Andrey Shapovalov: Didžiųjų duomenų panaudojimas rinkodarai

ADVERTISING AND DATA HANDLING
FOR THE COMPANIES WITH LARGE
CLIENT’S DATABASES
March 16, 2018

Education:
1993 – Moscow State university, oceanology
2009 – ESMT, Germany, MBA, general management
Work:
2007-2008– Yandex, search engine, Moscow, Russia
2010-2011 – Luxoft, Moscow, Russia
2013-2014 – Modnique, e-commerce, Vilnius, Lithuania
2015-2016 – MEC, GroupM, Moscow, Russia, advertising agency
2017 – Faberlic, Moscow, Russia, e-commerce and MLM
Main speaker
Andrey Shapovalov, PhD, MBA

LINA YARYSH,
Owox BI
ANDREY TYSHENKO,
Dynamic Yield

«It is a capital mistake to theorize before one has data. Insensibly one begins
to twist facts to suit theories, instead of theories to suit facts»
– Arthut Conan Doyle, Sherlock Holmes
«More data means more information, but it also Means more false information»
– Nassim Nicholas Taleb, Antifragile: Things That Gain from Disorder
«Information is the oil of the 21st century,
and analytics is the combustion engine»
– Peter Sondergaard, Gartner Research

DATA PREPARATION
Get as much as possible
Merge into a single database
DATA USE
Advertising
CRM marketing
Usability
DATA MEASUREMENT
Smart attribution
Omnichannel
Econometrics
We are here

WORKSHOP SCHEDULE
10.00 - 10.20 - Introduction. Significance of the topic.
10.20 - 11.30 - How to get the most from existing data? Data availability.
11.30 - 11.45 - Coffee break
11.45 - 13.30 - Down the purchase funnel . Marketing activities with unified database.
13.30 - 14.30 - Lunch
14.30 - 15.45 - How to be perfect in e-com marketing activities?
An ideal ad split, smart attribution and the power of econometrics
15.45 - 16.00 - Coffee break
16.00 - 16.45 - Wrapping up
16.45 - 17.15 - Group discussion

PART ONE: HOW TO GET THE
MOST FROM THE EXISTING DATA.
DATA AVAILABILITY

ACQUISITION OF CONTACT INFO BEFORE REGISTRATION

ACQUISITION OF CONTACT INFO DURING
REGISTRATION OR CHECKOUT PROCESS

BASIC DATA FROM THE CLIENT
NAME /
SURNAME
EMAIL /
PHONE
NUMBER
BIRTHDAY SEX INTERESTS

z
Give a reward to a client:
Discounts for birthday data,
special occasional discounts for
specific categories, etc.
MORE DATA FROM
THE CLIENT >>
MORE PROFIT

Merging online
and offline data
online offline

Reality: CRM data and
online data are in
separate databases
Offline data
Online data

SOLUTION: MERGE DATA WITH ONE PARAMETER

200-300 000
daily users
1 GB
of online stats
LARGE SITE – NO POSSIBILITY FOR
EXCEL-LIKE DATA BASE

GOOGLE BIGQUERY – A FULLY-MANAGED DATA
ANALYTICS SERVICE IN THE CLOUD

Google Analytics free account has
sampled reporting and sometimes
produces skewed data
GA STANDARD GA 360 (PREMIUM)
FREE
Sampled reporting from
500 000 sessions
$150 000+ / year
No sampling up to
100 000 000 sessions

USER ID TO GOOGLE ANALYTICS
USER/CUSTOMER ID
CRM SITE
GOOGLE
ANALYTICS

TYPES OF USERS WHO VISIT THE SITE
CLIENTS,
REGISTERED
CLIENTS,
NON-REGISTERED
NON-CLIENTS
Any info
available
No info
Can be obtained
it there is a
reason to sign in
No info
To create a
purpose for
signing up

HOW TO USE UNIFIED DATA
SINGLE DATABASE - ONE
SOURCE OF DATA
ADVERTISING
SITE
PERSONALIZATION
CRM MARKETING

TECHNICAL SOLUTION FOR CRM AND ONLINE
DATA UNIFICATION
an official distributor of Google
solutions and Google partner
for Cloud Console
LINA YARYSH

PART TWO: HOW TO THE DATA FOR
EXISTING AND PERSPECTIVE CLIENTS

SUMMARY OF THE FIRST SECTION
01Data from
clients (as
much as
possible)
02User-ID into
the site and
GA
03Online data
streaming
into Google
Big Query
04CRM data
regular
download
into Google
BigQuery

GDPR
56% customers like to be
personalized

In Google and in social media
Existing
customers
Similar audiences
to existing
customers = look-
alike audiences
Video LAL audience
Email list LAL audience
Conversion-based LAL
audience
Page likes LAL audience
LOOK-ALIKE AUDIENCES IN FACEBOOK

LOOK-ALIKE AUDIENCES - CONVERSIONS

LOOK-ALIKE AUDIENCES – PAGE LIKES

CASUAL GAMING INDUSTRY
Search for
top-notch
audience
Email list of
top-buying
audience
What should
be the size?
1*
OR AND

OPTIONS: REMARKETING, SIMILAR AUDIENCES, CUSTOMER MATCH

GOOGLE SIMILAR AUDIENCES – SEARCH AND SHOPPING

GOOGLE SIMILAR AUDIENCES – CUSTOMER MATCH

UPLOADED AUDIENCES – MATCH RATE

GOOGLE CUTOMER MATCH – CONVERSION RATE

EMAIL MARKETING AND CUSTOMER MATCH SYNERGY
Online shoe
shop
Email
marketing
Open
Not open
“Reminder: Get
shoes from new
collection! Use
Code XYZ For An
Extra 10% Off!”
“Introducing new
collection of
summer shoes for
your everyday
activities.”

FOR CUSTOMER MATCH
AUDIENCES!
NOT LESS THAN
2000ADDRESSES

Programmatic advertising – percentage of display advertising

PROGRAMMATIC ADVERTISING – HOW IT WORKS
STEP 1
Someone Clicks on
the webpage
STEP 2
The publisher of the
page puts up the ad
impression for auction
STEP 3
The publisher
holds the
auction among
the advertisers
competing for
the impression
STEP 5
The ad is delivered to
the prospective
customer
STEP 4
The advertiser willing to
bid the most for the
impression wins the right
to display their ad
STEP 6
Customer clicks on the
ad and the advertiser
converts them into
a sale and profits

PROGRAMMATIC – FIRST-, SECOND- AND THIRD PARTY DATA

PROGRAMMATIC – ENTIRE ECOSYSTEM

PROGRAMMATIC – THE ECONOMIST EXAMPLE

CRM MARKETING – HOW IT STARTED. AMAZON CASE

CRM MARKETING – MAJOR STEPS IN THE DEVELOPMENT
ONE MESSAGE
TO ALL
1st
STEP
2st
STEP
“PEOPLE WHO BOUGHT
THIS, ALSO BOUGHT THAT”
PERSONALIZED
MESSAGE
3st
STEP

This is Ruta
Affluent young mom,
Homeowner
shops at a national clothing retailer online, in the store,
and occasionally via the app
CRM MARKETING – RUTA’S
EXAMPLE

RUTA’S TIMELINE
Online purchase
FIRST PURCHASE
• In search of kids’ toys
• Found a toy that was
offered by the site
based on her previous
buying experience
Three days after
MESSAGE ON
A NEW TOPIC
• The retailer sends Ruta
a health-themed email
• Ruta watches a video
about raising healthy
kids
One week later
PURCHASE OF A
NEW PRODUCT
• Ruta receives an iPhone
message with 15 percent
one-day discount on baby
food
• Ruta purchases a bag
with infant fruit smoothie
“People who bought this,
also bought that”
Personalized message

CRM MARKETING – RUTA’S NEW
EXPERIENCE
Before
Now
Increase in lifetime value

PERSONALIZATION ROADMAP
01 Unite all possible data on the customer
02 Create triggers for messages
03 Craft the offers
04 Deliver messages

TRIGGERS – STEP TWO
Buy of a particular item
Visit of a particular page
Particular geolocation
No buy for a particular
period of time but internet
activity (visits) to the site
Birthday
Buy in a particular category
No buy for a particular
period of time
Buy a several
days/weeks/months ago
A LOT OF

the art of
composing chains
of messages
CRM MARKETING

TRIGGERS NEED THE DATA = SEGMENTS
SEGMENTS
HANDCRAFTED MACHINE LEARNING

CRAFTING THE OFFERS – STEP THREE
MESSAGESIT 01
02
03
04
MARKETING
DIGITAL
PRODUCT

PROGRAMS FOR SENDING MESSAGES – STEP FOUR
EMAIL SMS PUSH

Site personalization
“If we have
4.5 million
customers,
we shouldn't
have one
store, we
should have
4.5 million
stores”
Jeff Bezos,
1998

SITE PERSONALIZATION
+19%
of revenue
+56%
of customers like their
names mentioned
DYNAMIC PERSONALIZATION
Site changes on
the fly
Generates individual
experiences at the site
Content that perfectly fits
user’s session

SITE PERSONALIZATION
FACTORS
CUSTOMER
JOURNEY
PSYCHOGRAPHIC
CONTEXT GEOLOCATION
DEMOGRAPHY
LEVEL OF
ENGAGEMENT
DEVISE

SITE PERSONALIZATION – MAJOR CUSTOMER’S
CHARACTERISTICS NEEDED
• Demography: Age, race, gender, language, etc.
• Context: Situation in which the visitor views your content
• Customer Journey: Previously bought products, product
categories, average transaction value
• Psychographic: Habits, likes, interests, preferences, etc.
• Geo-location: Where the visitor lives and engages with the
content
• Engagement: Past and current visitor’s interactions with your
content across all channels
• Device: Computer, mobile, or tablet

SITE PERSONALIZATION – MAJOR TWO APPROACHES
Personalization
based on the
visitor
Personalization
according to the
product/service

TECHNICAL SOLUTION FOR SITE
PERSONALIZATION
The world’s first personalization
technology stack
ANDREY TYSCHENKO

PART THREE: HOW TO ASSESS
PROFITABILITY OF WHAT WE HAVE
DONE WITH THE DATA

TYPICAL MARKETING CAMPAIGN
ALLOCATE
BUDGETS
Divide campaign
into two-three parts –
performance, brand-
oriented, engagement
Assess the results:
- leads in performance
- views, clicks, BR in the
others

Performance campaign analytics – typical approach
LTV
calculation
for average service user
P&L
analysis
For the profit from the
user’s revenue
LTV and
profit
calculations
for segments of the
audience
Profit for users from
any audience segments

PERFORMANCE CAMPAIGN ANALYTICS –
PROFIT CALCULATION
SPENDINGS REVENUE PROFIT
SOCIALPROGRAMMATIC PAID SEARCH

Analytics of the segment – example of LTV calculation
Women 25-34, Kaunas
3 439
Meets criteria of Google and Facebook
Four years with
the service
320
Five years with
the service
31
LTV full
9 orders, revenue - 500 Euro, net profit – 175 Euro
Should we pay for a customer:
60 Euro
150 Euro
167 Euro

Fewer years – lower cost of lead
4 years • 175
Euro
3 years • 140
Euro
2 years • 110
Euro
1 year • 70
Euro

SMART ATTRIBUTION
an official distributor of Google
solutions and Google partner
for Cloud Console
LINA YARYSH

OMINCHANNEL VS MULTICHANNEL – DIFFERENCE IN PHILOSOPHY

https://www.youtube.com/watch?v=zdbumR6Bhd8
ONMICHANNEL VS MULTICHANNEL – AMAZON GO

WHY SO FEW COMPANIES USE OMNICHANNEL
APPROACH?
INVESTMENT
IN TECHNOLOGY
BUSINESS
CHANGE
COMPEXITY
WITH DATA
MANAGEMENT

INTRODUCTION OF ECONOMETRICS
ECONOMETRIC MODELS ARE
TARGETING MEASURING OPTIMIZING
OMNICHANNEL CAMPAIGNS

How can brands meet their marketing goals
across the purchase funnel?
What is the optimal allocation for your
campaign?
How can you make your mobile investment
work harder?
We know last click is lame, but what’s the
better alternative?
QUESTIONS FOR ECONOMETRIC MODELS

Econometrics – Factors
FMCG
“Sales”
Competitors’
investments
Promo activities
Ads
Price
Seasonality
Competitors’
promos
Competitors’ prices
Factors under control Not-controlled factors
New SKU Competitors’
activities
Distribution
ECONOMETRICS - FACTORS

Econometrics – main formula
Базовый уровень
ttt XXDepVar   ...2211
Показывают связь между каждым
фактором (т.к. цена) и зависимой
переменной. В качестве примера
“снижение цены на 1%, приведет к росту
продаж на 1.5%”
Всегда имеется ошибка, так как мы не
обладаем совершенной информацией и
поведение человека в определенной степени
непредсказуемо. Ключевым критерием
является отсутствие корреляции между
ошибкой и каждым из факторов Х
Зависимая переменная
(например, продажи или
трафик)
Каждый X является фактором, который имеет
систематическую, причинно-следственную связь с зависимой
переменной. Участвует столько X-ов, сколько требуется. Они
включают в сеюя такие вещи, как цена, ТВ, активность
конкурентов и т.д.

Econometrics – main formula
Period Sales Price Distribution Promotion Income Advt.
Constant/
Base
1
2
3
4
5
6
S1
S2
S3
S4
S5
S6
=
=
=
=
=
=
a x P1
a x P2
a x P3
a x P4
a x P5
a x P6
+
+
+
+
+
+
b x D1
b x D2
b x D3
b x D4
b x D5
b x D6
+
+
+
+
+
+
c x Pr1
c x Pr2
c x Pr3
c x Pr4
c x Pr5
c x Pr6
+
+
+
+
+
+
d x I1
d x I2
d x I3
d x I4
d x I5
d x I6
+
+
+
+
+
+
e x A1
e x A2
e x A3
e x A4
e x A5
e x A6
+
+
+
+
+
+
K
K
K
K
K
K

Econometrics – modelling graph - real
FMCG
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
500,000
11-Nov-00
9-Dec-00
6-Jan-01
3-Feb-01
3-Mar-01
31-Mar-01
28-Apr-01
26-May-01
23-Jun-01
21-Jul-01
18-Aug-01
15-Sep-01
13-Oct-01
10-Nov-01
8-Dec-01
5-Jan-02
2-Feb-02
2-Mar-02
30-Mar-02
27-Apr-02
25-May-02
22-Jun-02
20-Jul-02
17-Aug-02
14-Sep-02
12-Oct-02
9-Nov-02
7-Dec-02
Week Ending Saturday
UnitSales
Actual

0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
500,000
11-Nov-00
9-Dec-00
6-Jan-01
3-Feb-01
3-Mar-01
31-Mar-01
28-Apr-01
26-May-01
23-Jun-01
21-Jul-01
18-Aug-01
15-Sep-01
13-Oct-01
10-Nov-01
8-Dec-01
5-Jan-02
2-Feb-02
2-Mar-02
30-Mar-02
27-Apr-02
25-May-02
22-Jun-02
20-Jul-02
17-Aug-02
14-Sep-02
12-Oct-02
9-Nov-02
7-Dec-02
UnitSales
Actual Model
FMCG
Econometrics – modelling
graph – real vs forecasted by
model

FMCG
model -100,000
0
100,000
200,000
300,000
400,000
500,000
UnitSales
Price Distribution & Base Promotions TV Advertising
Demonstrations Seasonality Outdoor Advertising

model
What we saw and
what we can take
from this journey
1. How to get customers’ info
How to assemble all info on
customers
How to use it in marketing and
communication with clients
how to analyze marketing
campaigns

Andrey Shapovalov,
Internet marketing expert
Email:
ashapovalov@gmail.com
Phone: +7 926 215 7563

Andrey Shapovalov: Didžiųjų duomenų panaudojimas rinkodarai

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