using self-service analytics@ zulily to grow 1.5m customers

in one year

#TC18

Sasha BartashnikMarketing Analytics Manager

zulily

Sasha BartashnikManager, Marketing Analytics

@zulily

introduction

How we achieved 1.5m in customer growth

Our approach to marketing optimization

What’s next in marketing analytics

Q&A

agenda

zulily: a place for fun shopping

“

”

The fun of browsing is new ideas on what to buy. I am constantly getting inspired and updating. I

get a real endorphin rush when a new idea gets ignited in me.

routine(regular) shopping

funshopping

we launch

100 new sales every daytypically for 72 hours

1Based on customers that made purchases during 2017 calendar year. 2Based on orders placed from January to December 2017 by customers who previously purchased from zulily.

71%of orders placed on mobile

devices1

zulily today

A typical Costco has 4,500 SKUs;

we launch more than 9,000 product styles daily with minimal inventory

91%of orders placed by repeat

customers2

launch

hundredsof ads

serve

millionsof impressions

convert

tens of thousands

of customers

a day in the life of zulily marketing…

1An active customer is defined as an individual who has purchased at least once in the last twelve months, measured from the last date of a period. Active customers are in millions. All data as of Q2 2018.

6.4M Active Customers1

31% increase in active customers

13% increase in revenue

enabling growth

-

1

2

3

4

5

6

7

Q12014 Q22014 Q32014 Q42014 Q12015 Q22015 Q32015 Q42015 Q12016 Q22016 Q32016 Q42016 Q12017 Q22017 Q32017 Q42017 Q12018 Q22018

Act

ive

Cu

sto

mer

s (m

illio

ns)

Active Customers

self-service analytics

how we achieved growth?

Analytics teamcan move fast

without needingto involve IT

in everyday activities.

Business users get real-time access to key data

without needing to involve analysts

to generate basic insights.

marketing analytics team growth

BQ migration

develop self-service toolset

separate reporting from analytics

Create data science function

building the

Tableau + BigQuerypipeline

data teampushes all data —structured and unstructured,real-time and batch— into BigQuery

marketing analysts & data scientists join multiple data sources using BigQuery’s SQL

analysts develop models on BigQuery data marts using a variety of common data science toolsas well as internalETL platforms

marketers and analysts use Tableau for self-service analyticson data andmodel results stored in BigQuery

zulily Tableau + BigQuery pipeline

key centralized data in Tableau

Last Fiscal Week Efficiency

###

LTVLTV

######

Last Week Stats

Activation RateApp InstallsApp VisitsBounce RateConversion RateEmail ClicksEmail OpensOrders 0dOrders 14dOrder 365dRevenue 0dRevenue 14dRevenue 365dTotal CostVisits

2/5/2018

All data shown is masked to protect proprietary information.

All data shown is masked to protect proprietary information.

intra-day self-service insights

All data shown is masked to protect proprietary information.

holistic view of cross-company metrics

Revenue

Active customers

Brands on site

Order delivery

Cancellations

our approach to marketing optimization

1customer

acquisition

2lifecycle

management

3measuring the

brand

4monitoring

marketing analytics program components

add customer segmentation information

determine visit source

join to multiple traffic sources

collect raw data

foundational data layer with one view1 customer acquisition

use historical dataproduce variables using data

science model predict value days after

acquisition

1 customer acquisition

find existing highvalue customers

Which behaviors distinguish them from others?

Are new customersshowing these behaviors?

To what extent?

understand value of a new customer

What she uses to access zulily

What her first purchase was like

How she engages with us

Where she is located

How she found us

what goes into the zulily predictive model?

thousandsof variables considered

forthe model

hundredsof models tested

dozensof variables

chosen using gradient boosting machine

learning

+85%accuracy in predicting 1

year revenue

1 customer acquisition

$ $$$

1 customer acquisition

optimize new customer acquisition

a/b testing:

creative

timing

landing experience

paid vs unpaid

DR vs brand

shift budget & bids

lift by likelihood to be high value

mo

de

l sco

re

lift (%) in chance of purchase

lean in to these segments for offer

targeting

2 lifecycle management

targeted messaging

Favorite brands

Site and App Engagement

Purchase History

Email Behavior

Love this feature!

Text and Customer Service

using personalization to optimize2 lifecycle management

optimization based on response2 lifecycle management

All data shown is masked to protect proprietary information.

Email Performance Metrics YoY

Sen

d C

ou

nt

Op

en R

ate

Clic

k R

ate

Dem

and

/

Sen

d

Purchase/Churn Propensity Modeling

Attribution Model

Lookalike Modeling

Incrementality

Customer Level Profitability Modeling

Engagement Modeling

building the right models & tools2 lifecycle management

3 brands & partners

how we track the brand

TV response

NPS

customer surveys & VOC

All data shown is masked to protect proprietary information.

4 monitoring

monitoring data science models

Variable X Bin

All data shown is masked to protect proprietary information.

4 monitoring

monitoring inputs to data science models

what’s nextin marketing analytics

the future of zulily… and the industry

Increased integration between marketers

and data scientist– the rise of marketing engineers ex: advanced attribution

Using self-servicephilosophy to bemore efficient,

do “morewith less”

A renewed focus on sustainable growth,

long-term customer relationships

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thank you!any questions?

@zulilymedia@zulily.com