The document discusses how machine learning can be used to drive revenue along the shopper journey by providing personalized product recommendations within 150ms. It describes how the company was founded by former YouTube engineers and uses proprietary real-time ML models and scalable cloud architecture to optimize recommendations for both media and ecommerce. Key aspects of their approach include true real-time ML, easy catalog integration, and handling high volumes of products and users to generate over 200 billion recommendations per month at massive scale.

1. Now Moments
Using Machine Learning to Drive
Revenue along the Shopper Journey

2. We drive 10-20% Revenue lift with our proprietary
ML-driven Product Recommendations
Who We Are
Founded by team members who designed YouTube’s recommendation service,
LiftIgiter uses proprietary real-time ML models combined with extremely
scalable cloud-based global architecture to personalize each product
recommendation set in milliseconds.
• True Real-Time Machine Learning (<150ms response)
• Optimized for both media and eCommerce recommendations
• Easy set up– up in less than one week
• Catalog “scraping” for easy use
Our Tech
Building In-house is difficult because
• High volume of products to display to users
• User attention span: < 6 seconds
• 150M MAU
• 200B Reco’s/month
Scale and Power

3. What Makes Us Happy (Shoppers)
“I trust this
brand”
“I feel this brand
understands
me”
“I learn
something new
when I visit this
site”
“I can’t stop
browsing
through this site”
“I feel this brand
is ethical”
“I always seems
to find cool
things to add to
my shopping
cart at checkout”
“I feel good
when I purchase
from this brand”
“I discover
unique things to
buy that I like”
“Their emails
seem to know
exactly what I
would like to
buy”
“Their outreach to
me is always
welcomed and
relevant– not
creepy and
invasive”

4. We are all trying to get inside of here

5. Let’s start with some Behavioral
Science…
The WHY
SAVE TIMESAVE MONEY
NEED WANT
FOMO
HABIT ALTRUISM
INTEREST

6. Next, let’s look at how people find what
they want…
The How
DiscoveryDirected

7. Omotenashi: Can we learn from a 2000
year old tradition?

8. Discovery GOAL: Creating ‘Now Moments’

9. Point #1: Now Moments Need to be Now
Viewed
Scroll
activity
Clicked
Dwell
Time
Add to
Cart/Saved
Purchased
Remove
d from
Cart
Not
Clicked
Paired
Items
Hover
activity
Sequencin
g
1st Party Data Only

10. Point #2: Keep them out of an Echo
Chamber
Closest
pattern
match
Slight
color
variant
Closest
color
match
Slight
pattern
variant
Visual
style
match
Slight
color
variant
Visual
style
match
Large
color
variant
Style
variant
Color
Variant
Closest
match
1 Deg
Freedom
2 Deg
Freedom
1 Deg
Freedom
2 Deg
Freedom

11. Point #2B: A Fundamental ML Concept
EXPLOIT EXPLORE

12. Digging a bit deeper
EXPLOIT EXPLORE
NEED
PLANNED
PRACTICAL
MULTI-SITE
PRICE SENSITVITY
SPONTANEOUS
DISCRETIONARY
EMOTIONAL
TRUSTED SITE
MORE PRICE
INSENSITIVE

13. Key Takeaway: Now Moments Require
ML
Continuous Model
Recalc & Deploy
Trust the machine–
but A/B Experiments
Optimize Exploit vs.
Explore
Feed insights to rest
of Marketing Stack
Behavioral, not
Demographic
Real Time… Really
Real Time
Give an Omotenashi
experience
Consistent, Omni-
present Experience