Upload
dinhtuyen
View
218
Download
0
Embed Size (px)
Citation preview
ONE EMAIL DOES NOT FIT ALL
Our objective is to increase the value of email newsletters by making sure each recipient receives personalized recommendations of unread
articles directly to the inbox.
TO MEET OUR OBJECTIVE WE HAVE DEVELOPED AN ADVANCED RECOMMENDATION FEATURE
4
The recommendation feature is based on collaborative filtering as method.
”Collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating)”
For Schibsted this means that many subscribers read the same articles online (similar reading habit). There are however always articles that one individual have read that others with similar reading habits have not read. With the use of advanced algorithms such unread articles are pulled by the recommendation feature and further pushed to the right recipients.
THE RECOMMENDATION FEATURE PUBLISHES INDIVIDUAL RSS FEEDS ON THE WEB WITH
UNREAD ARTICLES THAT SUBSCRIBERS WITH SIMILAR READING HABITS HAVE READ
5
This allows us to individually merge the recommendations into marketing emails based on an unique ID (SPID ID) on each user.
WHEN SENDING A NEWSLETTER IN OUR ESP, ALL RECIPIENTS RECEIVES A NEWSLETTER WITH UNIQUE PERSONALIZED CONTENT
7
A custom built app are put in the newsletter template in the email builder in
our ESP. The content and design that should look the same for everyone (ielogo header) is put outside the app.
App settings define rss url. All urls are unique based on unique id. The ids are
pulled from data object on customer level in ESP (from CRM system)
The send generate unique emails with personalized content for all recipients. The newsletters is nicely designed with
CSS and html in the app settings.
APP
COLLABORATIVE FILTERING DRIVES UP TO 121% BETTER ENGAGEMENT (CLICK RATE)
COMPARED TO MANUALLY CURATED EDITORIAL NEWSLETTER
9
Preliminary results also show great numbers (54%) on article completion (read) after click (29% on regular).
REGULARREGULAR REGULARRSS RSS RSS
WEEK SEND SEND OPEN RATE OPEN RATE CLICK RATE CLICK RATE
39
40
42
43
44
47
45
46
48
Weekly A/B tests of manually curated (regular) and personalized (rss) newsletter:
COLLABORATIVE FILTERING IN EMAIL GROW DIGITAL READERSHIP AND ENGAGEMENT
10
With collaborative filtering in email our editorial newsletters has become much more engaging as our subscribers are encouraged
to read as well as discover more of our great content.
WITH COLLABORATIVE FILTERING IN EMAIL SCHIBSTED IS TAKING PERSONALIZATION
ONE STEP FURTHER COMPARED TO MOST OTHER PLAYERS IN THE MEDIA INDUSTRY
11
”By this development, (…) Schibsted get one step ahead of the competition in sending individual and relevant content to their
subscribers.” Arve Warholm, Deloitte Digital
WE WILL CONTINUE TO IMPROVE AND DEVELOP AS WELL AS LAUNCH THE RECOMMENDATION FEATURE ON ALL BRANDS IN 2017
13
1. First step was testing and proof of concept on Aftenposten
2. Next step is full launch on all Schibstedsubscription media brands
INNOVATION APPEAR WHEN SMART PEOPLE ACROSS THE ORGANIZATION WORK TOGETHER
14
This product shows what might come out of breaking down silos and working together across different units within the organization.
THE TEAM (from left): Eirik Winsnes(Development Editor, Aftenposten), Espen Tandberg (Product Manager Aftenposten, Schibsted Product & Tech), Hans Martin Cramer (Product Manager, SMP Curate), Ellinor Sande (CRM Designer, SchibstedNorge Abonnementsmedia), Adam Marklund (baby on the job with his father), Arnbjørn J. S. Marklund (CRM Manager, Schibsted Norge Abonnementsmedia)
THANK YOU
15
Category10:Bestideatogrowdigitalreadershiporengagement
CollaborativefilteringinemailEirik Winsnes – DevelopmentEditor,Aftenposten,[email protected]ørnMarklund- CRMManager,Schibsted,[email protected]