1. co-founder icomm
unified marketing cloud
Foto Speaker Hernán Litvac
@icomm_ok @Litvac @icomm_ok
https://www.linkedin.com/in/hernanlitvac/
Talk: Marketing Automation, the Best-Kept Secret.

2. 1600 customers
12 countries
80,000 million of newsletters sent
1,500 million of clicks generated
13 million of generated purchase

5. #DataDrivenMarketing

6. A recent passion?
micromobility.

11. Alternating
current
motor

12. Nikola Tesla
1856 - 1943
+700 inventions

13. How many technologies have we seen
emerge recently?

14. We’ve fallen in love with the Hype.

15. Hype Cycle for Emerging Technologies.

17. email marketing
Transaccional email
Push notifications
Navegation detection
Omnichannel processing
Social media integration
Abandoned carts
SMS
Whatsapp
First-party cookies
Browsing bubbles
NPS
Surveys
LeadNurturing
LeadScoring
Triggers
Flow Automation
Marketing Automation.

18. Marketing Automation.
→ Unique omnichannel “channel set”
→ Highest ROAS in the entire ecosystem 40:1
→ Contributes up to 30% of the transactions
→ Optimize the UX
→ Know the user (zero-party data)

19. Today, everything operates with
the consumer at the center.
Customer centric
Data driven marketing
Omnichannel
Frictionless

20. 80,000 million communications
1,500 million click generated
13 million purchases

21. Have you ever wondered:
what’s behind a click?

23. “Omnichannel is the harmonious expression of
the consumer's vision through data; channels
stimulate or respond in their frictionless
relationship (or purchase) process.”
icomm unified marketing cloud.

24. Marketing Automation is…

25. Marketing Automation is…
Hitting play on traditional marketing

26. Purchase
Interest
Consideration
Intent
Evaluation
Awareness
Lead
Generation
Lead
Nurture
Sales
It's the automation of
marketing processes:
segmentation and
personalization across
multiple channels.
Marketing Automation.

27. Marketing Automation.

30. De-frictionize
the Customer Journey
email marketing
webpush notif.
SMS, Wapp
KPI processing MKT Autom.
re-focusin
g on the
purchasin
g process
AI-driven
purchase
propensity
models
STEP 1
Buying incentive
STEP 2
Supporting them
throughout the
process
STEP 3
Complete the
transaction
STEP 4
Reevaluate the
indicators
Browsing bubbles, Abandoned acrts, we
miss you, mail transaccional, exit popup
120 KPI

31. 80,000 million of newsletters sent
1,500 million of clicks generated
13 million of generated purchase
ZERO PARTY DATA

32. Zero party data…
Third Party
Data
→ Big data
→ Data from third-party
(aggregators)
→ Data purchase
Significant compliance
issues
Data Typologies
First Party
Data
→ Collected by you own
company
→ Reliable data
Compliance
OK!
Second Party
Data
→ Complement to your
own data
→ Involves another
company
Compliance
issues

33. Zero Party Data
→ The user provides them voluntarily.
→ Explicitly provided.
→ Highly reliable.
→ Data depth.
Perfect compliance!
Third Party
Second Party
First Party
Data Typologies

34. Zero Party Data
We use declared data to generate
recurring purchases and extend the
lifetime of the customer relationship.

35. How do you use your own data?
#RetailMedia

36. https://adage.com/article/marketing-news-strategy/rise-retail-media-networks-and-implications-agencies/2376136

38. Data models with +100 KPIs updated daily per consumer
Synchronization, enrichment, and structuring of a powerful consumer database
ID Gender Days last
purchase
Date last
purchase
Amount last
purchase
Value last
purchase
Items last
purchase
Categories
last purchase
Brands last
purchase
Promotion
codes last
purchase
Last transactions
Basic data
Phone
number
Birthday Days first
purchase
Date first
purchase
Historical transaction data
Categories Brands Value Promotional
value
Purchase
quantity
Promotional
codes
Seller
Payment
method
Shipping
method
Any demographic field at the customer's
discretion (e.g., customer type, city,
address, marital status).
Custom variables
30 days 60 days 90 days 180 days 360 days Total days

39. Data models with +100 KPIs updated daily per consumer
Synchronization, enrichment, and structuring of a powerful consumer database
ID Gender Days last
purchase
Date last
purchase
Amount last
purchase
Value last
purchase
Items last
purchase
Categories
last purchase
Brands last
purchase
Promotion
codes last
purchase
Last transactions
Basic data
Phone
number
Birthday Days first
purchase
Date first
purchase
Historical transaction data
Categories Brands Value Promotional
value
Purchase
quantity
Promotional
codes
Seller
Payment
method
Shipping
method
Any demographic field at the customer's
discretion (e.g., customer type, city,
address, marital status).
Custom variables
30 days 60 days 90 days 180 days 360 days Total days
Consumer DNA
First Party Data

41. ADN del consumidor
First Party Data
Zero First
Party Data
Encuestas y Formularios
Zero Party Data
Zero - First - Second
Party Data
Data models with +100 KPIs updated daily per consumer
Synchronization, enrichment, and structuring of a powerful consumer database
Consumer DNA
First Party Data
Last transactions
Basic data
Historical transaction data
Custom variables
30 days 60 days 90 days 180 days 360 days Total days
Surveys and forms
Zero party data

42. Progressive registration
Progressive Profiling
Customer Dynamic Journey
#QualityData

43. Progressive
registration
Progressive
Profiling
Customer
Dynamic
Journey
Basics
Expert
continuos
improvement
Data Quality
Profiling Quality
Momentum
Fundamental data
Fundamental profile
Beyond range

44. Statistical segmentation and
classification models.
R.F.M.
Progressive registration & profiling

45. MONETARY VALUE
The value of the bet, measured in
money.
How much do the bets add up to
in X period of time?
FREQUENCY
The frequency, measured in the
number of bets made by the
customer.
How many bets has the customer
made in X period of time?
RECENCY
The proximity, measured in days,
of the customer's last bet.
How many days/months/years
ago was the last bet?
¿What is RFM?
It's a customer classification model that uses statistics to measure individual behavior in relation to three
variables: Recency, Frequency, and Monetary value.
Options for monetizing databases
$

46. Approach to building the RFM (interactions . transactions . purchases)
5
4
3
2
1
5
4
3
2
1
5
4
3
2
1
Segments
RFM
(Up to 5*5*5=125)
MOST RECENT
MOST ANCIENT
MOST FREQUENT
LESS FREQUENT
HIGHEST VALUE
LOWEST VALUE
Example
%
20
20
20
20
20
5
Very recent
transaction
4
High frequency
2
Low amount
Options for monetizing databases

47. VIP drain
Low tickets’
Loyal drain
Low tickets’
Loyal
VIP
VIP
in
risk
or
drain
M5
M4
M3
M2
M1
R1 R2 R3 R4 R5
LOYALS
Nearly
VIP drain
Low
tickets’
Recurrents
drain
Low tickets’
Recurrents
Nearly VIP
M5
M4
M3
M2
M1
R1 R2 R3 R4 R5
RECURRENTS
Potencial
VIP drain
Low
tickets’
First-time
users
drain
Low tickets’
First-time users
Potencial VIP
M5
M4
M3
M2
M1
R1 R2 R3 R4 R5
FIRST TIME

48. How do they use their own spaces?
#RetailData #RetailMedia
Detecting the momentum (customer
journey)

49. Marketing Automation ON.

50. A.I.

51. A.I.
low level high level

52. low level high level
A.I.

53. Chat GTP OpenAI as co-pilot

54. #switches of marketing automation
ZeroPartyData ProgressiveProfiling RetailMedia

56. co-founder icomm
unified marketing cloud
Foto Speaker Hernán Litvac
@icomm_ok @Litvac @icomm_ok
https://www.linkedin.com/in/hernanlitvac/
Talk: Marketing Automation, the Best-Kept Secret.