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Boosting Email Marketing Revenue with Personalised Recommendations
Our Presenters Chloe RigbyWeb Editor – Internet Retailing Rolf DoerrDirector, Business Development – Baynote Alistair McLeishCTO, Musicroom.com Richard EvansDirector, Marketing – Silverpop
Today’s Agenda Personalising the Web The Concept of Collective Intelligence Creating the Customer Experience  The Power of Triggered Email Marketing Personalising Email Marketing Live Q&A
MEETING REVENUE TARGETS IS STILL CHALLENGING FAILING TO MEET REVENUE EXPECTATIONS Expected Revenue    The Need:  Revenue Multiplier Effect Incremental Gains: Not Good Enough Widening Gap ,[object Object]
Conversion Rates Are Stuck
Average Order Size is Flat5
CONVERGENCE OF RECENT MARKET DYNAMICS Rising Commerce Complexity Higher eCommerce Revenue Targets The Battle for Consumer Attention eCommerce growing at 12.6%/yr. Senior execs and board perceive online channel as a “miracle cure” e-Commerce dominated by large players, brands selling direct, increasing global competition More sku’s, prices, bundles, terabytes of data to analyze, multi-channel touch points,internationalization Consumers  are Ahead of Merchants Social, mobile, local, flash sales, group buying, video, what’s next??? The Result: Increasingly Difficult to Make Your Revenue 3
BREAKING THROUGH THE 3% CONVERSION BARRIER Traditional online personalization strategies are based on past shopping history and profile data.  20% of online shoppers have an intent to buy, but only ~3% actually purchase. Personal in-store experience starts with understanding and acting on intent – “how can I help you today?” In comparison, 30% of shoppers who enter a physical store complete a purchase.* 7
TODAY’S REQUIREMENT: NEW APPROACH TO PERSONALISATION WHO are you? UNDERSTAND What is your intent, this time? ACT Deliver relevant  product & content at every moment RESPOND Continually adapt  to ever-changing  interests Traditional Profiles And Targeting Rules Buyer Intent Streaming Modeling Revenue Multiplier Effect … Across All Channels 6
POLL: Do you currently use recommendations on your website, in email or both?
UseRank ENGAGEMENT Adaptive Web: Automated, real-time, and personalised …page refers, links, entry trails, queries, page sizes, mouse movement, negative experience, virtual bookmarks, time spent, exit trails…
Baynote Secret Sauce: UseRank®
Musicroom.com
Music Sales Ltd. ,[object Object]
Europe’s largest online store for printed music and related materials for musicians, including tutor methods, instruments and accessories.
Started in 1996.
18 high street stores in the UK and Ireland.
Part of the Music Sales Group, an international family of music companies with interests in music copyright, printed music, book publishing, music retail & digital publishing. ,[object Object]
With 150,000+ products, some very niche, how to manage recommendations effectively.
Playing an instrument is personal
Listening to music is passive, playing it is active.
So targeting must include not only genre & instrument, but also skill level.
The Problem with Customers Also Bought...
Ignores new and long tail items with insufficient purchase history.
Ignores 95%-98% of user activity.
Doesn’t provide an alternative / up-sell technique.
Optimising Search
How to order search results?   ,[object Object]
As an alternatives/up-sell or cross-sell technique.
Search Results & Category Pages
Placing results that users currently find relevant at the top search.
Basket Page
Cross-sell complimentary items, missed deals etc.
Artist A-Z
Artist Tag cloud
Email Recommendations
Follow-up emails include related items.
Future: Using 10 different metrics to customise experience.,[object Object]
Musicroom.com Recommendations Search Results & Category Pages Placing results that users currently find relevant at the top search.
Musicroom.com Recommendations Basket Page Cross-sell complimentary items, missed deals etc.
Musicroom.com Recommendations Artist A-Z Popular Artist Tag cloud
Musicroom.com Recommendations Email Recommendations Follow-up emails include related items.
Recommendations - 2 examples ,[object Object]
Basic recommendations algorithms can suggest related products based on meta-data. i.e. With a flute, recommend flute strings, flute books, flute cleaning products.
Crowd-sourced recommendations can make associations that are not present in product data.
E.g. Recommend a music stand with a flute.
Real-time recommendations
Trends in how music is consumed often moves fast
Real-time recommendations can give products exposure without marketing intervention.
E.g. Ubi Caritas at the 2011 Royal Wedding ,[object Object]

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