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.

1. HOW DISTANCE & INTENT
SHAPE A LOCAL PACK

2. @STATrob/@getSTAT
#SearchLove
getSTAT.com/searchlove

4. DON’T IGNORE KEYWORDS
WITH IMPLIED LOCAL INTENT.
@STATrob
TWEETABLE TIP

5. 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

6. 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

7. THE CASE FOR
TRACKING LOCAL
@STATrob

11. COMPARING ZIP CODE AND
MARKET-LEVEL ORGANIC RESULTS
vs.10038 New York US-en market

12. 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

13. 5% OF ORGANIC RESULTS STAYED
IN THE SAME RANKING POSITION.
@STATrob

14. COMPARING ZIP CODE AND
MARKET-LEVEL SERP FEATURES
vs. US-en market10038 New York

15. SERP FEATURES ARE MORE AFFECTED BY
LOCALIZATION THAN ORGANIC RESULTS
10038 NY vs. US-en 97204 Portland vs. US-en
23% 20%similar similar

16. %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

17. %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

18. EVERY SERP IS A LOCALIZED SERP.
Google (almost) always knows where you are.
GROUND RULE
@STATrob

19. THE RESEARCH
@STATrob

21. GEO-MODIFICATION
When the searcher includes geographical terms in
their search query — i.e. [best shoes in Vancouver].
@STATrob

22. GEO-LOCATION
When the searcher’s device provides location
data as part of the search query.
@STATrob

23. CREATING THE BASE KEYWORDS
{Adjective +} Noun = Base
• {Chinese +} restaurant =
[Chinese restaurant]
• {acrylic +} nail salon =
[acrylic nail salon]
• {auto +} mechanic =
[auto mechanic]

24. 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

25. 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]

26. 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

27. HIGH-LEVEL FINDINGS
@STATrob

28. 73% OF KEYWORDS RETURNED
A LOCAL PACK.
@STATrob

29. Percentoflocalpacks
0%
25%
50%
75%
100%
"near me" Base "in [city]"
78%
52%
96%
APPEARANCE OF LOCAL PACKS
BY GEO-MODIFIER

30. GOOGLE IS HEDGING ITS BETS
WITH IMPLICITLY LOCAL
KEYWORDS.
@STATrob

31. LOCAL PACK OPPORTUNITY
VARIES BY GEO-MODIFIER.
GROUND RULE
@STATrob

32. HOW GOOGLE
INTERPRETS LOCAL
INTENT
@STATrob

33. MAP CENTRE POINT VS. ZIP CODE CENTRE POINT

34. 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

35. NO TWO GEO-MODIFIERS ARE
TREATED THE SAME.
GROUND RULE
@STATrob
Google can understand “near me” and far away intent.

36. THE INFLUENCE OF GEO-
MODIFICATION & GEO-
LOCATION ON LOCAL PACKS
@STATrob

37. COMPARING THE SIMILARITY OF LOCAL PACKS
WITH DIFFERENT GEO-MODIFIERS
vs.“near me” “in [city]”

38. 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

39. COMPARING THE SIMILARITY OF LOCAL
PACKS IN DIFFERENT ZIP CODES
vs.zip code 1 zip code 2

40. 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

41. LOCAL PACKS CARE MORE ABOUT
THE SEARCHER’S LOCATION THAN
THEIR GEO-MODIFIER.
GROUND RULE
@STATrob

42. TRACK MULTIPLE LOCATIONS FOR
A STRONG LOCAL PACK STRATEGY.
TWEETABLE TIP
@STATrob
Use different geo-modifiers where necessary.

43. THE INFLUENCE OF GEO-
MODIFICATION & GEO-
LOCATION ON ORGANIC
RESULTS
@STATrob

44. COMPARING THE SIMILARITY OF ORGANIC
RESULTS WITH DIFFERENT GEO-MODIFIERS
vs.“near me” “in [city]”

45. 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

46. COMPARING THE SIMILARITY OF ORGANIC
RESULTS IN DIFFERENT ZIP CODES
zip code 1 zip code 2vs.

47. 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

48. ORGANIC RESULTS CARE MORE
ABOUT THE SEARCHER’S GEO-
MODIFIER THAN THEIR LOCATION.
GROUND RULE
@STATrob

49. TRACK A VARIETY OF GEO-
MODIFIERS TO GET A DECENT
PICTURE OF ORGANIC RESULTS.
TWEETABLE TIP
@STATrob

50. LOCAL PACK
RANKING FACTORS
@STATrob

51. DISTANCE AWAY FROM
THE SEARCHER
@STATrob

52. 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

53. MEASURE THE DISTANCE OF
YOUR LOCAL PACK RESULTS,
THEN FIND COMPETITORS
WITHIN THAT RADIUS.
TWEETABLE TIP
@STATrob

54. GOOGLE RATING
@STATrob

55. 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]”

56. LOCAL PACKS CONSIDER THE
DISTANCE OF THE REQUEST
BEFORE THE RATING OF THE
BUSINESS.
GROUND RULE
@STATrob

57. ORGANIC RANKING
@STATrob

58. 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]"

59. 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

60. ADVERTISING
@STATrob

61. 16% OF LOCAL PACKS RETURNED
AN AD LISTING.
@STATrob

62. Percentoflocalpacks
0%
25%
50%
75%
100%
"near me" Base "in [city]"
19%
7%
26%
LOCAL PACK ADS BY GEO-MODIFIER

63. YOU’LL COMPETE WITH PAID
ADVERTISING LESS ON NON-
GEO-MODIFIED LOCAL PACKS.
TWEETABLE TIP
@STATrob

64. HOW LOCAL PACKS
HANDLE COMPETING
NEEDS
@STATrob

65. QUALITY
@STATrob

66. QUALITY MODIFIERS
“Best,” “good,” “recommended,” “excellent,”
“expert,” “trusted,” “professional,” “highly-rated,”
“five star,” “#1.”
@STATrob

67. 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

68. 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

69. FOCUS ON RATINGS WHEN
OPTIMIZING FOR QUERIES
RELATED TO QUALITY.
TWEETABLE TIP
@STATrob

70. AFFORDABILITY
@STATrob

71. AFFORDABILITY MODIFIERS
“Cheap,” “inexpensive,” “budget,” “deals,”
“discount,” “free,” “sale,” “affordable,”
“subsidized.”
@STATrob

72. 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

73. 0
0.3
0.6
0.9
1.2 1.171.20
DISTANCE AWAY FROM THE SEARCHER:
AFFORDABILITY
Miles
Affordability keywords Other keywords

74. 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

75. REACH IS FURTHER AND
RATINGS MATTER LESS FOR
PRICE POINT QUERIES.
GROUND RULE
@STATrob

76. BRAND
@STATrob

77. BRAND MODIFIERS
“Ford,” “Mazda,” “Hyundai,” “Honda," “BMW,”
“Mercedes,” “iPhone,” “Sony,” “Samsung,”
“Pixel,” “Apple,” “Blackberry.”
@STATrob

78. 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

79. 0
0.45
0.9
1.35
1.8
1.17
1.77
DISTANCE AWAY FROM THE
SEARCHER: BRAND
Miles
Brand keywords Other keywords

80. BRANDED KEYWORDS HAVE A
HUGE IMPACT ON LOCAL PACK
RESULTS.
TWEETABLE TIP
Segment them out.
@STATrob

81. WRAP-UP & KEY
TAKEAWAYS
@STATrob

82. EVERY SERP IS A LOCALIZED SERP.
GROUND RULE
@STATrob

83. LOCATION MATTERS, BUT
SO DO GEO-MODIFIERS.
TWEETABLE TIP
@STATrob

84. IF YOU’RE NOT SAMPLING SOME
LOCAL SERPS, YOU’RE NOT SEEING
WHAT YOUR SEARCHERS SEE.
TWEETABLE TIP
@STATrob

85. SMART SEGMENTATION IS
KEY TO LOCAL SEO SUCCESS.
TWEETABLE TIP
@STATrob
Each segment may require its own strategy.

86. YOUR LOCAL SERP TRACKING
STRATEGY PROBABLY ISN’T
SEGMENTED ENOUGH.
TWEETABLE TIP
@STATrob

87. Download the research at
getSTAT.com/searchlove.