Post on 12-May-2015
description
BASIC STATISTICS
for PAID SEARCH ADVERTISING
Katharine Mission-EstenzoSGS.com Search Engine Marketing LeadResearch & Testing Specialist andQuality Management Coordinator
PPC Pinas Meetup 2013May 31, 2013Cypress Towers, Taguig
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
TOPICS
Statistical Sampling and Analysis
Charts and Graphs
Common Numerical Misuses
Prediction and Forecasting
Statistical Process Control
4
STATISTICS
Collection
Analysis
Interpretation
Presentation
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
WHY USE A SAMPLE?
• Lower Costs• Faster Data CollectionResearch
• Validity of Results• Robustness of
Statistical Model• Statistical Significance
Testing
SA
MP
LIN
G E
RR
OR
S
• History
• Instrumentation
• Selection
• Sampling Distortion
7
SIGNIFICANT VS STATISTICALLY SIGNIFICANT
SIGNIFICANT
Important
Essential
Meaningful
STATISTICALLYSIGNIFICANT
Pattern
Behavior
Not by ChanceBefore making conclusions, always make sure that you have sufficient sample size. All test results are invalid if:
insufficient sample sizesampling errors
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
GRAPH IT!
HACCP ISO 22000 GMP FSSC 22000
BRC
0
200
400
600
800
1000
1200
1400
1600
Page VisitsJanuary 2013
Jan February March April
0
200
400
600
800
1000
1200
1400
1600
1800
Monthly Page VisitsJan - Apr 2013
HACCP GMP FSSC 22000
HACCP
33%
ISO 2200022%
GMP17%
FSSC 2200014%
BRC12%
Page VisitsJanuary 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
COMBINATION GRAPHS
Madrid Valencia Mallorca Zaragosa Tenerife
0
10
20
30
40
50
60
$0.00
$1.00
$2.00
$3.00
$4.00
$5.00
$6.00
Conversions vs CPA
Jan February March April
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
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%
Monthly Search Traffic Share
HACCP ISO 22000 GMP FSSC 22000 BRC
Algeria Nigeria Saudi Kurdistan Kenya
0
5000
10000
15000
20000
25000
Clicks Clicks ClicksClicks
Clicks
Impressions Impressions Impressions
ImpressionsImpressions
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
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
MEASURES OF CENTRAL TENDENCY
DayEarning(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.
MEANAverage
Minimal differences
Widely dispersed data
Extremes and outliers
MEDIANMiddle value
Most resistant to outliers and extreme values
If data points are even, this is the mean of the 2 middle values
MODEMost often appears
Most likely to be sampled
Not unique – data set may be mutli-modal
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 31145.83
%
AVERAGE = 36.46 %
AVERAGE = 29.52 %
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
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
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
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
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
QUESTIONS/ CONSULTATION
katha.mission@gmail.com
nina.mission
THANK YOU!www.sgs.com