Polar Analytics

We've all dealt with the frustration of user tracking issues due to iOS privacy changes. Modular Closets was no different. At first, they didn’t realize how significantly these changes affected their email performance. They were unaware that Klaviyo was missing crucial events. It wasn’t until they installed Polar that they saw the full extent of the missed opportunities and potential revenue slipping through the cracks. Our team approached Modular Closets introducing the Klaviyo Flows Enricher, a tool we recently designed to capture up to 70% more abandonment events and enhance email flows. This first-party solution spots missed events, connects them to previously subscribed email addresses using our in-house Identity Graph, and sends these addresses back to Klaviyo. By simply duplicating their existing flows and enriching them with this new data, Modular Closets reached an additional 4,188 sends in Browse Abandonment Flow emails with a 54% revenue boost in just 28 days. “While I am a huge fan of Polar for their fantastic software, which has streamlined my super-complicated data stack, the new offering has raised the bar even further with its immediate ROI, in terms of lost revenue recovered,” says Yisroel, from Modular Closets Curious about how they did it? Check out the link in the comments below!

🚀 We just launched the Klaviyo Flows Enricher. This game-changing data flow enables brands to reclaim up to 70% of missed email revenue. I'm thrilled to share it, so let me take you through our journey from 0 to 3,051 merchants in 3 years. 🔹 Why Polar? During my time as Head of Growth at Turo, one of the top data-driven marketplaces, we scaled to hundreds of millions in GMV and managed a multi-million ad budget. I saw a new generation of brands emerging; they needed tools to compete with established retail giants. Polar Analytics was born from this need — and the profound belief that better commerce drives peace and prosperity in society. 🔹 Step 0: From Data to Actions (2021 - 0 merchants) We first launched a recommendation engine that connected to all e-commerce data and showed which actions to take. Transformational moment: A chat with Louis from Merci Handy changed everything — He found the data stack behind our recommendations more valuable than the recommendations themselves. Our philosophy is: "whatever it takes to make brands grow." So, we pivoted; time for Polar V1. 🔹 Step 1: Centralizing Data: One dashboard to rule them all (2022: Number of merchants: 347) The need was clear, and a data platform was the solution. We evolved Polar into a master dashboard, bringing all data together. Transformation moment: Seeing first-hand the growth of Polène Paris, one of the most ambitious brands. They pushed us towards the ability to play with data like never before. We set out to build the most customizable platform on the market. 🔹 Step 2 First-Party Data: Navigate the post-iOS 14 world (2023: Number of merchants: 1,482) When iOS 14 shook up the data world, we adapted. We needed to do more than pull data; we needed to produce it. We launched the Polar Pixel to collect first-party data, helping brands navigate the new landscape with accurate attribution. Transformation moment: Bigger brands like Quad Lock, 2Modern, and The Frankie Shop were knocking at our door, so we launched Polar Enterprise. 🔹 Step 3, Today: Putting data to work (Mid-2024: Number of merchants: 3,051) Back to the original vision that data is meant to drive outcomes. And we're doing it with Abandonment Flows. Transformational moment: Klaviyo is missing up to 60% of abandoners. After Apple's updates, Klaviyo only receives cart, product, and checkout events from Shopify stores if the user is logged in or has completed an authentication event within seven days. Meet our Klaviyo Flows Enricher, the new tool that drives more revenue from emails by capturing and re-engaging missed abandoned shoppers using identity resolution. In under 30 days, we see results for top brands of +37% to +70% more abandoned cart revenue! More about it here: https://lnkd.in/ejNMBtc8 PS: we're already working on step 4 and cooking something big. Thanks for being part of our journey. Onwards & Upwards!

Happy Friday, everyone! I’m traveling today, but I still wanted to share some quick insights on Time Decay Attribution before the weekend. If you’ve ever noticed how those last-minute marketing efforts often seal the deal, the Time Decay Attribution model is a good option for your brand. It’s pretty straightforward—it values the marketing touches that happen closer to the sale more heavily. I like this model because we often see that initial ads kick things off, but the last-minute reminders usually clinch the sale. It’s similar to studying last minute for an exam; what you study last, you often recall best 📚 Picture this: you run an online store selling eco-friendly clothing. Early on, a customer sees your ad but just browses. As Earth Day approaches and your sale heats up, you increase your emails and social posts. A final email with a countdown timer gets them to purchase. Time Decay highlights that these last pushes—the crunch-time efforts—truly drive your sales, showing you the real power of your endgame strategies. Ever felt those final efforts pulling more weight? Seen this play out in your own marketing? Let’s chat in the comments! 🌍👕

While Amazon Seller Central has been part of the Polar Analytics family for a while now, Amazon Vendor Central has been a long-awaited data source (that we've just launched 🎉 ). This gives you access to Amazon Vendor’s rich data inside Polar so you can slice, dice, and compare it with other important sources for insights on: 🎯 Order Tracking Efficiency 💰 Cost Management 📊 Product Performance Analysis What integration would you like to see next and why?

Let’s end this week with a key challenge in ecommerce marketing: knowing which efforts count (or should count) most toward checkout 🛒 U-Shaped Attribution offers an interesting take and is one of my favorites, so let’s unpack it. According to multiple studies, it can take 6 to 8 interactions to convert a customer… U-Shaped Attribution does a great job of considering this while still assigning the majority of the credit to the crucial first and last touchpoints. Here’s a real-life example 👀 Imagine an online store specializing in bespoke furniture. A potential customer first discovers the brand through a targeted Facebook ad showcasing beautiful, custom-designed desks. This initial ad captures their attention, earning the first 40% of the credit. Over the next few weeks, the customer interacts with various emails and social media posts, gradually building interest (Sharing 20% credit). Finally, a compelling last-touch email offers a time-limited discount on their first purchase, which convinces the customer to buy. This last interaction secures the remaining 40% of the credit. What do you think? Do you use U-Shaped attribution?

I’ll keep saying it. The time and effort our Success and Support team puts into our customers is something special — it’s why we keep seeing reviews like this one. Extra shoutout to Lee A. who takes such good care of Waiākea 👏🏻

We’ve explored First and Last Click, but what about the whole journey? That’s where Linear Attribution enters the ring, especially in ecommerce. 💭 Imagine running an online store selling sports equipment, Monday rolls in and you sell a very expensive set of golf clubs 🏌🏻♂️… This isn’t an overnight decision so tracking just the final touchpoint doesn’t tell you how you got such a substantial order. You might have several campaigns running simultaneously: Facebook ads introducing new products, Instagram stories showcasing customer testimonials, and email blasts promoting a seasonal sale. Linear Attribution helps you see how each of these interactions—from initial discovery to final decision—contributes to a sale. It treats each step as equally important. This model is particularly useful for long-term campaigns where consistent engagement builds towards a purchase. It gives you a balanced view, so you can adjust your strategy based on what truly influences customer decisions at each stage. Curious about how Linear Attribution could help optimize your campaigns? Let’s discuss it in the comments! How do you think understanding every touchpoint could change your approach?

Our CTO, Charbel Seif 🐻❄️, said something yesterday that stuck with me. "An angry user is a user who cares!" He's right. Feedback, good or bad, is helpful. And users who are upset or have hit a wall in your product, give you the best advice. Because I promise you, if one person is experiencing an issue and talking about it, many others are too but just aren’t speaking up. We’ve actually recently started using Canny to collect feedback and requests directly from customers - we also use it to send us updates directly to Slack. It’s a great tool and is helping us be even more customer-focused.