Voyado

Your customer has already bought from someone else. Many win-back campaigns activate after 90 days of customer inactivity. The signal was visible at day 30. The trigger fires at day 90. That's 60 days you didn't have. The logic behind fixed-delay automation still holds for simple scenarios: welcome flows, post-purchase follow-ups, basic re-engagement offers. It breaks down when customer behavior is too varied for a fixed rule to catch in time. Declining purchase frequency, lower campaign engagement. Each signal might look like noise on its own. Together, they describe a customer who is already drifting. Agentic retention works by reading combinations of signals like loyalty activity, on-site behavior, and purchase timing, before a threshold trips. The intervention window is longer, the options are broader, and the customer is still reachable. That's where recovery rates improve. Getting started doesn't mean rebuilding every flow. It means finding two or three journeys where timing is the difference between a customer returning and not. Second purchase, churn prevention, and loyalty reactivation are usually where the lift shows up first.

Most retail teams track loyalty as a KPI. The teams getting more out of their data treat it differently. Every loyalty member who walks into a store is an attribution signal. Because they're identifiable, their purchases can be connected to specific campaigns, email opens, browsing sessions, and offers. When a loyalty member buys, you can trace the path. When a non-member buys, you're often guessing. That's why loyalty data is one of the most underused tools in retail attribution. Most teams track it separately — membership growth, points redeemed, churn rate. The teams getting more from their data use it as a backbone: linking digital engagement to offline revenue across a real customer profile. The difference in what you can measure is significant. Loyal customers buy more often, respond more predictably to campaigns, and give you the data needed to test incrementality and report by lifecycle stage. Attribution built on loyalty data is also less dependent on cookie tracking or last-click shortcuts. This is one of 10 strategies in the guide. The rest cover the online-to-offline gap, cross-channel journey analysis, and the attribution mistakes most retail teams are still making. View guide: https://lnkd.in/dpzz8e2G

Search conversion doubled after Ellis Brigham rebuilt how customers find products online. 🏔️ Mike Humphreys, Online Manager at the UK outdoor specialist, says shoppers who use site search now convert at twice the rate of those who don't. That's the return on making product discovery genuinely easy to use, not just technically capable. Personalization in search and navigation has been live for about two months. Early results lean positive, especially on navigation, where the team is tracking click depth to see how quickly people reach the product they came for. Another big shift is still ahead. Ellis Brigham is finishing a new POS rollout, and connecting it to Voyado means online and in-store data will finally sit in one place. In Mike's words: "For the first time, we're going to have a full 360 view of how our customers are shopping and how they're interacting with our brand." That's the real prize of unifying retail data. Not more dashboards, but a clearer read on the customer in front of you, wherever they're shopping. Watch the conversation with Mike Humphreys:

As Pour Moi Ltd's digital audience grew, so did their ambition. Rather than relying on more traffic to drive growth, they focused on making product discovery more effortless through smarter search, better product recommendations, and personalisation at scale. And the results followed: 📈 +12% increase in search conversion rate 📈 +9% increase in total conversion growth Read the full customer story and how Pour Moi drove these results: https://lnkd.in/edDBdAGA

Are you running lifecycle campaigns or a customer retention program? Running lifecycle campaigns and calling it retention is common. The emails go out. The numbers look fine: opens, clicks, unsubscribes all in range. Then someone asks why customer lifetime value isn't moving, and you have no clean answer. The shift most retail teams are making isn't from less automation to more. It's from measuring campaign activity to measuring customer behavior such as repeat purchase rate and time to second purchase. These are the numbers that show whether a program is retaining customers, or just filling inboxes. Agentic AI changes when the response happens. Instead of sending the same post-purchase flow on a fixed schedule, the system reads signals like slowing purchase frequency, fading loyalty engagement, or reduced browsing, and responds while the relationship is still active. Earlier intervention is the only kind that gives you real options. If your retention program is working, these are the numbers that should move: repeat purchase rate, time to second purchase, cohort retention, loyalty tier progression, and customer lifetime value. If only opens and clicks are improving, you may be optimizing campaigns and not customer retention.

No discounts. Just tiers and points built around one goal: making customers feel recognized. The loyalty program launched in October, and the team at MOS MOSH has been explicit about what they're optimizing for. Not a lower price, but a better experience. That's a harder thing to build than a discount code, and a much harder thing for competitors to copy. Getting there took more than a points structure. Alberte Skoffer, Email & CRM Specialist at Mos Mosh, says the most valuable part of the process was the close, ongoing work with Voyado to tailor the program specifically to their brand rather than dropping in a standard template. The lesson for anyone running loyalty: a generic program rewards anyone who shows up. A tailored one rewards the behavior you actually want to see. If your loyalty strategy is still measured by discount depth instead of recognition, you're solving for the wrong metric. Watch the full conversation with Alberte Skoffer and Stefan Frandsen here👇

The 4 moments where the omnichannel experience breaks down: 1️⃣ Browse online, buy in store → store teams can't see what the customer was looking at 2️⃣ First purchase made → no follow-up that reflects what they bought 3️⃣ Loyalty member visits a store → their tier status isn't recognized at the till 4️⃣ Returns an item in-store → still gets upsell emails for the same product These aren't technology failures. They're context failures and each one sends the same signal: we don't know you. Most retailers personalize well in one channel. Almost none do it consistently everywhere, and customers notice every gap. Fixing this doesn't start with adding new channels or new tools. It starts with one thing: a single customer profile that updates in real time, wherever the customer shows up. Loyalty activity, e-commerce behavior, in-store purchases, marketing preferences — all feeding the same view. When that's in place, personalization stops being a channel-by-channel problem and becomes something your whole operation runs on. The guide covers 7 practical steps for getting there. Read the guide here: https://lnkd.in/dbPkvfTm

Most fashion shoppers leave your site not because they don't want to buy, but because they couldn't find what they came for. 😬 That's the problem intelligent search and merchandising is built to solve. Last week in Amsterdam, we joined Ask Phill and Triple Whale for Ask Phill & Friends, a morning built for commerce leaders who want less guesswork and more growth in 2026, and one brand showed the room exactly what fixing that problem looks like in practice. Never Fully Dressed - the London-born, female-founded fashion brand known for inclusive, multi-wear styles and pure feel-good energy - took the stage with Fabio Cancemi, Head of Digital, who flew in from London to share their journey. NFD connected Shopify with Voyado's product discovery engine to layer intelligent search and personalized merchandising on top of their existing store - no rebuild, no compromise on the brand's feel-good identity. The result: shoppers find the styles they'll love, faster, and NFD gets scalable automation without losing the personal touch. After just a couple of months live, the results speak for themselves: 📈 Search conversion rate up 39% 🔍 Search's contribution to total conversions up 11% 🛒 Overall conversion rate up 16% 💳 Average order value up 4% Whether you're running a fashion brand in London or Linköping, the challenge is the same: help customers discover what they'll love without getting in their own way. Never Fully Dressed is proof that smart search and merchandising can do exactly that, all the while staying true to your brand's identity. Huge thanks to Fabio for making the trip and sharing the journey so openly, and to Ask Phill and Triple Whale for bringing the room together over great coffee, croissants, and even better conversations. #Retail #Ecommerce #Fashion #ProductDiscovery #CustomerExperience #AskPhillAndFriends

Two days of talking shop at K5 - Future Retail in Berlin left us buzzing with energy. After an incredible day at our stand, with lots of great conversations, we waited eagerly for the K5 Awards Ceremony to start. And Voyado won! The award for Best Conversational Commerce & CRM is ours now, thank you to everyone who voted. Another highlight was seeing our customer MOS MOSH take the stage talking about the great results they're seeing on their loyalty program powered by our Engagement Suite. No discounts, just great customer experience is their motto. We're excited to see how they will extend that same amazing customer experience in the future with Voyado Product Discovery 🤩. A big thank you to Stefan Frandsen & Alberte Skoffer for a stellar performance. Thank you to everyone who stopped by, to Mos Mosh for sharing their story, and to the K5 team for bringing the retail community together. Until next time. #K5 #Retail #Ecommerce #CRM #Loyalty #ProductDiscovery

Knowing the customer is no longer enough. At Love Generation, Lars Gezelius, Global Head of Digital at H&M, talked about a challenge that gets harder as assortments grow. He used a simple example: A customer who sometimes shops for herself, sometimes for her kids, sometimes for both. When she comes back, you probably already know who she is. The harder question is why she's here this time. Most systems weren't built to answer that. They were built around historical behavior, aggregated clicks, word matching. Useful — but a record of what happened, not a read on what someone is trying to do right now. "Relevance never goes out of fashion," Lars said. For years, retailers have invested heavily in understanding who their customers are. Increasingly, the harder challenge is understanding why they’re here today. Knowing who a customer is has always mattered. Understanding their intent in the moment is something else entirely.