How To Increase Newsletter Subscriptions With Personalized Recommendations - Rainmaker Media Solutions
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How To Increase Newsletter Subscriptions With Personalized Recommendations

How To Increase Newsletter Subscriptions With Personalized Recommendations

 by Assaf Dudai

Personalization is a growing segment of online marketing. If in the past a one-size-fits-all was enough, today’s audience demands more. The idea behind personalization is a straightforward one: personalize your message and you’ll see better results. Think about it like calling someone by their name instead of just shouting, “Hey you”; in which case are you more likely to get a response?

Be Personal, Be Relevant

Publishers are attempting to personalize the entire online experience, with personalized recommendations becoming a central tool for driving anonymous readers into deeper engagement and interaction. These personalized recommendations can come in the form of popups, calls-to-action, email segmentation, etc., and attempt to deliver the most relevant article or offer to each and every reader.

Relevancy is the name of the game. If you recommended the best and most comfortable ski gear, at an amazing price, to a person who never skied, it’s still not going to work. The quality of the recommendation is important but pales in comparison to the importance of being relevant.

How Personalization Works

So how does it actually work? Personalization can either be rule-based or algorithm-based. With rule-based personalization tools, you define visitor lists and decide what is the best and most relevant content for them. You can segment your visitors in any number of ways, depending on the specific tool you are using. In many cases, self-volunteered information from previous on-site conversions can be most effective.

With personalization algorithms there are two phases: first the system ‘learns’ your site by mapping content such as white papers, case studies and blog posts, using a combination of NLP (natural language processing) textual matching and taxonomy. The result is a content-map of the site, with highlights for unique content.

As a visitor arrives to the site, the algorithm then analyzes their on-site behavior – what was the starting page, what other pages the visitor viewed and how long he stayed on each. The algorithm then matches the two pieces and serves personalized recommendations to the visitor in real time. View Full Article >>

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