Picnic Big Data Expo
-
Upload
bigdataexpo -
Category
Data & Analytics
-
view
297 -
download
0
Transcript of Picnic Big Data Expo
@daniel_gebler
@picnic
How Picnic became the Supermarket in your Pocket
Our Value Proposition
Groceries
+
Mobile Home On-Time Lowest Price
+ + + =
Our Mobile Shopping Ecosystem
10s of cities
100s of suppliers
1,000s of local products
10,000s of global products
100,000s of customers
3 Challenges
30 Articles in 3 Minutes
The Mobile Shopping Challenge
The Mobile Shopping Solution - Bulk recommendations
• Set of 4, 8 or 12 articles
• Buy all with a single tap
• 1-click shopping for half of your basket
• Purchase confidence >90%
• Covering repetitive & boring items
Challenges of bulk recommendations
Precision challenge
• 90% single item precision 28% precision for 12 items
• 90% precision for 12 items 99% single item precision
• Factor 10 better recommendations required!
Item challenge
• Seasonal availability
• Seasonal variations
• Event-based buying patterns
Our Shopping Time Journey
0
50
100
150
200
250
300
350
400
Aug-16 Sep-16 Oct-16 Nov-16 Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 May-17
Sess
ion
tim
e (i
n s
eco
nd
s)
2
1
3
Formalization of the Challenge
PR(next= |hist=(( ,t-4),( ,t-7),( ,t-9)))
Input Hidden Layers Output
Monday(wk -2)
Friday(wk -2)
Wednesday(wk -1)
Tomorrow
Solution 1: Deep Recurrent Neural Network (LSTM)
• Item likelihood to buy• Cat likelihood to buy• Next 7 days• Item/cat buying interval
• Order history (articles, dates) • Normalized quantities• Days between orders
x 1
x 2
x 0
x 3
x 0
x 1
x 2
x 2
x 0
x2
x 1
x 1
Item - Item relations
Item - Day relations
Itemset - Day relations
y2
x1
x2
x3 y3
y1
z2
z3
z1
50%
Shallow data
Small training set
Pre-Training
Solution 2: RFM-based order prediction
Get last 10 orders
Select top items (80% orders)
Rank by [freq, qty]
Display best items(min 4, max 12)
…
| | |
Filter byseasonality
… …
… …
… …
Result: Big and Deep data for optimal RFM prediction parameters
65%
70%
75%
80%
85%
90%
0 20 40 60 80 100 120 140
Pre
cisi
on
Number of orders
Big data(lack of depth)
Big & Deep data Deep data(lack of breadth)
BIG DATAInsights from scale of collected data points (large sample)
Insights from depth of stories (small sample)
DEEP DATA
DEPTH OF INSIGHTS
N
Summary: Deep Learning requires both Big Data and Deep Data
1000s of suggestions each week
The Co-Creation Challenge
Step 1: Create Visibility, Encourage Accountability, Celebrate Success
Step 2: ML-based classification of product suggestions
Customer input (free text)
Picnic Retail Platform(storage)
Force.com(processing & analytics)
Zendesk(Customer feedback)
Picnic Retail Platform(status update)
Azure ML(NeuralNet classification)
Result: Auto-classification 3 out of 4 suggestions
Insufficient training data
Max 91% accuracy
Data Science is the MVP for AI Products
The Distribution Challenge
99% on-time delivery
1% no show
5-star rating
Formalization of the Challenge
Tdrop = Carea + C1 + Cambient|chilled·Nambient|chilled + Cfrozen·Nfrozen + Tdelta
Fitness Measure
RMSE =σ𝑖=1𝑛 ∆𝑇
𝑑𝑟𝑜𝑝(𝑖)2
𝑛
Adjusted drop time deviation• Asymmetry (early vs. late)• Error filtering
Normalization• Time of day• Driver• Vehicle
Calibration process▪ Daily update (params)▪ Weekly review (params)▪ Monthly review (model)▪ Granularity (PC6 vs. address)
Parametrization▪ Init by defaults (avg. optima)▪ Household size▪ Proximity House vs. Street▪ City Maturity▪ Hub Maturity▪ Runner Maturity
Result: 50% improvement after 10 drops, oscillating convergence
-10
-5
0
5
10
20 40 60 80
Dro
p T
ime
dev
iati
on
(in
sec
on
ds)
Drop time deviation
Moving average
Creating a mobile super service
@picnic
@daniel_gebler