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A Head-to-Head Analysis of Top Shopping Apps


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As the retail space evolves from brick and mortar to digital, budgets are pouring in to rapidly acquire and engage consumers that are eager to open their wallets. In this environment, we’ve seen pure peer-to-peer shopping apps like LetGo and OfferUp race up the app store charts, despite competition from traditional brick-and-mortar brands like Nordstrom, Wal-Mart and Target, as well as massive investment in e-commerce sites like Amazon and EBay.  

With the retail-apocalypse in headlines every day, we used Appnique’s affinity-based algorithm to better understand how shoppers are segmented, and provide some insight for up-and-coming shopping apps looking to accelerate their user acquisition.


After all, targeting the right users with the right message can be the difference between climbing the charts, or bidding up cost for too-limited of an audience, resulting in a hit to ROAS and lower than expected acquisition rates.


For the sake of the analysis, we separated relevant shopping apps into three categories and analyzed the top 50 related apps ranked by affinity:


-       Mobile-first marketplace apps (OfferUp and LetGo)

-       eCommerce branded apps (Amazon, eBay)

-       Traditional brick-and-mortar branded apps (Nordstrom, Target, Walmart)


Our analysis focused on determining:


-       Similarities and differences in the install bases among apps in the same sub-category

-       Takeaways for app marketers in each category


Key takeaways

1.) Value proposition is the best predictor of performance

Overall, the strongest correlation in terms of audience overlap wasn’t within subcategories (peer-to-peer marketplace, eCommerce brands, brick and mortar) among competitors. Rather, there was a stronger correlation to price sensitivity and variety & convenience. For example, LetGo had more in common with Walmart’s price-conscious install base than it did with OfferUp – a direct competitor.


2.) Branded interests are limited in scale

With an essentially unlimited number of affinity-based Facebook interests to target, competition is lower for these interests. Not only is this important for app marketers building using interest targeting on Facebook, but for those who are using lookalike audiences that dilute with scale.


3.) Creative and messaging should reflect affinity

Considering which apps correlate most strongly to your app can provide important insight that should be considered when building out messaging and positioning. In the examples above, price vs. convenience emerged as an important factor, but your own analysis might uncover more nuanced points of differentiation. These insights should also be considered for a re-engagement strategy that delivers a consistent and holistic experience for acquired users.  


Highlights by Category & Brand

1.) Mobile-first marketplace apps

Overall, price sensitivity seemed to differentiate LetGo’s install base from OfferUp’s. Despite having similar categories over-index among the top 50 results, there was less-than-expected overlap in specific app titles.


Casual games titles ranked 1 or 2 for both LetGo and OfferUp. However, LetGo’s casual game apps trended more toward free puzzle games, while OfferUp’s install base were most likely to be Action and Casino games – some of which were paid titles.


The same pattern held up for dating apps. While both had Tinder among top results, other dating apps had very little overlap. LetGo’s install base had dating apps that focused on “free” and “discrete” - a pattern that didn’t exist among OfferUp’s install base.


Top Categories

-       Mobile-first aggregators – (e.g.Ibotta, Flipp, WISH)

-       Casual Games – Puzzles

-       Dating apps



Top Categories

-       Casual Games – Action & Casino

-       Finance

-       Dating Apps


2.) eCommerce Branded Apps

A compare and contrast with Amazon and eBay showed us that eBay shares more in common with the peer-to-peer marketplace apps than it does with Amazon’s install base.  OfferUp, LetGo and Wish all made the top 20 highest correlation in affinity for eBay, and had a much looser correlation to Amazon.


Amazon’s top 50 highest-correlating apps can be broken into two basic tiers. Tier 1 is comprised of traditional brick-and-mortar brands, including apps like Target and Best Buy, as well as a suite of productivity and entertainment apps.


It’s not until Tier 2 that we see peer-to-peer marketplace apps showing strong correlation. OfferUp and Etsy indexed near the top of the second tier. It is of note that even among peer-to-peer shopping apps that comprised the second tier for Amazon, they are not ones with price sensitive install-bases. Arcade games and other casual games also had a strong showing here.



Top Categories

-       Productivity apps – Google’s suite of apps indexed high here

-       Shopping – brick and mortar brands

-       Shopping – peer-to-peer


Top Categories

-       Shopping – peer to peer marketplace

-       Finance

-       Casual Games


3.) Brick-and-mortar brands

A comparison between Target and Walmart install base had some interesting results. Walmart’s install base reflects a price-sensitive consumer. Deal apps including EbatesIbottaReceipt Hog and ShopKick all indexed in the top 20.  Interestingly, there was significantly more overlap in app affinity between Walmart and the LetGo app, which leaned more price-sensitive, than it did with Target or Amazon.


Target’s install base seemed random in terms of category, but was full of blue chip consumer brands. Brands like Starbucks, Facebook, Yelp and Amazon highlighted a brand-focused top tier of high-affinity apps. Tier 2 reflected an install base concerned with price and utility with deal apps, food & drink coupon apps, and eCommerce branded apps.



Top Categories

-       Major consumer brands – Starbucks etc.

-       Food & Drink coupons – Dunkin’ Donuts, Dominoes, Subway etc.

-       Casual games - puzzles



Top Categories

-       Shopping apps – price sensitive

-       Food & Drink coupons – Dunkin’ Donuts, Dominoes, Subway etc.

-       Tools


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