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Copyright 2000 Sam Tutterow and NetPerceptions
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Collaborative Filtering and Net PerceptionsNovember 15, 2000
The Darden School
Sam Tutterow JD/MBA ‘99
The Darden School
Agenda
How CF works
Where it works (and doesn’t)
Some results
The privacy issue
A quick history of Net Perceptions
Getting a job in high tech marketing
Q&A throughout
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The Darden School
How collaborative filtering works
How it works
Mathematically
Philosophically (marketing)
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The ratings databaseP1 P2 P3 P4 P5 P6 P7 P8 P9 P10 …
C1 1 0 0 0 1 0 0 0 1 0 …C2 0 0 0 1 0 0 1 0 0 0 …C3 0 1 0 1 0 0 1 0 0 1 …C4 1 0 0 0 0 1 0 0 1 0 …C5 0 0 1 0 0 0 0 0 0 1 …C6 0 1 0 0 0 1 0 0 0 0 …C7 0 1 0 1 1 0 1 0 0 1 …C8 0 0 0 0 0 1 0 0 1 0 …C9 0 1 0 1 1 0 1 0 0 1 …C10 1 0 0 0 1 0 0 0 0 0 …… … … … … … … … … … … …
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A customer comes to the web siteP1 P2 P3 P4 P5 P6 P7 P8 P9 P10 …
C1 1 0 0 0 1 0 0 0 1 0 …C2 0 0 0 1 0 0 1 0 0 0 …C3 0 1 0 1 0 0 1 0 0 1 …C4 1 0 0 0 0 1 0 0 1 0 …C5 0 0 1 0 0 0 0 0 0 1 …C6 0 1 0 0 0 1 0 0 0 0 …C7 0 1 0 1 1 0 1 0 0 1 …C8 0 0 0 0 0 1 0 0 1 0 …C9 0 1 0 1 1 0 1 0 0 1 …C10 1 0 0 0 1 0 0 0 0 0 …… … … … … … … … … … … …
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Then we find their “soul mates” in real-timeP1 P2 P3 P4 P5 P6 P7 P8 P9 P10 …
C1 1 0 0 0 1 0 0 0 1 0 …C2 0 0 0 1 0 0 1 0 0 0 …C3 0 1 0 1 0 0 1 0 0 1 …C4 1 0 0 0 0 1 0 0 1 0 …C5 0 0 1 0 0 0 0 0 0 1 …C6 0 1 0 0 0 1 0 0 0 0 …C7 0 1 0 1 1 0 1 0 0 1 …C8 0 0 0 0 0 1 0 0 1 0 …C9 0 1 0 1 1 0 1 0 0 1 …C10 1 0 0 0 1 0 0 0 0 0 …… … … … … … … … … … … …
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The soul mates determine the best offerP1 P2 P3 P4 P5 P6 P7 P8 P9 P10 …
C1 1 0 0 0 1 0 0 0 1 0 …C2 0 0 0 1 0 0 1 0 0 0 …C3 0 1 0 1 0 0 1 0 0 1 …C4 1 0 0 0 0 1 0 0 1 0 …C5 0 0 1 0 0 0 0 0 0 1 …C6 0 1 0 0 0 1 0 0 0 0 …C7 0 1 0 1 1 0 1 0 0 1 …C8 0 0 0 0 0 1 0 0 1 0 …C9 0 1 0 1 1 0 1 0 0 1 …C10 1 0 0 0 1 0 0 0 0 0 …… … … … … … … … … … … …
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How collaborative filtering works
How it works
Mathematically
Philosophically (marketing)
Key characteristics of CF
Focus is on individuals, not on segments
Learns individual preferences in real-time
An adaptive system that gets smarter with every use
Viewed as a customer benefit, not an ad
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Business problems CF helps solve
Customer objectives
• Getting the first purchase
• Increasing average transaction sizes
• Getting more profit out of the customer base
Product objectives
• Moving overstock inventory
• Promoting high margin items
• Launching new items effectively
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Where it works - on the web site
Seven key applications
1. Home page
2. Product detail page
3. Shopping cart cross-sells
4. Check-out page
5. Recommendation center
6. Product category sorting
7. Search results sorting
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Where it works - on the web site
Seven key applications of NPEC
• Home page
• Product detail page
• Shopping cart cross-sells
• Check-out page
• Recommendation center
• Product category sorting
• Search results sorting
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The Darden School
Where it works - on the web site
Seven key applications of NPEC
• Home page
• Product detail page
• Shopping cart cross-sells
• Check-out page
• Recommendation center
• Product category sorting
• Search results sorting
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Results - on the web site
“The click-through is twice as high as anything we get without the software. It has twice the sales impact, too.”
Jim LaBelleVP Marketing, Wine.com1to1, November 2000
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Other places where CF works
In the call center
Through email campaigns
Other customer touch points such as cell phones, PDAs, kiosks, POS systems
“walk-talk-click-beep”
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* Converted from British Pounds
With Net With traditional Perceptions’
cross-sell realtime methods recommendations
Ave. Cross-Sell Value $19.50* 60%higher
Cross Sell Success Rate 9.8% 50%higher
Results - in the call center
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Results - through email
Click-throughResponse Rate 10% 19.1% 2X
Purchase Rate 0.25% 2.7% 10X
Industry NETP versusAverage* Delivered Industry
* Forrester Research
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Some of our personalization customers
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Where CF works best
Best environment• Lots of products, lots of customers
• Where preferences are expressed and observable through customer behavior (not a “spec’d” purchase)
• Where customers are uniquely identifiable
Best applications• Retail commerce
• Knowledge management
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The knowledge management applicationP1 P2 P3 P4 P5 P6 P7 P8 P9 P10 …
C1 1 0 0 0 1 0 0 0 1 0 …C2 0 0 0 1 0 0 1 0 0 0 …C3 0 1 0 1 0 0 1 0 0 1 …C4 1 0 0 0 0 1 0 0 1 0 …C5 0 0 1 0 0 0 0 0 0 1 …C6 0 1 0 0 0 1 0 0 0 0 …C7 0 1 0 1 1 0 1 0 0 1 …C8 0 0 0 0 0 1 0 0 1 0 …C9 0 1 0 1 1 0 1 0 0 1 …C10 1 0 0 0 1 0 0 0 0 0 …… … … … … … … … … … … …
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The privacy issue
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The privacy issue
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The privacy issue
• Lots of media coverage over the past year
– But coverage has slowed recently
• Privacy issue lies on a spectrum, not in absolutes1. Type of info tracked (anon clicks, mailing address, personalfinancials?)
2. How I consent (opt in immediately, opt out possible, opt out not honored?)
3. Where the info will be used (today, this site only, sold to others?)
4. Value I receive (great experience, save some time, info used to harm me?)
Pause forQuestions and Answers
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Agenda
How CF works
Where it works (and doesn’t)
Some results
The privacy issue
A quick history of Net Perceptions
Getting a job in high tech marketing
Q&A throughout
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Net Perceptions (A)
“Establishing credibility”
Key events
• Amazon as first customer on the web (pre IPO)
• IPO (4/23/99)
• GUS results demonstrate value of the technology in call center (9/20/99)
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The Darden School
Net Perceptions (B)
“Catching the wave”
Key events
• Significant customer, partner, and industry leader recognition (November 99)
• ABC and NBC profile the company (December 99)
• Acquire KD1 analytics company (January 99) -
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Net Perceptions (C)
“The dot-com collapse”
Key events
• Secondary offering raises over $100 million (3/24/00)
• Dot-com market collapses (early April 2000)
• Miss Q3 revenue and earnings targets (9/28/00)
• Lay off 17% of work force (10/19/00)
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Where we are today - markets and competitionMarkets
• Multi-channel retailers
• Fortune 1000 (knowledge management)
Closest competitors in retail
• Analytical CRM (Epiphany, Broadbase, Blue Martini)
• Retail-specific ERP (JDA Software, Retek)
• “Build it in-house”
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Where we are today - products
Hosted APPS
Software AdvertisingAdvisor
E-commerceAnalyst
RetailAnalyst E-commerce Marketing
Campaigns Call Centers
CoreTechnology
Business Intelligence Personalization“ P e r s o n a l i z a t i o n M a n a g e r ”
Business Intelligence Engine
Knowledge Discovery Engine - Recommendation Engine
AdvertisingAdvisor
E-commerceAnalyst
RetailAnalyst
*Powered by Angara
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Business intelligence customers
Hudson’s Bay
Questions and Answers
Discussion: Job hunting in high tech
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