Approach to marketing technology with open source components, customer behavior analysis and marketing automation.

1. MARKETING
TECHNOLOGY
Karol Bzik
9.12.2015

2. 2
”Personalization and automation are taking
center stage as retailers work to deliver more
relevant messages more efficiently”
Source – http://www.criteo.com/media/2265/etail-trends-in-digital-retail.pdf

3. 70%
ONE-TIME
BUYERS
98%
VISITORS NEVER
CONVERT
7X
COSTS OF
NEW VS. REPEAT
ORDER
SHORT
LIFECYCLE
67%
PURCHASES ARE
ABANDONED
Source – http://www.retentionscience.com/why-measuring-your-customer-churn-rate-increases-revenue/,
http://uk.businessinsider.com/heres-how-retailers-can-reduce-shopping-cart-abandonment-and-recoup-billions-of-dollar s-in-lost-sale s-2014-4
30%
OF CUSTOMERS BUY
ONLY ONCE
3

4. GROWTH FRAMEWORK
4
P
EARNED MEDIA
OWNED MEDIA
PAID MEDIA
UX, UI
CONVERSION
(micro & macro conversions)
RETENTION
(repeat order, reference)
ACQUISITION
(new customer)
O
E
U
P
O
E
U
P
O
E
U
P
A
C
R
O
E
U
A
R
C
PRODUCT
PRICING
PROMOTIONS
CUSTOMER CARE
BUSINESS

5. PROBABILITY & TIME BETWEEN NEXT PURCHASE
5
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
1 2 3 4 5 6 7 8 9
Repeatpurchaseprobability
Number of orders
1-7 days
17%
7-14 days
18%
14-30 days
15%
30-90 days
27%
90-180 days
16%
over 180 days
7%
TIME TO NEXT
PURCHASE
PROBABILITY OF NEXT
PURCHASE
Source – https://rjmetrics.com/resources/reports/ecommerce-buyer-behavior/

6. GROWTH FRAMEWORK
6
O
E
U
P
O
E
U
P
O
E
U
P
A
C
R
PRODUCT
PRICING
PROMOTIONS
CUSTOMER CARE
BUSINESS
ACQUISITION
• NC (New Customer)
• CAC (Cost of Customer Acquisition)
• NCAC (Cost of New Customer
Acquisition)
• LTVNC,T (Predictive Lifetime Value)
• ROAS (Return on Ad Spend)
• ROILTV (Return on Investment
with LTV)
• TBEP (Time to Break Even Point)
• T1stP (Time to First Purchase)
CONVERSION
• Conversions & CR (Conversion Rate)
• Microconversion & mCR
• CPmC (Cost Per microConversion)
• Funnels
• Churn rate
• Exits/abandonments
• Customer Journeys
RETENTION
• RCAC (Cost of Repeat Customer
Acquisition)
• RO (Repeat Order)
• LTVt (Lifetime Value)
• LTVn,p (Predictive Lifetime Value)
• Pn (Probability of n-th Purchase)
• Tn (Time to n-th Purchase)
• Lifecycle Stage
• RGU (Revenue Generating Unit)
• IU (Installed Units)

7. HUE + HADOOP – 4D ANALYSIS IN E-COMMERCE
7
ON-LINE
PURCHASES
ANALYSIS
PRODUCT
ANALYSIS CUSTOMER
ANALYSIS
OFF-LINE
PURCHASES
ANALYSIS
Source – http://gethue.com/

8. CUSTOMER JOURNEY – SEQUENCES & LIFECYCLE ANALYSIS
8
WEB
MODEL
CUSTOMER
WEB
PROMO
HUNTER
WEB
TYPICAL
MAN
WEB
GIFT
BUYER
PURCHASE
E-MAIL
GSN
GDN
Subscribed to
newsletter
E-mail with
promo-code
Remarketing with
sale promotion
Seasonal
sale
Broad
campaign
in Google
E-mail with
discount code
VISITS

9. RFM SEGMENTATION & ANALYSIS
9
ADDITIONAL
DIMENSIONS
Visits
LTV (monetary)
Lifecycle
0-3031-6061-9091-180181-365366+
10+
6-9
4-5
3
1
2
WIN-BACK
E-MAIL
REMARKETING
CAMPAIGN
NEWAT-RISK
PROMISING
LOYALLOYAL AT-RISKFORMER LOYAL
FORMER NEW
TIME SINCE LAST PURCHASE
PURCHASES
Source – http://retentiongrid.com/
CUSTOMER IN
RFM MATRIX

10. EXAMPLE OMNICHANNEL ORCHESTRATION ANALYSIS
10
WEB
E-MAIL
INFOSITE
WEB (APP)
MOBILE (APP)
OFF-LINE
CUSTOMER CARE
SMS
CALL CENTER
CREATING ACCOUNT MOBILE APP – INSTALLATION
AND ACTIVATION
CUSTOMER IN
BANKING

11. EXAMPLE OMNICHANNEL AUTOMATION
11
WEB
E-MAIL
INFOSITE
WEB (APP)
MOBILE (APP)
OFF-LINE
CUSTOMER CARE
SMS
CALL CENTER
GETTING LOAN E-INVOICE ACTIVATION
LANDING PAGE
E-MAIL
POP-UP
GDN
GSN
CALL CENTER
E-mail „How to save money
with e-invoice?”
Personalized landing page
„e-invoice for you”
Push notification „You can
activate e-invoice here!”
Remarketing „Check
personalized loan offer!”
Welcome pop-up with
redirection to personalized offer
Exit
Remarketing „Are you
looking for loan?”
Customer care – call, talk
about account conditions
and bank offer

12. KIBANA + HADOOP + D3.js – OMNICHANNEL ANALYTICS
12
OMNICHANNEL ATTRIBUTION
MOBILE „LAST SCREEN/BUTTON
BEFORE ABANDONMENT”
Source – https://www.elastic.co/products/kibana, http://d3js.org

13. EXAMPLE WIN-BACK CUSTOMER JOURNEY
13
„AT-RISK”
CUSTOMERS
START
STOP
WIN-BACK
E-MAIL 1
TIME
AND
LOOP
WIN-BACK
E-MAIL 2
TAG AS
„NON-
RESPONSIVE”
STOP
STOP
WIN-BACK
SMS
STOP
WAIT
10 S
WIN-BACK
POP-UP
STOP
1 TIME A DAY,
EVERYDAY AT 5
A.M., EVERY 30
DAYS ONCE PER
USER
ALL
CUSTOMERS
IN „AT-RISK”
RFM
SEGMENT
NEWSLETTER
SUBSCRIPTION?
OPENED?
OPENED?
SMS
SUBSCRIPTION?
VISIT
HOMEPAGE?

14. EXAMPLE CLOSING SALE CUSTOMER JOURNEY
14
ALL
CUSTOMERS
START
STOPASSISTANCE
POP-UP
TIME
AND
LOOP
STOP
3 ERRORS ON FORM
ANONYMOUS
CUSTOMERS
START
STOPSUBSCRIPTION
POP-UP
TIME
AND
LOOP
STOP
SUBSCRIBED
CUSTOMERS
START
STOP
TIME
AND
LOOP
STOP
WAIT
2h
ABANDONED
CART
E-MAIL 1
WAIT
2h
ABANDONED
CART SMS
STOP
STOP
WAIT
48h
ADD TAG
„UNACTIVE”
STOP
EXIT OVERLAY
ABANDONED CART
EVERY 30 DAYS
ONCE PER USER
ALL CUSTOMERS,
IDENTIFIED AND
UNIDENTIFIED
3 ERRORS IN
ONE SESSION?
ONCE PER
SESSION
UNIDENTIFIED
CUSTOMERS
CURSOR OVER
BROWSER
ALL CUSTOMERS,
SUBSCRIBED TO
NEWSLETTER
EVERYDAY
BETWEEN 8 AM
AND 11 PM,
EVERY 30 DAYS
ONCE PER USER
ABANDONED
CART WITH
PRODUCT INSIDE
VISITED ANY
PAGE IN 2h?
OPENED?
PURCHASE?

15. 15
HOW TO BUILD
MARTECH SOLUTION?

16. EVOLUTION OF MARTECH
16
947 MARTECH VENDORS IN 2014 1876 MARTECH VENDORS IN 2015
Source – http://chiefmartec.com/

17. OPEN SOURCE ARCHITECTURE
17
MARTECH
MODULE

18. EXAMPLE OF MARTECH TEAM
18
MarTech engineer
• Transfers his experience from
e-commerce sales marketing, to
the construction and use of
marketing technology;
• Develops and coordinates
MarTech implementation in terms
of its substance;
• Conducts training of the operation
and/or operates MarTech
systems.
Big data scientist
• Transforms numerical and
statistical analysis to business
conclusions;
• Creates analytical and statistical
models, e.g. a model of
probability, segmentation,
correlation;
• Prototypes solutions in statistics
languages e.g. R language.
Big data developer
• Creates key MarTech
components;
• Develops solutions in a scalable
technologies, e.g. Hadoop, Spark,
Scala, Cloudera.
Project Manager
• Organizes and improves work;
• Ensures the continuity and
completeness of work;
• Organizes and manages sprints, so
that they are delivered on time.

19. EXAMPLE OF MARTECH DEVELOPING PROCESS
19
Customer behaviour
analysis
Prototype of
personalization
elements
Testing
personalization
prototypes
Designing a dedicated
MarTech solution
Implementation
and integration
Goal – to detect key purchasing
habits, system constraints and
develop the concept of solution
and project scope.
Realization – workshop, input data
analysis (database analysis in the
areas of trade, product and
customer), IT systems analysis;
preliminary technical analysis.
The effect of work – conclusions
from the conducted analyses (used
in marketing, sales, IT and UX)
MarTech and personalization
development plan, a preliminary
plan of MarTech and
personalization mechanisms
application in the organization.
Goal – to develop the first version of
personalization and MarTech
components (segmentation
mechanisms, recommendation
mechanisms, data aggregating and
processing mechanisms) along with a
plan of their use/ implementation.
Realization – creating concept,
mockups, developing prototypes of
mechanisms operating independently
of the current IT system.
The effect of work – prototypes of
personalization and MarTech
mechanisms and a plan for testing
them.
Goal – to test and optimize
personalization and MarTech
prototypes.
Realization – research/testing,
optimizing the mechanisms (conceptual
work, mockups, developing prototypes
of mechanisms operating
independently of the current IT
system).
The effect of work – tested and
approved prototypes of personalization
and MarTech mechanisms; revised
MarTech and personalization
development plan.
Goal - to design the final version of
MarTech and personalization solutions,
create mockups, and the
implementation backlog.
Realization – creating final Axure
mockups, preimplementation analytics,
The effect of work – Axure mockups,
implementation backlog, planned
implementation analytics (IT and the
mechanism application in the
organization).
Goal - implementation of
personalization and MarTech
mechanisms, using the gained
knowledge in the current sales and
marketing activities.
Realization - IT implementation carried
out under the strict supervision of a
MarTech engineer.

20. EXAMPLE OF MARTECH ARCHITECTURE
20
Web logs
Logs
Market data and
events
Crm data
Social media data
Hadoop
Relational databases
DATA
SOURCES
MERGING
PROCESOR
INTEGRATE &
PERSONALIZE
PROCESOR
MARTECH
INTERFACES
Omnichannel
analytics module
Omnichannel
marketing
automation module
Site personalization
moduleClearing and
connecting data
Spark
Logstash
Personalize
Orchestrate
Predict
Client monitor
CMS
SMS/VMS
AdServers
(DoubleClick)
Mobile app
E-commerce
CRM
E-mail
Landing pages
Call center
ERP
TECHNOLOGY:

21. 21
EXAMPLE
SOLUTIONS

22. MARKETING TECHNOLOGY – 4 KEY CATGORIES
22
Client acquisition
• Dashboard for monitoring and
managing communication in paid
media, e.g. Google AdWords,
DoubleClick, Google Shopping,
affiliate networks, aggregators and
price comparison sites, social media;
• Centralized media plan;
• Aggregation of marketing activities;
• Remarketing aggregation;
• Aggregation of a client acquisition
cost (actual cost);
• Combining data from marketing, CRM,
call centers and other off-line sources;
• Antifraud systems;
• A network of dynamic landing pages;
• Unified analytics - connecting tools,
e.g. Google Analytics, Gemius, CMS.
Purchasing retention
• Dashboard for monitoring and
managing communication with clients
in owned media, e.g. e-mail, SMS,
push notification;
• Marketing automation;
• Customer segmentation;
• Product recommendations;
• Loyalty programs;
• Customer scoring (customer
assessment and valuation);
• Unified analytics - connecting tools
e.g. Google Analytics, Gemius, CMS,
system marketing automation.
Direct sales
• Vendor dashboards for managing
communication with clients in
on-line and off-line media;
• Monitoring customer health;
• Cross- and up-selling web/marketing
mechanisms for use by vendors;
• Predefined components for
communicating with customers, e.g.
everyday brochures ready to send;
• Mechanisms of product/service
recommendation;
• Mechanisms supporting direct sales,
e.g. potential and risk customer
alerts.
CRO/UX automation
• Layout personalization;
• Product recommendations;
• Search engine personalization;
• Navigation personalization;
• Management dashboards for website
personalization.

23. UX AUTOMATION & MANAGEMENT
23
LAYOUT AUTOMATION
Automation management for elements like: homepage,
navigation, slider, merchandising, pop-ups

24. ACQUISITION AUTOMATION & MANAGEMENT
24
FEED MANAGEMENT
LP
LANDING PAGE MANAGEMENT
Source – https://www.feedoptimise.com/, https://www.mautic.org/

25. PERSONALIZED LANDING PAGES CONNECTER WITH REMARKETING
25

26. MARKETING AUTOMATION – PERSONALIZED FUNNELS
26
LANDING PAGE
FOR SERVICE
PERSONALIZED
LANDING PAGE
PERSONALIZED
REMARKETING
PAID MEDIA
e.g. Google
CONSULTANT

27. RETENTION AUTOMATION & MANAGEMENT
27
WIN-BACK CAMPAIGNS REPLENISHMENT CAMPAIGNS
Source – http://www.gymboree.com, https://www.justrightpetfood.com

28. MOBILE MARKETING AUTOMATION – PUSH NOTIFICATION & SMS
28

29. DIRECT SALES – AUTOMATION & PERSONALIZATION FOR SALES
29
SALES COCKPIT WITH AUTOMATION
Detecting customers „at-risk”
Preparing templates for communication
Generating recomendations for self-management
Source – http://www.yesware.com/, https://www.mautic.org, https://canopylabs.com

30. THANK YOU
Karol Bzik
e-commerce performance director @Divante
kbzik@divante.pl
https://linkedin.com/in/karolbzik
30