The document discusses the importance of measuring lifetime value (LTV) in marketing, particularly in the mobile age, highlighting the shift from volume-based to value-based marketing strategies. It emphasizes predicting LTV through various metrics and models, taking into account factors such as user engagement and retention. The document also stresses the need for aligning LTV predictions with attribution models to optimize marketing effectiveness and campaign strategies.

2. Measure what matters.
From volume to value-based
marketing in a mobile age
Demian Matarazzo
Marketing Science Head of South Cone

3. Lifetime value is a strategic lever to deliver sustained growth
INSTALLS
ACQUISITIONS
EVENTS
LIFETIME
VALUECLICKS
PROXY VALUE
From quantity to quality

4. Conversions don’t always translate into value
Same day 7 days 14 days 21 days 28 days
The majority of
installs do not
translate into
a revenue-
producing event
Source: “Loyalty Isn’t Over, It’s Now On Demand” by Facebook IQ, Jan 2018.

5. Campaign/audience profitability will depend on future events
Businesses need to predict LTV
1. p(t) = Retention rate at any given day (t)
2. R(t) = ARPDAU at any given day (t)
3. Pe(t) = Probability of engagement at any give day (t)
4. R(t) = Revenue by engagement at any give day (t)
W H E R E
Historical information
(LTV)
!
!"#
$
p t r t
Prediction
(pLTV)
!
!"$
%
P& t R(t)+ =
LTV
When do I start
to predict?
How long is a
“lifetime”?
What revenue
sources are
included in value?

6. q Does a model exist elsewhere in the business? (Data Science, Product, Monetization, CRM)
q How accurately does the prediction estimate the period I care most about at each point in time?
q How quickly can I get a prediction? (D1,D3,D7….)
q What are the inputs to “value” for the model?
q Is the model interlinked with my attribution approach? (Cohort models, Acquisition channel variable)
q Aside from statistical accuracy, how often does the model choose a winning campaign/strategy when
compared to other methods?
q How transparent is the model? Will I understand volatile results?
q How well does the model identify different behaviours in my user base (e.g. Whales, Non/late payers)?
ü .
ü .
ü .
ü .
ü .
ü .
ü .
ü .
What do I need to know about pLTV?

7. Knowing your user value curve
Predict as early as
possible

8. How pLTV interlinks with attribution approach?
Marketing Budget
Number of conversions
÷
Value of conversions
(historical/realized)
CPA ROAS X days
÷
Marketing Budget
attributed
Be mindful of the underlying attribution model
!
Signals
Conversions
Attribution

10. ARPU = $4.76
This is the value of the users
that installed your app within
7 days of clicking on a FB ad
after all other touchpoints
ARPU= $4.54
This is the value of the users
that installed your app within 7
days of clicking on a FB ad after
all other touchpoints
ARPU = $5.02
Machine learning or
econometric model built on
top of path to conversion
data.
Attribution and LTV
Validate with randomized experiments when possible
7 days click 28 days click Data driven

11. How do you calculate incremental value?
INCREMENTAL
Converted due to ads
Saw an ad, but would have
been converted on their own
Converted and
didn’t see any ads

12. What impact does your retention marketing activity have on your LTV prediction?
Understanding incremental LTV
1. p(t) = Retention rate at any given day (t)
2. R(t) = ARPDAU at any given day (t)
3. Pe(t) = Probability of engagement at any give day (t)
4. R(t) = Revenue by engagement at any give day (t)
W H E R E
Historical information
(LTV)
!
!"#
$
p t r t
Post Re-engagement
!
!"'
%
P& t R(t)
Prediction
(pLTV)
!
!"$
%
P& t R(t)+ + =
Incremental
LTV

13. How useful is pLTV model?
10,000 $100,000 3.1% $1.2
10,000 $100,000 2.9% $1.9
Campaign 1
Campaign 2
Model A: Accuracy 80%
Model B: Accuracy 85%
Incremental
Installs
Deposit
% D7Spend
Model A
pLTVARPU D7
Model B
pLTV
Actual
LTV
$2.3
$3.8
$3
$2.8
$2.7
$2.9
Illustrative example

14. AD
I love
this IP
I have a
new
device
I watch
a lot of
RV
I love
this
genre
Missed audienceMissed audience
1- Lift tests to calibrate attribution model
2-Acquisition+engagement incremental value
3-Optimization:
- pLTV based LAL vs. VBLAL Targeting
- Accuracy vs Usability – speed of prediction
4- Scaling spend – optimal level of spend
Gaming companies Learning Agenda example

15. • 23% more incremental transactions.
• 15% incremental users that did a first
purchase during the period.
• 29% more pre-paid recharges.
Optimize for 28 days purchases vs App Installs
Source: Facebook case study Jul 2020 https://www.facebook.com/business/success/2-uala

17. Appendix
NOT USED

18. Internal Only – presentation flow
• Marketing de performance evoluciono a maximizar el valor del
negocio. La idea es brevemente contar el enfoque de la industria de
Gaming, la mas avanzada en su estrategia en FB en el topico.
• Predecir el Life Time Value es complejo pero hoy modelos son open
source.
• lo importante es
• tener un enfoque holistico.
• evaluar como conectar el pLTV con el modelo de atribucion.
• Para marketing, evaluar que modelo elije a las campañas medios que major performan
ex-post, ni importa tanto el accuracy del pLTV vs LTV
• En Fb y otras plataformas, usar la informacion de pLTV para optimizar y testear.

19. Ensure your ads reach the most responsive audiences
Targeting
Your defined
target audience
Optimization
The outcome you’ve
defined as
important
Who will see
your ad
Location
Demographics
Interests
App installs
App event
Value