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SEO and Analytics

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SEO and Analytics

  1. 1. @AlexisKSanders
  2. 2. @AlexisKSanders Landscape architects design where paths will be paved. (they do other stuff too, buuuut that’s outside this analogy)
  3. 3. @AlexisKSanders Often times there is a path we walk on, which doesn’t exist.
  4. 4. @AlexisKSanders We could ignore these people-made paths…
  5. 5. @AlexisKSanders Or we could build around them (least-resistant design)
  6. 6. @AlexisKSanders The same concept holds true in reporting. Start with insights, then automate, and iterate.
  7. 7. @AlexisKSanders Time is either being spent: • Strategizing • Executing • Analyzing results Time of an SEO Manager Strategizing Analyzing Executing
  8. 8. @AlexisKSanders How it actually works out… b/c stakeholders Strategizing Analyzing Executing
  9. 9. @AlexisKSanders Analytics is a moving target, keep iterating getting answers to questions etc.automating pulse- reporting building interactive analytics which allow us to derive insights progressive development (example)
  10. 10. @AlexisKSanders • Don’t worry about having the perfect report the first time (accurate insights are a great starting point) • Incremental improvement in analytics reporting is normal • Get the data and insights you need first, then automate, and iterate • Use least-resistance design mentality!
  11. 11. @AlexisKSanders + Insights + Dashboards + Visualization + BI platforms + Testing + Forecasting + Scripting + Math Analytics (from most to least practical)
  12. 12. @AlexisKSanders Analytics: insights (data is not useful without insights)
  13. 13. @AlexisKSanders Separate out metrics KPI diagnostic measure smoke alarm KPI: A business outcome or measure of success Diagnostic Measure: A metric used to identify which lever(s) will have the most impact on KPIs Smoke Alarm: A metric no one pays attention to unless it suddenly goes way up or down
  14. 14. @AlexisKSanders • It measures performance against a goal • Someone is accountable for performance • There is context for whether the value is good or bad A metric is not a KPI unless…
  15. 15. @AlexisKSanders  understand what the metrics mean  E.g., what qualifies as a new user?  know what you’re working with  30 day last click?  what metrics/dimensions?  what are top three KPIs?  what data are you capturing?  is site leveraging the data layer?  do you have channel stacking? Know your analytics package
  16. 16. @AlexisKSanders visual modified from @smrvl via @DannyProl data information knowledge insight wisdom metrics segmented data dashboarding & reporting insights (data forensics) strategy Determine your top data views
  17. 17. @AlexisKSanders • Seasonality • Another channel’s performance • What’s going on in paid? social? email? • External • Algorithm shifts • Site changes • Content added, removed, etc. • Tracking/Analytics issues misattributions Evaluating fluctuations in organic search performance
  18. 18. @AlexisKSanders • Significant site updates (both good and bad) • Your site updates • Other digital channels updates • Company in the news • Relevant search industry updates Maintain (a) list(s) of:
  19. 19. @AlexisKSanders Know what’s happening on your site Visual Ping Uptime robot Little Warden Selenium/Jenkins Repeat with me: I am not a robot, I have a life. I am going to use a robot to do this.
  20. 20. @AlexisKSanders •Know your KPIs •Know what could cause data fluctuations versus shifts and how to identify them •Determine your top data views •Block off calendar time for building case studies
  21. 21. @AlexisKSanders Analytics: dashboards
  22. 22. @AlexisKSanders visualization = important for quick understanding microcopy = important for broad audiences clarity = important for ensuring communication automation = important for our sanity Concepts important for dashboards
  23. 23. @AlexisKSanders There are many potential dashboarding hosts…
  24. 24. @AlexisKSanders • Integrations with: • Google Analytics • Google Search Console • Google Sheets • SEMrush (if you’re tracking keywords) • Adobe (if you have Adobe, you can use Workspace) • Supermetrics … w/Google Data Studio
  25. 25. @AlexisKSanders https://bit.ly/al eyda-gds-gsc Aleyda made a stellar GSC dashboard (which you can apply to your own data!)
  26. 26. @AlexisKSanders Add top KPI scorecards
  27. 27. @AlexisKSanders Able to add relevant filters, which can be updated on-the- fly
  28. 28. @AlexisKSanders Can integrate with Google sheets to understand top events/initiatives
  29. 29. @AlexisKSanders See how KPIs flow from traffic to conversion
  30. 30. @AlexisKSanders • Be lazier • Automate repeat analyses • Get a working case study template • Maintain a record of performance-impacting site events • Know your data
  31. 31. @AlexisKSanders Analytics: data visualization
  32. 32. @AlexisKSanders Classic decision chart
  33. 33. @AlexisKSanders Buuuuut there are a ton of visuals that exist (even interactive ones like these from d3 library)
  34. 34. @AlexisKSanders Do the visuals answer a business question? (Are the charts labelled with the question they endeavor to answer?)
  35. 35. @AlexisKSanders Aim for visual clarity > coolness
  36. 36. @AlexisKSanders We notice things that stand out (aka, pre-attentive features) https://medium.com/design-at- zoopla/building-purposeful-ui-using-pre- attentive-attributes-9c5ee5dcc25c; The Big Book of Dashboards
  37. 37. @AlexisKSanders Some colors have meanings
  38. 38. @AlexisKSanders Some colors have meanings The Big Book of Dashboards; https://slideplayer.com/slide/14386894/
  39. 39. @AlexisKSanders Avoid dual axis charts
  40. 40. @AlexisKSanders Cut the visual graffiti
  41. 41. @AlexisKSanders Watch out for the spaghetti monster charts
  42. 42. @AlexisKSanders Be careful with pie charts, it’s hard to differentiate small differences try it – pick a pie chart, order it from most to least The Big Book of Dashboards; https://www.businessinsider.com/pie-charts- are-the-worst-2013-6
  43. 43. @AlexisKSanders The Big Book of Dashboards; https://www.businessinsider.com/pie-charts- are-the-worst-2013-6 Be careful with pie charts, it’s hard to differentiate small differences try it – pick a pie chart, order it from most to least
  44. 44. @AlexisKSanders • Play around with different visuals • Use whichever best answers the question • Use business question as chart title • Avoid repetitiveness • Clarity > “coolness” • Answer: I choose to _____ because “It looks pretty” or “It communicates better” • Checkout, read, skimThe Big Book of Dashboardsby Wexler, Shaffer, Cotgreave
  45. 45. @AlexisKSanders Analytics: BI platforms
  46. 46. @AlexisKSanders I’m talking about…
  47. 47. @AlexisKSanders • dataset = too big for excel (and you’re sick of waiting) • want to connect two disparate datasets • want visualizations that combine multiple datasets When to use
  48. 48. @AlexisKSanders Answering questions requiring lots of data or joining datasets
  49. 49. @AlexisKSanders Example use cases: competitive dashboards w/pages
  50. 50. @AlexisKSanders Text Filter is really useful for chopping up data Example use cases: competitive dashboards w/kws
  51. 51. @AlexisKSanders Example use cases: keyword/sv analytics
  52. 52. @AlexisKSanders Forecasting performance
  53. 53. @AlexisKSanders Attempting to correlate things (looks like title tag length != correlating w/rankings)
  54. 54. @AlexisKSanders • Competitive dashboard • Paid/organic report • Keyword analytics • Ranking factor correlation visualizations • Forecasting • Maps (with local data) • Anything you’d use an R script with Example use cases
  55. 55. @AlexisKSanders 1. Download Power Bi (search [download Power Bi]) 2. Log in w/ office email 3. Wil Reynold’s videos  Part I (30 minutes)  Part II (30 minutes)  Get paid conversion data, map against current rankings 4. Download a dataset on Kaggle.com, create visualizations 5. Watch these videos on relationships within Power BI  Understanding Relationships in Power Bi (18 m)  Power Bi Relationship Step-by-Step Example (12 m)  Looking at Many to Many Relationships (8 m) 6. Download organic ranking data (e.g., SEMrush, aHrefs) and a crawl (from Screaming Frog) for 3x competitors  Answer: does word length correlate with better rankings? (go through each value in SF)  Answer: what categories are competitors outperforming? (Note: will have to categorize KW data) 7. Do a mini-challenge on who can create best visual (winner gets small prize) how to get yourself and your team hooked on Power Bi (in ½ day’s worth of work)
  56. 56. @AlexisKSanders Analytics: testing
  57. 57. @AlexisKSanders • A-B tests (side note: form of statistical inference) • Control versus challenger • Click heatmaps • Qualitative testing • Interviews • Focus groups • Surveys • Usability tests • Screen recordings for basic tasks • Tree testing / card-sorting for information findability • Biology-based • Pulse • Eye tracking • Sweat • Statistical inference Types of traditional UX/UI site tests https://www.userinterviews.com/ux-research- field-guide-chapter/user-research-tools
  58. 58. @AlexisKSanders • A-B tests • Control versus variant(s) • Pre-/post-analysis • Do some campaign/intervention and compare pre- performance and post • Casual inference • Comparing estimated performance (based on pre-period and possibly control data) versus actual performance SEO Campaign testing
  59. 59. @AlexisKSanders Forecasting (for id’ing lift of initiatives)
  60. 60. @AlexisKSanders • We’re making an assumption that the past has something to do with the future • Some models require stationarity (meaning that the mean and variance are constant) • Creating very high-quality forecasts requires substantial experience* • There are multiple levers to pull / parameters to adjust • High potential area for technical debt • Always going to be some level of uncertainty Challenges w/ forecasting time series data *From FB Prophet paper -> https://peerj.com/preprints/3190.pdf
  61. 61. @AlexisKSanders Perception of modeling types… “least” to “most” cool LSTM Exponential smoothing BSTS (S)ARIMA(X)Linear Regression Moving Average AutoRegression There are a lot of forecasting models… less cool coolest The coolest doesn’t mean best results. Additive Model RNN standard level of cool
  62. 62. @AlexisKSanders Takes actuals, uses pre-period to create estimated, shows different post-intervention. CasualImpact for id’ing (potential) causation Black line = actual metric trended Blue dot = estimated Light blue = confidence interval Actual minus the estimated performance Cumulative change https://bit.ly/causalimpact-seo https://www.distilled.net/diy-splittester/ https://bit.ly/pshapiro-ci
  63. 63. @AlexisKSanders Facebook Prophet
  64. 64. @AlexisKSanders Facebook Prophet also provides general trend and weekly and monthly seasonality trends
  65. 65. @AlexisKSanders • Many analysts use FB Prophet: https://facebook.github.io/prophet/ • Forecasting overview python notebook: https://bit.ly/forecasting- overview • There are a lot of models for forecasting • We’ll talk about evaluating (soon young grasshopper) • Book on time series https://bit.ly/time-series-4 (recommended by my stat professor colleague, ty Kari!!)
  66. 66. @AlexisKSanders Analytics: scripting (python and R) (cough… I know very little about R… so there’s nothing in here for you R people)
  67. 67. @AlexisKSanders • Categorizing keywords (by URL or theme) • Casual Inference models • Forecasting • E.g., SARIMA, RNN LSTMs, FB Prophet, etc. • Machine learning stuff • Easy to import tensorflow, keras, sklearn, fbprophet • Anything you want to do in Excel… buuut in Python Use cases
  68. 68. @AlexisKSanders 7 Python Basics
  69. 69. @AlexisKSanders import [library-name] as [library-nickname] Now we don’t have to spell [pandas] every time! from [library-name] import [function-name] 1. import libraries We only want these functions from the library.
  70. 70. @AlexisKSanders Search [pip install library] online, use !pip install [library-name] 1.5. what if it says I don’t have the library! Find the conda install, use Anaconda prompt to install
  71. 71. @AlexisKSanders 1. Add to same folder as Jupyter Notebook 2. Use pandas to read_csv(data) 3. Check data with [dataset- name].head()  (you can also look at [dataset-name].tail() for last rows of data or just type the dataset name and it will print dataset (w/ellipses in middle if too large) 2. pd.read_csv(data)
  72. 72. @AlexisKSanders w/ matplotlib pyplot.plot(data) 3. .plot(trended-data) w/ plotly(requires a biiit more setup, but is more interactive/interesting)
  73. 73. @AlexisKSanders • Functions (aka, methods) are used to transform data or return information • You’ve probably used len(), concatenate(), sum() in Excel • it’s the same idea here, we provide inputs to function, function gives us outputs 4. dataset.function(arguments*)
  74. 74. @AlexisKSanders • Start with def function- name (arguments): • Add the logical steps (it’s like a math proof) indented below (with a tab or four spaces) • Buuuut we often use functions created by other programmers 5. We can define our own functions
  75. 75. @AlexisKSanders • w/ python use type(object- name) • w/ pandas use object- name.dtypes 6. Python doesn’t require programmer to specify the data type… so, uh… don’t worry about it* (…until you need to worry about it…) *everyone should be cognizant of data types
  76. 76. @AlexisKSanders • dir(object-name) for list of attributes and methods • object-name.__doc__ for documentation on the object (if it exists) 6. Python doesn’t require programmer to specify the data type… so, uh… don’t worry about it (…until you need to worry about it…) *everyone should be cognizant of data types
  77. 77. @AlexisKSanders • In Pandas they call a table a “data frame” • “Series” is a basically a column of data • pandas.dataframe_name.to_csv() to export df to csv • pandas.dataframe_name.drop_duplic ates('column-name') to remove duplicate values from a column • Check out https://www.dataschool.io/python-pandas- tips-and-tricks/ • Follow @justmarkham 7. Pandas useful stuff
  78. 78. @AlexisKSanders One installation option – Anaconda (that saved me a lot of heartache)
  79. 79. @AlexisKSanders 1. Open [Anaconda Navigator] 2. Launch [Jupyter Notebook] 3. New > Python Notebook 4. Type Python into cells 5. Click run 6. Repeat step 4-5 until objective achieved (or soul is crushed…. haha) Opening Jupyter Notebooks w/ Anaconda
  80. 80. @AlexisKSanders If you want to test out before installing, try https://colab.research.google.com/
  81. 81. @AlexisKSanders • Data Analysis in Python with Pandas (~ 8 hours, buuuut it’s very fun, engaging and useful!) • FB Prophet: https://facebook.github.io/prophet/docs/quick_start.html#python-api • SQL  Python (~1.5 hours) • Forecasting with LSTM • Tips on installing Tensorflow and Keras (that was actually helpful) Learning practical data science python
  82. 82. @AlexisKSanders Analytics: math! (data highlights in under 5 minutes)
  83. 83. @AlexisKSanders Highly correlated (closer to one) Correlations are a relationship Some correlation No correlation (closer to zero)
  84. 84. @AlexisKSanders Correlation vs. Causation correlation causation values A and B align, i.e., a relationship exists A causes B
  85. 85. @AlexisKSanders Correlation is hard….
  86. 86. @AlexisKSanders Descriptive: Explains data (e.g., there are 5 red) Descriptive versus Inference Statistics population Inference: assumptions based on “representative” sample (e.g., 10% chance of red ball*) sample *note: we know it’s actually only 5%... so there is some margin of error
  87. 87. @AlexisKSanders • Step 1: Create a hypothesis (H1), (aka, alternative) • H1: campaign sales mean for testing period > 0 • (meaning: the campaign drove an increase in sales) • Step 1.5: Reverse for null hypothesis (H0) • H0: campaign sales average ≤ 0 A common form of inference statistics = hypothesis testing
  88. 88. @AlexisKSanders Concept: You can show that the alternative hypothesis is true by disproving the null hypothesis. So by rejecting the null hypothesis, the only situation that could possibly exist is the world in which the alternative hypothesis is true.
  89. 89. @AlexisKSanders Step 3: Sample the population, step 4: calculate the p- value • p-value = chances H0 is valid • p-value < alpha = statistically significant = we reject the H0
  90. 90. @AlexisKSanders • Generally: 0.05 • Means we will reject the null hypothesis if there is <5% that we would find a sample as “Extreme” as we found Step 5: Decide statistical significance (alpha) criteria
  91. 91. @AlexisKSanders • Effect size = how much the intervention affects data • TL;DR: with a large enough sample, you will often be able to find statistical significance, even if the effect size isn’t big enough to care Effect size matters
  92. 92. @AlexisKSanders Under- and overfitting (e.g., using clustered for viz) under-fitting (too simple) appropriate- fitting over-fitting (forcefitting)
  93. 93. @AlexisKSanders Now what happens if I give my algo new data…. under- appropriate- over- we need to ensure the algo is general enough to be useful.
  94. 94. @AlexisKSanders Over- and underfitting maybe under- maybe over- creatively over-
  95. 95. @AlexisKSanders What regression over-, under-, and appropriate fitting looks like Huge thanks to Kari Nelson for providing samples!
  96. 96. @AlexisKSanders About partitioning your data to test how accurate your models are. Training, validation, and test set Training Validation Test use this for training and fitting data with model. midterm test, practice rounds for testing model accuracy (don’t look at dataset… ever)
  97. 97. @AlexisKSanders Step 2: sum distance between point and line (i.e., the error), square (to remove negatives) Step 1: how many points? We evaluate each by checking error? (e.g., a way = Root Mean Squared Error) n=10 This much error (20)2 Step 3: multiply 1/n (i.e., average) * amount of error 1/10 * (20)2 = 400/10 = 40 Step 4: take root (inverse of square) √40 ≈ ~6.325
  98. 98. @AlexisKSanders Buuuuut an analyst would never do that by hand(you’d use something like sklearn.metric’s mean_squared_error), an easy alternative to use is simply plotting residuals Huge thanks to Kari Nelson for providing samples!
  99. 99. @AlexisKSanders • In inference statistics there is always margin of sampling and non- sampling error • p-value < alpha (your chosen statistical significance criteria) = statistical significance • When modeling data, be aware of under- and overfitting models • Use validation and test data to assess model accuracy
  100. 100. @AlexisKSanders Closing thoughts
  101. 101. @AlexisKSanders • It’s not about starting with the perfect, automated reporting, it’s about iteration • Simple, accurate analyses are okay • A case study can always be revisited • Collect knowledge • Play around with visualizations, options available in BI systems, and scripting
  102. 102. @AlexisKSanders
  103. 103. @AlexisKSanders

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