socketify.py
Bloom Analytics
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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.
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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.
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.
Bloom Analytics's answer:
-CAPS -Curio Blvd -OMOYE -thecupcakequeens
Bloom Analytics's answer:
It is budget friendly, It focuses on Profit calculation and attribution, also helps in customer journey and company performances Profitability.
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.
Bloom Analytics's answer:
While working with Shopify brands, we kept hearing the same feedback that weโre making sales, but we still donโt 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 whatโs working. So, we built Bloom Analytics. Itโs 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.
Based on our record, socketify.py seems to be more popular. It has been mentiond 2 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
These "benchmarks" are useless, they're not testing anything real world except the performance of uWebsockets. There are copy errors all over the place. And then an advertisement: https://github.com/cirospaciari/socketify.py#briefcase-comme... Is this a professional framework that produces proper, real-world benchmarks and... - Source: Hacker News / over 3 years ago
After starting the project called socketify.py at https://github.com/cirospaciari/socketify.py, I got pretty good results and reviews, but many people asked if socketify.py could be used to create a WSGI and ASGI server. WSGI and ASGI have a lot of overhead, that's is why I choose not to use them in the first place, but adding an ASGI and WSGI server allows a lot of code already written to run faster! Source: over 3 years ago