Utilizing Customer Data Analytics

“211 SQLs → $7,500 x 9 deals → $67,500 for client Bobby in just 35 days with cold email” “Cody, where have you been in the last month?” → Dialing in hyper-scale cold email systems. Everyone need to become *the best* at One thing. My One thing = high volume cold email. P.S. This does NOT work if you have a small TAM (Total Addressable Market) / account-based-marketing. 🐊: This is, however, *the* best strategy for any B2B that has a product/offer where economics make sense at a large scale. You’ll know if you have a high TAM product & backend to deliver. before the LinkedIn up-bound/around-bound failed outbound agency owners say “outbound is dead, you shouldn’t make that much money from email!!” 🤡 These automated campaigns were a button push. There was little effort after the initial setup other than lead management. It’s absolutely not as easy as it was in 2021. Cold email is difficult. Mass market advice is.. “…buy inboxes from us, VAs to deliver lead lists, copy/paste templates to ICP then hundreds of SQLs appear out of thin air.” The success rate ^ is probably 10%, especially in the 1st month. If a “marketer” tells you otherwise, question them. Here’s what’s been working well: ✅ Weekly Domain blacklist checks ✅ Weekly Inbox Placement Tests + replace bad inboxes [see if you’re going to spam] ✅Open tracking off (Non-negotiable) Don’t send links or videos or images ✅ Apollo / Sales nav to build initial (top of funnel) lists ✅ Clay to qualify accounts at scale + remove non-ICP fits + add in gpt4o personalizations to qualified leads ✅ Findymail to enrich & double-validate email addresses [Findymail is the best email enrichment tool on the market, A/B test it vs every other provider and it will will] ✅After 3-4 months of list building, you’ve built a niched ICP database of your best ideal customers with enriched data/personalization columns. Recycle this list infinitely with new angles. 100% of the time, prospects forget your email the next day, after a few months or sooner reach back out. ✅ Cold outbound messaging based on customer interviews/sales calls, although you can spawn ideas from thin air. Here’s what has NOT been working well in the outbound space in the last 45 days: 👉 Don’t use ANY “private infrastructure” sellers. You will waste your money, 99.9% of the time. I know just about every high level outbound person in this space, we’ve all tried MailScale, Mailforge, InfraForge – they’re not good. They are marketers selling a bad product, they have fantastic marketing angles. Ultimately, make your own choice + question everyone. 👉 Google inboxes sending to other google inboxes. Microsoft is starting to become a foundation to solve this. 👉 Listening to mass market outbound advice. Cody

If your CX Program simply consists of surveys, it's like trying to understand the whole movie by watching a single frame. You have to integrate data, insights, and actions if you want to understand how the movie ends, and ultimately be able to write the sequel. But integrating multiple customer signals isn't easy. In fact, it can be overwhelming. I know because I successfully did this in the past, and counsel clients on it today. So, here's a 5-step plan on how to ensure that the integration of diverse customer signals remains insightful and not overwhelming: 1. Set Clear Objectives: Define specific goals for what you want to achieve. Having clear objectives helps in filtering relevant data from the noise. While your goals may be as simple as understanding behavior, think about these objectives in an outcome-based way. For example, 'Reduce Call Volume' or some other business metric is important to consider here. 2. Segment Data Thoughtfully: Break down data into manageable categories based on customer demographics, behavior, or interaction type. This helps in analyzing specific aspects of the customer journey without getting lost in the vastness of data. 3. Prioritize Data Based on Relevance: Not all data is equally important. Based on Step 1, prioritize based on what’s most relevant to your business goals. For example, this might involve focusing more on behavioral data vs demographic data, depending on objectives. 4. Use Smart Data Aggregation Tools: Invest in advanced data aggregation platforms that can collect, sort, and analyze data from various sources. These tools use AI and machine learning to identify patterns and key insights, reducing the noise and complexity. 5. Regular Reviews and Adjustments: Continuously monitor and review the data integration process. Be ready to adjust strategies, tools, or objectives as needed to keep the data manageable and insightful. This isn't a "set-it-and-forget-it" strategy! How are you thinking about integrating data and insights in order to drive meaningful change in your business? Hit me up if you want to chat about it. #customerexperience #data #insights #surveys #ceo #coo #ai

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.

instantly.ai just analyzed billions of cold email interactions across 700,000+ businesses for their 2026 benchmark report. The infrastructure section is where the real lift sits. Here's what stood out:  1. The reply rate hierarchy  • Average: 3.43%  • Top quartile: 5.5%+  • Elite tier (top 10%): 10%+ Same offers, same volume, the gap is infrastructure. Inbox placement drives engagement, which improves inbox placement. Your sending setup runs that loop. 2. The 10 deliverability non-negotiables Pulled directly from the report:  • Catch-all email caution  • Gradual domain warmup  • Domain rotation and aging  • Inbox placement monitoring  • Engagement signal management  • Enterprise email gateway awareness  • Bounce rate discipline, under 2%  • Domain authentication (SPF, DKIM, DMARC)  • Consistent sending patterns, no volume spikes  • Infrastructure distribution across multiple domains I see teams rewriting subject lines for the third time while their SPF record is broken. 3. Where most outbound stacks break  • Single sending provider (Google or Microsoft)  • Shared IP pools with millions of other senders  • One account suspension and the whole pipeline goes down Concentrating all volume on one domain is the cleanest way to lose a quarter overnight. 4. The +15-20% lift sitting in the infrastructure layer  • Stable domain health = +15-20% higher reply rates  • Domains fatigue with heavy use, they need rotation  • Erratic sending kills deliverability faster than bad copy That's the cleanest path from tier 3 toward tier 2, and it lives entirely in infrastructure. Instantly.ai turned 50B sends of their own data into AirMail. We've started testing it on client stacks for the IP isolation alone: → $4 per inbox / month → Live in 5 minutes, no manual config → Isolated IP pools, no shared reputation → SPF/DKIM/DMARC automatic from day one Runs alongside Google and Microsoft, so volume distributes across three layers instead of one. What's the biggest deliverability fix in your stack?

𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗳𝗼𝗿 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗧𝗮𝗶𝗹𝗼𝗿𝗲𝗱 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 Does your organization want to stand out? Then, personalization is the key. By leveraging advanced data analytics tools, organizations can track customer behavior to offer personalized recommendations that drive engagement and foster loyalty. For instance, analytics platforms like Google Analytics 360, Salesforce Einstein, or Adobe Experience Cloud gather insights from every customer interaction whether online, in-app, or in-store. This data is then analyzed to understand preferences, buying patterns, and even the best time to engage. The result? Highly targeted recommendations that resonate with each individual. Imagine a customer frequently browsing outdoor gear on your website. Advanced analytics would recognize this behavior and automatically push personalized recommendations for hiking equipment or exclusive deals on similar products. This level of relevance not only increases conversion rates but also strengthens long-term loyalty by making customers feel understood. The key is continuous optimization. As data is collected, analytics tools refine their algorithms to offer increasingly precise recommendations, turning one-time buyers into repeat customers. By using data to create personalized experiences, organizations can meet customer expectations, boost satisfaction, and stay competitive in a crowded market. Are you leveraging Data Analytics to personalize Customer Experiences? Let’s explore your strategies at Digital Transformation Strategist #digitaltransformation #dataanalytics #personalization #customerexperience #customerloyalty

🔮 From Raw Data to Product Gold: The AI Advantage Product managers are drowning in data but starving for insights. Here's how AI is changing that equation: 1. Customer Journey Intelligence AI doesn't just track user flows – it predicts them. By analyzing millions of interactions simultaneously, it can spot hidden patterns in user behavior and identify critical dropoff points that traditional analytics might miss. 2. Automated Sentiment Analysis Manual feedback analysis is becoming obsolete. Modern AI can process thousands of customer comments in seconds, clustering issues by theme and detecting emerging problems before they become widespread concerns. 3. Predictive Feature Impact AI can simulate feature impact using historical data and behavioral patterns, helping PMs understand potential adoption rates and user response before investing development resources. Getting Started Today with LLMs 🚀 Before diving in, crucial first step: Data Privacy - Remove all PII (names, emails, IDs) - Replace user identifiers with anonymous keys - Strip out sensitive business metrics - Remove location data unless essential - Validate compliance with your data policies Ready to start? Here's how: 1. Begin with customer feedback analysis - Feed anonymized support tickets into GPT-4/Claude - Ask for theme clustering and sentiment patterns - Request prioritized action items based on impact 2. Product usage patterns - Share anonymous usage logs - Ask LLMs to identify common user flows - Discover unusual patterns or friction points 3. Feature requests analysis - Input sanitized feature requests - Get automated categorization and priority suggestions - Identify underlying user needs and pain points Pro Tips for LLM Analysis: - Break large datasets into digestible chunks - Use clear prompts for consistent results - Always validate AI insights against business context - Keep a human in the loop for critical decisions The next evolution in product management isn't about having more data – it's about having better insights, faster. #ProductManagement #AI #DataAnalytics #ProductStrategy #Innovation What data analysis challenges are you facing in your product role? Let's discuss below 👇

I remember years ago working with a coffee brand, and we discovered some fascinating insights from analyzing customer buying behavior. We had two types of purchases: subscriptions and one-time buys. When we dug into the data, we found a significant pattern. Only 18% of one-time buyers made a second purchase. But if they did, there was an 85% chance they’d order a third time, and the repeat order rate stayed high after that. This showed us a major bottleneck. The founder initially wanted to focus all incentives on attracting first-time buyers, but the data told a different story. We saw the value in driving that crucial second purchase. So, we overhauled our approach: 1. Revamped Fulfillment Kits: The first order kit included incentives for a second purchase. 2. Updated Email Campaigns: Emails were tailored to encourage a second buy. The results? We boosted the second purchase rate to nearly 30%, leading to a significant increase in overall sales and customer lifetime value (LTV). Even with pushing more people into that second order, we only saw a small dip in the number of people who went from a 2nd to a 3rd order, moving from 85% to 83%. This experience shows the power of slicing your data by cohorts to uncover bottlenecks and then addressing them directly. Sometimes, the biggest gains come from focusing on the steps beyond the initial sale.

𝗛𝗼𝘄 𝗜 𝘁𝘂𝗿𝗻 𝗺𝗲𝘀𝘀𝘆 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 — 𝗶𝗻 𝟲 𝘀𝘁𝗲𝗽𝘀. 𝘈𝘴𝘬 → 𝘗𝘳𝘦𝘱𝘢𝘳𝘦 → 𝘗𝘳𝘰𝘤𝘦𝘴𝘴 → 𝘈𝘯𝘢𝘭𝘺𝘻𝘦 → 𝘚𝘩𝘢𝘳𝘦 → 𝘈𝘤𝘵 You can apply it to any project, in any industry. Let’s bring it to life with an example 👇 You’re a Data Analyst at a food delivery startup. One morning, your manager says: “Our delivery times have gone up by 15% this month. Customers are complaining. Can you find out what’s happening?” Here’s how the framework is useful. 𝗦𝘁𝗲𝗽 𝟭 – 𝗔𝘀𝗸 Clarify the problem: • Are delays everywhere or only in certain cities? • Is it during all hours or just peak times? • Did anything change in operations recently? 𝗦𝘁𝗲𝗽 𝟮 – 𝗣𝗿𝗲𝗽𝗮𝗿𝗲 Gather data: • Order history from the last 3 months • Delivery partner logs • Traffic & weather data for affected areas 𝗦𝘁𝗲𝗽 𝟯 – 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 Clean the mess: • Remove duplicates • Fix wrong timestamps • Fill or remove missing delivery partner IDs 𝟰 – 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 Dig for patterns: • Compare average delivery times before & after the drop • Spot that 70% of delays are during evening peaks in two major cities 𝗦𝘁𝗲𝗽 𝟱 – 𝗦𝗵𝗮𝗿𝗲 Tell the story: • Dashboard showing the spike in delays • Map visual highlighting hotspots • Clear narrative explaining why it’s happening 𝗦𝘁𝗲𝗽 𝟲 – 𝗔𝗰𝘁 Drive change: • Add more delivery partners during evening peaks • Test traffic-aware routing in problem areas 𝗥𝗲𝘀𝘂𝗹𝘁? Delivery times drop. Complaints decrease. Customer satisfaction rises. All because you followed a structured process. Learned something new? 𝗛𝗶𝘁 𝘁𝗵𝗲 "𝗟𝗶𝗸𝗲" 𝗯𝘂𝘁𝘁𝗼𝗻, 𝗮𝗻𝗱 𝘀𝗵𝗮𝗿𝗲 𝗶𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗻𝗲𝘁𝘄𝗼𝗿𝗸—𝗶𝘁 𝗺𝗶𝗴𝗵𝘁 𝗵𝗲𝗹𝗽 𝘀𝗼𝗺𝗲𝗼𝗻𝗲 𝗲𝗹𝘀𝗲! ✅ #DataAnalytics #Data #BusinessImpact

A new APRF email reporting standard tells you whether your emails hit spam or inbox. I was so excited that deployed it across 100+ companies we consult for, to compare the IETF draft theory with real data. One mail stream sent almost 20,000 emails to Comcast users, according to Comcast's DMARC reports, while Comcast APRF reported placement on only 4,400 of them. The numbers in the reports are rounded to buckets of 100 and not every email sent is reported, but the signal itself is useful for understanding overall performance with Comcast users. Now we're keeping an eye on a stream where the spam bucket starts showing up on days it didn't before. And that's what an early spam-placement signal looks like before deliverability tanks.