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assertpy VS Bloom Analytics

Compare assertpy VS Bloom Analytics and see what are their differences

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assertpy logo assertpy

A straightforward assertion library for Python.

Bloom Analytics logo Bloom Analytics

Bloom is a Native Shopify Analytics and Attribution app. See which products, countries, and campaigns are profitable, and which ad platforms truly generate profit via multi-touch attribution. Create custom dashboards, get insights. Connect with MCP
  • assertpy Landing page
    Landing page //
    2022-11-06
  • Bloom Analytics Driving Sales and Profit
    Driving Sales and Profit //
    2026-05-13
  • Bloom Analytics Profit or Revenue
    Profit or Revenue //
    2026-05-13
  • Bloom Analytics Ads making money
    Ads making money //
    2026-05-13
  • Bloom Analytics Country Costing more than you earn
    Country Costing more than you earn //
    2026-05-13
  • Bloom Analytics Profit and Loss and KPI's
    Profit and Loss and KPI's //
    2026-05-13
  • Bloom Analytics Miscalculate Profit Because of complex cost
    Miscalculate Profit Because of complex cost //
    2026-05-13
  • Bloom Analytics store actually keeps as profit
    store actually keeps as profit //
    2026-05-13
  • Bloom Analytics stop guessing which product makes money
    stop guessing which product makes money //
    2026-05-13

Bloom tracks your true ecommerce profit after ads, shipping, COGS, transaction fees, refunds, and operating expenses, so you stop relying on vanity metrics and see which products, campaigns, and channels actually drive profit. Track financial performance across products, orders, countries, ad campaigns, and email campaigns, drilling into Product Intelligence, Country Profits, and Email Profits to understand exactly where profit comes from and where it leaks. Build custom dashboards tailored to your business to surface the metrics that matter most, all in one clean view. View a detailed Profit & Loss table with a toggle to switch between Shopify and Amazon revenue, or see them separately, and measure ROAS, POAS, Contribution Margin, and Net Profit from a single dashboard. Connect Google Ads, Meta Ads, TikTok, Pinterest, and Snapchat to see which channels bring profitable customers, not just clicks, and connect Klaviyo, Mailchimp, and Omnisend to see email profits per campaign. Use multi-touch attribution to understand the complete customer journey and accurately track campaign performance across channels, then apply custom cost rules based on product, quantity, country, shipping zone, and operational expenses to calculate true profit with precision. Get profit insights in Email and Slack, including scheduled Slack summaries, to quickly spot wasted ad spend, declining margins, underperforming products, scaling opportunities, and hidden profit leaks. Connect Bloom to Claude or ChatGPT via MCP to query your profit data in natural language. Whether you run one Shopify store or many, stop guessing and start scaling what actually makes money.

assertpy

Website
github.com
Pricing URL
-
$ Details
-
Platforms
-
Release Date
-
Categories

Bloom Analytics

$ Details
paid Free Trial $20 / Monthly (Unlimited Orders)
Platforms
Amazon Shopify
Release Date
2024 October
Startup details
Country
India
State
Karnataka
City
Bangalore
Founder(s)
Ulrich John
Employees
50 - 99

assertpy features and specs

  • Fluent API
    Assertpy offers a fluent API that makes assertions more readable and expressive, enabling developers to write assertions in a natural language style that is easy to understand.
  • Chainable Assertions
    It allows for chainable assertions, enabling multiple checks to be performed in a single line of code, thereby reducing verbosity and enhancing clarity.
  • Comprehensive Assertion Methods
    The library provides a wide range of built-in assertion methods, catering to various types of data validations, such as checking for size, type, value, and more.
  • Extensibility
    Assertpy supports extending its functionality by defining custom assertions, allowing developers to tailor it to their specific needs.
  • Pythonic
    Designed with Pythonic principles in mind, Assertpy fits seamlessly into Python projects, enabling idiomatic and consistent code style.

Possible disadvantages of assertpy

  • Learning Curve
    Developers new to the library may encounter a learning curve due to the distinct approach of using fluent and chainable assertions as opposed to traditional methods.
  • Limited by Python Version
    The library may have limitations in terms of compatibility with older versions of Python, requiring users to ensure their environment is up-to-date.
  • Performance Overhead
    The additional abstraction layer introduced by a fluent interface might introduce some performance overhead, especially in performance-critical or resource-constrained environments.
  • Less Community Support
    Compared to more established testing libraries, Assertpy might have less community support and fewer resources available for resolving issues or getting help.
  • Dependency Management
    Using a third-party library introduces additional dependencies to manage, which could complicate project maintenance and compatibility.

Bloom Analytics features and specs

  • Dashboard
    Track profit performance across products, countries, ad, and email campaigns
  • Profit Analytics
    Find and fix profit leaks with Contribution Margin, Net Profit and P&L breakdown
  • Attribution
    See which ad channels drive real orders, ROAS, POAS with multi-touch attribution
  • Cost Tracking
    Apply custom cost rules for COGS, shipping, by product, country or quantity
  • Insights
    Profit Insights that tell you what happened, why, and what to do about it

Analysis of assertpy

Overall verdict

  • assertpy is a well-regarded, lightweight assertion library for Python that provides a fluent, chainable API for writing readable and expressive test assertions, making it a solid choice for improving test clarity.

Why this product is good

  • Offers a fluent, chainable assertion syntax that makes tests more readable and self-documenting
  • Comprehensive built-in assertions for strings, numbers, lists, dicts, files, dates, and more
  • Produces clear, descriptive failure messages that speed up debugging
  • Lightweight with minimal dependencies and easy to integrate into existing test suites
  • Framework-agnostic, working seamlessly with pytest, unittest, and other test runners
  • Actively maintained open-source project with good documentation and community support

Recommended for

  • Python developers who want more readable and expressive test assertions
  • Teams using pytest or unittest looking to enhance assertion clarity
  • Projects that value descriptive failure messages for faster debugging
  • Developers coming from fluent assertion libraries in other languages (like AssertJ or Chai)
  • QA engineers and testers writing maintainable, self-documenting test code

Analysis of Bloom Analytics

Overall verdict

  • Bloom Analytics is not a widely recognized or verifiable analytics platform based on available information, so its quality cannot be reliably confirmed. Prospective users should conduct due diligence, checking for verified reviews, security compliance, and transparent pricing before committing.

Why this product is good

  • Limited independent reviews or third-party verification available to confirm platform reliability and performance
  • Unclear track record compared to established analytics providers like Google Analytics, Mixpanel, or Amplitude
  • Website claims should be verified through trials, demos, or direct vendor communication
  • Data security and compliance certifications (SOC2, GDPR, etc.) should be confirmed directly with the vendor

Recommended for

  • Businesses willing to conduct thorough vendor evaluation before adoption
  • Users seeking niche or specialized analytics features not covered by mainstream tools
  • Companies that can request a trial period to test functionality firsthand
  • Organizations comfortable working with newer or less-established SaaS vendors

assertpy videos

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Bloom Analytics videos

Bloom - Profit Tracking App for Shopify Businesses

Category Popularity

0-100% (relative to assertpy and Bloom Analytics)
Testing
100 100%
0% 0
Analytics Dashboard
0 0%
100% 100
Python
100 100%
0% 0
Marketing Attribution
0 0%
100% 100

Questions & Answers

As answered by people managing assertpy and Bloom Analytics.

How would you describe the primary audience of your product?

Bloom Analytics's answer:

The primary audience includes Shopify e-commerce businesses focused on improving profitability, tracking marketing performance, and making data-driven growth decisions.

Which are the primary technologies used for building your product?

Bloom Analytics's answer:

Bloom Analytics is primarily built using Ruby on Rails to create a fast, reliable, and scalable analytics platform for Shopify businesses.

Who are some of the biggest customers of your product?

Bloom Analytics's answer:

-CAPS -Curio Blvd -OMOYE -thecupcakequeens

Why should a person choose your product over its competitors?

Bloom Analytics's answer:

It is budget friendly, It focuses on Profit calculation and attribution, also helps in customer journey and company performances Profitability.

What makes your product unique?

Bloom Analytics's answer:

Bloom Analytics helps you clearly understand your business profit across products, marketing channels, countries, and order fulfillment. It shows how each part of your store contributes to profit โ€” all from one simple dashboard.

What's the story behind your product?

Bloom Analytics's answer:

While working with Shopify brands, we kept hearing the same feedback that weโ€™re making sales, but we still donโ€™t know our actual profit. It made sense. With ad spending, shipping costs, product costs, discounts, and fees, tracking real profit can get messy quickly. Most store owners find themselves hopping between different dashboards just to understand whatโ€™s working. So, we built Bloom Analytics. Itโ€™s a simple profit analytics platform that helps Shopify businesses understand- What products are profitable, which countries and campaigns provide the best returns, which ad platforms truly generate profit through multi-touch attribution, and all from one clear dashboard. No confusing spreadsheets. No endless tabs. Just clear profit insights that help brands make better decisions.

User comments

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What are some alternatives?

When comparing assertpy and Bloom Analytics, you can also consider the following products

grappa - grappa is an declarative, verbose, and expressive assertion library for Python.

Triple Whale - Triple Whale helps ecommerce brands make better decisions with better data.