SGS is not directly affiliated with PPC Pinas. Katharine is a full-time employee of SGS and a member of PPC Pinas. SGS is the world's leading inspection, testing, certification and verification company. PPC Pinas is a community for Filipino paid search professionals and individuals who have interest in search engine marketing, digital media buying and related activities.

1. BASIC STATISTICS
for PAID SEARCH ADVERTISING
Katharine Mission-Estenzo
SGS.com Search Engine Marketing Lead
Research & Testing Specialist and
Quality Management Coordinator
PPC Pinas Meetup 2013
May 31, 2013
Cypress Towers, Taguig

2. 2
OBJECTIVE & SCOPE
 Introduce Statistical Concepts and Tools to efficiently
manage campaigns and results
 Correct common misuses and misconceptions on Basic
Statistical concepts
 Not a Statistics crash course - Guaranteed formula-free
presentation!

3. 3
TOPICS
 Statistical Sampling and Analysis
 Charts and Graphs
 Common Numerical Misuses
 Prediction and Forecasting
 Statistical Process Control

4. 4
STATISTICS
 Collection
 Analysis
 Interpretation
 Presentation

5. 5
STATISTICAL SAMPLING?
Population
Target
Population
Samples
Selected
SAMPLING TECHNIQUES
 Simple Random Sampling
 Systematic Sampling
 Stratified Sampling
 Probability-proportional-to-size sampling
 Accidental / Purposive Sampling
 Quota Sampling
 Clustered Sampling
SAMPLING PROCESS
 Define the population of concern
 Specify a sampling frame
 Develop a sample plan
 Implementing the sample plan
 Sampling and data collecting

6. 6
WHY USE A SAMPLE?
• Lower Costs
• Faster Data CollectionResearch
• Validity of Results
• Robustness of
Statistical Model
• Statistical Significance
Testing
SAMPLINGERRORS
• History
• Instrumentation
• Selection
• Sampling
Distortion

7. 7
SIGNIFICANT VS STATISTICALLY SIGNIFICANT
SIGNIFICANT
 Important
 Essential
 Meaningful
STATISTICALLY
SIGNIFICANT
 Pattern
 Behavior
 Not by Chance
Before making conclusions, always make sure that you
have sufficient sample size. All test results are invalid if:
insufficient sample size
sampling errors

8. 8
SAMPLING ERROR = MARGIN OF ERROR
Sampling Error
• Failure to capture the profile of the true
population- under representation.
Margin of Error
• The difference of the estimated value to the true
population value

9. 9
GRAPH IT!
0
200
400
600
800
1000
1200
1400
1600
HACCP ISO 22000 GMP FSSC
22000
BRC
Page Visits
January 2013
0
200
400
600
800
1000
1200
1400
1600
1800
Jan February March April
Monthly Page Visits
Jan - Apr 2013
HACCP GMP FSSC 22000
HACCP
33%
ISO 22000
22%
GMP
18%
FSSC
22000
15%
BRC
12%
Page Visits
January 2013
 Discrete/ count data –
Impressions, Clicks,
Conversions
 Comparing data based on
a single category/ criteria
 Change in magnitude/
quantity
 Continuous data – CTR,
Conv Rate, CPC
 Tracking changes over time
 Trends
 Correlations
 Portions/ percentages of a
whole – Geo performances
One variable at a time
 Limit your data – use bar
charts for more than six
variables
 Avoid using 3D rotation -
deceiving

10. 10
COMBINATION GRAPHS
$0.00
$1.00
$2.00
$3.00
$4.00
$5.00
$6.00
0
10
20
30
40
50
60
Madrid Valencia Mallorca Zaragosa Tenerife
Conversions vs CPA
33.39% 31.90% 27.38% 27.16%
22.26% 24.07% 28.33% 28.86%
17.47% 19.06% 18.79% 16.98%
14.47% 13.14% 11.33% 11.88%
12.42% 11.84% 14.16% 15.11%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
Jan February March April
Monthly Search Traffic Share
HACCP ISO 22000 GMP FSSC 22000 BRC
Clicks
Impressions
0
5000
10000
15000
20000
25000
Algeria Nigeria Saudi Kurdistan Kenya
When using combination graphs (or
even simple graphs), keep in mind that
your objective is to simplify data
presentation. Present trends and changes
in the simplest form.
Do not complicate your graphs just to
give the impression of “advanced” analysis
and/or analytical skills.

11. 11
LIES, DAMNED LIES AND STATISTICS
The Danger of Averages
 Bill Gates walk into a bar; on average, everybody in the bar is a
millionaire.
 The average human has one breast and one testicle. ~Des McHale
 The interesting thing about averages is that they hide the truth very
effectively. ~Avinash Kaushik

12. 12
MEASURES OF CENTRAL TENDENCY
Day
Earning
(USD)
Day 1 350.00
Day 2 400.00
Day 3 400.00
Day 4 5,500.00
Day 5 150.00
Day 6 300.00
Day 7 400.00
Day 8 400.00
Day 9 400.00
Day 10 400.00
Total 8,700.00
ON IMPULSE:
My average daily
earning is USD 870.00.
MEAN
Average
Minimal differences
 Widely dispersed
data
 Extremes and
outliers
MEDIAN
Middle value
Most resistant to
outliers and extreme
values
If data points are
even, this is the mean of
the 2 middle values
MODE
Most often appears
Most likely to be sampled
Not unique – data set may be mutli-modal

13. 13
Percentage Fallacies and Misuses
 Using pure percentage values to measure effectiveness
CTR
Conversion Rates
 Averaging Percentages – valid or not?
Trials Successes %
10 6 60.00%
25 10 40.00%
30 10 33.33%
40 5 12.50%
Totals 105 31 145.83%
AVERAGE = 36.46 %
AVERAGE = 29.52 %

14. 14
The Excuse of Trends and Seasonality
 TREND - General tendency of a series of data points to move in a
certain direction over time
Consecutive data points moving in a single direction
Majority of data points moving in a single direction
Extreme values, singular peak values and outliers (Noise) are flattened in trend analysis
 SEASONALITY – Characteristic of a time series in which the data
has regular and predictable changes on a specific period recurring
every calendar year
Always check previous data for the same time period
Not all holidays are causal to seasonality

15. 15
PREDICTION AND FORECASTING
 TIME SERIES
 A sequence of data points measured successively in uniform time
intervals
 Use of a statistical model to predict future values based on previous
observations
! Assuming that conditions stay the same.
 REGRESSION ANALYSIS
 A technique for estimating the relationships between variables
 The value of a dependent variable is affected by the behavior of the
values of the independent variables
! Check data for conformance to statistical assumptions.

16. 16
STATISTICAL PROCESS CONTROL
FMEA – Failure Mode and Effects Analysis
 Identifying potential mistakes before they happen to determine whether the
effects are tolerable or not
 FME(C)A – includes criticality analysis
Efficient assessment of
best option
Evaluate effects of
proposed changes on
processes & performances
Manage risks associated
with system failures and
changes
Standardize procedures
and practices

17. 17
Design
Measure
Analyze
Improve
Control
DMAIC – Six Sigma Core Concept
 Campaign Objectives
 Nature of Business
 Advertising Channels
 Type of Testing
 Gap analysis/ Benchmark
 Historical Data
Data Collection/ Testing
 Identify sources of variation
 Identify critical factors
 Validation of results
 Discover process
relationships
Implement optimization/
improvements
 FMEA
 Documentation
 Develop Control Plan
 Monitoring

18. 18
REMINDERS
 TEST! Don’t rely on assumptions.
 Efficiency – cost, time, energy
 Always define objectives and targets clearly
 Plan carefully – ensure objectives are met
 Understand your data – how, where, what and when
 Statistics – Bane or Boon?

19. 19
QUESTIONS/ CONSULTATION
katha.mission@gmail.com
nina.mission

20. THANK YOU!
www.sgs.com