Review miningGuide - retail apps and App Store search - getting ahead
What is in this resource:
How to build your ASO strategy from end to end
Learn to pick target keywords, write metadata, follow rank and base every decision on data.
From data to action
Simplify the keyword data and stay in line with App Store rules.
Tools that lift rank
Carry the keywords drawn from competitor reviews into your metadata and take your app out of the cusp band into the top 10.
What this document holds, and what it does not
Shopping is one of the App Store's most crowded categories and its competition has a different shape from the others. This document is about how to measure that shape and what to decide from it.
This is a method document. It describes how to measure the shopping category, which numbers mean something, and which patterns are particular to it.
What it does not hold: market share, download and revenue figures. Apple publishes none of them, and the estimates in circulation are computed backwards from rank. Written into a table, a reader takes an estimate for a measurement.
The second thing it does not hold is a frozen list of numbers. Category densities do not change weekly but they do change monthly. A density written without a date is still taken as true months later.
What it holds instead is everything you need to run the same measurement for your own product and your own country. A category report is that work done in advance; knowing the method, you can produce it yourself.
Three things that make shopping different
Every category has its own shape, and shopping's differs at three points.
The first is brand weight. A large share of searching here carries a store's name: the user is looking for a specific retailer. Brand searches have far higher volume than generic ones, and ranking for them is impossible unless you own the brand.
The practical consequence: the top of the popularity ordering is full of brand phrases, and a team that looks at that list and works the top three is spending its time. No list built here without separating brand phrases works.
The second is seasonality. The category moves markedly in certain parts of the year. When popularity rises so does the number of apps entering those phrases, which means holding the same rank gets harder. Comparing against last month misleads; the comparison has to be against the same period last year.
The third is country divergence. Shopping is local work. Store names, payment methods and shipping terms in one country are not searched at all in another. Divergence between countries is higher here than in most categories.
- Brand phrases fill the top of the popularity list; no list is built without separating them.
- Seasonality is pronounced; compare against the same period last year.
- Country divergence is high; local terms do not travel between storefronts.
The four numbers to measure
Category measurement is made of four numbers and all four come from open data.
- DensityHow many apps appear in the first 200 for a phrase. In shopping density usually comes out high, but that alone does not mean the phrase is valuable - it is often the accumulation of stale metadata.
- Title shareHow many of the top 20 carry the phrase in the app name. Because brand names sit in app names here, title share for generic phrases can come out lower than expected - which means entering from the subtitle is possible.
- The strength thresholdThe median rating count of the top 10. In shopping this tends to be high, because large retail apps sit at the top of the category.
- Band spreadWhich rank bands the apps cluster in. Typically a few large apps at the top and many small ones in the lower bands - easy to enter, hard to climb.
How to run the measurement
There are five steps to producing those four numbers for your own country and your own sub-category.
Step one: build the universe. Apple's ranking list for the shopping category is public; take the first hundred apps. If a hundred is too many, start with fifty - the universe shrinks but the result does not break, as long as you do it knowingly.
Step two: extract the phrase pool. Pull two- and three-word phrases from the names, subtitles and descriptions of the apps in the universe. The country's letter-folding rules have to be applied, or the same phrase appears as two separate rows.
Step three: separate the brand phrases. In this category that step is compulsory. Phrases matching the app names in the universe move to a separate list; what stays in the main list are the generic phrases.
Step four: measure. Search each remaining phrase once in that country and read the first 200. Density, title share and band spread all come out of that one search.
Step five: add popularity and date it. Where no search suggestion returns, leave the field blank; do not write zero. Store the table with its measurement date - the real value is in the difference between two tables.
Where to enter this category
The measurement is done and the category came out crowded. That does not mean stay out; it means change how you go in.
The first way is niche phrases. Three-word phrases are searched less but their relevance is very high and their competition low. In shopping they are usually a product type plus a qualifier. Alone they bring little traffic; gathered in clusters they produce a real total.
The second is country selection. Divergence between countries is high here. A phrase crowded in a large market can be nearly empty in a smaller one - the same metadata, a different result.
The third is rival reviews. Review volume in shopping apps is high and users write concretely: which product they were looking for, what they could not find, which shipping or returns problem they hit. Those words are in no keyword tool's pool.
The fourth is preparing before the season. Changing metadata when a seasonal phrase's season arrives is late - the index takes a few days to settle and the season has started. The move has to be made a few weeks before.
The fifth is waiting. If the strength threshold is very high, the distance metadata can cover is short. What ASO should say then is that the work has moved to the product.
| Situation | Approach |
|---|---|
| Brand phrases at the top of the list | Move them to a separate list; work the generic ones. |
| High density, low popularity | A trap - a pile of stale metadata; do not spend effort. |
| Strength threshold very high | Not taken with metadata; the work moved to the product. |
| A seasonal phrase | Make the move a few weeks before the season. |
| High country divergence | The same metadata gains more in the smaller market. |
Metadata traps in this category
Four traps come up repeatedly when writing metadata for shopping.
The first is rival brand names. Writing a store's name into the keyword field is tempting because it is searched. Apple does not accept it and it leads to rejection at review. Even if accepted, the arriving user is looking for a different app and conversion is low.
The second is product names. Writing the name of a product your app does not sell can get you ranked for it, but the arriving user does not find what they came for. Low-rated reviews weaken your strength across every phrase.
The third is generic shopping words. "Shopping", "store", "discount" are heavily searched, but the category's largest apps sit there. A new app entering those with metadata is not practical.
The fourth is copying between countries. Because terms are local here, the cost of copying is larger than in other categories.
How sub-areas diverge
Shopping is a broad heading holding very different businesses, and their competition does not resemble one another. The figures for the category as a whole may not be true for your sub-area.
Retail apps. The area with the highest brand weight; most searching carries a store's name. Competition on generic phrases is very high because of the large retailers' rating counts. A new app entering with metadata is not practical.
Second-hand and marketplaces. Generic phrases are more reachable because brand searches are less dominant. Three-word phrases in the form of product type plus qualifier work well here.
Coupons and discount tracking. The sub-area with the sharpest seasonality. Phrase popularity multiplies in certain parts of the year and so does the number of apps entering them.
Comparison and price tracking. One of the least contested sub-areas, but search volume is low too. Niche phrases gathered in clusters produce a meaningful total.
Shipping and delivery tracking. The area where local terms dominate most; country divergence is highest here. Courier company names in one country are not searched at all in another.
The practical consequence: building the universe from the apps in your own sub-area rather than the category as a whole gives a far more accurate competition picture.
If you are new to the category
If your app is entering shopping for the first time, the work of the first three months differs slightly from other categories.
Month one: separate the brand phrases and build the generic pool. That step cannot be skipped here; skipped, the list spends its first three rows on brand searches.
Month two: settle on your sub-area and narrow the universe to it. The category-wide figures mislead you; twenty apps from your own sub-area give a truer picture than a hundred from the category.
Month three: gather the local terms. Country-specific words here - payment methods, shipping terms, local store types - are less contested than the generic phrases.
What should not be done across those three months: spending metadata space on generic shopping words. The category's largest apps sit there and entering is not metadata work.
And timing: if you are near a seasonal period, do not make a large metadata change until it passes. The measurement window is at its dirtiest during the busiest part of the year.
Frequently asked
- Why is there no download or market-share figure for the category?
- Apple publishes neither. The estimates in circulation are computed backwards from rank and their error bars vary widely by category; written into a table, a reader takes an estimate for a measurement.
- Why do I have to separate the brand phrases?
- In this category the top of the popularity list is full of brand searches, and ranking for those is impossible unless you own the brand. A list built without separating them wastes its first three rows.
- When should I prepare for a seasonal phrase?
- A few weeks before the season. The index takes a few days to settle; a move made after the season starts does not reach that season.
- Should I measure my sub-area separately?
- Yes. Shopping is a broad heading; retail and coupon apps do not face the same competition. Building the universe from the apps in your own sub-area gives a truer result.
- Do rival reviews really work in this category?
- Where review volume is high, markedly - because users write concretely: which product they wanted, what they could not find. Those words are in no keyword tool's pool.
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