An efficient framework for Send Time Optimization was proposed in this presentation by profiling and segmentation of recipients. Also, machine learning approaches were used to predict and analyze the recipients' behaviors based on the available contents by considering any prior knowledge about the recipients.

2. Recommendation SystemRecommendation System
Recipient’s Interaction Data in
Target Business (RIDTB)
Recipient’s Interaction Data in
Other Businesses (RIDOB)
Recipient’s User Data in Target
Business (RUDTB)
Prior Knowledge in Target
Business (PKTB)
RIDTB: Recipient’s historical interaction data in current business like recipient’s open date time, click date time and so on
RIDOB: Recipient’s historical interaction data in other business (if available) like recipient’s open date time, click date time and so on
RUDTB: Recipient’s user data in current business like recipient’s age, sex, favorites, points, and so on
RCDTB: Recipient’s content data in current business like title and content of emails/messages opened/clicked by recipients
PKTB: Prior side information available regarding to optimal email marketing such as optimal send day and time
Recipient’s Content Data in
Target Business (RCDTB)
Optimal Send Channel
Per Active Recipient
Optimal Send Time
Per Active Recipient
1
User Active/Inactive
Status? Y/N
Is User Interested in
Message Content? Y/N

3. Recipients Profiling: Construct interaction and content profile
vector per recipient based on his historical interaction and content
data
Recipients Segmentation: Segment recipients into different clusters
based on their historical interaction, content and user data and
predict for each recipient an interaction profile vector
Recipients Prediction: Train a prediction model to predict
interaction profile vector for each recipient based on all users data
available
Recipients Content-based Interest Analysis: Analyze the interest of
recipients into content. For each recipient, this module generates a
content interest vector based on all users data.
Recommendation System: Get the optimal time and
channel for each recipient to send message.
Prior Knowledge Analysis: Analyze the prior knowledge on send
time optimization. Any prior knowledge from research, best
practices, articles, and so on is welcome.
ProfilingProfiling
RIDTB
RCDTB
SegmentationSegmentation
Recipient Predicted Interaction Profile Vector (RPIPV-1)
RUDTB
RIDOB
PredictionPrediction
Recipient Predicted Interaction Profile Vector (RAIPV-2)
Recipient Historical Content Profile Vector (RHCPV)
RUDTB
Content-based
Interest Analysis
Content-based
Interest Analysis
RUDTB
RCDTB
Recipient Content Interest Profile Vector (RCIPV)
Recommendation
System
Recommendation
System
Recipient Historical Interaction Profile Vector (RHIPV)
Ranking of optimal time per user
Prior Knowledge
Analysis
Prior Knowledge
Analysis
PKTB
Prior Knowledge Send Time Vector (PKSTV)
Does recipient like the next email content? Yes/No
1-1
1-2
1-3
1-4
1-5
1-6
Optimal channel per user

4. Recipients Profiling: Construct interaction and content profile vector per recipient based on his historical interaction and content data
1-1-1: Extraction of
recipients historical
interaction profile vectors
RIDTB
RCDTB
RIDOB
Recipient Historical Interaction Profile Vector (RHIPV)
1-1-6: Extraction of
recipient historical content
profile vectors
1-1-2: Applying the effect of
time to the extracted vectors
Recent interactions of recipients should be respected more
1-1-4: Weighted
combination of the
extracted vectors
1-1-3: Smoothing the
extracted vectors
1-1-7: Applying the effect of
time to the extracted vectors
Recent interactions of recipients should be respected more
1-1-5: Thresholding and
click sprees (click
sessions) detection
Recipient Historical Interaction Profile Mask (RHIPM)
Recipient Historical Content Profile Vector (RHCPV)1-1-9: Weighted
combination of the
extracted vectors
1-1-8: Smoothing the
extracted vectors
1-1-10: Thresholding
and content profile
mask generation
Recipient Historical Content Profile Mask (RHCPM)

5. Recipients Segmentation: Segment recipients into different clusters based on their historical interaction, content and user data and predict for each recipient an
predicted interaction profile vector
1-2-1: Recipients clustering based on
their hourly, daily and weekly
interaction and content patterns
RHIPV
RHCPV
1-2-2: Clusters
representatives extraction
RUDTB 1-2-4: Recipients clustering based on
their user data
1-2-5: Clusters
representatives extraction
1-2-3: Finding recipients similar
to the current recipient
Current recipient user data
Recipient Predicted Interaction Profile Vector (RPIPV-1)
1-2-6: Thresholding and
interaction profile mask
generation
Recipient Predicted Interaction Profile Mask (RPIPM-1)

6. Recipients Prediction: Train a prediction model to predict interaction profile vector for each recipient based on all users data available
RUDTB
1-3-1: A prediction model that predicts an interaction vector
based on user data for each recipient.
Current recipient user data
Recipient Predicted Interaction Profile Vector (RPIPV-2)
1-3-2: Thresholding and interaction
profile mask generation
Recipient Predicted Interaction Profile Mask (RPIPM-2)

7. Recipients Content-based Interest Analysis: Analyze the interest of recipients into content. For each recipient, this module generates a content interest vector based on
all users data.
1-4-1: Regression model between
recipients user data and contents (or
topics)
1-4-3: Comparison of the
predicted and next message
content models
RUDTB
1-4-5: Decision making based on the
recipient’s interest into the next
message
Are similar? Yes/No
Current recipient user data
Does the recipient like next message content? Yes/No
RCDTB
Next message content data
1-4-4: A classifier that classifies each user into interested or
not-interested user based on his user and content data
(Interested or not-interested in next message content)
Interested? Yes/No
Current recipient user data
Next message content data
Recipient Content Interest Profile Vector (RCIPV)
1-4-2: Next message content vector
model
RUDTB

8. Prior Knowledge Analysis: Analyze the prior knowledge about the optimal send time. Any prior knowledge from research, best practices,
articles, and so on is welcome (e.g. )
1-5-1 : Prior knowledge that explicitly has
reported about the optimal send time for
recipients
1-5-3: Combination
PKTB
Prior Knowledge Send Time Vector (PKSTV)
1-5-2: Statistical analysis of existing prior
knowledge about email marketing

9. Recommendation System: Get the optimal time and channel for each recipient to send message.
Ranking of optimal time per user
Recipient Predicted Interaction Profile Vector (RPIPV-1)
Recipient Predicted Interaction Profile Vector (RAIPV-2)
Recipient Content Interest Profile Vector (RCIPV)
1-6-1: Ensemble model for profile vectors combination. This
modeling is done for every channels and the optimal
channels is based on the likelihood.
Recipient Historical Interaction Profile Vector (RHIPV)
Recipient Historical Content Profile Vector (RHCPV)
Does recipient like the next email content? Yes/No
Prior Knowledge Send Time Vector (PKSTV)
1-1
1-1
1-2
1-3
1-4
1-4
1-5
1-6-2: Active/Inactive
recipients classification
Active user? Yes/No
Optimal channel per user