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DMSS: SEO Insights, Analysis & Reporting: Visualizing Your SEO Data

Learn how to visualize your SEO data in an interpretable way.

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DMSS: SEO Insights, Analysis & Reporting: Visualizing Your SEO Data

  1. 1. Sam Partland Analysis, Insights & Reporting
  2. 2. About Me – Sam Partland • 12 years digital marketing experience • Lead generation & affiliate marketing for various niches • Covered all aspects of digital marketing for small businesses through my own business • Have worked agency side and in-house, and currently a growth marketer at Airtasker
  3. 3. Whilst agency side, I got bored of the repetitiveness Copy and pasting over and over Individually analysing keywords and mapping data Worst of all – monthly reporting
  4. 4. 16 hours of work every month generating 3-page PDFs Comments, SEO visits & rankings, SEM traffic & spend data across 20 hotels. Great fun!
  5. 5. So I worked out how to automate it with a button! And saved 12 hours a month. Google analytics, ranking data, & SEM data exported into a single Excel file. One button would; - Cycle each hotel - Merge SEO & SEM Data - Integrated comments - Generates 20 PDFs
  6. 6. I then worked out how to take a list of keywords
  7. 7. And efficiently group them into related topics / categories Car Truck Van
  8. 8. Which had me thinking, what else could I optimise or do better? Keyword dashboards Competitive overviews Ranking reports Opportunity analysis Combining analytics & ranking data Keyword mapping Search market overview
  9. 9. Why is any of this important? Analysis, Insights & Knowledge Forecasting & Reporting
  10. 10. The basics
  11. 11. Preparing your data • Separate sheet with a RAW table full of the data you need • One month / keyword per row in most cases • New column for anything you want to filter the data by
  12. 12. A KeywordRAW & SERPsRAW will create almost anything! KeywordRAW
  13. 13. A KeywordRAW & SerpsRAW will create almost anything! KeywordRAW SerpsRAW
  14. 14. Efficiently Categorise & Classify keywords in bulk The Magical Formula:
  15. 15. How the magical categorisation formula works 1. Find One of These
  16. 16. How the magical categorisation formula works 1. Find One of These 2. In Here
  17. 17. How the magical categorisation formula works 1. Find One of These 2. In Here 3. Then Use Matching Category
  18. 18. How the magical categorisation formula works 1. Find One of These 2. In Here 3. Then Use Matching Category 4. In Here
  19. 19. And we get thousands of keywords categorised in seconds
  20. 20. And we can also apply this to URLs
  21. 21. Pivot tables present your data in a digestible manner When you update the RAW, you can ‘refresh’ your pivot table. • Easily filter • Apply styling / formats • Calculated Fields • Power graphs
  22. 22. Slicing & filtering will let you analyse your data based on the categories A ‘slicer’ will do this most efficiently …and they’re prettier.
  23. 23. Use a default, or create custom table & slicer designs Match your clients’ colours and they’ll love you! It will take time to setup, but once done you can keep re-using!
  24. 24. Estimate traffic using a CTR model This model ‘guesses’ what clicks a site will receive. Whilst not extremely accurate, it does allow us to set a benchmark for comparison. Rank 1: 33.6% of searches Rank 7: 2.1% of searches Rank 16: 0.9% of searches
  25. 25. How the traffic estimation works 1. Find The Rank
  26. 26. How the traffic estimation works 1. Find The Rank 2. In Here
  27. 27. How the traffic estimation works 1. Find The Rank 2. In Here 3. Then Multiply Search Volume
  28. 28. How the traffic estimation works 1. Find The Rank 2. In Here 3. Then Multiply Search Volume 4. By The Matching Approx. CTR
  29. 29. How the traffic estimation works 1. Find The Rank 2. In Here 3. Then Multiply Search Volume 4. By The Matching Approx. CTR Rank 1 CTR: 33.6% 480 Volume x 0.336 = 161 Visits
  30. 30. Putting it all to use..
  31. 31. Faster Keyword Mapping Leverage categorisation to automate keyword mapping. You will need; • Categorised keywords & URLs
  32. 32. Start with a raw table that has categorised keywords & landing pages Merging both allow you to use a single pivot table
  33. 33. Pivot by category structure, then add URLs & Keywords LocationCategory URL Keyword
  34. 34. Copy paste this out of the pivot table Drag down the URLs & remove blanks
  35. 35. Search Market Overview The search market overview is going to show you what categories / locations get searched, and the top keywords. You will need; • Keywords with search volume • Categorisation setup & applied
  36. 36. Start with pivots for anything you want volume graphs for
  37. 37. Then create separate pivot tables for the top categories Include their keywords with volumes
  38. 38. Then just join it all together, and clean it up! We get an overview of the top categories, locations & keywords within a niche.
  39. 39. Keyword Dashboard Reports aren’t always the best way to present data, and are best avoided where possible! You should try using dashboards. You will need: • Categorised keyword data • Search volumes
  40. 40. Empower clients to pull their own insights Through the use of Pivot Tables & Slicers, you can send a client an interactive document.
  41. 41. Setup categorised overviews with the keywords (or subcategories) below Expanded to the keywords within by clicking the ‘+’
  42. 42. You can also slice the data to filter by other categories Truck 4WD
  43. 43. The best way to present keyword data to a client
  44. 44. You could look at the top locations for van hire keywords
  45. 45. Then see the top keywords for Melbourne van hire
  46. 46. Competitive Landscape The competitive landscape document will show who the top players are in your categories. You will need; • Top 10 (or 20) domains ranking for a keyword set • Estimated traffic formula applied.
  47. 47. Create a pivot table & graph for the top domains Use estimated traffic to weigh them up against each other.
  48. 48. Then add separate pivot tables for top sites per category
  49. 49. Once put together, we get an overview of top competitors within a niche This will assist in setting your performance goals, as you now know who ranks where.
  50. 50. And you could even add a category slicer to the graph
  51. 51. And you could even add a category slicer to the graph
  52. 52. And you could even add a category slicer to the graph
  53. 53. Opportunity Analysis An opportunity analysis will allow you to identify key categories of interest for your client. You will need: • Categorised keyword data • Search volumes • Rankings
  54. 54. Split your data into the categories you want
  55. 55. And use two calculated fields / metrics ‘If #1’ – ‘Search Volume’ x ‘#1 CTR’ (0.336)
  56. 56. And use two calculated fields / metrics ‘If #1’ = ‘Search Volume’ x ‘#1 CTR’ (0.336) ‘%Share of #1’ = ‘Est Traffic’ / ‘If #1’)
  57. 57. And then just piece it together
  58. 58. Another way to look at opportunity This is how you could value up step change increase of rank.
  59. 59. Ranking Snapshot A ranking snapshot is ideal as a once off, or initial, report to show where a website stands. You will need: • Categorised keyword data • Search volumes • Rankings
  60. 60. Create a pivot table that filters to clients Est Traffic And add a calculated field for % Share of #1. % Share of #1 = 'Est. Traffic‘ / '#1‘ Where ‘#1’ is the estimated traffic for #1 (Volume * 0.336)
  61. 61. Then split out categorised keywords with their rankings
  62. 62. Piece together and we get a good performance overview This also roughly touches on how they are performing compared to how #1 would be performing.
  63. 63. Weighted Rankings Not all keywords are equal. An ‘average rank’ treats them like they are.
  64. 64. At a quick glance, this report is not showing anything positive
  65. 65. Individual keywords actually show some positive movement though But the graph still points south! 
  66. 66. Using ‘Est. Traffic’ / ‘Volume’ we can get a weighted market share Since the highest volume terms still increased in rank, the overall market share movement was actually positive.
  67. 67. Good way to show ranking movement, and cover all bases Could also just show ‘estimated traffic’ instead of market share. However, that doesn’t help understand the potential.
  68. 68. Onedrive OneDrive allows you to present your data, in an online dashboard. Don’t send your client a file, send them a link!
  69. 69. Save your data to onedrive There is an approx. 5MB file limit for this to work, but that will handle a lot of data still.
  70. 70. Then just create a sharing link
  71. 71. You can now share the link with anyone and view in the browser
  72. 72. Custom SERP Analysis Analyse the top ranking websites for a large keyword set in Bulk. You will need; • Top 10 (or 20) domains ranking for a keyword set • Estimated traffic formula applied.
  73. 73. Add anything URL / Domain specific to your SERPraw for comparison I’m going to use Page Titles
  74. 74. We can now see the top ranking page titles by category You don’t always need to be different.
  75. 75. Clicking each of the slicers reveals the top titles per category Bus Truck
  76. 76. Better yet though – you could analyse any metrics for YOUR serps
  77. 77. Analyse keywords in bulk for ranking potential
  78. 78. Build a list of sites that you think you are your closest competition Check the top sites list And build a table in your SERPs ranking raw file
  79. 79. Give each ranking domain their score By using a vlookup to the domain table =IFERROR(VLOOKUP([@Domain],Competitors,2,0),0)
  80. 80. Then each keyword gets a total score Using a SUMIF that matches the keyword from Serps RAW to Keyword Raw. =SUMIF(serpraw[Keyword],[@Keyword],serpraw[Domain score?])
  81. 81. Pivoting this, we see estimated traffic by that ‘rankability’ score
  82. 82. And most importantly – the low scoring keywords
  83. 83. Why’d they get a low score? - International keywords - Niche / to targeted Filtering by a score under 2… - Some complete scraps that slipped through
  84. 84. I love this type of keyword analysis • Removes junky keywords from your research • Removes ‘dictionary’ keywords • Filters our keywords that might prove too difficult • Highlights international / niche keywords Let Google tell you what to try target based on your competitors.
  85. 85. Google Analytics Exporting your Google Analytics data allows you to expand upon it, analyse it, and present it how you would like to. You also don’t need to train a client on how to use GA to be able to see their performance.
  86. 86. Create a GA custom report, and export the data you want to analyse This month-by-month report has been split out channel.
  87. 87. Create a GA custom report, and export the data you want to analyse This month-by-month report has been split out channel. Insert Month, and Year to analyse YoY data.
  88. 88. Insert pivot tables for each metric
  89. 89. Setup a ‘current’ and ‘previous’ pivot table Two slicers are setup to select a month (or period) for the report, and a comparison month (or period). These control pivot tables with the associated metrics.
  90. 90. Great Google Analytics overview Using a slicer, you can generate channel specific reports, and create a small set of easily-updated pages for the report. Once data is updated, all you need to do is press the new date!
  91. 91. Automate your comments based on increases / declines The comments are even being auto-magically generated based on whether a metric has increased or decreased. Add a personal touch to the comment, and you’ve saved yourself a bit of time each month.
  92. 92. Merging analytics & ranking performance
  93. 93. Have your analytics and ranking data merged in one RAW 1. 1. Separate between traffic / rankings
  94. 94. Have your analytics and ranking data merged in one RAW 1. 1. Separate between traffic / rankings 2. Keyword or landing page 2.
  95. 95. Have your analytics and ranking data merged in one RAW 1. 1. Separate between traffic / rankings 2. Keyword or landing page 3. Same category columns to collate data 2. 3.
  96. 96. Have your analytics and ranking data merged in one RAW 1. 1. Separate between traffic / rankings 2. Keyword or landing page 3. Same category columns to collate data 4. Any visitor metrics 2. 3. 4.
  97. 97. Have your analytics and ranking data merged in one RAW 1. 1. Separate between traffic / rankings 2. Keyword or landing page 3. Same category columns to collate data 4. Any visitor metrics 5. Ranking & estimated visitor data by month 2. 3. 4. 5.
  98. 98. Create pivot tables for Rankings, Est Traffic, Visits, & Conversions You will need a separate table for anything that you want to have a separate graph for.
  99. 99. Create a graph from each table
  100. 100. Add your category slicers
  101. 101. Clean it up to have an organic performance dashboard Category or location performance insights Confirm whether ranking movement is impacting visits on a landing page level
  102. 102. So by clicking ‘car’ & ‘Melbourne’ we can filter the report And can see Melbourne was the reason for the June spike
  103. 103. With a full use case being a dashboard I built agency side
  104. 104. So, we’ve covered • Keyword & URL categorisation and what to do with it • Presenting various forms of SEO data ‘prettily’ • Leveraging dashboards rather than always using reports • Analysing the SERPs to determine what is ranking • Merging keywords & landing page performance
  105. 105. Thanks!  seoinexcel.com/dmss sam_partland linkedin.com/in/sampartland/

Learn how to visualize your SEO data in an interpretable way.

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