Applying Martech In Vietnam Lessons Learnt

1. Applying Martech In Vietnam
Lessons Learnt
TRIỀU NGUYỄN
CEO & Founder @

2. 06 Key Lessons from Vietnam Martech Adoption in 2021-2022
HOT
1
PEOPLE OPS ISSUE Age of
RETENTION
Age of
RULE-BASED
2 3 4 5 6
Data Co Ops
(Open Data)

3. Knowing customers in their {lifetime} journey
Browsing
Sign-up, Login,
Checkout, Submit
Known User
Anonymous User
Advertising &
Social
Lifecycle Engagement
Acquisition
Public Website Web / Apps / Portal
Call, Feedback, Issue,
Complaint
Review, NPS, CSAT,
Loyalty
Payment, Refund,
Redeem
In-store, Kiosk, IOT,
POS, eCommerce
Third-party First-
party
Retention, Growtah & Suppression

4. HOT TOPICS BECOME STANDARD
1
DX and CX, Omnichannel, Engagement
Customer Data, CDP, Personalization
”WHY” is confirmed
“WHAT” and “HOW” are questionable!

5. Strategy
& Purpose
New Personalized Offers
Resilient Operations
TECH
DATA
AI ML
HUMAN
People
Agile Org
DX Outcomes
Business Purposes
DX Enabers
Understand the role of data, but not what and how to make it
IDENTITIES & DEMOGRAPHICS
Identities, age, gender, location, career,
income class…
+ Other computed attributes
INTERACTIONS
Engagement with marketing – sales –
service
• View, Click, Add to cart, Chat, Search Info…
• Campaigns, Messages
• Service & Support
PURCHASE HISTORY
• Orders, Revenues
• Items, Categories
• Price, Promotions
• Revenue ($)
• Preference Stores, channels
PREDICTIONS
• Segment Models: CLV, RFM,
• Propensity scores
• Next best offers (NBA)
• Best channel, Best times
• Churn detection & prevention

6. IDENTITIES & DEMOGRAPHICS
Identities, age, gender, location, career,
income class…
+ Other computed attributes
INTERACTIONS
Engagement with marketing – sales –
service
• View, Click, Add to cart, Chat, Search Info…
• Campaigns, Messages
• Service & Support
PURCHASE HISTORY
• Orders, Revenues
• Items, Categories
• Price, Promotions
• Revenue ($)
• Preference Stores, channels
PREDICTIONS
• Segment Models: CLV, RFM,
• Propensity scores
• Next best offers (NBA)
• Best channel, Best times
• Churn detection & prevention
Understand the role of data, but not what and how to make it

7. PEOPLE & STRUCTURE
Lack of practical experience
Learning issue
Lacking of collaboration (policy, structrure)
2

8. OPS TECH IS NOT MATURE…
While the ”storm of martech” landed,
Many companies are still struggling in ensuring smooth operational
systems
E-com, RMS, OMS, WMS, ERP,….
3

9. Marketing and Operation systems are always connected and depended
Omnichannel
Touch points
(E-com, Store, Social, Call)
UX/CX
Management Unified Customer 360
(CDP)
Integrations to others
(CRM, OMS, WMS, Loyalty, Payment, Delivery…)

10. Age of RETENTION
Acquisition is the $$$ game
Retention is the focus, increasing ROMI, CLV, Profit and Growth
4

11. 1. More customers, or More Revenue?
2. Lower customer acquisition cost (CAC)
3. Higher {Customer Value} (CLV)
Revenue (Growth) Profit
• Acquisition cost = 5X to 10X of retention
• Average retention rate (RR): <20%
• Incease of 5% RR ! trigger 25% - 90%
increase of profit
76% companies agree that CLV (Customer
Lifetime Value) is a key business metric
Understand {Customers}
+
Deliver {Personalized Experience}
=
Customer Lifetime Value ($) * Growth
CLV = Frequency * Order value (AOV) * Lifetime
Why Retention? And what drives {customer value}?

12. Primary Channel: Offline Retail
Most popular strategies
(1) O2O strategy: drive ONLINE users to OFFLIINE stores
(2) Digitalize at store (product placement, queue optimization)
(3) Location-based media
Tactics: QR code to discover promos, Identify users with games
SOCIAL & ADS
What’s drivers:
- Personalized message
(offers)
- Promotions
How to measure?
• By Promo Code
• By Identity (phone,
name)
Business Outcomes:
Increase customer metrics:
Returned Rate: 1.2X – 2X
Frequency: 1.5X – 3X times
!Lead to CLV & Revenue Growth
!Less cost of ads: {Higher Profit}
At Store:
• Pre-oder
• Redeem promo code
• Identify customers (ID)
Web Mobile

13. The Age of Rule-based
Rule-based engagement is still KING
ML-based /Data Model-based Recommendation & Segmentation are
rare in some enterprises (Banking & Finance, Group)
5

14. Rule-based Model-based
▪ Experience-led Design (easy to start)
▪ Can cover most of ”known” scenarios
- Cannot cover ”complex” scenarios
- Lack of ”unknown” cases
- Limit of quality ! no differentiation from competitors
▪ Machine Learning-powered, trained knowkedge
▪ Create smarter results (Segments, Recommendations…)
! Make differentiation from competitors (Business Secrets)
- Expensive ($, Human), need skills and time
- Not always bring good result (short-term, poor data)
5%
95%

15. Model-based common examples in finance, retail, …
Find best segments for:
▪ Propensity to purchase product A at Price <xyz
▪ Churn score (prevent from churn)
Recommendations
▪ Next best offer (product items, categories, price range, promo)
▪ Next best campaign

16. Segmentation

17. Next Best Actions
Next best offer, best campaign

18. Data Coalition is in ideation
Executives have initial idea of DATA COOPS between internal and external business entities
! The future of shared data in coalition platforms
6

19. Data Co Ops: companies share customer data and receive better acquisition/reach
Company A Company B Company Z
Merged customer data -> better segment -> engage

20. “Great changes won’t happen in one day
Be patience, as you are on the journey of the differentiators”

21. Why PRIME ?
CDP Platform Analytics
Consulting
Data Service
• Segmentation
• Strategy
• Data-driven Growth
Digital Strategy
Customer Experience
Unified Customer Profile
Omnichannel Personalized Engagement
We bring our own CDXP and expertises to help brands own New Competitive Growth engine

22. Have a great day!