1) The document discusses forecasting and predictive digital marketing. It defines forecasting as using past observations to predict future events in digital business. 2) It discusses different forecasting timeframes (short, medium, long term), common issues with forecasting like lack of patterns or anomalies, and how forecasting differs from planning which describes what should happen rather than what will happen. 3) It provides an example application of forecasting digital marketing data using exploratory data analysis, data collection and processing, modeling and validation, and the Prophet forecasting model to generate forecasts.

1. Forecasting and
Predictive Digital
Marketing

2. Hello!
Daniele Donzella
Gianluigi Spagnuolo
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3. Forecasting
An introduction
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4. A definition
A forecast report is a key factor for predicting future events in
digital business, based on past observations.
A forecast helps to plan resources, set goals and KPI and
identify outlier.
Identify Factors,
Relations and
Problem Definition
Data Collection Data Analysis Model Selection Model Validation
Forecasting Model
Deployment
Monitoring
Forecasting Model
Performance
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5. A definition
Classification: short-term, medium-term, long-term
Short
Term
days, weeks, months one to two years
Medium
Term
many years
Long
Term
we are here
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6. Known issues
When we are about to create a forecast, we can meet different problems:
● no patterns present in the data: we do not always have data with
strong patterns
● highly uncertain future: if unpredictable elements will occur in the
future
● anomalies in the dates: for example, months that do not have the
same number of days, or the variations regarding holidays time, etc
This points out that predicting future events is not a trivial operation. For
this purpose, there are several libraries for different languages ​​that use
different algorithms and approaches.
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7. Planning vs Forecasting
Forecasting is commonly confused with planning, that tells us
how the information should be and not how it actually will be.
SHOULD / WILL
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8. When to do it
When the patterns are clear and obvious, there is no need for
forecasting. Predictions can be easily made on the time series.
Forecasting is relevant when patterns, even if they exist, are
not intuitive and where we can not make predictions based
only on experience.
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9. Application
Anatomy of an application to create forecasting
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10. Problem definition
The aim is to capture the relationships between advertising,
different promotional strategies and total number of sales, to be
able to plan resources and goals.
Problem Definition Data Collection Data Analysis Model Selection Model Validation
Forecasting Model
Deployment
Monitoring
Forecasting Model
Performance
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11. Are results
satisfactory?
Plans
Implement plans
Planning
Process
Forecast
Monitoring
Outcomes
DataUsers
Yes
Forecasting
Methods
No
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12. Data Collection: a premise
Since we only have limited data, referring to a little bit more
than a year, we can not accurately make a forecast or
modelling the data only on a long term.
But it is possible to make predictions by evaluating shorter
periods.
Problem Definition Data Collection Data Analysis Model Selection Model Validation
Forecasting Model
Deployment
Monitoring
Forecasting Model
Performance
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13. Exploratory Data Analysis
From an exploratory data analysis (EDA), being a time series reflecting users’
behavior, it is clear that:
● there are several periodic events both annual and weekly
● there are some outliers
● data are affected by the launch of products and advertising campaigns
Problem Definition Data Collection Data Analysis Model Selection Model Validation
Forecasting Model
Deployment
Monitoring
Forecasting Model
Performance
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14. Exploratory Data Analysis
From an exploratory data analysis (EDA), being a time series reflecting users’
behavior, it is clear that:
● some data is missing or incomplete due to:
○ tracking malfunctions
○ incorrect implementation of the Garante’s provisions regarding cookies
○ implementation of the GDPR
Problem Definition Data Collection Data Analysis Model Selection Model Validation
Forecasting Model
Deployment
Monitoring
Forecasting Model
Performance
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15. DATA COLLECT &
PROCESSING
Data: Google Analytics
Google DoubleClick
EXPLORATORY DATA ANALYSIS
Data analysis to summarize their
main characteristics
FORECAST REPORT
Forecasting Model
Deployment
MODELING
Creation of a model based
on EDA
FORECAST EVALUATION
Model selection and
validation
TROUBLESHOOTING
Possible errors and problems
highlighted by the validation
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16. DATA COLLECT &
PROCESSING
Data: Google Analytics
Google DoubleClick
EXPLORATORY DATA ANALYSIS
Data analysis to summarize their
main characteristics
FORECAST REPORT
Forecasting Model
Deployment
1. MODELING
Creation of a model based
on EDA
2. FORECAST EVALUATION
Model selection and
validation
3. TROUBLESHOOTING
Possible errors and problems
highlighted by the validation
1. creation of a model based
on a human interpretation
of parameters (EDA)
2. creation and validation of
forecasting based on the
newly created model and
with a baseline created
from historical data
3. if this forecasting is not
good enough, the model is
made more compliant and
the process starts again
from step 2
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Google Analytics 360
Google DoubleClick
Teradata CRM
Data
Quantitative &
Additive Regression
Model
Type & Model
Marketing
Application Area
3 Months
Forecast Horizon
Monthly
Forecast Interval

18. Facebook Prophet
“Prophet is a procedure for
forecasting time series data. It is
based on an additive model
where non-linear trends are fit
with yearly and weekly
seasonality, plus holidays.
Prophet is robust to missing data,
shifts in the trend, and large
outliers.”
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● Open source
● Easy and fast
● It is possible to alter the model
to apply your experience
and/or external data
● It creates reliable results
● Available for Python

19. Prophet model
The model that underlies
Prophet consists of three
components:
● trends
● seasonal effects
● holiday/special events
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Compared to classic ARMA/ARIMA
models, the advantages are:
● better flexibility, it is possible to
easily adapt irregular data
● we can directly evaluate the
contribution of each
component and handle it
y(t) = g(t) + s(t) + h(t) + ϵt

20. Prophet in the application
Prophet allowed us to intervene at several points:
● dates of the products’ launch, of the marketing strategy
change, and of the advertising campaigns have been
specified;
● dates of the holidays and their weight have been specified,
as for the seasonality;
● furthermore, various parameters can be specified: for
example, how the data on seasonality will influence
forecasting in the future.
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21. Future
developments
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Photo by Rodion Kutsaev on Unsplash

22. Conclusions
In addition to forecasting, which
predicts sales volumes, an analysis
that tells us the reasons for such
behavior should also be performed.
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23. References
● Facebook Prophet https://github.com/facebook/prophet
● Exploratory Data Analysis
https://www.itl.nist.gov/div898/handbook/eda/eda.htm
● Google Analytics https://analytics.google.com
● Google DoubleClick
https://www.doubleclickbygoogle.com
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Thanks
Questions?
donzella@alturalabs.com
spagnuolo@alturalabs.com