1.
HOW DISTANCE & INTENT
SHAPE A LOCAL PACK

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

3.
DON’T IGNORE KEYWORDS
WITH IMPLIED LOCAL INTENT.
@STATrob
TWEETABLE TIP

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.
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.
THE CASE FOR
TRACKING LOCAL
@STATrob

7.
COMPARING ZIP CODE AND
MARKET-LEVEL ORGANIC RESULTS
vs.10038 New York US-en market

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

10.
COMPARING ZIP CODE AND
MARKET-LEVEL SERP FEATURES
vs. US-en market10038 New York

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

15.
THE RESEARCH
@STATrob

16.
GEO-MODIFICATION
When the searcher includes geographical terms in
their search query — i.e. [best shoes in Vancouver].
@STATrob

17.
GEO-LOCATION
When the searcher’s device provides location
data as part of the search query.
@STATrob

18.
CREATING THE BASE KEYWORDS
{Adjective +} Noun = Base
• {Chinese +} restaurant =
[Chinese restaurant]
• {acrylic +} nail salon =
[acrylic nail salon]
• {auto +} mechanic =
[auto mechanic]

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.
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.
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.
HIGH-LEVEL FINDINGS
@STATrob

23.
73% OF KEYWORDS RETURNED
A LOCAL PACK.
@STATrob

24.
Percentoflocalpacks
0%
25%
50%
75%
100%
"near me" Base "in [city]"
78%
52%
96%
APPEARANCE OF LOCAL PACKS
BY GEO-MODIFIER

25.
GOOGLE IS HEDGING ITS BETS
WITH IMPLICITLY LOCAL
KEYWORDS.
@STATrob

26.
LOCAL PACK OPPORTUNITY
VARIES BY GEO-MODIFIER.
GROUND RULE
@STATrob

27.
HOW GOOGLE
INTERPRETS LOCAL
INTENT
@STATrob

28.
MAP CENTRE POINT VS. ZIP CODE CENTRE POINT

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

31.
THE INFLUENCE OF GEO-
MODIFICATION & GEO-
LOCATION ON LOCAL PACKS
@STATrob

32.
COMPARING THE SIMILARITY OF LOCAL PACKS
WITH DIFFERENT GEO-MODIFIERS
vs.“near me” “in [city]”

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

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

37.
TRACK MULTIPLE LOCATIONS FOR
A STRONG LOCAL PACK STRATEGY.
TWEETABLE TIP
@STATrob
Use different geo-modifiers where necessary.

38.
THE INFLUENCE OF GEO-
MODIFICATION & GEO-
LOCATION ON ORGANIC
RESULTS
@STATrob

39.
COMPARING THE SIMILARITY OF ORGANIC
RESULTS WITH DIFFERENT GEO-MODIFIERS
vs.“near me” “in [city]”

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

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

44.
TRACK A VARIETY OF GEO-
MODIFIERS TO GET A DECENT
PICTURE OF ORGANIC RESULTS.
TWEETABLE TIP
@STATrob

45.
LOCAL PACK
RANKING FACTORS
@STATrob

46.
DISTANCE AWAY FROM
THE SEARCHER
@STATrob

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

49.
GOOGLE RATING
@STATrob

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

52.
ORGANIC RANKING
@STATrob

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.
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.
ADVERTISING
@STATrob

56.
16% OF LOCAL PACKS RETURNED
AN AD LISTING.
@STATrob

57.
Percentoflocalpacks
0%
25%
50%
75%
100%
"near me" Base "in [city]"
19%
7%
26%
LOCAL PACK ADS BY GEO-MODIFIER

58.
YOU’LL COMPETE WITH PAID
ADVERTISING LESS ON NON-
GEO-MODIFIED LOCAL PACKS.
TWEETABLE TIP
@STATrob

59.
HOW LOCAL PACKS
HANDLE COMPETING
NEEDS
@STATrob

60.
QUALITY
@STATrob

61.
QUALITY MODIFIERS
“Best,” “good,” “recommended,” “excellent,”
“expert,” “trusted,” “professional,” “highly-rated,”
“five star,” “#1.”
@STATrob

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

65.
AFFORDABILITY
@STATrob

66.
AFFORDABILITY MODIFIERS
“Cheap,” “inexpensive,” “budget,” “deals,”
“discount,” “free,” “sale,” “affordable,”
“subsidized.”
@STATrob

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

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

71.
BRAND
@STATrob

72.
BRAND MODIFIERS
“Ford,” “Mazda,” “Hyundai,” “Honda," “BMW,”
“Mercedes,” “iPhone,” “Sony,” “Samsung,”
“Pixel,” “Apple,” “Blackberry.”
@STATrob

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

76.
WRAP-UP & KEY
TAKEAWAYS
@STATrob

77.
EVERY SERP IS A LOCALIZED SERP.
GROUND RULE
@STATrob

78.
LOCATION MATTERS, BUT
SO DO GEO-MODIFIERS.
TWEETABLE TIP
@STATrob

79.
IF YOU’RE NOT SAMPLING SOME
LOCAL SERPS, YOU’RE NOT SEEING
WHAT YOUR SEARCHERS SEE.
TWEETABLE TIP
@STATrob

80.
SMART SEGMENTATION IS
KEY TO LOCAL SEO SUCCESS.
TWEETABLE TIP
@STATrob
Each segment may require its own strategy.

81.
YOUR LOCAL SERP TRACKING
STRATEGY PROBABLY ISN’T
SEGMENTED ENOUGH.
TWEETABLE TIP
@STATrob

82.
Download the research at
getSTAT.com/searchlove.