Purchase Funnel Analysis

Funnel analysis is essential for understanding where and why users drop off in structured workflows like onboarding, checkout, or sign-up flows. Unlike clickstream analysis, which maps the broader user journey, or session analysis, which focuses on individual interactions, funnel analysis zeroes in on goal-driven processes, tracking user progression and highlighting abandonment points. What’s evolving today is how we approach funnel analysis. With more natural behavioral data and machine learning enhancements, we’re moving beyond static drop-off reporting. AI-driven insights now allow teams to predict drop-offs before they occur, identifying early warning signs like hesitation patterns or inefficient navigation loops. This proactive approach enables UX researchers to refine workflows dynamically, improving user retention before friction escalates. Advanced segmentation is also revolutionizing funnel tracking. Instead of analyzing drop-offs solely through broad demographic data, researchers can now segment users based on behavioral clusters - how they interact with key touchpoints, their engagement duration, or even their likelihood of return. This behavioral-first approach allows for personalized interventions that cater to different user types, ensuring a more seamless experience for all. Beyond traditional conversion tracking, we’re incorporating statistical methods like survival analysis to estimate how long users remain engaged in a funnel and Markov modeling to understand the probability of transitioning between different steps. Instead of treating drop-offs as simple yes/no outcomes, these approaches quantify the likelihood of users completing a process based on their prior actions, leading to more precise and actionable insights. Funnel analysis is no longer just about counting conversions, it’s about deeply understanding user intent, predicting disengagement, and designing experiences that encourage progression. The shift from static reporting to predictive UX optimization is already underway.

🚨The greatest drop-off is from Product Details Page To Cart Page, so we must improve our Product Details Page! Not so fast ✋ In today's age of data obsession, almost every company has an analytics infrastructure that pumps out a tonne of numbers. But rarely do teams invest time, discipline & curiosity to interpret numbers meaningfully. I will illustrate with an example. Let's take a simple e-commerce funnel. Home Page ~ 100 users List Page ~ 90 users Product Display Page ~ 70 users Cart Page ~ 20 users Address Page ~ 15 users Payments Page ~12 users Order Confirmation Page ~ 9 users A team that just "looks" at data will immediately conclude that the drop-off is most steep between Product Details Page & Cart Page. As a consequence they will start putting in a lot of fire power into solving user problems on Product Display Page. But if the team were data "curious", would frame hypothesis such as "do certain types of users reach cart page more effectively than others?" and go on to look at users by purchase buckets, geography, category etc and look at the entire funnel end to end to observe patterns. In the above scenario, it's likely that the 20 cart users were power users whilst new & early purchasers don't make it to this stage. The reason could be poor recommendations on the list page or customers are only visiting the product display page to see a larger close up of the product. So how should one go about looking at data ? Do ✅ Start with an open & curious mind ✅ Start with hypothesis ✅ Identify metrics & counter metrics that will help prove/disprove hypothesis ✅ Identify the various dimensions that could influence behaviours - user type, geography, category, device type, gender, price point, day, time etc. The dimensions will be specific to your line of business. ✅ Check for data quality and consistency ✅ Look at upstream and downstream behaviour to see how the behaviour is influenced upstream and what happens to the behaviour downstream. ✅ Check for historical evidence of causality Dont ❌ Look at data to satisfy your bias ❌ Rush to conclude your interpretation ❌ Look at data in isolation - - - TLDR - Be curious. Not confirmed. #metrics #analytics #productmanagement #productmanager #productcraft #deepdiveswithdsk

Just watched another entrepreneur blow through his marketing budget. $100K conference booth. $250k ad spend. Cold email campaigns. Zero clue which (if any) actually work. How most entrepreneurs approach real estate sales: • Sponsor a $25k conference booth • Pay channel partners $15K referral fees • Launch cold email campaigns Wonder why they don’t know what’s working. The numbers they're missing: • Cost per acquisition by channel • Value of each funnel stage • Which touchpoints actually drive revenue 100% of them are surprised when I show them the funnel math. The systematic approach: Take a $200/month PropTech tool: 2.5 year average customer life = $5,000 LTV Smart entrepreneurs work backwards from LTV to value each interaction: • 1.5% website visitor to lead conversion • 20% lead to demo conversion • 15% demo to close conversion Suddenly every touchpoint has clear value: • Each website visitor = $15 • Each lead = $1,000 • Each demo = $750 Why this changes everything: That $500 cost-per-lead suddenly makes perfect sense. That $1,500 broker referral fee? Easy decision. You stop throwing money at channels that don't convert. The buyer complexity problem: But here's where most entrepreneurs still fail. Real estate has multiple decision makers. Your messaging needs to match the role: Asset Manager: Cares about operational efficiency Pitch: "Reduces operating costs by 15%, increasing NOI" Head of Acquisitions: Focused on deal flow and speed Pitch: "Analyze 3x more deals in half the time" Facilities Manager: Worried about day-to-day operations Pitch: "Eliminates manual processes, reduces staff workload" Development Director: Thinking about project timelines Pitch: "Accelerates project delivery, reduces delays" What separates winners from losers: Winners know: • Exactly what each funnel stage costs and converts • Who the real decision maker is (vs who takes the meeting) • Which stakeholders hold veto power • How to tailor messaging to each role's priorities Losers treat every prospect the same and wonder why deals stall. The bottom line: Start thinking systematically about funnel economics and buyer roles. Track every interaction. Know your numbers. Match your message to your audience. Details for our next workshop in the comments.

Most sellers focus on top-of-funnel signals. But there is serious power in also using signals that surface after the demo – when buyers go quiet or deals stall. I was chatting with my friend Saad Khan at Aligned this week, and he broke down how they use Digital Sales Rooms (DSRs) to track buying signals deeper in the funnel. Most people use DSRs as a content dumping ground. But here’s how to actually turn it into a bottom-of-funnel signal engine: 1. Map the real buying committee Every org is different. Use your DSR to track who’s engaging – not just your known champion. → Cross-check with your account map → Talk with your champion about these new players → Tailor content for the real decision-makers 2. Use engagement (or silence) as a signal No activity = no deal. If your room’s been dead for 2 weeks, that’s a sign. Time to re-engage, reposition, or de-prioritize. 3. Stack signals from other sources Combine DSR data with: → Former users re-engaging → Trial activations → Job listings tied to your initiative → Competitor activity Example: The procurement team is deep in your DSR looking at competitive content while your competitor’s AE is liking their exec’s posts. That’s not random. That's a signal. The best sellers today don’t just read signals in isolation. They connect the dots.

"Our funnel is broken." No. Your benchmark is. Every deep tech marketer I talk to is comparing their numbers to a SaaS funnel — because that's the only benchmark that exists. There is no published funnel benchmark for semiconductors, MedTech, or industrial deep tech. Not one. So they look at a 0.1% SQL rate and panic. In deep tech, that's not failure. That's the shape of the game. I reconstructed the funnel from the closest defensible proxies (industrial B2B) and mapped what actually wins each stage: 🔹 VISITORS Who · engineers & R&D, not procurement Move · technical SEO where engineers actually search 🔹 LEADS (~2%) Who · self-identified problem-owners Move · gate depth, not contact — trade real engineering value 🔹 MQL (~0.6%) Who · the buying committee forming Move · arm the champion to sell internally when you're not in the room 🔹 SQL (~0.1%) — the steepest drop Who · economic buyer + procurement Move · arm sales with the business case. This is where deep tech leaks most. 🔹 DESIGN WIN Who · the whole account, not one champion Move · treat the win as the start of the next design cycle ~5,000 visitors per single design win. And that win won't generate revenue for another 12–18 months. The funnel shape is fixed: by physics, by procurement, by multi-year qualification cycles. You don't get to change that. The only variable you control is how deliberately you work each stage. If you run deep tech demand gen and your numbers differ, that's the most useful thing you could post below. Where does your funnel actually leak and is it the SQL stage too? #DeepTech #GTM #B2BMarketing #ProductMarketing #DemandGen #Semiconductors #DeepTechToMarket

I’ve been playing around with Customer Journey Analytics, and here’s what I realized: If you look at it in isolation, it doesn’t tell you much. But once you start comparing different time periods, and especially once you define your own rates, like the add-to-cart drop-off rate or whatever makes sense for your brand , that’s where it gets really interesting. When you start tracking those over time, it becomes insanely insightful. Every time we make a change — running Brand Tailored Promotions, coupons, new ad strategies, or AMC audiences— I go back to this tool. I use it to see if those experiments actually changed how people move through the funnel. Here’s one example: let’s say we target people who added to cart with a Brand Tailored Promotion. Some people might say, “You’re just cannibalizing — they were gonna buy anyway.” Maybe. But I don’t like guessing — I want proof. So I look at how many people added to cart but didn’t buy. Then I track that drop-off rate over time. If the drop-off goes down after our promo, great — it worked. If not, maybe we’re just handing out discounts for no reason. That’s what I love about this tool — it’s not just a funnel snapshot. It’s a way to see how your experiments actually impact behavior over time.

About a third of the dashboards I've designed have been funnel analysis. The biggest mistake I see people make is trying to show everything at the same time. The visualization pattern you choose depends on what questions your stakeholders are asking and what capabilities they have to influence the results. Here are four design patterns I use for Funnel Analysis: Spark Funnel A sparkline paired with a bar chart to show performance across time for each funnel stage. Use a dropdown to switch between metrics like step retention, conversion rate, and volume. Where I've seen this work: New or established products where cross-functional teams need to monitor trends. BANS + Decomp Each stage is shown in funnel order, the first and last shows volume, while each in between step shows the retention percentage. The decomp below provides comparison between segments. Where I've seen this work: Executive reporting where retention patterns are more important than volume, especially post-launch weeks when numbers are still small. Sankey + Table A flow diagram maps the user journey with line thickness representing volume between steps, paired with a reference table showing segment breakdowns and additional metrics. Where I've seen this work: Funnels with many steps where a map helps stakeholders understand the complete journey. Retention Heatmap Focuses on post-acquisition retention rather than funnel stages. Each cell is a cohort's retention rate at a specific time interval, with color intensity showing churn patterns. Where I've seen this work: Established subscription products where improving retention has more impact than adding volume. The pattern you choose depends on which part of the customer journey your stakeholders can influence. Marketing fills the funnel, Product keeps people engaged, Operations maintains support, and Executives orchestrate resources across all of it. Over time your analysis will evolve and your visualizations need to keep up with the maturity and sophistication of your audience to diagnose and communicate the health of their business. #DataAnalytics

𝗦𝘁𝗼𝗽 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗮𝘁 𝗮 𝗹𝗼𝗻𝗴 𝗹𝗶𝘀𝘁 𝗼𝗳 𝗯𝗿𝗮𝗻𝗱 𝗞𝗣𝗜 𝘀𝗰𝗼𝗿𝗲𝘀 𝗶𝗻 𝗶𝘀𝗼𝗹𝗮𝘁𝗶𝗼𝗻. 𝗬𝗼𝘂𝗿 𝗳𝘂𝗻𝗻𝗲𝗹 𝘁𝗲𝗹𝗹𝘀 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝘀𝘁𝗼𝗿𝘆. Most brand managers get obsessed with individual metrics: "Awareness is up 3 points!" or "Consideration hit 47%!" But here's what they're missing: It's not about the scores—it's about the ratios. Your marketing funnel doesn't just show where consumers are. It reveals how well you move them from one stage to the next. Here's the smarter way to analyze your funnel: 𝗦𝘁𝗼𝗽 𝗱𝗼𝗶𝗻𝗴 𝘁𝗵𝗶𝘀: 🚫 Comparing absolute scores to last year 🚫 Celebrating awareness gains while ignoring conversion 🚫 Looking at metrics in isolation 𝗦𝘁𝗮𝗿𝘁 𝗱𝗼𝗶𝗻𝗴 𝘁𝗵𝗶𝘀: ✅ Calculate conversion ratios at each stage (Familiar ÷ Aware, Consider ÷ Familiar, etc.) ✅ Compare YOUR ratios against your top competitor's ratios ✅ Find the gaps—then tell the strategic story 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Your brand and your competitor both have 90% awareness. Great. But when you look at the ratios: Your conversion from awareness to consideration: 65% Their conversion: 85% That 20-point gap tells you where you're bleeding. 𝗧𝗵𝗲 𝘄𝗶𝗱𝘁𝗵 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗳𝘂𝗻𝗻𝗲𝗹 𝗮𝗹𝘀𝗼 𝗿𝗲𝘃𝗲𝗮𝗹𝘀 𝘄𝗵𝗲𝗿𝗲 𝘆𝗼𝘂 𝘀𝗶𝘁 𝗼𝗻 𝘁𝗵𝗲 𝗯𝗿𝗮𝗻𝗱 𝗹𝗼𝘃𝗲 𝗰𝘂𝗿𝘃𝗲: 🔹 𝗜𝗻𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗯𝗿𝗮𝗻𝗱𝘀 = skinny funnels (low awareness, lower purchase, almost no loyalty) 🔹 "𝗟𝗶𝗸𝗲 𝗶𝘁" 𝗯𝗿𝗮𝗻𝗱𝘀 = strong at the top, narrow at purchase (bought only on deal) 🔹 "𝗟𝗼𝘃𝗲 𝗶𝘁" 𝗯𝗿𝗮𝗻𝗱𝘀 = robust funnel, slight leak at loyalty 🔹 𝗕𝗲𝗹𝗼𝘃𝗲𝗱 𝗯𝗿𝗮𝗻𝗱𝘀 = the most robust funnels across every stage Your funnel is your brand's health report. It shows you exactly where to invest, where you're leaking, and what strategy will move you from "nice to have" to "can't live without." So next time someone asks how the brand is doing, don't just rattle off scores. Show them the funnel. Tell them the story. Then fix the gaps. Want to dive deeper into funnel analytics and brand strategy? 𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝗼𝘂𝗿 𝗳𝘂𝗹𝗹 𝗴𝘂𝗶𝗱𝗲: https://lnkd.in/g8BZJr9i #BrandStrategy #Marketing #MarketingAnalytics #BrandManagement

Most brands lose money on Meta Ads for one reason. They try to sell too early. Customers don’t open Instagram or Facebook thinking "Let me buy something immediately." They move through a journey. Awareness → Trust → Purchase → Loyalty. But most advertisers only focus on the last step. After working on multiple campaigns, I realized something simple: Meta Ads performance improves when your content matches the funnel stage. Here’s the structure I use 👇 1️⃣ TOFU — Awareness (50% of content) Goal: Introduce the brand and grab attention. Content that works: • Short videos (15–30s) • Problem–solution posts • Educational carousels • Behind-the-scenes brand stories At this stage, you’re not selling. You’re earning attention. 2️⃣ MOFU — Consideration (30%) Goal: Build trust and show product value. Best content: • Testimonials and review videos • Feature & benefit carousels • Demo or how-to videos • Comparison charts Now the audience starts thinking: "This product might actually solve my problem." 3️⃣ BOFU — Conversion (15–20%) Goal: Turn interest into sales. Content examples: • Offer-based ads • Scarcity or urgency campaigns • Retargeting ads • Bundle promotions This is where intent meets the right offer. 4️⃣ Retention — Loyalty (10%) Goal: Turn buyers into repeat customers. Content ideas: • Loyalty or referral offers • New product launches • Reorder reminders • UGC & customer reviews Because the cheapest customer is the one who already bought from you. When the funnel is structured like this, three things usually happen: ✔ Lower CPA ✔ Higher ROAS ✔ Stronger brand trust Most advertisers run only conversion ads. Smart marketers build the entire funnel. Question for marketers here: What percentage of your content is actually focused on awareness? #MetaAds #DigitalMarketing #PerformanceMarketing #MediaBuying #MarketingStrategy

Search Query Performance Report on Seller Central is an extremely powerful report for growing on Amazon Amazon is a search led platform, and in most categories at least 60-70% sales originate through a search query. And this report gives all the metrics ( search volumes, impressions for that query, clicks from that query, add to carts from that query, purchases from that query) for the top 1000 relevant search queries for your brand. And you get both the category level data as well as your brand data and your brand's share Eg: You can find out for the search term "ceiling fan", what were the total impressions, your brand impression share, total clicks, your brand click share, total add to carts, your brand add to cart share,total purchases and your brand purchase share etc Now this is extremely powerful data. This includes both organic and paid clicks/sales You can basically map your brand funnel vis-a-vis the category funnel for every relevant keyword Eg: Lets say for the keyword "ceiling fan", my impression share is 7%, click share is 8%, add to cart share is 9% and purchase share is 10% The immediate actionable would be to increase impression share by increasing spends on the Keyword "ceiling fan". And because this is a high volume keyword and my funnel is stronger than the category, I would start a single KW exact match campaign with high budgets and bids for this keyword And if the funnel holds, very soon the impression share will increase Similarly, if impression share>click share, it means the Hero image/Title/offer needs working If Click share>Purchase Share, it means the offer ( pricing/TAT) and the content ( images, bullets, A+ etc) need to do a better job at convincing the consumer Now imagine if you do this rigorously for 1000 keywords and bring incremental improvement for many search queries, how the benefits could stack up. Both market share and TACOS will improve Extremely powerful report if used well. Doing this rigorously helped us a lot in the last 12-18 months ( This report didn't exist when we started 10 years back) in scaling up Amazon even faster than we used to and gain almost 300-400 bps market share on platform. Also helped a lot in scaling up the new categories How to Access? Seller Central>> Brands>>Brand Analytics>> Search Query Performance And once there, you can look at the data week wise, month wise, quarter wise