0 1 00:00:01,350 --> 00:00:05,000 Now how do you come up with these key words in the first place? 1 2 00:00:05,160 --> 00:00:10,770 Well, an easy starting point is going into the app store and going into the search tab. 2 3 00:00:10,800 --> 00:00:17,100 Now for example, if you search for Ninja, the App Store will automatically suggest to you some of the 3 4 00:00:17,100 --> 00:00:22,810 variations that users have used when they're looking for things that are related to the word Ninja. 4 5 00:00:22,830 --> 00:00:26,840 So some of these words might be good keywords for you to jump on. 5 6 00:00:26,850 --> 00:00:32,760 So this is basically the easiest and the least effort intensive way of coming up with keywords. 6 7 00:00:32,940 --> 00:00:37,800 Now I'm going to show you a slightly more refined workflow for coming up with app store keywords. 7 8 00:00:37,980 --> 00:00:41,970 The first thing you have to do is go and find a reverse dictionary online. 8 9 00:00:42,090 --> 00:00:47,400 So one of my favorite is a dictionary called OneLook and it's free to use and it's basically at one 9 10 00:00:47,400 --> 00:00:48,430 look.com. 10 11 00:00:48,470 --> 00:00:53,850 Now don't go into that dictionary search because this only gives you definitions and don't go into the 11 12 00:00:53,850 --> 00:00:54,480 theasaurus. 12 13 00:00:54,510 --> 00:00:57,420 Instead what you want is the reverse dictionary. 13 14 00:00:57,540 --> 00:01:00,740 Now a reverse dictionary is different from a thesaurus. 14 15 00:01:00,750 --> 00:01:06,240 So whereas a thesaurus gives you all the synonyms of a particular word, the reverse dictionary gives 15 16 00:01:06,240 --> 00:01:10,500 you words that are used in conjunction with your word. 16 17 00:01:10,500 --> 00:01:18,120 So for example, if we type in coffee then we'll get a whole bunch of words that are used when people 17 18 00:01:18,120 --> 00:01:19,170 use the word coffee. 18 19 00:01:19,170 --> 00:01:26,250 So for example, Java or coffee bean or chocolate or coffee berry, espresso, latte, tea. And the words that 19 20 00:01:26,250 --> 00:01:32,260 come out a reverse dictionary are pretty much perfect for use as keywords. 20 21 00:01:32,370 --> 00:01:37,470 Now you do have to do a little bit of sifting through this because some of these are not ever going 21 22 00:01:37,470 --> 00:01:40,220 to be searched like umber, burnt umber. 22 23 00:01:40,290 --> 00:01:43,530 I have a feeling there's probably one search for that on the App Store per month. 23 24 00:01:43,530 --> 00:01:48,680 So how so then the question is how do you figure out which of all of these words, 24 25 00:01:48,690 --> 00:01:53,420 I mean there's pages and pages of this, are worth using as your keyword? 25 26 00:01:53,550 --> 00:01:56,370 Well there's a very easy workflow for this. 26 27 00:01:56,370 --> 00:02:00,590 Now this is where a service like Sensor Tower or App Annie comes into play. 27 28 00:02:00,750 --> 00:02:06,840 So there's many of these such services out there and you can try and figure out which one you prefer 28 29 00:02:06,870 --> 00:02:08,970 or which one has a better pricing model. 29 30 00:02:08,970 --> 00:02:13,500 Personally, we use Sensor Tower. And what you can do with Sensor Tower is that if you go into the 30 31 00:02:13,500 --> 00:02:20,490 products tab you can go to the App Store optimisation feature and what you can do is you can research 31 32 00:02:20,550 --> 00:02:22,100 a whole bunch of keywords. 32 33 00:02:22,110 --> 00:02:25,810 Now this is where those words from the reverse dictionary comes into use. 33 34 00:02:25,830 --> 00:02:31,800 You can either go ahead and just copy these words one by one into your keyword space or if you're looking 34 35 00:02:31,800 --> 00:02:36,960 for a little bit more efficiency than what I use is a free Chrome plugin code scraper. 35 36 00:02:37,170 --> 00:02:42,900 Now once you've added that to Chrome, what you can do is you can highlight the type of data that you're 36 37 00:02:42,900 --> 00:02:43,560 looking for. 37 38 00:02:43,560 --> 00:02:49,380 So in this case is actually this piece of data and then you can right click and you can click on this 38 39 00:02:49,500 --> 00:02:52,050 scrape similar button. 39 40 00:02:52,050 --> 00:02:58,230 Now what that does is that it looks through the website and looks for data that's structured similarly 40 41 00:02:58,500 --> 00:03:00,070 to that word Java. 41 42 00:03:00,240 --> 00:03:06,030 Now then the next thing that you can do is you can hit copy to clipboard and copy all of that data and 42 43 00:03:06,030 --> 00:03:13,710 paste it into here. So you will have the curate and add some commas between these word and maybe get 43 44 00:03:13,710 --> 00:03:16,450 rid of some of these because they're not really going to help you. 44 45 00:03:16,560 --> 00:03:20,070 Okay. So now we've added commas in between all the different words 45 46 00:03:20,130 --> 00:03:24,680 and I've gotten rid of some of the words which I think probably are never going to be searched. 46 47 00:03:24,720 --> 00:03:31,540 So now that we've got all our key words, all you have to do is hit track keyword and you'll get a number 47 48 00:03:31,570 --> 00:03:34,120 of really important metrics. 48 49 00:03:34,120 --> 00:03:38,030 Now the first thing that you want to look at is this column called traffic. 49 50 00:03:38,050 --> 00:03:45,120 So as they say, this is an estimate of how many people are actively searching for this particular keyword. 50 51 00:03:45,250 --> 00:03:48,370 So the higher the score, then the more traffic there is. 51 52 00:03:48,430 --> 00:03:50,790 And the other important column is difficulty. 52 53 00:03:50,830 --> 00:03:57,360 So this index lets you know how difficult it is to rank in the App Store for this particular keywords. 53 54 00:03:57,370 --> 00:04:02,790 So that means the higher the score, the more apps are targeting that particular keyword. 54 55 00:04:02,800 --> 00:04:06,940 So you're basically going to be in a large pool of a large number of apps. 55 56 00:04:06,970 --> 00:04:13,330 So the sweet spot that I find is when the traffic score now the scores are different for different services. 56 57 00:04:13,420 --> 00:04:19,900 But a sense of how what we found is that once the traffic score is above 3 and the difficulty is 57 58 00:04:20,050 --> 00:04:24,580 below 3, then that is usually a pretty solid keyword. 58 59 00:04:24,580 --> 00:04:30,610 And what you want to do is to create a Excel file or Google sheet to keep track of those keywords that 59 60 00:04:30,610 --> 00:04:37,450 you have selected. And once you've made a full list of all the keywords, so this is usually 100 or 150, 60 61 00:04:37,630 --> 00:04:43,510 then you can sort them and figure out what are your top keywords that you will cram into that 100 character 61 62 00:04:43,810 --> 00:04:45,040 keyword field. 62 63 00:04:45,040 --> 00:04:46,780 So this is pretty much our work flow 63 64 00:04:46,840 --> 00:04:53,050 and in my opinion it's the best way of going about this. Instead of employing a ASO expert who is probably 64 65 00:04:53,050 --> 00:04:55,030 going to do something very similar to this, 65 66 00:04:55,090 --> 00:04:59,260 you can in fact just DIY and do it yourself. And you can optimize your keywords this way.