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Deep Learning for online Retail

@daniel_gebler@picnic

Our Value Proposition

Groceries

+

Mobile HomeOn-TimeLowest 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

Challenges

30 Articles in 3 Minutes

The Mobile Shopping Challenge

Our Shopping Time Journey

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Aug-16 Sep-16 Oct-16 Nov-16 Dec-16 Jan-17 Feb-17 Mar-17 Apr-17 May-17

Sess

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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

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%

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Pre

cisi

on

Number of orders

Big data(lack of depth)

Big & Deep data Deep data(lack of breadth)

Each week

100s of products

1,000s of suggestions

10,000s of interactions

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

Artificial Neural Network classifier Leading to ~75% correct categorization

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

The Interface Challenge

Everything

Everytime

Everywhere

Convenient

1. Dream Big, Act small

2. Mission first, Data as support

3. Data Science first, AI second

4. Launch first, Scale second

5. Great products come from small teams

Learnings from a disruptive Scale-up

Creating a mobile super service

@picnic

@daniel_gebler