Algolia Acquires Velou for Intelligent Product Discovery

Competitor research as a solo founder isn't about copying - it's about finding the missing data point. As I continue learning how to gather and use marketing data to inform what I sell at Brine and Bard Designs, one of my biggest takeaways has been redefining what "competitive research" actually means for a small business. It’s easy to look at similar makers and feel overwhelmed by how crowded a market seems. But when you view competitor analysis as data collection, the goal changes. You aren't looking at what to copy - you're looking for the white space. Here is how I use competitive landscape data to guide my inventory: 1️⃣ Mapping Price & Format: Where are other makers pricing similar categories, and what format (singles, sets, bundles) moves best? 2️⃣ Identifying Unmet Demand: What are customers buying elsewhere that could be improved or given a unique visual twist? 3️⃣ Validating Niche Interest: Seeing demand for pickle motifs or gaming accessories across the broader market validates that the trend has legs. Studying the market gives you macro-level data on what works generally. In-person testing gives you micro-level data on what works for your specific brand. The sweet spot lives right where those two datasets meet. When you notice a broader market trend in your industry, how do you evaluate whether it fits your brand strategy? #CompetitorAnalysis #MarketResearch #ECommerce #ProductStrategy #SoloFounder #BrineAndBardDesigns

The problem with “knowing your customer”? Sometimes, your customer doesn’t fully know themselves. They can tell you what they think they want. But their actual behavior can tell you something very different. And this is where customer behavior analysis becomes so important. 👉 IBM found that 62% of consumers said they were willing to change their purchasing habits to reduce environmental impact. But only 31% said sustainable products made up most or all of their last purchase. That’s a pretty big intention vs. action gap. So when doing buyer persona research, don’t stop at: ❌ “What would make you buy this?” Look at: → What do they actually search for? → Which alternatives do they compare? → What makes them abandon a purchase? → What reviews do they trust? → What questions keep appearing in comments, forums and support tickets? → What do they do after discovering your product? Good buyer persona research methods combine interviews and surveys with behavioral evidence. Because people are not always good at explaining their own decision-making. Your persona shouldn’t describe what customers say they do. It should help you understand the patterns behind what they actually do. 🔎 That’s where research becomes much more useful than assumptions. I create professionally designed buyer persona templates to help marketers structure this research: 👉 MarketingMuse on Payhip: https://lnkd.in/evVmqKWQ What’s one customer behavior that surprised you after you started looking at the data? #BuyerPersonas #CustomerBehavior #CustomerResearch #MarketingStrategy #MarketResearch #ConsumerBehavior #BuyerResearch #MarketingResearch #CustomerInsights

if I have junglescout/h10/keepa api do I still need another api or they have everything i need? these tools are amazing. But they are meant for more on the analytics side of things. ⤷ like sales volumes of an asin ⤷ keyword search volumes.. ⤷ historical prices of an asin ⤷ brand category share last 6months all very valuable. but how they function is they scrape and store sht ton of data on their own timelines they don't care YOU want ⤷ every 15 minutes sponsored competitor positions in the most busy hour competition on a keyword ⤷ they wont give who's buybox owner in every hour of the day ⤷ they won't go depth putting every zip/state in US to give you all organic rank differences they are not customized that way. My api (https://asgardata.com/) born out of these realtime fresh data needs you click my api > it go scrape NOW > give you the freshest data available on amazon whereas, all 3 above have a database they update on their own timelines not yours. imo if everyone uses the same competitive intel noone has competitive intel:) so having an api that YOU schedule the frequency/geolocation etc. is actual intel to give you an edge

Extract Styli Product Data with API-Powered Scraping 👗🛍️📊 Looking to access structured Styli product data at scale? Our Styli Product Data Scraping API helps businesses collect valuable eCommerce intelligence for product research, price monitoring, competitor analysis, and market insights. With automated data extraction, you can collect: 👗 Product Names & Detailed Information 💰 Prices, Discounts & Offers 🏷️ Brands, Categories & Product Attributes 📦 Product Availability & Stock Data ⭐ Ratings & Customer Reviews 🎨 Colors, Sizes & Product Variants 🔍 Competitor & Market Intelligence Whether you’re tracking fashion and lifestyle products, comparing assortments, monitoring prices, analyzing promotions, or researching competitors, turn Styli product data into structured, actionable business insights. 🚀 Automate product data collection and make smarter eCommerce decisions with Actowiz Solutions. Explore our Styli Product Data Scraping API: https://lnkd.in/duV_kVPn #StyliData #StyliScraping #ProductData #FashionData #EcommerceData #APIScraping #DataScraping #WebScraping #PriceMonitoring #ActowizSolutions

AI-powered shopping is only as smart as the product data behind it. That’s what makes this one so exciting. Velou (now part of Algolia) is officially joining Algolia, bringing even more product intelligence into the discovery experience as shopping moves from search bars to conversations, recommendations, and AI agents.

Search was won on speed. Discovery is won on intelligence. That’s why Algolia has acquired Velou (now part of Algolia)! For many brands and retailers, product data arrives from multiple sources — often inconsistent and missing the attributes shoppers actually search for. The result? Merchandising teams spend valuable time manually fixing catalogs with tags, synonyms and rules — a job that never really ends as catalogs constantly change. Velou fixes that at the source. Its AI reads product text and imagery to automatically create missing attributes — from colour and material to style, occasion and more — mapped to a retail taxonomy and refreshed as the catalog evolves. Algolia then uses that richer product understanding to retrieve, rank and personalise the right products based on shopper intent. The impact: less manual work, more discoverable inventory, greater product visibility, better conversion and more revenue. Velou understands the product. Algolia connects it to shopper intent. Weve partnered with Velou for a while — but starting today, we're one company. Welcome to Algolia, Velou! 🚀

Customers don’t understand that agents are limited to the information that they are trained on and have access to. Customers expect that agents are all knowing. This where Velou fills the gap in agent knowledge across all types of product attributes. We are excited to have Velou as a part of the team.

Great ranking can't find what the data never described. Today Algolia acquires Velou (now part of Algolia) to fix that at the source. Every search engine, recommendation model, and AI shopping agent runs on product data. When a listing reads “Blue Size 00 T-Shirt” with no tags, a parent searching “6 month baby boy clothes” never sees it. Velou's computer vision and language models read product photos and descriptions, then fill in the attributes retailers never had time to enter: neckline, sleeve length, material, age, season. Trained on retail data and tuned with merchandiser feedback, it knows buffalo plaid from gingham. With Velou inside Algolia, the data and retrieval layers come from one team. Cleaner catalogs feed NeuralSearch, personalization, and the agents our customers build in Agent Studio. We've partnered for a while. Starting today, we're one company. Welcome to the team Gavin Hewitt, Sadee Gamhewa and the Velou team. Explore what’s next: https://lnkd.in/gqXT6nsd

Most companies spend significant budgets on customer research surveys. Then they ignore the questions prospective buyers ask their website every single day. Think about it. When someone arrives on your site looking for a specific solution or pricing structure and cannot find it, what happens? In the traditional web model, they leave. In standard analytics, they are just an anonymous bounce. You learn nothing about what they actually wanted. When your website can hold an intelligent conversation, something fundamental changes. Visitors tell you exactly what they are looking for. They ask if your offering supports their specific use case. They ask about constraints you haven't addressed on the page. They tell you the exact problem they are trying to solve right now. Suddenly, your website becomes a real-time market sensor. You do not need a three-month research cycle to discover what buyers wish you offered. Your visitors are asking for it in plain language every day. That intelligence should not stay locked in a conversation log. It should inform your product roadmap, your messaging, and your commercial strategy. I wrote about how to capture and use that intelligence here: https://lnkd.in/g_GB_52i #AIStrategy #MarketIntelligence #ExecutiveStrategy

Funny how your career dots connect when you look backward. My time in the retail media and ecommerce world taught me how powerful performance data can be. Platforms like Pacvue help brands understand media performance, commerce signals, incrementality and where opportunities exist. Companies like Netrush bring another layer, connecting marketplace performance, marketing, operations and fulfillment. And now, at Bizrate Insights, I get to look at another piece of that same puzzle: The customer. Because sometimes the dashboard tells you WHAT happened. ROAS dropped. Conversion changed. A product took off. Repeat purchase slowed. Traffic increased, but sales didn't. But it can't always tell you WHY. That's where direct customer feedback becomes really interesting. What stopped the shopper from buying? What information was missing? Did the product meet expectations? Did shipping change their perception of the brand? What would make them come back? To me, the future isn't choosing between behavioral data, retail media data, operational data and customer feedback. It's connecting them. Performance tells us where to look. The customer helps tell us why. And together, we have a much better idea of what to do next. Maybe the next evolution of commerce intelligence isn't MORE data. Maybe it's getting better at connecting the dots we already have.