"bus time" App Store Keywords Research Case | ASOTools
1.Preparing Research App Store Keywords
When you are working on App Store Optimization Services, in order to help you choose the best App Store keywords in Google Play or Apple App Store , it is worth studying this report carefully. This keyword research report about “bus time” will use the data collected by ASOTools for a comprehensive analytic. For example, long tail keyword data: “starling bank”, “calculadora imc”, “songs videos download”, “peacock”, “dophin”, “вода”.
If you are doing App Store keyword research with the keyword inspector, we recommend that you create a specification for your app store keyword optimization. For instance：
| Question Keywords |
User Keywords |
The action Keywords |
Function Keywords |
The location Keywords |
| Question keywords describe why users search for applications. | User Keywords represent the role your target audience plays when using the application. | The action keyword specifies how to handle your application and use it. |
Function keywords are basic functions of an application. | The location keyword describes where people use your application. |
Well, after establishing the theoretical basis of App Store optimization, let’s make a case analytic and study the specific data performance of this keyword in Google Store and Apple App store with app store optimization tools.
2.Research Keyword “bus time” with Google Play Data
Extended Long tail words
Obviously, once the root of the core keyword is determined, you need to expand more long tail words. For example, I have collected the top 5 long tail words related to the keyword bus time from the ASOTools, as follows:
| Top 5 Keywords | Search Volume | KD | Search results | Head apps |
| bus timer | 48 | 7 | 250+ | 109 |
| mta bus time | 41 | 5 | 250+ | 74 |
| bus times | 31 | 13 | 250+ | 152 |
| bus arrival time | 29 | 4 | 249 | 74 |
| bus time sg | 12 | 5 | 250+ | 138 |
The search volume of the core keyword bus time in Google App Store reached 38, its difficulty level reached 18, and the number of apps related to it reached 250+, among them, there are 126 apps with more than 1,000 comments, and the top 10 apps are selected. They are:
| # | App Icon | App Name | Top1 Keyword | Search Volume | KD |
| 1 |
 |
MTA Bus Time | mta bus time | 41 | 5 |
| 2 |
 |
Transit: Bus & Subway Times | traset | 100 | 29 |
| 3 |
 |
NYC Bus Time - New York Bus Tracker | bus new york city | 7 | 10 |
| 4 |
 |
Bustime | atyrau bus | ≤5 | 25 |
| 5 |
 |
Bus Times - Live Arrivals for Public Transit | bus times | 31 | 13 |
| 6 |
 |
Moovit: Timing & Navigation for all Transit Types | moved | 51 | 18 |
| 7 |
 |
NYC Mta Bus Tracker | mta tracker | ≤5 | 4 |
| 8 |
 |
MyTransit NYC Subway, MTA Bus, LIRR & Metro North | staten island railroad | 6 | 15 |
| 9 |
 |
New York Bus Time App | new york bus time | ≤5 | 30 |
| 10 |
 |
NextBus | next bus | 37 | 11 |
The top 5 keywords mentioned above are just one of the selected cases. You can select keywords in any range according to your own criteria. Generally, the search volume for keywords should be between 20 and 80, the difficulty should be less than 50, and then analyze the keyword data of the competing App according to the keywords.
ASOTools Suggested Keyword
| Top 5 Keywords | Search Volume | KD | Search results | Head apps |
| traset | 100 | 29 | 250+ | 186 |
| movitas | 68 | 58 | 250+ | 237 |
| bla bla car | 66 | 22 | 250+ | 140 |
| new york | 65 | 70 | 250+ | 45 |
| mappy itinéraires | 63 | 57 | 248 | 242 |
In the era of big data analysis of app store keywords, computers are sometimes smarter than us. Therefore, ASOTools provides us with a new function, that is, based on your search target keywords, it is extremely intelligent to recommend accurate keywords for potential traffic.
Top 10 apps related to the App Store keywords:
| # | App Icon | App Name | Top1 Keyword | Search Volume | KD |
| 1 |
|
Transit: Bus & Subway Times | tranportation | 53 | 42 |
| 2 |
|
MyTransit NYC Subway, MTA Bus, LIRR & Metro North | staten island railroad | 6 | 15 |
| 3 |
|
NextBus | next bus | 37 | 11 |
| 4 |
 |
OneBusAway | one bus away | 13 | 12 |
| 5 |
 |
myStop® Mobile | sunline | 18 | 37 |
| 6 |
 |
Whiz • Live Subway & Bus Times | whiz | 25 | 39 |
| 7 |
 |
NJ TRANSIT Mobile App | newjersey | 53 | 2 |
| 8 |
 |
Metro Transit | metro transit | 32 | 5 |
| 9 |
 |
Rocketman – Bus & Train Times | rocketman | 30 | 32 |
| 10 |
 |
TransLoc Rider | rider app | 13 | 10 |
These five keywords: “traset”, “movitas”, “bla bla car”, “new york”, “mappy itinéraires”. If you think the traffic is good, you can seriously consider optimizing it into your App store. You can also carefully study these ten apps: “Transit: Bus & Subway Times”, “MyTransit NYC Subway, MTA Bus, LIRR & Metro North”, “NextBus”, “OneBusAway”, “myStop® Mobile”, “Whiz • Live Subway & Bus Times”, “NJ TRANSIT Mobile App”, “Metro Transit”, “Rocketman – Bus & Train Times”, “TransLoc Rider”, look at their optimized core words.
3.Conclusion
Now, do you know how to find the best keywords? The above is a case of my research keyword “bus time”. You can also research keywords according to this idea in the process of the app store optimization, and then use the intelligent services provided by ASOTools.
There are many App store keyword tool, which can be used together to ensure the accuracy of keyword data. I recommend these ASOTools: