Bionic Bookselling Nathan Maharaj

Sr. Director, Merchandising        

Summer 2016

The Far Side® and the Larson® signature are registered trademarks of FarWorks, Inc. 

Copyright © 2000, 2007 FarWorks, Inc. All Rights Reserved. 

</debate> ?

<discussion>

How we got to now

Let’s learn about machine learning!

Why?

Segmentation

ü  Identify Promotions that skew strongly to a category ü  Identify Readers who have purchased books from that category ü  Distribute the promotion to only those readers

Ø  Lots of readers’ purchases don’t fit neatly into a single category

Ø What if readers / publishers / booksellers don’t agree on which categories a book belongs to?

Ø  Lots of readers aren’t expressing a genre preference in their purchases (i.e. the Hunger Games problem)

Segmentation shortcomings

Enter: Science!

Collaborative filtering

https://www.slideshare.net/erikbern

Affinity Score

# of people with books X and Y # of people with either X or Y

0.7  

0.6  

Books  you  have  

Books  other  people  have   Sorted  

0.1      

0.9  

1.5      0.7      0.1      0      

Sorted. For you.

1. 2. 3. 5x106.

Focus on readers & books

About last summer…

0.000000

0.500000

1.000000

1.500000

2.000000

2.500000

3.000000

3.500000

0 10 20 30 40 50 60

Affinity Score

0.000000

0.500000

1.000000

1.500000

2.000000

2.500000

3.000000

3.500000

4.000000

0 10 20 30 40 50 60

Affinity Score

Who is this for? Who is this for?

Targeting Personalization

Focus on readers reading

Targeting: Romance

More targeting…

We’re obsessed with relevance

https://www.flickr.com/photos/10295270@N05/

Personalization Challenge: New Releases

•  No actual sales •  Some pre-orders •  Pre-orders unevenly distributed among categories •  No API for stuff merchandisers have learned from sales reps •  Offer personalized sorting on Day 0

Small data

Thanks!

Questions?