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tactics Behind the Data Science: Sending Item Alerts to the Right Users

Recommendation algorithms usually try to optimize the best set of items (products) to show a specific user. In certain situations it is necessary to obtain the best set of users that might be interested in a specific item. Many digital marketers face the hard problem of targeting the right users not only for their promotional emails, but also for specific item triggers. [...] [more]
retentionscience.com    Intelligence, Recommendation

tactics Design and Models for a Personalized Product Recommendations Engine

Before discussing specific models, it’s important to know a bit about how we store and serve product data for the Bronto Marketing Platform. Our product service uses Solr to support efficient, scalable searches on products based on arbitrary product fields, and it stores data in Apache HBase as a master storage database. The product service already uses Solr to allow customers to search for products (either in the Bronto Marketing Platform user interface or in the Recommendations Standard app), so [...] [more]
bronto.com    Intelligence, Recommendation

tactics 3 Major Recommendation Algorithm Mistakes Fortune 500 Companies Make

Several recommendation algorithms power email-marketing campaigns as well as on-site product recommendations. With Amazon’s success in driving revenue and engagement from product recommendations, several companies leverage these algorithms to cross-sell/up-sell products to users. [...] [more]
retentionscience.com    Intelligence, Recommendation

tactics Scaling Recommendation Engine from 15,000 to 130M Users

Delivering users with precise product recommendations (recs) is the creative force that drives Retention Science to continue to iterate, improve and innovate. In this post, our team unveils our iteration from a minimum viable product to a production-ready solution. Here’s the chronology of events: [...] [more]
retentionscience.com    Intelligence, Recommendation

discussion Dark social: It's worse than we thought in Asia-Pacific

Though we have mentioned dark social before, new research shows that the problem is worse than previously thought, especially in Asia-Pacific and Australia. Here's why. Dark social exists because the web works by passing information from the source of a click to the destination site. That is, from a marketer's perspective, every time a user clicks through to your website to download a web page, the user's browser also sends a string to the site. [...] [more]
econsultancy.com    Webanalytics, Recommendation

social How WeddingWire lifted re-pins from email 31% through testing [VID]

“Our subscribers are very active, they really engage with our content … but one thing that’s really important to highlight with our audience … was how quickly they went to mobile,” said Bart Thornburg, Senior Email Marketing Manager, WeddingWire, adding that the brand crossed the 50/50 threshold in 2012. The team at WeddingWire wanted to increase the brand's presence on the social media site Pinterest, which is known for having a strong wedding planning community. Specifically, they wanted to increas [...] [more]
marketingsherpa.com    Social, Study, Test, Recommendation

discussion Research: The Million Domain Challenge: Broadcast Email Prioritization by Cross-domain Recommendation [PDF]

With email overload becoming a billion-level drag on the economy, personalized email prioritization is of urgent need to help predict the importance level of an email. Despite lots of previous effort on the topic, broadcast email, an important type of emails with its unique challenges and intriguing opportunities, has been overlooked. The most salient opportunity lies in that effective collaborative filtering features can be exploited due to thousands of receivers of a typical broadcast email. [...] [more] 
kdd.org    Study, Recommendation

 discussion MailChimp Brings Product Recommendations To Small Stores

The company wants to bring mom-and-pop e-commerce vendors the kind of automated recommendations made famous by Amazon and Netflix. [...] [more]
fastcompany.com    Intelligence, Trend, Recommendation

tactics How to deliver millions of personalised newsletter emails on AWS

At Zalando, we strive to help our customers find the most relevant fashion they can imagine. Zalando is known for its great fashion assortment and its huge selection of products. In order to ensure that our customers are not overwhelmed by this vast selection of products, the Recommendations Team builds systems that ensure that customers find products in an easy and convenient way. [...] [more]
zalando.de    Recommendation

social New Facebook Sharing Options: What Marketers Need to Know

Do you want more people to share your content on Facebook? Are you taking advantage of Facebook’s sharing features? Facebook offers a variety of new buttons and plugins that make it easier than ever for readers to share and engage with your content on Facebook. In this article you’ll discover how the new social sharing buttons and plugins from Facebook can help increase shares of your content. [...] [more]
socialmediaexaminer.com    Social, Recommendation

strategy Dashlane uses customer reviews to increase clickthrough rate on optimized newsletter

As more and more personal data is stored online, the most important question password manager Dashlane needs to answer for customers is, "Can you be trusted?"To do this, the marketing team began collecting customer reviews, and utilizing them in paid ad campaigns, landing pages and email marketing. They were able to collect over 300 reviews, 97% of which were four stars or higher. [...] [more]
marketingsherpa.com    Study, Recommendation

tactics Pinterest email test results in 31% lift in re-pins from email

Incorporating social media icons in email sends is a common practice for most companies. However, when online wedding marketplace WeddingWire wanted to increase its Pinterest presence, it looked to these icons and discovered how ineffective they actually were. Through testing, the brand was able to develop an email strategy that grew its Pinterest following without drastically altering its sends. Learn how WeddingWire was able to increase its email re-pins 31% and build its Pinterest community by [...] [more]
marketingsherpa.com    Social, Study, Recommendation

tactics How to Use Social Proof in Email Marketing

“Billions and billions served.” It may be the canonical example of social proof. We’ve all seen it and most of us are among the billions served. McDonald’s simple slogan connotes ubiquity and community. Some version of that line reaches 69 million customers each day. It’s wise to borrow strategies from companies like McDonald’s. After all, they’ve spent millions on research and advertising to arrive at a four-word slogan. If they believe social proof works, there’s a pile of data to back it up. [...] [more]
getvero.com    Copywriting, Social, Recommendation

stats Chart: Tactics used by the top 1% of viral emails

Your email marketing success is not limited by the size of your list. Every subscriber on your list has friends, family and colleagues that can be reached through email forwards.How do you reach beyond your own list to get your email sends out into this social network?In this week's chart, we explore factors that influence commercial email forwards based on data from 400,000 email sends. Read on to discover what these successful emails have in common and how you can create emails that your customers [...] [more]
marketingsherpa.com    Study, Recommendation

stats Slides: How to Make Your Emails Go Viral [PDF]

We tend to associate “going viral” with social media, but email forwarding is a much more powerful action-driver because its sharing is more targeted and more urgent. In this session, presented at Salesforce Connections 2015, Chad White and Justine Jordan shared insights into email virality gained from analyzing 1 million emails tracked through Litmus. Covering benchmarks for email forward-to-open rates, different tactics for spurring email sharing, and real-world examples of highly viral commercial [...] [more]
slideshare.net    Study, Recommendation
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