her actions across the site to build up an individual profile of her overall preferences.
For example, if a person regularly consumes content or views product pages for shoes, or lawn care, or gardening, or office supplies, you should analyze that engagement to determine what that means for her affinities. Those insights can inform the recommendations and experiences she sees across the site.
Make both searchable
Once your system can understand both products and content, you can allow visitors to search for both on the site at the same time. When your visitors aren’t able to search for both at once, they can only search your CMS for content and your e-commerce platform for products. That requires them to know exactly which type they are looking for.
But when you bring both together, if a visitor searches for “lawn care” on your home improvement site, you can suggest both products for lawn care as well as how-to guides for growing and maintaining a healthy lawn, based on his preferences that you have obtained throughout his engagement with your site.
Recommend both at the right time
Even if a person does not search for a topic, you should be able to recommend the right pieces of content to pair with a product a person is viewing or the best products to recommend to someone engaging with a piece of content. With machine-learning algorithms that leverage data in each shopper’s individual profile, you can automatically connect the two together without manually curating specific products that are related to certain pieces of content and vice versa (an approach that doesn’t scale).
For example, if a visitor reads multiple articles about growing and caring for lilacs on a garden supply e-commerce site, his preferences for lilacs should be incorporated into the product recommendations he sees.
In the other direction, if a travel site visitor is researching flights to London, the site should recommend articles on activities to do in London. By reading up on things to do in London, she may be more likely to book the trip!
And across the homepage and other areas of the site, contents and products can be promoted together at the best time to appeal to each visitor.
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