@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