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SearchLove London 2018 - Rob Bucci - How Distance and Intent Shape a Local Pack

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Since we can’t always rely on a searcher to state when their intent is local, we should look to keywords where that intent is implied. But is Google any good at interpreting implicit local intent? And is it treated the same as explicit intent?

Google decides what queries mean, so it’s important to understand that decision when building out a tracking strategy. In his talk, Rob will unpack the local pack, revealing how Google handles different kinds of intent and what elements go into shaping this local SERP mainstay.

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SearchLove London 2018 - Rob Bucci - How Distance and Intent Shape a Local Pack

  1. 1. HOW DISTANCE & INTENT SHAPE A LOCAL PACK
  2. 2. @STATrob/@getSTAT #SearchLove getSTAT.com/searchlove
  3. 3. DON’T IGNORE KEYWORDS WITH IMPLIED LOCAL INTENT. @STATrob TWEETABLE TIP
  4. 4. IT’S UP TO GOOGLE TO INTERPRET THE INTENT 
 OF A SEARCHER’S QUERY. But it’s up to us to understand Google’s decision. @STATrob
  5. 5. EACH QUERY COULD BE ASKING FOR SOMETHING DIFFERENT [sushi near me] — close proximity? [sushi in Seattle] — anywhere’s good? [sushi] — restaurant or general info? [best sushi] — highest quality? @STATrob
  6. 6. THE CASE FOR TRACKING LOCAL @STATrob
  7. 7. COMPARING ZIP CODE AND 
 MARKET-LEVEL ORGANIC RESULTS vs.10038 New York US-en market
  8. 8. MARKET-LEVEL SERPS ARE MISSING 70% OF THE RESULTS THAT SEARCHERS ACTUALLY SEE 10038 NY vs. US-en 97204 Portland vs. US-en 33% 30%similar similar
  9. 9. 5% OF ORGANIC RESULTS STAYED IN THE SAME RANKING POSITION. @STATrob
  10. 10. COMPARING ZIP CODE AND MARKET-LEVEL SERP FEATURES vs. US-en market10038 New York
  11. 11. SERP FEATURES ARE MORE AFFECTED BY LOCALIZATION THAN ORGANIC RESULTS 10038 NY vs. US-en 97204 Portland vs. US-en 23% 20%similar similar
  12. 12. %ofSERPS 0.0% 0.5% 1.0% Home services Jobs 0.01%0.00% 0.10% 0.20% 0.09% 0.25% 97204 Portland 10038 New York US-en market PERCENT OF SERPS WITH 
 HOME SERVICES & JOBS RESULTS
  13. 13. %ofSERPS 0.0% 1.0% 2.0% 3.0% 4.0% Featured snippets 2.94% 1.73% 2.09% 97204 Portland 10038 New York US-en market PERCENT OF SERPS WITH 
 FEATURED SNIPPETS
  14. 14. EVERY SERP IS A LOCALIZED SERP. Google (almost) always knows where you are. GROUND RULE @STATrob
  15. 15. THE RESEARCH @STATrob
  16. 16. GEO-MODIFICATION When the searcher includes geographical terms in their search query — i.e. [best shoes in Vancouver]. @STATrob
  17. 17. GEO-LOCATION When the searcher’s device provides location data as part of the search query. @STATrob
  18. 18. CREATING THE BASE KEYWORDS {Adjective +} Noun = Base • {Chinese +} restaurant = 
 [Chinese restaurant] • {acrylic +} nail salon = 
 [acrylic nail salon] • {auto +} mechanic = 
 [auto mechanic]
  19. 19. Qualifier + {Adjective +} Noun = Base • best + {Chinese +} restaurant = 
 [best Chinese restaurant] • affordable + {acrylic +} nail salon = 
 [affordable acrylic nail salon] • Porsche + {auto +} mechanic = 
 [porsche auto mechanic] CREATING THE BASE KEYWORDS
  20. 20. CREATING THE GEO-
 MODIFIED KEYWORDS Base + Geo-modifier = Geo-modified • best Chinese restaurant + near me = 
 [best Chinese restaurant near me] • affordable acrylic nail salon + in Portland = [affordable acrylic nail salon in portland] • Porsche auto mechanic + in New York = [porsche auto mechanic in new york]
  21. 21. THE TRACKING STRATEGY Each set of three keywords was tracked in two 
 zip codes in New York and Portland — 30% on 
 desktop, 70% on mobile. @STATrob
  22. 22. HIGH-LEVEL FINDINGS @STATrob
  23. 23. 73% OF KEYWORDS RETURNED A LOCAL PACK. @STATrob
  24. 24. Percentoflocalpacks 0% 25% 50% 75% 100% "near me" Base "in [city]" 78% 52% 96% APPEARANCE OF LOCAL PACKS BY GEO-MODIFIER
  25. 25. GOOGLE IS HEDGING ITS BETS WITH IMPLICITLY LOCAL KEYWORDS. @STATrob
  26. 26. LOCAL PACK OPPORTUNITY VARIES BY GEO-MODIFIER. GROUND RULE @STATrob
  27. 27. HOW GOOGLE INTERPRETS LOCAL INTENT @STATrob
  28. 28. MAP CENTRE POINT VS. ZIP CODE CENTRE POINT
  29. 29. Miles 0 0.2 0.4 0.6 0.8 1 1.2 1.4 "near me" Base "in [city]" 1.37 0.99 0.62 DISTANCE BETWEEN CENTRE POINTS BY GEO-MODIFIER
  30. 30. NO TWO GEO-MODIFIERS ARE TREATED THE SAME. GROUND RULE @STATrob Google can understand “near me” and far away intent.
  31. 31. THE INFLUENCE OF GEO- MODIFICATION & GEO- LOCATION ON LOCAL PACKS @STATrob
  32. 32. COMPARING THE SIMILARITY OF LOCAL PACKS WITH DIFFERENT GEO-MODIFIERS vs.“near me” “in [city]”
  33. 33. GEO-MODIFIERS CHANGE A LOCAL PACK BY ROUGHLY 20% “near me” vs. “in [city]” “near me” vs. base “in [city]” vs. base 81% 81% 78%similar similarsimilar
  34. 34. COMPARING THE SIMILARITY OF LOCAL 
 PACKS IN DIFFERENT ZIP CODES vs.zip code 1 zip code 2
  35. 35. 26% 49% 64%similar similarsimilar GEO-LOCATION HAS A BIG INFLUENCE ON LOCAL PACKS, AND VARIES BY GEO-MODIFIER Base “in [city]”“near me” zip code 1 vs. zip code 2 zip code 1 vs. zip code 2 zip code 1 vs. zip code 2
  36. 36. LOCAL PACKS CARE MORE ABOUT THE SEARCHER’S LOCATION THAN THEIR GEO-MODIFIER. GROUND RULE @STATrob
  37. 37. TRACK MULTIPLE LOCATIONS FOR A STRONG LOCAL PACK STRATEGY. TWEETABLE TIP @STATrob Use different geo-modifiers where necessary.
  38. 38. THE INFLUENCE OF GEO- MODIFICATION & GEO- LOCATION ON ORGANIC RESULTS @STATrob
  39. 39. COMPARING THE SIMILARITY OF ORGANIC RESULTS WITH DIFFERENT GEO-MODIFIERS vs.“near me” “in [city]”
  40. 40. GEO-MODIFIERS HAVE A SIGNIFICANT INFLUENCE ON ORGANIC RESULTS “near me” vs. “in [city]” “near me” vs. base “in [city]” vs. base 32% 40% 19%similar similarsimilar
  41. 41. COMPARING THE SIMILARITY OF ORGANIC RESULTS IN DIFFERENT ZIP CODES zip code 1 zip code 2vs.
  42. 42. GEO-LOCATION CHANGES ORGANIC RESULTS BY ROUGHLY 20% “near me” Base “in [city]” zip code 1 vs. zip code 2 zip code 1 vs. zip code 2zip code 1 vs. zip code 2 75% 80% 81%similar similarsimilar
  43. 43. ORGANIC RESULTS CARE MORE ABOUT THE SEARCHER’S GEO- MODIFIER THAN THEIR LOCATION. GROUND RULE @STATrob
  44. 44. TRACK A VARIETY OF GEO- MODIFIERS TO GET A DECENT PICTURE OF ORGANIC RESULTS. TWEETABLE TIP @STATrob
  45. 45. LOCAL PACK RANKING FACTORS @STATrob
  46. 46. DISTANCE AWAY FROM THE SEARCHER @STATrob
  47. 47. Miles 0 1 2 3 4 5 New York Portland 3.4 1.2 2.9 1.1 2.5 1.2 Rank 1 Rank 2 Rank 3 DISTANCE AWAY FROM THE 
 SEARCHER BY RANK
  48. 48. MEASURE THE DISTANCE OF YOUR LOCAL PACK RESULTS, THEN FIND COMPETITORS WITHIN THAT RADIUS. TWEETABLE TIP @STATrob
  49. 49. GOOGLE RATING @STATrob
  50. 50. GOOGLE RATINGS BY GEO-MODIFIERPercentoflocalpacks 0% 3.5% 7% 10.5% 14% Google rating 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 “near me” Base “in [city]”
  51. 51. LOCAL PACKS CONSIDER THE DISTANCE OF THE REQUEST BEFORE THE RATING OF THE BUSINESS. GROUND RULE @STATrob
  52. 52. ORGANIC RANKING @STATrob
  53. 53. ORGANIC RANK OF LOCAL PACK RESULTS Percentoflocalpacks 0% 0.1% 0.2% Rank 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 "near me" Base "in [city]"
  54. 54. LOCAL PACKS PREFER FIRST PAGE ORGANIC RANKS. Base and “near me” keywords pull most from rank four, while “in [city]” keywords like rank seven. TWEETABLE TIP @STATrob
  55. 55. ADVERTISING @STATrob
  56. 56. 16% OF LOCAL PACKS RETURNED AN AD LISTING. @STATrob
  57. 57. Percentoflocalpacks 0% 25% 50% 75% 100% "near me" Base "in [city]" 19% 7% 26% LOCAL PACK ADS BY GEO-MODIFIER
  58. 58. YOU’LL COMPETE WITH PAID ADVERTISING LESS ON NON- GEO-MODIFIED LOCAL PACKS. TWEETABLE TIP @STATrob
  59. 59. HOW LOCAL PACKS HANDLE COMPETING NEEDS @STATrob
  60. 60. QUALITY @STATrob
  61. 61. QUALITY MODIFIERS “Best,” “good,” “recommended,” “excellent,” “expert,” “trusted,” “professional,” “highly-rated,” “five star,” “#1.” @STATrob
  62. 62. MORE BUSINESSES CAN BE CLASSIFIED 
 AS HIGH QUALITY Quality keywords zip code 1 vs. zip code 2 zip code 1 vs. zip code 2 43% 45%similar similar Other keywords
  63. 63. GOOGLE RATINGS: QUALITYPercentoflocalpacks 0% 4% 8% 12% 16% Google rating 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 Quality keywords Other keywords
  64. 64. FOCUS ON RATINGS WHEN OPTIMIZING FOR QUERIES RELATED TO QUALITY. TWEETABLE TIP @STATrob
  65. 65. AFFORDABILITY @STATrob
  66. 66. AFFORDABILITY MODIFIERS “Cheap,” “inexpensive,” “budget,” “deals,” “discount,” “free,” “sale,” “affordable,” “subsidized.” @STATrob
  67. 67. FEWER BUSINESSES CAN BE CLASSIFIED 
 AS AFFORDABLE Affordability keywords zip code 1 vs. zip code 2 zip code 1 vs. zip code 2 46% 44%similar similar Other keywords
  68. 68. 0 0.3 0.6 0.9 1.2 1.171.20 DISTANCE AWAY FROM THE SEARCHER: AFFORDABILITY Miles Affordability keywords Other keywords
  69. 69. GOOGLE RATINGS: AFFORDABILITYPercentoflocalpacks 0% 5% 9% 14% 18% Google rating 1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5 Affordability keywords Other keywords
  70. 70. REACH IS FURTHER AND RATINGS MATTER LESS FOR PRICE POINT QUERIES. GROUND RULE @STATrob
  71. 71. BRAND @STATrob
  72. 72. BRAND MODIFIERS “Ford,” “Mazda,” “Hyundai,” “Honda," “BMW,” “Mercedes,” “iPhone,” “Sony,” “Samsung,” “Pixel,” “Apple,” “Blackberry.” @STATrob
  73. 73. BRANDS ARE MORE LIKELY TO APPEAR IN A 
 LOCAL PACK WHEN SEARCHED 59% 42%similar similar zip code 1 vs. zip code 2 zip code 1 vs. zip code 2 Other keywordsBrand keywords
  74. 74. 0 0.45 0.9 1.35 1.8 1.17 1.77 DISTANCE AWAY FROM THE 
 SEARCHER: BRAND Miles Brand keywords Other keywords
  75. 75. BRANDED KEYWORDS HAVE A HUGE IMPACT ON LOCAL PACK RESULTS. TWEETABLE TIP Segment them out. @STATrob
  76. 76. WRAP-UP & KEY TAKEAWAYS @STATrob
  77. 77. EVERY SERP IS A LOCALIZED SERP. GROUND RULE @STATrob
  78. 78. LOCATION MATTERS, BUT SO DO GEO-MODIFIERS. TWEETABLE TIP @STATrob
  79. 79. IF YOU’RE NOT SAMPLING SOME LOCAL SERPS, YOU’RE NOT SEEING WHAT YOUR SEARCHERS SEE. TWEETABLE TIP @STATrob
  80. 80. SMART SEGMENTATION IS KEY TO LOCAL SEO SUCCESS. TWEETABLE TIP @STATrob Each segment may require its own strategy.
  81. 81. YOUR LOCAL SERP TRACKING STRATEGY PROBABLY ISN’T SEGMENTED ENOUGH. TWEETABLE TIP @STATrob
  82. 82. Download the research at getSTAT.com/searchlove.

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