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BASIC STATISTICSfor PAID SEARCH ADVERTISINGKatharine Mission-EstenzoSGS.com Search Engine Marketing LeadResearch & Testing...
2OBJECTIVE & SCOPE Introduce Statistical Concepts and Tools to efficientlymanage campaigns and results Correct common mi...
3TOPICS Statistical Sampling and Analysis Charts and Graphs Common Numerical Misuses Prediction and Forecasting Stati...
4STATISTICS Collection Analysis Interpretation Presentation
5STATISTICAL SAMPLING?PopulationTargetPopulationSamplesSelectedSAMPLING TECHNIQUES Simple Random Sampling Systematic Sam...
6WHY USE A SAMPLE?• Lower Costs• Faster Data CollectionResearch• Validity of Results• Robustness ofStatistical Model• Stat...
7SIGNIFICANT VS STATISTICALLY SIGNIFICANTSIGNIFICANT Important Essential MeaningfulSTATISTICALLYSIGNIFICANT Pattern B...
8SAMPLING ERROR = MARGIN OF ERRORSampling Error• Failure to capture the profile of the truepopulation- under representatio...
9GRAPH IT!02004006008001000120014001600HACCP ISO 22000 GMP FSSC22000BRCPage VisitsJanuary 20130200400600800100012001400160...
10COMBINATION GRAPHS$0.00$1.00$2.00$3.00$4.00$5.00$6.000102030405060Madrid Valencia Mallorca Zaragosa TenerifeConversions ...
11LIES, DAMNED LIES AND STATISTICSThe Danger of Averages Bill Gates walk into a bar; on average, everybody in the bar is ...
12MEASURES OF CENTRAL TENDENCYDayEarning(USD)Day 1 350.00Day 2 400.00Day 3 400.00Day 4 5,500.00Day 5 150.00Day 6 300.00Day...
13Percentage Fallacies and Misuses Using pure percentage values to measure effectivenessCTRConversion Rates Averaging ...
14The Excuse of Trends and Seasonality TREND - General tendency of a series of data points to move in acertain direction ...
15PREDICTION AND FORECASTING TIME SERIES A sequence of data points measured successively in uniform timeintervals Use o...
16STATISTICAL PROCESS CONTROLFMEA – Failure Mode and Effects Analysis Identifying potential mistakes before they happen t...
17DesignMeasureAnalyzeImproveControlDMAIC – Six Sigma Core Concept Campaign Objectives Nature of Business Advertising C...
18REMINDERS TEST! Don’t rely on assumptions. Efficiency – cost, time, energy Always define objectives and targets clear...
19QUESTIONS/ CONSULTATIONkatha.mission@gmail.comnina.mission
THANK YOU!www.sgs.com
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Basic Statistics for Paid Search Advertising

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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.

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Basic Statistics for Paid Search Advertising

  1. 1. BASIC STATISTICSfor PAID SEARCH ADVERTISINGKatharine Mission-EstenzoSGS.com Search Engine Marketing LeadResearch & Testing Specialist andQuality Management CoordinatorPPC Pinas Meetup 2013May 31, 2013Cypress Towers, Taguig
  2. 2. 2OBJECTIVE & SCOPE Introduce Statistical Concepts and Tools to efficientlymanage campaigns and results Correct common misuses and misconceptions on BasicStatistical concepts Not a Statistics crash course - Guaranteed formula-freepresentation!
  3. 3. 3TOPICS Statistical Sampling and Analysis Charts and Graphs Common Numerical Misuses Prediction and Forecasting Statistical Process Control
  4. 4. 4STATISTICS Collection Analysis Interpretation Presentation
  5. 5. 5STATISTICAL SAMPLING?PopulationTargetPopulationSamplesSelectedSAMPLING TECHNIQUES Simple Random Sampling Systematic Sampling Stratified Sampling Probability-proportional-to-size sampling Accidental / Purposive Sampling Quota Sampling Clustered SamplingSAMPLING PROCESS Define the population of concern Specify a sampling frame Develop a sample plan Implementing the sample plan Sampling and data collecting
  6. 6. 6WHY USE A SAMPLE?• Lower Costs• Faster Data CollectionResearch• Validity of Results• Robustness ofStatistical Model• Statistical SignificanceTestingSAMPLINGERRORS• History• Instrumentation• Selection• SamplingDistortion
  7. 7. 7SIGNIFICANT VS STATISTICALLY SIGNIFICANTSIGNIFICANT Important Essential MeaningfulSTATISTICALLYSIGNIFICANT Pattern Behavior Not by ChanceBefore making conclusions, always make sure that youhave sufficient sample size. All test results are invalid if:insufficient sample sizesampling errors
  8. 8. 8SAMPLING ERROR = MARGIN OF ERRORSampling Error• Failure to capture the profile of the truepopulation- under representation.Margin of Error• The difference of the estimated value to the truepopulation value
  9. 9. 9GRAPH IT!02004006008001000120014001600HACCP ISO 22000 GMP FSSC22000BRCPage VisitsJanuary 2013020040060080010001200140016001800Jan February March AprilMonthly Page VisitsJan - Apr 2013HACCP GMP FSSC 22000HACCP33%ISO 2200022%GMP18%FSSC2200015%BRC12%Page VisitsJanuary 2013 Discrete/ count data –Impressions, Clicks,Conversions Comparing data based ona single category/ criteria Change in magnitude/quantity Continuous data – CTR,Conv Rate, CPC Tracking changes over time Trends Correlations Portions/ percentages of awhole – Geo performancesOne variable at a time Limit your data – use barcharts for more than sixvariables Avoid using 3D rotation -deceiving
  10. 10. 10COMBINATION GRAPHS$0.00$1.00$2.00$3.00$4.00$5.00$6.000102030405060Madrid Valencia Mallorca Zaragosa TenerifeConversions vs CPA33.39% 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 AprilMonthly Search Traffic ShareHACCP ISO 22000 GMP FSSC 22000 BRCClicksImpressions0500010000150002000025000Algeria Nigeria Saudi Kurdistan KenyaWhen using combination graphs (oreven simple graphs), keep in mind thatyour objective is to simplify datapresentation. Present trends and changesin the simplest form.Do not complicate your graphs just togive the impression of “advanced” analysisand/or analytical skills.
  11. 11. 11LIES, DAMNED LIES AND STATISTICSThe Danger of Averages Bill Gates walk into a bar; on average, everybody in the bar is amillionaire. The average human has one breast and one testicle. ~Des McHale The interesting thing about averages is that they hide the truth veryeffectively. ~Avinash Kaushik
  12. 12. 12MEASURES OF CENTRAL TENDENCYDayEarning(USD)Day 1 350.00Day 2 400.00Day 3 400.00Day 4 5,500.00Day 5 150.00Day 6 300.00Day 7 400.00Day 8 400.00Day 9 400.00Day 10 400.00Total 8,700.00ON IMPULSE:My average dailyearning is USD 870.00.MEANAverageMinimal differences Widely disperseddata Extremes andoutliersMEDIANMiddle valueMost resistant tooutliers and extremevaluesIf data points areeven, this is the mean ofthe 2 middle valuesMODEMost often appearsMost likely to be sampledNot unique – data set may be mutli-modal
  13. 13. 13Percentage 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. 14The Excuse of Trends and Seasonality TREND - General tendency of a series of data points to move in acertain 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 datahas regular and predictable changes on a specific period recurringevery calendar yearAlways check previous data for the same time periodNot all holidays are causal to seasonality
  15. 15. 15PREDICTION AND FORECASTING TIME SERIES A sequence of data points measured successively in uniform timeintervals Use of a statistical model to predict future values based on previousobservations! 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 thevalues of the independent variables! Check data for conformance to statistical assumptions.
  16. 16. 16STATISTICAL PROCESS CONTROLFMEA – Failure Mode and Effects Analysis Identifying potential mistakes before they happen to determine whether theeffects are tolerable or not FME(C)A – includes criticality analysisEfficient assessment ofbest optionEvaluate effects ofproposed changes onprocesses & performancesManage risks associatedwith system failures andchangesStandardize proceduresand practices
  17. 17. 17DesignMeasureAnalyzeImproveControlDMAIC – 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 processrelationshipsImplement optimization/improvements FMEA Documentation Develop Control Plan Monitoring
  18. 18. 18REMINDERS 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. 19QUESTIONS/ CONSULTATIONkatha.mission@gmail.comnina.mission
  20. 20. THANK YOU!www.sgs.com

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