PCC Plants

PCC Plants icon

ASO Keyword Dashboard

Tracking 18 keywords for PCC Plants in Apple App Store

Developer: David Bourne Category: books_and_reference

PCC Plants tracks 18 keywords (1 keyword ranks; 17 need traction). Key metrics: 100% top-10 coverage, opportunity 70.4, difficulty 38.1, best rank 5.

Tracked keywords

18

1  ranked •  17  not ranking yet

Top 10 coverage

100%

Best rank 5 • Latest leader —

Avg opportunity

70.4

Top keyword: project

Avg difficulty

38.1

Lower scores indicate easier wins

Opportunity leaders

  • project

    Opportunity: 73.0 • Difficulty: 42.2 • Rank —

    Competitors: 95

    68.0
  • model

    Opportunity: 73.0 • Difficulty: 40.5 • Rank —

    Competitors: 109

    65.5
  • found

    Opportunity: 73.0 • Difficulty: 43.4 • Rank —

    Competitors: 230

    68.3
  • native

    Opportunity: 73.0 • Difficulty: 40.5 • Rank —

    Competitors: 69

    66.7
  • identification

    Opportunity: 72.0 • Difficulty: 37.7 • Rank —

    Competitors: 30

    60.6

Unranked opportunities

  • project

    Opportunity: 73.0 • Difficulty: 42.2 • Competitors: 95

  • model

    Opportunity: 73.0 • Difficulty: 40.5 • Competitors: 109

  • found

    Opportunity: 73.0 • Difficulty: 43.4 • Competitors: 230

  • native

    Opportunity: 73.0 • Difficulty: 40.5 • Competitors: 69

  • identification

    Opportunity: 72.0 • Difficulty: 37.7 • Competitors: 30

High competition keywords

  • provide

    Total apps: 55,830 • Major competitors: 560

    Latest rank: — • Difficulty: 48.4

  • important

    Total apps: 49,101 • Major competitors: 520

    Latest rank: — • Difficulty: 48.7

  • training

    Total apps: 30,145 • Major competitors: 309

    Latest rank: — • Difficulty: 45.6

  • part

    Total apps: 29,306 • Major competitors: 408

    Latest rank: — • Difficulty: 48.2

  • accurate

    Total apps: 27,865 • Major competitors: 328

    Latest rank: — • Difficulty: 45.5

All tracked keywords

Includes opportunity, difficulty, rankings and competitor benchmarks

Major Competitors
diverse selection691003147

646 competing apps

Median installs: 650

Avg rating: 4.2

55

17

major competitor apps

provide681004879

55,830 competing apps

Median installs: 350

Avg rating: 4.1

560

major competitor apps

accurate701004674

27,865 competing apps

Median installs: 450

Avg rating: 4.1

328

major competitor apps

part701004874

29,306 competing apps

Median installs: 400

Avg rating: 4.1

408

major competitor apps

project731004268

12,359 competing apps

Median installs: 350

Avg rating: 4.1

95

major competitor apps

important691004978

49,101 competing apps

Median installs: 400

Avg rating: 4.1

520

major competitor apps

training701004674

30,145 competing apps

Median installs: 400

Avg rating: 4.2

309

major competitor apps

model731004166

8,725 competing apps

Median installs: 400

Avg rating: 4.0

109

major competitor apps

plants711003756

2,263 competing apps

Median installs: 500

Avg rating: 4.1

40

major competitor apps

found731004368

12,847 competing apps

Median installs: 500

Avg rating: 4.1

230

major competitor apps

native731004167

10,247 competing apps

Median installs: 450

Avg rating: 4.1

69

major competitor apps

alien711003554

1,858 competing apps

Median installs: 900

Avg rating: 4.1

39

major competitor apps

identification721003861

4,423 competing apps

Median installs: 350

Avg rating: 3.9

30

major competitor apps

ml691002744

470 competing apps

Median installs: 300

Avg rating: 4.0

5

major competitor apps

restoration701003250

958 competing apps

Median installs: 500

Avg rating: 4.1

21

major competitor apps

anticipated691003243

383 competing apps

Median installs: 700

Avg rating: 4.2

12

major competitor apps

encouraged701003050

995 competing apps

Median installs: 300

Avg rating: 4.2

2

major competitor apps

prairie671002135

133 competing apps

Median installs: 250

Avg rating: 4.2

0

major competitor apps

18 keywords
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App Description

This is part of a prairie restoration project whereby native plants can be encouraged and alien plants removed. Accurate identification of the plants at PCC is important to this project. It is anticipated that training the ML model with plants found at PCC will provide more accurate results.