Ecommerce

Buyers are showing up to sales calls more informed than ever. That means sellers need to show up more prepared than ever. In the past, buyers would reach out to sales when they still knew very little about a product. They wanted to learn more about the features, pricing, and how it compares to competitors – and they expected the salesperson to provide that information. Today, buyers are gathering all that information (and more) long before they talk to sales. They’re reading review sites like G2. They’re scrolling communities like Reddit. They’re watching product walkthroughs on YouTube. Most significantly, they’re doing deep research with LLMs like ChatGPT, asking questions like “Is Product A or Product B better for my business?” Now, when a buyer gets on a call with sales, they expect more than basic information. Instead, they're looking for: Detailed examples of how other companies in their industry are using the product. Custom demos that show how the product works in their specific use case. Clear plans for how the product will be implemented and adopted. Here’s the good news. Just as buyers use AI to learn more about products, salespeople can use it to learn more about prospects. If I were in sales again, I would: 1. Use an AI assistant to do advanced research about your prospects before every call. 2. Use AI to find the best examples of similar companies seeing success with your product. 3. Build bespoke demos that highlight the most relevant features. Buyers today are more informed than ever. The best sellers I know are more prepared than ever. The result? More productive conversations, deeper connections and higher trust.

We analyzed 4 million recruiting emails sent through Gem. Most get opened. But only 22.6% get replies. Half those replies are "thanks, but no thanks." We dug into what actually works. Here are 8 factors that drive REAL responses: 1. Strategic timing beats everything else - 8am gets 68% open rates. 4pm hits 67.3%. 10am lands at 67% - Most recruiters blast at 9am when inboxes are flooded - Avoiding peak times alone can boost your opens by 7-10% 2. Weekend outreach is criminally underused - Saturday/Sunday emails get ≥66% open rates consistently - Why? Empty inboxes. Zero competition. Candidates actually have time - Yet few recruiters send on weekends. Their loss is your gain 3. Keep messages between 101-150 words - Shorter feels spammy. Longer gets skimmed - You need exactly 10 sentences to nail the essentials - Every word beyond 150 drops performance 4. Generic templates kill response rates - Generic templates: 22% reply rate - Personalized outreach: 47% increased response rate - Even adding name + company to subject lines boosts opens by 5% 5. Subject lines need 3-9 words - Include company name + job title for highest opens - "Senior Engineer Role at [Company]" beats clever wordplay - 11+ words can work if genuinely intriguing, but why risk it? 6. The 4-stage sequence is optimal - One-off emails are dead. Send exactly 4 follow-up messages - You'll see 68% higher "interested" rates with proper sequencing - After stage 4, engagement completely flatlines. Stop there 7. Get the hiring manager involved - Having the hiring manager send ONE follow-up boosts reply rates by 50%+ - Yet most recruiters don't use this tactic - Weekend advantage: Minimal competition for attention 8. Leadership involvement is a cheat code - Role-specific timing (tech vs non-tech) matters - Technical roles: 3 of 4 best send times are weekends - Engineers check email differently than salespeople. Adjust accordingly TAKEAWAY: These aren't opinions. This is what 4 million emails tell us. Most recruiting teams are stuck in 2019 playbooks wondering why their reply rates won't budge. Meanwhile, recruiters who implement these 8 factors see dramatically better results. The data is right there. The patterns are clear. The only question is: will you actually change how you operate? Or will you keep sending the same tired emails at 9am on Tuesday? Your call.

"Is $20/month too much for our product?" Instead of guessing, we used the Van Westendorp method to find our pricing sweet spot. 4 questions revealed exactly what users would pay (and we haven't touched our pricing since). Here's the framework any founder can steal: 1. Send a survey to actual users, not prospects We surveyed people already using Gamma. They understood the real value of our product, not hypothetical value. Too many founders survey their waitlist or randomly select people who have never used their product. That's like asking someone who's never driven about car prices. 2. Ask these 4 specific questions - At what price would this be too expensive for you to consider it? - At what price is it expensive but still delivering value? - At what price does it feel like a bargain? - At what price is it so cheap you'd question if it's reliable? These create bookends for perceived value. You're mapping the entire spectrum of price psychology, not just asking "what would you pay?" 3. Plot the responses and find where the lines intersect Graph responses from lots of users. Where "too expensive" and "too cheap" lines cross: that's your acceptable range. Where "expensive but fair" meets "bargain": this is your optimal price point. 4. Test within the range, don't just pick the middle The intersection gives you a range, not a number. We ran pricing experiments within that range to see actual conversion rates. A survey shows willingness to pay; testing reveals actual behavior. 5. Lean towards generous (especially for product-led growth) We chose to be more generous with AI usage than our "optimal" price suggested. Word-of-mouth growth matters more than maximizing initial revenue. Not everything shows up in the numbers. 6. Lock it in and stop tinkering Once you find the sweet spot through data, stick with it. We haven't changed pricing in 2 years. Every month debating pricing is a month not improving product. Remember: pricing is a signal, not just a number (Image: First Principles)

Replenishment isn’t a side feature, it’s a force multiplier. This is a big mistake. We’ve seen replenishment flows outperform promos and win-back emails combined. They convert better every time with the right timing and zero customer effort. Brands overspend on ads to win new customers, then forget to win them again. They need to predict exactly when a customer needs to repurchase and trigger the message at the perfect moment. Not too soon, not too late. Just right. ++ 𝗪𝗵𝘆 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗗𝗼𝗻’𝘁 𝗥𝗲𝗼𝗿𝗱𝗲𝗿 – 𝗔𝗻𝗱 𝗛𝗼𝘄 𝘁𝗼 𝗙𝗶𝘅 𝗜𝘁 ++  𝗧𝗵𝗲𝘆 𝗙𝗼𝗿𝗴𝗲𝘁 ✅ Fix: Replenit’s AI triggers proactive reminders across channels exactly when customers are likely to run out, via the brand's own marketing automation vendors, without any migration. 𝗣𝗼𝗼𝗿 𝗧𝗶𝗺𝗶𝗻𝗴 𝗼𝗿 𝗖𝗵𝗮𝗻𝗻𝗲𝗹 ✅ Fix: Multichannel orchestration (SMS, push, email) with personalized timing based on consumption behavior. 𝗡𝗼 𝗖𝗹𝗲𝗮𝗿 𝗜𝗻𝗰𝗲𝗻𝘁𝗶𝘃𝗲 ✅ Fix: Smart upsell bundles, urgency messages (“running low?”), and loyalty integration improve reorder ROI.   • Food & Beverage, pet food and treats, wellness & beauty products hold the highest repeat purchase potential, being very high due to frequent, perishable-driven consumption patterns. • Online groceries and FMCG rank high in habitual/impulsive behavior, presenting a strong fit for mobile push and SMS-driven replenishment campaigns. Brands like Glosel turned a leaky bucket into a revenue engine with Replenit’s AI-powered multichannel replenishment flows. 🚀 53.75% more automation revenue 🛒 +28% higher AOV 📲 100% of the Multichannel approach, email, SMS & Push channel revenue -12X Higher Engagement Rate Why does it work? Because Replenit activates timely, no-effort reorders across email, SMS, push, and more. Most brands forget to remind customers. ++ 𝟯 𝗧𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗥𝗲𝘁𝗮𝗶𝗹𝗲𝗿𝘀 ++ 1️⃣ Make Replenishment an Always-On Growth Engine Don’t treat it as a postscript. Integrate replenishment flows as a core revenue pillar in your retention strategy. 2️⃣ Automate Across Channels With Smart Triggers Use AI-powered solutions to trigger SMS, email, and push notifications based on usage cycles, not guesswork. 3️⃣ Track and Optimize With First-Party Data Loops Leverage Replenit’s dashboards to identify top retention products, run experiments on timing, and iterate continuously. 𝗧𝗼 𝗮𝗰𝗰𝗲𝘀𝘀 𝗮𝗹𝗹 𝗼𝘂𝗿 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗼𝗹𝗹𝗼𝘄 ecommert® 𝗮𝗻𝗱 𝗷𝗼𝗶𝗻 𝟭𝟰,𝟮𝟬𝟬+ 𝗖𝗣𝗚, 𝗿𝗲𝘁𝗮𝗶𝗹, 𝗮𝗻𝗱 𝗠𝗮𝗿𝗧𝗲𝗰𝗵 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 𝘄𝗵𝗼 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲𝗱 𝘁𝗼 𝗲𝗰𝗼𝗺𝗺𝗲𝗿𝘁® : 𝗖𝗣𝗚 𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝗚𝗿𝗼𝘄𝘁𝗵 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. About ecommert We partner with CPG businesses and leading technology companies of all sizes to accelerate growth through AI-driven digital commerce solutions. #CPG #ecommerce #Replenishment #AI #FMCG

🗺️ AirBnB Customer Journey Blueprint, a wonderful practical example of how to visualize the entire customer experience for 2 personas, across 8 touch points, with user policies, UI screens and all interactions with the customer service — all on one single page. AirBnB Customer Journey (Google Drive): https://lnkd.in/eKsTjrp4 Spotify Customer Journey (High-res): https://lnkd.in/eX3NBWbJ Now, unlike AirBnB, your product might not need a mapping against user policies. However, it might need other lanes that would be more relevant for your team. E.g. include relevant findings and recommendations from UX research. List key actions needed for next stage. Add relevant UX metrics and unsuccessful touchpoints. That last bit is often missing. Yet customer journeys are often non-linear, with unpredictable entry points, and integrations way beyond the final stage of a customer journey map. It’s in those moments when things leave a perfect path that a product’s UX is actually stress tested. So consider mapping unsuccessful touchpoints as well — failures, error messages, conflicts, incompatibilities, warnings, connectivity issues, eventual lock-outs and frequent log-outs, authentication issues, outages and urgent support inquiries. Even further than that: each team could be able to zoom into specific touch points and attach links to quotes, photos, videos, prototypes, design system docs and Figma files. Perhaps even highlight the desired future state. Technical challenges and pain points. Those unsuccessful states. Now, that would be a remarkable reference to use in the beginning of every design sprint. Such mappings are often overlooked, but they can be very impactful. Not only is it a very tangible way to visualize UX, but it’s also easy to understand, remember and relate to daily — potentially for all teams in the entire organization. And that's something only few artefacts can do. Useful resources: Free Template: Customer Journey Mapping, by Taras Bakusevych https://lnkd.in/e-emkh5A Free Template: End-To-End User Experience Map (Figma), by Justin Tan https://lnkd.in/eir9jg7J Customer Journey Map Template (Figma), by Ed Biden https://lnkd.in/evaUP4kz Free Figma/Miro User Journey Maps Templates https://lnkd.in/etSB7VqB User Journey Maps vs. Service Blueprints (+ Templates) https://lnkd.in/e-JSYtwW UX Mapping Methods (+ Miro/Figma Templates) https://lnkd.in/en3Vje4t #ux #design

🤔Understanding 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 Let me break it down for you: In the 𝟭𝟵𝟵𝟬𝘀, with the rise of Ecommerce, the first payment gateways came into existence. However, they lacked today's advanced collection and reconciliation tools. The 𝟮𝟬𝟬𝟬𝘀 saw integrations between developers and gateways due to limitations in serving all customers through one gateway. By the 𝟮𝟬𝟭𝟬𝘀, PSPs transformed, introducing alternative payment methods, fraud prevention, and global payments in local currencies. The 𝟮𝟬𝟮𝟬𝘀 witnessed a shift, with over 60% of retailers using multiple payment providers and payment orchestration becoming essential for businesses. What is 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻? Drawing from the world of music, payment orchestration functions similarly to a maestro harmonizing an orchestra🎼 This system blends multiple payment processes, offering an efficient and streamlined transaction route. It centralizes various gateways, ensuring a smooth consumer checkout. Integrated reporting provides a unified data view, and "smart routing" auto-directs transactions through the best route. Europe's e-commerce data shows that roughly a quarter of Mastercard's payment authentications in early 2021 failed. Smart routing in payment orchestration aims to combat such issues. Business Research Insights predicts that by 2027, the payment orchestration market will be valued at nearly $5 billion. Key advantages of payment orchestration include: 1️⃣ Cost and Time Efficiency: Merchants can choose lower transaction fees from a range of providers. 2️⃣ Increased Conversion: Improved customer experience boosts conversion rates. Factors like smart routing, diverse payment methods, and local currency support play significant roles. 3️⃣ Transaction Success: With the rise in digital payments, ensuring transaction success becomes vital. Payment orchestration can notably reduce decline rates. 4️⃣ Customer Loyalty: Offering preferred payment methods enhances the buying experience, fostering customer loyalty. 5️⃣ Global Expansion: For businesses aiming globally, understanding regional payment preferences is crucial. 6️⃣ Rapid Scaling: Merchants can swiftly integrate solutions supporting business growth. 7️⃣ Fraud Reduction: A consolidated platform with multiple payment methods aids in fraud prevention. 8️⃣ Automatic Reconciliation: This feature minimizes errors, saving internal resources and enhancing efficiency. 9️⃣ Real-time Ledgers (RTLs): RTLs provide almost instant financial data visibility, ensuring transactional integrity. Source: Axerve Find this helpful? [ 𝗿𝗲𝗽𝗼𝘀𝘁 ] Anything to add about this subject? [ 𝗶𝗻𝘃𝗶𝘁𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗺𝗲𝗻𝘁 ] Nice story, Marcel. Next! [ 𝗹𝗶𝗸𝗲 ]

Early in my facilitation career, I made a big mistake. Spent hours crafting engaging activities and perfecting every little detail… Thinking that amazing learning design is what would make my workshops stand out and get me rehired. Some went great. Some bombed. You know the ones, sessions where: - One participant dominated the conversation. - People quietly disengaged, barely participating. - half the group visibly frustrated but not saying anything. I would push through, hoping things would course-correct. But by the end, it was a bit… meh. I knew my learning design was great so... What was I missing? Why the inconsistency between sessions? 💡I relied too much on implicit agreements. I realised that I either skipped or rushed the 'working agreements'. Treating it like a 'tick' box exercise. And it's here I needed to invest more time Other names for this: Contract, Culture or Design Alliance, etc... Now, I never start a session without setting a working agreement. And the longer I'm with the group, the longer I spend on it. 25 years of doing this. Here are my go-to Qs: 🔹 What would make this session a valuable use of your time? → This sets the north star. It ensures participants express their needs, not just my agenda. 🔹 What atmosphere do we want to create? → This sets the mood. Do they want an energising space? A reflective one? Let them decide. 🔹 What behaviours will support this? → This makes things concrete. It turns abstract hopes into tangible agreements. 🔹 How do we want to handle disagreement? → This makes it practical. Conflict isn’t the problem—how we navigate it is. ... The result? - More engaged participants. - Smoother facilitation. - Ultimately, a reputation as the go-to person for high-impact sessions. You probably already know this. But if things don't go smoothly in your session. Might be worth investing a bit more time at the start to prevent problems later on. Great facilitation doesn't just happen, It's intentional, and it's designed. ~~ ♻️ Share if this is a useful reminder ✍️ Have you ever used a working agreement in your workshops? What’s one question you always ask? Drop it in the comments!

So, here’s a quick story about how I managed to take our app ratings at airtel from a 3.2 to a solid 4.3 in just 30 days. I was on a call with our account executive at MoEngage where we were discussing the RFM model. If you’re not familiar, RFM stands for Recency, Frequency, Monetization—it’s basically a way to understand customer behavior based on how often they use the app, how recently they’ve been active, and if they’ve made any purchases. After the call, I started thinking—how can we use this data beyond just targeting users for offers or notifications? And then it clicked: we could use this to improve our app ratings. Here’s what I did next: instead of showing the app rating prompt to everyone (which was clearly not working), I decided to get more specific. I created a segment of users who were really engaged—people who were listening music for at least 20-30 minutes a day and opening the app 5-6 times daily. These were our power users, the ones who were already loving the app. But I didn’t just stop there. I made sure the rating prompt would only pop up after an “aha moment,” like after they listened to five songs or changed their hello tune. I wanted to catch them at a high point when they were already feeling good about their experience. Plus, we capped the prompt to only show up once a week, so we weren’t bombarding them. And guess what? It worked! By focusing on the users who were most likely to give us positive feedback, we managed to take our ratings from 3.2 to 4.3 in just a month. It was all about understanding who to ask, when to ask, and how to make that moment feel seamless.

We're moving away from charging for *access* to software and toward a model of charging for the *work delivered* by a combination of software and AI agents. Let’s dive into what’s happening and what it means for you ⤵️ 1. The rise of disruptive AI pricing models Tech companies are realizing they can't solely rely on seat-based subscriptions in an age of AI, automation and APIs where value is disconnected with how many people are logging in. Perhaps Salesforce going all-in on Agentforce (and charging $2 per conversation) was the push the industry needed. Each product category has its own flavor of disruptive pricing. - Legal AI products might charge for a demand package generated by AI or an AI-generated summary. - Creator AI products might charge for the content that gets produced such as a video generation or amount of video created. - GTM products might charge for specific tasks completed or workflows executed by the AI. 2. Selling work, not necessarily success As a customer, I wish I only had to pay for software when it delivered results. But the reality is that true success-based billing won’t work for the vast majority of today’s products. Most products should charge for work output instead. The issue is attribution. You want the customer to get a fantastic outcome — and you want them to recognize that your product powered that outcome. As soon as you start charging for success, the customer begins to rethink the results. 3. Goodbye ARR as we know it? Shifting to these newer value-based pricing models isn't a simple pricing change you can just announce in a press release. It's a business model evolution that looks a lot like the shift from on-prem to SaaS in the first place. These new AI pricing models might mean greater volatility in both usage and spend. Variable margin profiles across products and customers. Seasonal revenue fluctuations. The potential for project-based, non-recurring use cases. Put simply, annual recurring revenue (ARR) continues to get dethroned. — Full post in today’s Growth Unhinged newsletter: https://lnkd.in/ea5eTrVD Things are about to get interesting 🍿 #ai #pricing #saas

Digital wallets (DWs) are the number 1 and fastest growing payment method globally. Yet not all DWs are the same. This is an analysis of the different players and business models behind them. These are 3 reasons why you should pay attention to DWs: —  5.2 bn users globally by 2026 —  50% e-com global share ($3.1 tn) —  30% POS global share ($10.8 tn) To understand how DWs differ (#strategy, positioning) we need to categorize them. These are my criteria: 1)  The types of players that are behind them: SuperApps, BigTechs, e-commerce players, banks, crypto providers, telecoms, big brands, etc. 2)  How they manage funds: DWs such as Apple or Google Pay (pass-through) don’t have their own balance, others such as PayPal process funding and #payments in separate stages, whereas Alipay and WeChat Pay are stored wallets, pre-loaded with funds. 3)  The kind of use cases they support (online or in-store with P2P, C2B, B2C, B2B, C2G and G2C variations). 4)  Their #technology: QR-codes (widespread in Asia) vs NFC (popular in Europe) or crypto wallets are examples. 5)  Their target audience: merchants, marketplaces, big brands, niche users, etc. 6)  The payment methods they support: credit or debit cards, bank accounts (A2A transfers), crypto, etc. Based on the above, I have identified 10 distinctive DW plays: 1. SuperApps in Asia that have evolved from simple wallets facilitating payment use cases to huge ecosystem behemoths with multiple plays (consumer, merchant, government, lending, etc).   2. Bigtechs like Apple and Google using DWs as vehicles to monetize their user base and expand beyond their core offering. 3. #ecommerce platforms like Amazon, Mercado Pago or Rakuten looking to boost their business and create new growth opportunities. 4. Ecosystem players in local or regional markets that use DWs to bring payments, digital platforms and mobile banking functionalities under one umbrella. 5. Banks looking to compete with new value-chain challengers (fintechs, platforms) on their own (front-end) customer-facing game. 6. A2A players like Venmo or Zelle focusing on social features, P2P payments, instant transfers, bank integration and competitive pricing to expand their offering. 7. Niche players using customization, vertical focus, rewards and loyalty programs and specialized offerings to service specific use cases (i.e. gambling, gaming, FX). 8. Crypto & blockchain players using DWs to bridge the gap with the fiat world and to offer new use cases. 9. Big brands like Starbucks leveraging DWs to build closed-loop FS ecosystems. 10. Telecoms in Africa employing DWs as a replacement for core-banking infrastructure.   DWs’ spectacular rise is not only democratizing access to #payments and to broader FS faster than any other point in history but it is also forcing players across the value chain (providers, merchants, banks, platforms, fintechs) to re-think their entire positioning and strategy. Opinions and graphics: Panagiotis Kriaris