Analyzing Referral Outcomes

Everyone wants to know if and how fast traffic from LLMs is growing. And if their website is meeting industry averages. I published a case study of 6 B2B SaaS, showing that referral traffic from AI Chatbots 5x’ed over the last 6 months. Viola Eva then compared the numbers for an additional 20 B2B SaaS and found the exact same trend! So, what are the implications? 👉 AI chatbot and LLM referral traffic is poised to become a lot more significant, especially now that Chat GPT launched its search engine for all users (not just paying). ChatGPT is the leading referral source for most sites analyzed. 👉 The volume is still tiny in comparison to classic organic traffic. 0.14% for Kevin’s clients and 0.2% for Flow Agency (formerly: Flow SEO) clients, with peaks at 1.3%. BUT: 👉 Over the last year, LLM referrals have grown at a 20% median monthly rate across all 26 SaaS sites. 👉 Over the last 3 months, it has grown at a 36-54% median monthly rate, so growth is accelerating. For now, it makes sense for AI chatbot referrals to be much lower than organic traffic - users complete more of their journey before clicking through to websites. When they do click to your website, they are highly relevant and interested though. Average conversion rate from LLMs for Flow Agency’s clients has been 0.7% with peaks at 2.3%. Here is what we both tell our (B2B) clients: ✅ Monitor LLM crawlers, referral traffic, and conversions by landing page to figure out which content gets crawled and performs well in AI chatbots. ✅ Monitor visibility in Chat GPT, Perplexity, Copilot/Bing and Gemini because we don’t yet know whether “AI chatbot optimization” will lead to the same results for all chatbots - likely not. ✅ Test net-new content and content adjustments to provide better answers in AI chatbots. Now is the time to write the playbook. ✅ Keep doing classic SEO since AI chatbots still lean heavily on their results to ground answers.

My best referral source isn't satisfied patients. It's the ones who developed complications I openly predicted. Rahul, 32. Fracture neck femur. Fixed with cancellous screws. During discharge, I said what most surgeons avoid: "Your fracture will heal. But there's a 20% chance of AVN - where the hip loses blood supply. Not from surgery, from the fracture itself. If it happens, ceramic hip replacement will fix it." 8 months later: Fracture healed. Groin pain started. X-ray: AVN confirmed. Most surgeons act surprised. Make excuses. Lose trust. What I did: "Remember our AVN discussion? This is it. Exactly as predicted. Let's fix it." The family's response: "We trust you completely. When can we do the hip replacement?" Post-surgery: Rahul's back to normal. 6 months later: He sent me 9 patients. His exact referral: "He told me what could go wrong. When it happened, he had the solution ready." The data after tracking 100+ cases: → AVN managed transparently: 4.8x more referrals → Perfect outcomes: 1.1x referrals → Upfront risk discussion: 87% higher trust Here's why: When you predict complications, you're not wrong - you're prophetic. When you have solutions ready, you're not scrambling - you're prepared. The Transparency Protocol: 1. State the risk clearly: "20% chance of AVN" 2. Explain why: "The fracture damages blood supply" 3. Offer the solution: "Hip replacement works excellently" 4. Own the biology: "This is how some fractures behave" 5. Document recovery: They become your advocates Most surgeons promise perfection and deliver biology. Smart surgeons promise honesty and deliver solutions. Your complications aren't practice killers. Your communication about them is. Every young patient with fracture neck femur is a potential AVN. Every AVN is a potential practice builder. But only if you prepare them before, not explain after. How do you handle complication discussions?

Insights we've learned from analyzing 300k+ conversations with home owners, as it relates to referrals and reviews.... 🔍 New vs. Legacy Customers: New customers are 5x more likely to leave a referral compared to legacy customers. This underscores the critical need to engage with new customers early and often. Their initial excitement and positive experiences are powerful catalysts for generating referrals. By prioritizing new customers, you can leverage their enthusiasm to drive growth. 🧠 Top of Mind is Earned: It takes an average of about 2.6 messages to secure a first referral and ~40% of new referrals come from customers who have left at least one referral. You can't expect to send out one message with a referral link and expect results. You also can't expect someone who has left one referral to send more if they have a poor experience with your referral program. You can't reap where you don't sow. It takes consistent personalized nurturing + a delightful referral experience (how a referral is submitted, updates on referral progress, easy reward collection, access to a customer advocate point of contact, etc) to earn the top of mind with your customers. But it's well worth the effort!! ⭐ Win, Win, Win: When you put energy into referral marketing and nurturing your customers through personalized conversation, everyone wins: 1. The customer wins as they feel valued and are rewarded for referring their friends. 2. The brand wins with cost efficient referrals and quality reviews to build their reputation and social proof their brand 3. The referrals win as they are buying with confidence from a brand they know their friend had a good experience with #Referrals #Reviews #WordOfMouthMarketing #CustomerEngagement

87% of our business came from referrals. That's the figure for last month. No cold calls. No tedious proposals. No competing against 5 other agencies. Just warm leads who already trusted us. When I started DesignLion, we had no referral strategy. We just hoped happy clients would spread the word. Some did. Most didn't. So we created a systematic approach that transformed occasional referrals into our primary growth engine: - Results documentation - Strategic Timing - Specific Asking Referral Rewards - For every successful referral, we offer: - $2,500 credit toward future work - Free website maintenance for 3 months - Complimentary strategy session The numbers tell the story: Before this system: - Referral rate: 12% of clients - Referral quality: Mixed - Referral close rate: 40% After implementation: - Referral rate: 64% of clients - Referral quality: Mostly enterprise-level - Referral close rate: 78% Our total marketing budget decreased by 41%. Most importantly, these referred clients start with trust already established. They value our expertise from day one and are 3.4x more likely to accept our strategic recommendations. Stop chasing cold leads when warm introductions are waiting to be activated. The best marketing strategy isn't always about reaching new audiences. Sometimes it's about fully leveraging the relationships you've already earned. What systematic approach could you implement to transform occasional referrals into a predictable pipeline?

Think you know what's really driving your sales growth? I am willing to bet quite a lot that whatever you think...it's wrong. Why? Because a gaping hole lies at the center of almost every marketing and sales attribution model I have ever seen. Worse yet, almost no one knows it's there. In the latest episode of Customer Confidential, Fred Reichheld and I open up one of our working sessions to explore the mysterious world of referrals and their value. Failing to properly measure and attribute referral impact leads to wildly misallocated resources. It's a massive blind spot. It's ironic that referrals have driven profitable business growth for centuries and everyone knows it. Yet here we are in 2024. We have gigantic datasets available for analysis. We have exceptionally sophisticated digital tools at our fingertips. We have machine learning-based attribution models. And yet, only a tiny fraction of companies even attempts to track and quantify referral behavior in any meaningful way. To begin making progress, we have got to develop a shared language and a "referral taxonomy." We need to align on how to answer questions like: ➡ What exactly is a referral? ➡ What do we call the person who makes a referral? What do we call the person who receives it? ➡ What motivates authentic referrals? ➡ Which types of referrals deliver more or less value to customers and to the company? ➡ What do we call each of these referral types? Next, we need an analytic framework for understanding the value of a referral. For example, how much of our total customer acquisition costs do we charge against customers who come in through referrals? Does it matter what type of referral we think they got? How do we know how valuable the referral was relative to sales, advertising, and marketing activities? These are thorny questions without clear answers today. That's why we need the best minds in the business to weigh in and help forge a path forward. Cracking the referral code offers a huge untapped growth opportunity for most businesses. Getting a simple datapoint on every new customer – "How important was a recommendation, referral, or reference in your decision to buy our product/service?" - would be transformative. But almost no one is doing it. We need your help working through this, so we are asking you to contribute your perspective and experience. This is uncharted territory. If we all begin working on this now – if we share experience along the way – we'll start to make real progress in one of the least understood but most impactful dimensions of customer value. Imagine the competitive advantage for companies that truly figure this out. Your marketing budget (and your CFO) will thank you. Listen: https://lnkd.in/gqwBEaCM Share your experience and perspective: https://lnkd.in/eQy5cG79 #CustomerExperience #Referrals #MarketingROI #Loyalty #Advocacy #CLV #CLTV #LTV Bain & Company

A 98% referral-to-application rate is the kind of stat I’d call BS on if I hadn’t seen it myself. But I have—a client of ours consistently achieves this rate, and it isn’t a handful of referrals inflating the numbers. We’re talking hundreds of monthly referrals consistently converting into applicants. Most referral programs consider a 50% application rate impressive, making this near-perfect conversion a true standout. Nearly 40% of those applicants became hires, compared to the industry standard of 1–5%. Those numbers directly translate into significant cost savings. Instead of paying external recruiters, the company now redirects nearly half of their recruiting dollars into employee bonuses, increasing loyalty and strengthening their internal community. The strategy behind these results is straightforward and specific. Employees receive clear, frequent communication about exactly which roles need filling and what makes a referral relevant. This ensures each referral genuinely fits the job rather than adding unnecessary volume. This same client recently ran a referral campaign and achieved these impressive results despite only a fraction of their workforce actively participating. With increased internal promotion, they’re positioned to see these exceptional numbers climb even higher. Results like these changed my thinking. I used to believe referral success depended primarily on aggressive promotion or large rewards. These outcomes clearly show that targeted clarity and consistent communication produce meaningful hires and stronger overall results. If your referral conversion rate feels stuck, reconsider your approach to clarity. Clear communication about who you need consistently leads to more effective referrals.