Post on 04-Dec-2014
description
Intelligent SearchScandinaivan eBusiness Camp, 26th of May
Introducing Apptus
• Pioneering relevance engine technology
• Search, recommendations & content targeting for e-Commerce
• Boosts site profits; streamlines marketing & merchandising back-office
• Founded in 2000, profitable and VC backed
• HQ & development in Lund - Sweden• Sales offices & partner network in
Europe and North America
15% of the buyers
know exactly what they are looking for
70% have a clue,
but they are open to
guidance
Is it possible to guide & influence without being
relevant?
Relevant
interactions lead to more and better
transactions
Where Who
When ChannelWh
at
The many dimensions of relevancy
WHAT
• Only 10% of the users know the exact name, article id or specifics of what they are looking for
• 80% of searches address 20% of the database• The margin of the long tail is between 50-400%
higher than the top list
WHO
• Visit history(duration, time, length, outcome)• Search, Navigation and click track• Preferences (revisits to specific information entities)• Segment orientation and persona• Membership in any VIP or loyalty programs• Purchase history(duration, average order size, context)• Social network and influence• Contract specific terms for Business-2-Business
WHERE
• Nearest store where the buyer can explore the product
• Nearest location where the buyer can fetch or return the goods
• Location specific purchase/delivery terms
WHEN
• Seasonal influences • Trends(site-specific and general) • Ongoing marketing activities
Channel
• Web, Mobile, eMail, MMS, Store tillsLogged in
Anonym
ous
Ever met a great salesman who suffered amnesia?
* What we have shown* To Who, when and why* In what context* The outcome
We need to recall:
Learning from the crowd:automating the personalisation process
Behavioural database”Collective consciousness”
Fingerprints from past users –clicks, searches, purchases
New user
Michael Jackson
Book
Personalized Search & Navigation
Intelligent Search
Incremental search
Search Spellingcorrections
Auto-complete
Did you mean?
Implicitsynonyms
Combines multiple inputs• Product catalogue search• Browsing history• Learning from the crowd• Purchase history
to achieve the most relevant result
Multiple languagesMultiple channels
Auto-complete
Top of list: match to most
popular products
Search chosen fields in catalogue
Filtering auto-complete
Show how many hits for
eachOnly show
most important of
the total matches
Pick the most popular
matches for ‘bruc...’
Implicit synonyms
Implicit synonyms: look at what users did after searching
Spell-tolerant recommendations
Recommendations allow for common spelling
mistakes
Dynamic Navigation
Personalised, dynamic navigation simplifies product selection
Refines search results• Help shoppers zero in on what they want• Highlight factors influencing buying decisions• Shoppers will never see ‘no results found’
More ways to browse • Encourage shoppers to linger• Opportunity for up-sell and cross-sell
Personalize by relevance for higher conversion • Rank relevant attributes higher• Include user ratings
Dynamic Navigation
Navigation on brand landing page
Context-sensitive filtering
Product
Product
Product
eSales search finds best match to what user is looking for in each category…
…optimises use of page real estate
Faceted search personalised
Search, navigation and layout optimised for maximum conversions based on relevance & crowd learning
Category-based recommendations
Customers who bought things in
this category bought...
User-driven recommendations
People who bought this bought that...
Attribute-based recommendations
Other products with similar attributes....
Recommendations
Product
Product
Product
& selects and positions most effective
Product
Product
Product
Product
Product
Product
eSales creates recommendations using pre-build & custom merchandising tactics
Content Targeting
eSales automatically tests and chooses content to maximise sales outcome
Image
Image
Image
Image
Image
Image
Controlling merchandising
• Drag and drop deployment merchandising panels simplifies change
• Easily guides personalisation – e.g. boost products based on stock level
Displays
51150Inspects
27997Commissions
10233Commissions / Displays
19%Inspects / Displays
67%Commissions / Inspects
37%
Displays
32156Inspects
18356Commissions
5467Commissions / Displays
17%Inspects / Displays
57%Commissions / Inspects
30%
• Continuous feedback on performance guides improvements
Understanding performance
CombinedBehavioralSales
Text Match
Boosting relevancy from 20% to 80%
CombinedBehavioralSales
Text Match
Avoiding cold starts by combining technologies
Proven approach
By year-end 2013 over 30% of the 100 most popular websites will use search technology or content analytics to target content at users.
• 62% consumers find recommendations useful
• 15% admit to purchasing when they see recommendations
• “Retailers told us … that between 2% and 20% of their revenue could be attributed to recommendations”
Börge Olsen, Sales Manager
Personalised promotions double retention rates to 16%
CDON – the Amazon of the nordics
Challenges:• Slow search response times• Unstable IT environment and service
outages @ peak load• Irrelevant products shown to buyers
Results with Apptus eSales:• Lightening fast response time regardless of
load • 99.97% uptime and excellent reliability
during peak hours• Record sales in 2010 thanks to relevant
products exposed to the buyers in different contexts
• 10 million products
• 4.2 million searches/day; peak 2,000 searches/sec
• 5 million attribute updates/day
Reference case: CDON
Mikael Olander, CEO