The document discusses the implementation and analysis of small cells, specifically picocells, to enhance telecom network capacity and coverage while reducing costs. It outlines a case study that employs causal impact analysis to demonstrate a statistically significant increase in traffic by 61% due to picocell deployment. Visualizations and models are used to support the findings, indicating the effectiveness of picocells in improving network performance.

1. Introduction
 In simple terms, small cell is a
miniature version of the traditional
Macro cell. Helps Telecom operators
with capacity and coverage.
 Attractive business case - longer term
solution at reduced capital and
operational expenditure
(CAPEX/OPEX)
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2. Part-1. Main Technical Case
 Our goal is to determine if there is a
“lift”/increase in the total traffic in the
area under study where the Picocells
were deployed that can be attributed
to addition of the Picocells.
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3. Solution Approach
 Prepare Tidy data
 Perform Visual Tableau exploratory data
analysis, plot Moving Averages, Trend
charts, Seasonality, Time series
decomposition
 Perform Causal Impact analysis using R
CausalImpact package from Google.
 Present Conclusion
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4. Tableau Visualizations-Time
series
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5. Periodic patterns
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6. Seasonal Trend
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7. Time Series Decomposition
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8. Causal Impact
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9. Causal Impact chart-
Explanation
 Original (First) Graph: Solid, Black Line: Observed data
before the intervention
Dotted, Blue Line: Model predicted values for what would
have occurred without the intervention
 Point wise (Second) Graph: The net difference between the
observed and predicted response on the original scale, or the
difference between the solid, black line and the dotted, blue
line on the original graph.
 Cumulative (Third Graph): Dotted, Blue Line: Individual
causal effects added up in time, day after day.
 For all three graphs, the light blue shaded area represents
the results in a 95% confidence level. The farther that the
graph extends past the beginning of the intervention, the less
certain of the causal effect; hence, the larger the shaded
area.
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10. Summary of Impact
 In relative terms, the response
variable showed an increase of +61%.
The 95% interval of this percentage is
[+40%, +81%].
 The probability of obtaining this effect
by chance is very small (Bayesian
one-sided tail-area probability p = 0).
This means the causal effect can be
considered statistically significant.
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11. Questions?
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