sales volume forecasting using recurrent neural networks · sales volume forecasting using...
TRANSCRIPT
Sales Volume Forecasting Using
Recurrent Neural Networks
Z w i c k T a n g
A l e x L i a n g
A l l i s o n R o g e r s
K e v i n P e d d e
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Overviewof Red Ventures.
H I S T O R Y
B Y T H E N U M B E R S
3,500+ Employees
Locations
• USA - 13 Locations
• Brazil - Sao Paulo
• United Kingdom - London
1 Culture
Founded as Red F in 2000
Red Ventures launched in 2004
General Atlantic & SilverLake minority strategic investors.
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Partners
RV’s Business Model
Consumer Platforms
The digital companion that help consumers to make purchasing decisions on home, financial and healthcare services
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370MM unique visits and 6.1MM calls per year; 50TB of data; 1300+ cloud servers
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RY Sales Call Volume Forecasting at RV
Time HorizonSeconds Minutes Hours Days Weeks Months
H i r i n gS t a f f i n g /
S c h e d u l i n g
F r o n t - e n d M a r k i n g E f f o r t
S a l e s C a l l F o r e c a s t i n g
F i n a n c i a l P l a n n i n g
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RY Sales Call Volume Forecasting at RV
Time HorizonSeconds Minutes Hours Days Weeks Months
H i r i n gS t a f f i n g /
S c h e d u l i n g
F r o n t - e n d M a r k i n g E f f o r t
S a l e s C a l l F o r e c a s t i n g
Other external factors• Seasonality• Holidays• Social events• Business specific
events (e.g. offer changes)
F i n a n c i a l P l a n n i n g
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RY How it was done in the past at RV
• Simple linear models + business logics
• Built and maintained by each business team with dedicated resources
• Labor intensive, not utilizing all the information available
• 10+% prediction errors in daily sales volume forecasting, not ideal for staffing and scheduling
𝒳calls
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RY How it is done at RV today
• Automated – can adjust itself to changes in the business and market
• Standardized – can be easily repurposed from one business to another
• Flexible – can take various inputs in addition to the time series history
• Adequate – more accurate and predictable performance for intra-day staffing and scheduling
𝒳callsLong Short-Term Memory Networks
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RY Why Recurrent Neural Networks
Source: Anton Milan, AAAI, 2017
Multi-object Tracking
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Automatic Text Generation
Source: Andrej Karpathy, Stanford Univ.
Source: Anton Milan, AAAI, 2017
Multi-object Tracking
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Automatic Text Generation
Source: Andrej Karpathy, Stanford Univ.
Source: Anton Milan, AAAI, 2017
Multi-object Tracking
Financial Forecasting
Source: Ravichandiran, YouTube
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RY Background – Artificial Neural Networks
Input
Output
Hidden Layer
Weights
Standard ANN
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RY Background – Artificial Neural Networks
Summation
Activation
Input
Output
Hidden Layer
Weights
Standard ANN
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RY Background – Artificial Neural Networks
Summation
Activation
Input
Output
Hidden Layer
Weights
Activation FunctionsStandard ANN
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RY Background – Recurrent Neural Networks
Input
Output
Hidden Layer
Weights
H
X
Y
W
Standard ANN
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RY Background – Recurrent Neural Networks
Input
Output
Hidden Layer
Weights
H
X
Y
W
H
X
Y
W
Standard ANN RNN
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RY Background – Recurrent Neural Networks
Input
Output
Hidden Layer
Weights
H
X
Y
W
H
X( t - 1 )
Y ( t - 1 )
W
H
X( t )
Y ( t )
W
H
X( t+1 )
Y ( t + 1 )
W…
…
…
…
H
X
Y
W
Standard ANN RNN RNN unfolded
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RY Time Series Forecasting us RNN
H
X( t - t ba ck)
W
H
X( t )
X ( t + 1 )
W
H
X( t+1 )
X ( t + 2 )
W…
…
…
…
H
X( t+ t ahead)
W
…
…
Prediction Target
Historical Data
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RY Time Series Forecasting using RNN - Issues
H
X( t - t ba c k)
W
H
X( t )
X( t +1 )
W
H
X( t +1 )
X( t +2 )
W…
…
…
…
H
X( t + t a he a d)
W
…
…
Prediction Target
Historical Data
Diminishing or Exploding Gradient
M o d e l b u i l t t h r o u g h b a c k p r o p a g a t i o n
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RY Time Series Forecasting using RNN - LSTM
L S T M
X( t - t ba ck)
W
L S T M
X( t )
X ( t + 1 )
W
L S T M
X( t+1 )
X ( t + 2 )
W…
…
…
…
L S T M
X( t+ t ahead)
W
…
…
Prediction Target
Historical Data
LSTM: Understanding Long Short-Term Memory Networks by Colah
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RY Results
Methods Forecasting Error
RNN-LSTM 6.4% ± 5.2%
Baseline 11.2% ± 5.3%
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RY Results
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RY Results
• Being rolled out to 7 different businesses with RV
• Free up business analyst resources
• Improve sales resource utilization
• Reduce call abandon rate
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