How do I determine which specific assets to pull into the content pool from which recommendations are made?
Do not focus on analyzing data and insights around the buyer’s journey on any given page because a buyer’s journey is far from linear.
Work at ensuring that the content pools from which you are making recommendations represent a mix of varying journey stages, such as TOFU, MOFU, and BOFU. Each stage is based on the context of the page or site section. The buyer is empowered to pick their own journey; and you are well-positioned to nudge the visitors down the funnel. You should recommend the content that best maps to the buyer's interests from each pool.
For example, you can insert a dynamic blade at the bottom of a page and a single recommendation on the right rail. On the bottom of the page, you make a recommendation from a content pool that is comprised purely of TOFU thought leadership content. On the right rail, however, you recommend MOFU content to nurture your audience and to provide opportunities to nudge visitors down the funnel.
How do I improve resource page recommendations to yield more form submissions?
If your pages have little text, you may be creating a light topic cloud for your assets, which means that personalized recommendations have to use fewer keywords. While those keywords might be appropriate, it might not be well-personalized. A light topic cloud may also mean light interest enrichment of the individual's interest profile after they consume that page. Try to enrich a page to improve your ability to map the best resource page to the visitor and ensure that the individual’s interest profile accurately represents their interests.
With content changes, is SEO helped or hurt?
No impact on SEO. Using dynamic content to replace the static content located within the specific CSS selector with new, dynamically generated content variations is considered SEO-friendly. Google’s ranking algorithms rely on the default, static version of a page – instead of on the potentially unlimited amount of personalized variations that a page can have.
Does adding the widget affect page load time?
Can Content Recommendations make recommendations from a pool of gated content only?
Can I add case studies, tools, webinars and events to Content Recommendations?
Yes. Case studies and webinars are already sectioned. Upcoming events require constant supervision in the instance, and date/time for events must be removed from the recommendation engine programmatically, so you should not add these to the recommendation engine.
Can I know whether a user used a paid search or organic search?
You can filter by channel in Adobe to get these insights.
Does Google know when AI is used on the site?
As a general rule, personalization does not work against you. By improving digital experiences, you can improve engagement metrics, increase loyalty, and build a stronger brand. From an SEO perspective, penalties occur when you deliberately attempt to manipulate organic rankings via optimization initiatives that target specific variations to search engine User-Agents (like Googlebot) and another to human visitors.
Googlebot is not targeted with one set of content while showing other content to users. Content Recommendations does not redirect or negatively impage page loading.
You should make sure that content that is important for SEO appears in the static source code and do not rely on the dynamically injected content. this way, most bots, including Googlebot, are exposed to the content that is important for your SEO strategy.
What content should be recommended right after set up?
Content Recommendations ingests every piece of content from across your digital properties. When ingesting content, two things happen:
- Content Recommendations makes a copy of all the visual components of content, and available metadata in the page source code is also captured and stored (title, URL, Image, Publish Date, and so on).
- Content Recommendations applies NLP to the content, which automatically reads and extracts many topics from every piece of content, assigning each piece of content its own weighted topic cloud. The objective of the machinelearning is to automatically create a uniform and consistent taxonomy across content that is granular enough to differentiate every piece of content’s topical profile from one another.
User profiles are built simultaneously that may be stitched to certain client-persistent unique identifiers available in the browser. As users engage with content, the content’s topic clouds get attributed to the user, creating individual interest profiles. With each subsequent engagement with content, that profile gets recalculated and updated in-session, factoring in topic-weighting in content, topic position, recency, and frequency of topic engagement.
When next-best-content recommendations are made, the AI identifies the content that best matches the user’s individual interest profile. The recommendation decision is made from across “all content ingested" or narrowed down and defined (such as “blog content” or content containing a specific UT code).
Can Content Recommendations identify what keywords the user typed to Google Search?
Also, can it also Identify what search terms they are using in the internal search box?
Content Recommendations does not identify keywords searched by the user. Content Recommendations only captures interactions with content pages and aggregates the topics associated with the first-party interactions in real time when creating the individual interest profile. To the extent that keywords typed into Google search or other search box exist as topics on pages identified by our NLP, they are automatically included in the user’s individual interest profile.