Marketing KPI Dashboard

A very easy way to improve your Amazon ads efficiency by at least 10% Let’s say you’re spending ₹4–5 lakhs/month on Amazon ads. Your ACoS looks okay. Conversion rate seems fine. But your gut tells you—you’re still wasting some money on irrelevant traffic You’re not wrong At Atomberg, we had found that some of our Amazon spend was going toward search terms that had no business seeing our ads: - “cheap fan” -“rechargeable fan” - “usb fan under 1000” None of these users were in-market for a ₹3,000+ BLDC ceiling fan. But we were still showing up. And paying for those clicks. And it’s not just us. I’ve seen 6–7 brands' Amazon ad accounts across categories over the last few years—same problem, every single time The fix? N-gram analysis Takes less than an hour. You don’t need to be a performance marketing expert. But the results compound What’s N-gram analysis? It’s breaking down every search term into its word components—1-grams, 2-grams, 3-grams—and then identifying patterns that consistently drive waste… or conversion. Example: “cheap rechargeable fan for hostel room” turns into: 1-grams: cheap, rechargeable, fan, hostel, room 2-grams: rechargeable fan, hostel room 3-grams: fan for hostel, etc. When you do this across all your search terms, you start seeing the real picture. Why this matters more than just checking your search term report: Search terms ≠ keywords a) One keyword can trigger 100s of different queries. Some convert. Most don’t. You need to find the patterns. b) Waste is diluted across low-volume terms. Maybe “rechargeable fan for hostel” spent ₹300. You ignore it. But what if 12 other queries with “rechargeable” spent ₹6,000 in total with zero conversions? c) Long-tail is infinite. N-grams are finite. You can’t negate every bad search. But you can block the core terms—“cheap”, “usb”, “mini”—once and be done with it. d) It helps you scale campaigns too. You can find goldmine phrases like “white ceiling fan”, “silent BLDC fan”, “fan for living room”—with 5x+ ROAS. Those became exact match campaigns What you should do: a) Pull last 3 months of search term data b) Break them into unigrams, bigrams, trigrams c) Create a pivot with spend, orders, ROAS by N-gram d) Negate high-spend, low-conversion N-grams (e.g., “cheap”, “rechargeable”) e) Boost high-ROAS ones (e.g., “bldc”, “ceiling fan white”) f) Add exact match campaigns g) Rinse and repeat monthly Try it. Guaranteed to improve efficiency at whatever scale you are operating If you want to read an expanded version of the post, link is in the first comment

{🤹♀️ E-commerce Metrics} What matters at each stage There are tons of metrics, but you don't need to juggle them all. Focus on the important stuff at each stage of development for a smoother ride to success. 🌱 When you have less than 500 orders per month: 1/ Conversion rate → [❤️ healthy value on this stage = 2%-5%] 2/ Customer Acquisition Cost (CAC) → [$10-$30] 3/ Customer Feedback and Satisfaction → [NPS = 20-30] 🧭 When you have 500-1,500 orders per month: 1/ Average Order Value (AOV) → [$100-$150] 2/ Return on investment (ROI) → [300%-500%] 3/ Inventory Turnover Ratio → [4-6 times per year] 🚗 1,500-3,000 orders per month: 1/ Customer Lifetime Value (#LTV) → [Order fulfillment accuracy = >$300] 2/ Churn Rate → [5%-8%] 3/ Traffic Sources and Channels → [Share of top-performing channels 30%-40%] 🚀 3,000-10,000 orders per month: 1/ Repeat Purchase Rate → [20%-30%] 2/ Operational Efficiency Metrics → [Fulfillment time = 1-2 days; Support resolution time = <24 hours] 3/ Market Expansion Metrics → [Growth rate = 15%-20%] 🏆 >10,000 orders per month: 1/ Supply Chain Performance → [Inventory turnover ratio = 8-10 times per year; Order fulfillment accuracy = 99%] 2/ Global Expansion Metrics → [Growth rate = 20%-30%] 3/ Brand Equity and Recognition → [Brand NPS = 40-50] By paying close attention to these #metrics as your company grows, you'll be able to make smart choices that lead to lasting success and scalability. Or you think these sets should be changed? #ecommerceanalytics

🔮 UX Metrics and KPIs Cheatsheet (Figma) (https://lnkd.in/en9MK4MD), a helpful reference sheet for UX metrics, with formulas and examples — for brand score, desirability, loyalty, satisfaction, sentiment, success, usefulness and many others. Neatly put together in one single place by fine folks at Helio Glare. To me personally, measuring UX success is focused around just a few key attributes — how successful users are in completing their key tasks, how many errors users experience along the way and how quickly users get through onboarding to first meaningful success. The context of the project will of course request specific, custom metrics — e.g. search quality score, or brand score, or engagement score or loyalty — but UX metrics are all around delivering value to users through their successes. Here are some examples: 1. Top tasks success > 80% (for critical tasks) 2. Time to complete top tasks < Xs (for critical tasks) 3. Time to first success < 90s (for onboarding) 4. Time to candidates < 120s (nav + filtering in eCommerce) 5. Time to top candidate < 120s (for feature comparison) 6. Time to hit the limit of a free tier < 7d (for upgrades) 7. Presets/templates usage > 80% per user (to boost efficiency) 8. Filters used per session > 5 per user (quality of filtering) 9. Feature adoption rate > 30% (usage of a new feature per user) 10. Feature retention rate > 40% (after 90 days) 11. Time to pricing quote < 2 weeks (for B2B systems) 12. Application processing time < 2 weeks (online banking) 13. Default settings correction < 10% (quality of defaults) 14. Relevance of top 100 search queries > 80% (for top 5 results) 15. Service desk inquiries < 35/week (poor design → more inquiries) 16. Form input accuracy ≈ 100% (user input in forms) 17. Frequency of errors < 3/visit (mistaps, double-clicks) 18. Password recovery frequency < 5% per user (for auth) 19. Fake email addresses < 5% (newsletters) 20. Helpdesk follow-up rate < 4% (quality of service desk replies) 21. “Turn-around” score < 1 week (frustrated users -> happy users) 22. Environmental impact < 0.3g/page request (sustainability) 23. Frustration score < 10% (AUS + SUS/SUPR-Q) 24. System Usability Scale > 75 (usability) 25. Accessible Usability Scale (AUS) > 75 (accessibility) Each team works with 3–4 design KPIs that reflect the impact of their work. Search team works with search quality score, onboarding team works with time to success, authentication team works with password recovery rate. What gets measured, gets better. And it gives you the data you need to monitor and visualize the impact of your design work. Once it becomes a second nature of your process, not only will you have an easier time for getting buy-in, but also build enough trust to boost UX in a company with low UX maturity. [continues in comments ↓] #ux #design

Not sure if your product meets your customers' needs? 🤔 When we first launched, understanding our customers' needs was crucial. We used surveys and feedback to gain deep insights into their preferences and pain points. This information helped us refine our product to better meet market demands. The insights we gained were eye-opening! 🌟 It felt amazing to know exactly what our customers wanted and to see our product evolve based on their feedback. Here are 5 strategies to gain customer insights through surveys and feedback: 1️⃣ Customer Surveys: Send regular surveys to gather detailed information about customer preferences. 2️⃣ Feedback Forms: Implement feedback forms on your website or app to collect ongoing input. 3️⃣ Net Promoter Score (NPS): Use NPS surveys to measure customer satisfaction and loyalty. 4️⃣ User Interviews: Conduct one-on-one interviews with customers for in-depth insights. 5️⃣ Social Media Polls: Use social media platforms to run quick polls and gather immediate feedback. How do you gather customer insights for market validation? Share your strategies and experiences in the comments below! 👇 #MarketingStrategy #DigitalStrategy #Marketing #SurveysandFeedback ----------- 👍 Like this post? 👀 Want to see more? Ring the 🔔 on my Profile 🤝 Connect with me

The most important competence for building a sustainable DTC strategy: Data-Driven Customer Insights. Over the last decade direct-to-consumer marketers have suffered a 15% CAGR in CPM inflation for digital #advertising, according to research by Frederic Fernandez & Associates, dramatically increasing cost per acquisition. #DTC companies hence need to much better understand their target consumers, their path-to-purchase metrics, barriers/ drivers/ triggers & 4Ps preferences, and design a new omnichannel acquisition strategy. In my view, its time for DTC companies to build truly immersive and personalized customer acquisition strategies based on data driven customer insights. Data-driven customer insights are essential in the following 5 marketing areas: 🙋 Understanding Customer Behavior: To create personalized experiences, brands need to understand their customers' behaviors, preferences, and pain points. #Data analytics enables companies to track and analyze customer interactions across all touchpoints, providing deep insights into their journey and decision-making processes. 🎯 Personalization at Scale: Leveraging customer data allows brands to segment their audience and deliver tailored content, offers, and recommendations. This level of #personalization can significantly enhance customer satisfaction and loyalty, as consumers are more likely to engage with content that is relevant to their needs and interests. 📢 Optimizing Marketing Efforts: Data insights help brands to optimize their #marketing strategies and campaigns. By analyzing which tactics are most effective, companies can allocate resources more efficiently and improve their return on investment. ❤️ Enhancing Customer Engagement: Real-time data analysis enables brands to engage with customers at the right moment with the right message. This timely #engagement can drive higher conversion rates and foster a stronger emotional connection with the brand. 📈 Continuous Improvement: Data-driven #insights provide a feedback loop that allows brands to continuously refine their products, services, and customer interactions. This iterative process helps in adapting to changing customer expectations and market trends. By investing in data collection, advanced analytics, and skilled personnel, #DTC companies can create truly immersive and personalized customer experiences that drive engagement and loyalty.

How often do we receive a notification or an alert from a company about an issue before we even realize there’s a problem? Whether it’s a bank flagging suspicious activity, a delivery service notifying us of a delay, or a telecom provider offering compensation for downtime, proactive engagement is reshaping the customer experience landscape. Here’s an interesting fact: 67% of customers globally have a more favorable view of brands that offer or contact them with proactive customer service notifications. Yet, many businesses still focus solely on reactive support, missing the opportunity to elevate customer loyalty through preemptive action. In my opinion, the most impactful customer experiences don’t happen when customers reach out for help. They happen when businesses anticipate their needs and address them before they even ask. How, then, can businesses transform their CX strategies to embrace proactive engagement? Here are three essential strategies to lead the way: 1. Anticipate Customer Needs with Data and Insights The first step in proactive engagement is understanding your customers on a deeper level. Businesses can predict potential issues by analyzing behavioral patterns, feedback, and usage trends and offer solutions in advance. For example, monitoring a subscription service’s usage data could reveal customers at risk of disengagement, prompting a personalized offer to re-engage them. According to the 2024 Edelman Trust Institute Barometer, Saudi Arabia ranks first globally in trust in government leadership at 86%. The Kingdom is a clear example of how data-driven policies can foster trust. Businesses can follow this model by leveraging data insights to predict and address customer needs proactively. 2. Personalization: Beyond Generic Engagement Proactive engagement is most effective when tailored to individual preferences. Personalization goes beyond addressing customers by name; it involves delivering messages that resonate with their unique journeys. For instance, an e-commerce platform could recommend products based on browsing history or alert customers about restocks of their favorite items. 3. Solve Problems Before They Arise The ultimate goal of proactive engagement is to reduce friction. Offering solutions before customers encounter issues—like sending reminders for payments or proactively addressing service disruptions—can turn potential frustrations into positive experiences. At X-Shift, we’re committed to proactive engagement strategies that mirror these principles. While technology like AI is opening doors to automation, the human element—listening, anticipating, and personalizing—remains irreplaceable. The future of CX is proactive. Let’s lead the way! #Vision2030 #CustomerExperience #CX #Personalization #DigitalTransformation #SaudiArabia #CXTrends #CustomerLoyalty

Your Google Ads Metrics Are Lying to You! 🚨 Most lead gen advertisers in India focus on CPL (Cost Per Lead), CPC, and CTR—thinking lower costs mean better results. But in 2025, a ₹500 lead is useless if it doesn’t convert into revenue. If you’re not tracking the right metrics, you might be: ❌ Generating ₹50 leads that never turn into customers ❌ Wasting budget on low-intent clicks ❌ Scaling campaigns based on vanity metrics Let’s fix that. The 5 Google Ads Metrics That You Can Consider for Lead Gen in 2025 1. Cost Per Qualified Lead (CPQL) > Cost Per Lead (CPL) Not all leads are equal. A high-intent lead is worth more than a random form fill. ✅ CPQL = Ad Spend / Sales-Qualified Leads (SQLs) 📊 If a ₹500 lead has a 40% close rate, it’s better than a ₹100 lead with a 5% close rate. 2. Lead-to-Customer Conversion Rate > Total Conversions 100 leads mean nothing if only 5 convert into paying customers. ✅ Formula: (Customers / Leads) * 100 📊 A high conversion rate means your campaigns are attracting the right audience. 3. Cost Per Revenue-Generating Lead > Cost Per Click (CPC) Clicks don’t pay the bills—customers do. ✅ If you spend ₹50,000 on ads and generate 50 SQLs, but only 5 turn into paying customers, what’s the real cost per acquisition? 📊 A ₹1,000 CPL is fine if it generates ₹50,000 in revenue per customer. 4. Pipeline Value > ROAS A campaign that delivers high-value deals is better than one with a high ROAS but small-ticket sales. ✅ Pipeline Value = Sum of potential revenue from all SQLs. 📊 Example: If Google Ads generates ₹5,00,000 in pipeline revenue from 100 SQLs, your ad spend should be based on revenue impact, not just CPL. 5. Revenue Per Lead (RPL) > Cost Per Lead (CPL) Instead of just tracking lead cost, track how much revenue each lead generates. ✅ Formula: Total Revenue from Ads / Number of Leads. 📊 If 10 leads at ₹500 each bring in ₹1,00,000 in sales, RPL = ₹10,000 per lead. That’s the real performance metric. 🚀 In 2025, successful lead gen advertisers in India are not just measuring lead volume, They’re tracking lead quality, conversion rates, and revenue impact. If you’re still optimizing for low CPL and dependent on sales to do most of the heavy lifting, It’s time to rethink your strategy. #digitalmarketing #leadgen #marketing

“Blogging is dead.” // “AI killed the blog” // “No one reads blog posts” // “Google is dead” — These are some of the wild (misguided) takes flooding the feed and inboxes right now… Here’s the harsh truth though: That’s all false. The real issue is that most marketers are creating reports that aren’t connected to what matters. They’re not talking about RESULTS.. Most marketers track page views and social shares, but real ROI is about revenue impact. Here’s how to show the ROI of blogging: 1. Define What “Return” Means for You Not all blogs are designed for direct revenue. Some drive leads, some build brand authority, and others improve retention. Choose the right KPI: ✅ Lead Generation – Track blog-assisted form fills, newsletter signups, and gated content downloads. ✅ Sales Impact – Analyze closed-won deals where a blog was a touchpoint. ✅ SEO Value – Measure the cost savings from organic search traffic vs. paid traffic (organic traffic value). ✅ Customer Retention – Track whether blog readers have a higher LTV (lifetime value). 2. Content ROI Modeling: Connect Content to Business Outcomes The biggest mistake? Giving blog posts content zero credit: ➡ First-touch attribution: When a blog is the first interaction before a lead enters your CRM. ➡ Last-touch attribution: When a blog post is the final touchpoint before conversion. ➡ Multi-touch attribution: Assigns weighted value across all touchpoints, showing how blogs contribute throughout the journey. Use tools like: • Google Analytics: Event-based tracking + attribution modeling. • CRM Reports (HubSpot, Salesforce): Tie blog traffic to closed deals. • UTM Parameters: Track conversions from blog-specific campaigns. And ask: “How’d you hear about us?” 3. Lead Quality: Not Just Quantity Traffic means nothing if it doesn’t convert. • Measure Traffic-to-Lead Ratio: (Total Leads from Blog / Total Blog Traffic) x 100 • Analyze MQL to SQL Progression: Are blog leads actually converting into sales-qualified leads (SQLs)? • Check Lead Source Data: Identify high-intent pages driving conversions. 4. Revenue Per Asset: The best way to quantify blog impact? Directly assign revenue. Use CRM + analytics tools to calculate: (Total Revenue from Blog-Assisted Deals / Number of Blog Posts Published) = Revenue Per Blog Post. Example: If 10 deals closed in a quarter where a blog was a touchpoint, and those deals totaled $100K, that blog is worth $10K. 5. Is Your Blog Profitable? Calculate true content ROI using: Blog ROI = (Revenue Attributed to Blog – Blog Production Costs) / Blog Production Costs x 100 • Include writer salaries, SEO, distribution, and promotion in costs. • If a blog generates $50K in sales and costs $10K to create, ROI = 400%. The Bottom Line: Blogging isn’t just about traffic. It’s about leads, opportunities, conversion rates, and revenue impact. If you’re not optimizing for this — you’re leaving money on the table. #ContentMarketing #SEO

“If you’re not thinking segments, you’re not thinking.” - Theodore Levitt Here’s a brief history of market segmentation: 1950s: Segmentation started with basic demographics—age, location, gender—because that was the easiest data to collect and analyze. 1960s: Marketers began adding psychographics, gathering insights into customer attitudes and traits to create more specific profiles. 1970s: The rise of large transaction databases enabled real-time point-of-purchase data collection, leading to segments based on purchase behavior. 1980s: Needs-based segmentation emerged, driven by powerful computers and advanced clustering techniques. This allowed researchers to group customers based on desired product features and benefits. While needs-based segmentation was a step forward, it often missed the mark because customers aren’t product engineers. They struggle to articulate what specific products or features they need. But here’s the thing: Customers excel at describing the outcomes they want to achieve when using a product to get a "job" done. When discussing their desired outcomes, they can identify 100 to 150 different metrics to describe success at a granular level. Today's most effective market segmentation? It focuses on understanding how customers rate the importance and satisfaction of each outcome. This insight allows marketers to craft targeted messages and develop products that resonate deeply with each segment. Here’s 3 examples of Outcome-Based Segmentation in action: 1. J.R. Simplot Company identified a segment of restauranteurs who needed a French fry that stays appealing longer in holding, leading to a tailored product solution. 2. Dentsply found a segment of dentists who believed that the quality of a tooth restoration depended on consistently achieving solid bonds, allowing them to tailor their products to this need. 3. Bosch discovered a segment of drill–driver users who primarily wanted a tool optimized for driving, rarely using it as a drill. This insight helped Bosch create targeted and effective marketing strategies. Outcome-based segmentation represents a significant leap forward. It focuses on real opportunities... ...and measurable activities that are underserved by the competition. Outcome-based segments provide a clear path to innovation and market success.

Speed and scale at which you can monitor competitor pricing has changed… Over a decade ago I would painfully and manually scrap a competitors website. The manual effort could only track a basket of products vs the entire catalog, so a lot of assumptions were made. Then that data was added to an excel file to be compared with our catalog. A reco was presented to the manager for price change approval. And then his manager would need to sign a price change sheet. That was done via one of those inter department mails. (You know which ones). Mostly for audit trail. Once signed those changes would then be added to an AS400 system that would schedule an overnight job to add price change for a specific date. C And on the date you would have to audit manually to make sure changes have been complete. Then it would take weeks before we could analyze the impact of the price change. Don’t even ask me about getting that data !! Today a good price management system can do this while you sleep: -scrap, -connect, -analyze and -make recos -push price changes onto systems, -analyze the change -Repeat it all over again Lot of tools which with a combination of scraping, rule based prices, and AI are speeding up the rate at which price changes are inplemented. These tools can vary between scraping and monitoring tools like: Prisync | Dynamic Pricing PriceSpider Price2Spy Octoparse - Octopus Data Inc. To more complex tools built to implement more critical pricing processes and beyond like: Competera Pricing Platform Luca DataWeave No budget for tools ? Price scraping is a widely available skill on upwork for customized price monitoring. Research has shown the more dynamic the price changes the higher the profit and sales !! Take a look at dynamic pricing at Uber and Amazon. Don’t sleep on continuous price monitoring and price changes. That’s exactly what your customer is doing on a smaller scale before making purchase.. If you know of or have used any such tools then add them to the comments. It would benefit the pricing community immensly. ——————— Follow for more pricing, revenue management, discounting and career advice.