Charbel Seif | Data-Empowered Brands 🐻‍❄️

@scottmartinis @codyplof Yes exactly, our semantic layer is built on a big graph connecting all datasets together

@codyplof Do they support graph relationships?

Polar came though. My team is very impressed with their platform. Gonna have our data warehouse in Claude to cook with.

This brand owner discovered their "best performing" ads were stealing credit from their email flows. They'd been running the same playbook everyone else does - dumping budget into Facebook, watching the platform tell them they're crushing it, assuming that's the whole story. But something felt off. The numbers didn't match the bank account. So they ran a proper attribution analysis using @polar_analytics and discovered: → Ads claimed 60% revenue attribution → Email got credit for only 15% → Server-side tracking showed the opposite was true Classic case of platforms marking their own homework. Their email flows were actually driving 3x more incremental revenue than ads. The ads weren't converting cold traffic - they were just catching people at the finish line who were already sold through email nurturing. That's the gap between platform attribution (what Facebook wants you to believe) and real attribution with first-party data tracking. Most brands never figure this out. They keep feeding the ad machine because that's where the "results" show up, while their email flows do the actual work in the shadows. Stop letting platforms tell you where your revenue comes from. Track it yourself or you're just guessing with extra steps. This is exactly why I’m a #proudpartner with @polar_analytics - one of the few tools that actually shows you what's driving incremental growth versus what's just good at taking credit.

@seb_tam @kurtinc @klaviyo Yup @kurtinc hit me up I can help :)

@kurtinc @klaviyo There is with @charbseif MCP using Polar Analytics connected to Shopify 👌 Not tried yet but looking forward to it!

I set up the @klaviyo MCP in Claude for a client’s account yesterday. So far, it seems great. Like having a more powerful Shopify Sidekick. I’d love to see something similar for Shopify too.

One question for the builders here: we want to make MCP render Polar chart components to speed up React generation (that’s the big bottleneck right now) and make outputs prettier. Does anyone have recommendations? We’re looking into Shopify’s new MCP-UI: lnkd.in/epZ-6Q9C

We’re moving fast on the Polar Analytics 🐻‍❄️ MCP, so expect to hear from me more often :D

We’re at step 2 now, and your ratings/feedback is what will make the insights better.

The way we think about the AI Data Analyst: 1. Get very good at what questions (junior analyst). 2. Get better and better at insights/why (senior analyst). 3. Deliver strategic recommendations that just come to you (strategic analyst)

Now we’re taking it one step further: "why" questions and insights, more like what you’d expect from a senior analyst.

Yesterday I showed how the Polar MCP can handle "what" questions, the kind of questions you’d ask a junior analyst: 👉 “What were my sales in the last 6 months?” 👉 “How much did I spend today?”

From there, the possibilities open up: richer visualizations, custom agents, and workflows that can help automate parts of your business. We’re opening early access to a small group of select partners, reach out if you’d like to join.

The future of AI in eCommerce starts with the Polar MCP. In this short demo, I walk through how easy it is to connect Claude to a Polar data source through MCP, and instantly start creating charts on top of your eCommerce metrics.

The key difference is that Polar brings its semantic layer into the loop. Instead of AI just querying raw tables, it understands your business metrics and definitions. That’s what makes the outputs accurate and trustworthy, not just pretty.

You can think of it as a single connection that makes all your data connectors (Shopify, Walmart, Amazon, Meta Ads, Tiktok, etc.) and all your business context available in your preferred chat interface.

There’s something magical about B2B customer support on weekends. You’re helping operators and founders, just as passionate about it as you are - hustlers who respect the grind. One of our largest deals even began with a 5-minute reply request on a Saturday afternoon. I’m all for it.

I'm super excited about our latest project at @polar_analytics that's already generating over 10% increases in email revenue for brands. Almost every brand prioritizes getting their newsletters out each week because they miss their revenue targets if they don't. But finding time to properly segment that audience and customize campaigns accordingly is a constant struggle. This can be complicated without the right tool, and many brands fall back on generic campaigns that aren't actually tailored to anyone. Bespoke is our latest AI agent for email marketing. With Bespoke, you can personalize newsletters on a one-to-one basis every time. You just need to upload a catalog of creative Klaviyo campaign emails. From there, the AI matches the right campaign to each recipient based on factors like order history, browsing history, and inventory levels. This AI-powered segmentation has been a huge unlock for brands looking to increase the ROI of their Klaviyo account. We have a few openings left for June. DM me if you want to beta-test it.

1) Top of the pile is @polar_analytics which provides out of the box, yet customisable dashboards, and supports a whole slew of data streams: Shopify, Amazon, TikTok Ads, and Meta. Pricing: Starts at $300/m - probably restrictive for smaller businesses, but a no brainer for the more established. Shopify App Store: 4.8 stars (122 reviews)

Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future! Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build. This is why I’m excited about the future of Product Management, the discipline of developing and managing software products. I’m especially excited about the future of AI Product Management, the discipline of developing and managing AI software products. Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce. Perhaps engineers will step in to do some of this work, but if it remains the purview of specialized Product Managers, then the demand for these roles will grow. This change in the composition of software development teams is not yet moving forward at full speed. One major force slowing this shift, particularly in AI Product Management, is that Software Engineers, being technical, are understanding and embracing AI much faster than Product Managers. Even today, most companies have difficulty finding people who know how to develop products and also understand AI, and I expect this shortage to grow. Further, AI Product Management requires a different set of skills than traditional software Product Management. It requires: - Technical proficiency in AI. PMs need to understand what products might be technically feasible to build. They also need to understand the lifecycle of AI projects, such as data collection, building, then monitoring, and maintenance of AI models. - Iterative development. Because AI development is much more iterative than traditional software and requires more course corrections along the way, PMs need be able to manage such a process. - Data proficiency. AI products often learn from data, and they can be designed to generate richer forms of data than traditional software. - Skill in managing ambiguity. Because AI’s performance is hard to predict in advance, PMs need to be comfortable with this and have tactics to manage it. - Ongoing learning. AI technology is advancing rapidly. PMs, like everyone else who aims to make best use of the technology, need to keep up with the latest technology advances, product ideas, and how they fit into users’ lives. Finally, AI Product Managers will need to know how to ensure that AI is implemented responsibly (for example, when we need to implement guardrails to prevent bad outcomes), and also be skilled at gathering feedback fast to keep projects moving. Increasingly, I also expect strong product managers to be able to build prototypes for themselves. The demand for good AI Product Managers will be huge. In addition to growing AI Product Management as a discipline, perhaps some engineers will also end up doing more product management work. The variety of valuable things we can build is nearly unlimited. What a great time to build! [Original text: deeplearning.ai/the-batch/issu… ]

Thrilled to share that Polar has raised $18 million, a significant milestone in our journey to unlock growth for millions of consumer brands with Data. Our journey began with an ambition inspired by Polaris, the North Star. We wanted to provide independent brands with clarity and direction so they could compete in retail’s fast-paced landscape. Today, we're closer to that goal, supporting a new generation of brands that deserve to be seen and celebrated. The Data Challenge Today, brands face a “data blind spot.” The loss of third-party cookies and fragmented data channels have made it more complicated than ever for brands to understand their customers and make strategic decisions. Data can feel overwhelming—but that’s where Polar steps in. Our Impact So Far Three years in, we’re proud of the impact we’ve made. Today, Polar supports over 3,715 merchants in 45 countries, including brands like The Frankie Shop, Doên, Allbirds Korea, and RIPNDIP. Our customers see real results: a 20% reduction in customer acquisition costs (CAC) and a 30% increase in email flow revenue, often within the first month of using our platform. What's Next: Doubling Down on Growth With this funding, bringing our total to $28.5 million, we’re focusing on: 1. Product Development: Building advanced data-driven automation to help brands convert data into revenue, allowing them to thrive amidst rising ad costs and leverage first-party data effectively. 2. Expanding the Team: By the end of 2025, we aim to grow to 100+ team members (our Polar bears!), scaling our product and GTM teams across Europe and the U.S. A Vision for Retail’s Future Our vision is ambitious: to be the ultimate source of truth for retail brands, helping them scale to nine-figure revenues. We’re here for the small brands with big dreams. Today, we celebrate this milestone. Tomorrow, we’re back to work. Onwards and upwards. For more details, please check the links in the comments for our blog post, along with coverage from Axios.

@DaveRekuc Totally agree

Best shopify report: Sales over Time - Filterable by almost everything - Add/remove almost any column - Can replace an order export for almost any analysis - Fast - Medium sexy graph (bring back bars) - Real time - Exportable

Nvidia CEO: people with really high expectations have very low resilience