Troubleshooting Common Issues

Many are asking me... Should I continue to track "Open Rates" on Cold Emails? It's still no. My answer hasn't changed. I had predicted this about 9 months ago if you want to look back. Why? Analyze the image in the post. Does the position of the "Report as Spam" increase the amount of people who click it by 3 on 1,000 recipients? If you said yes, you agree with me. This is a subtle way Google is asking you for more feedback on the quality of your outbound campaigns. Here are 5 reasons NOT to use Open Tracking for Cold Email: Reason 1: Limits Your Use Of Plain Text Emails Plain Text Emails get superior deliverability. Open Trackers can't be used in Plain Text emails. Reason 2: Inconsistent Tracking Open Trackers identify "opens" differently and ultimately can't prove someone opened the email. Every sequencer has a different way of tracking it. Reason 3: Email Fingerprints Open Trackers provide a fingerprint for your domain reputation. It's shared amongst everyone using the sequencer your company uses. Do you want to be part of this group? Reason 3: Misleading Data Secure Email Gateways open emails for their users to protect their privacy. Budget has increased significantly here and will continue to go up. Most of these systems will put your email in spam because of it. Reason 4: Easy To Block Even simple rules can block emails with open trackers. No AI required. It's simple. Reason 5: Bad Metric Teams and internet gurus are obsessed with open tracking. However, it doesn't mean your email has been opened. It could mean that, but it depends who you emailed. Here are 3 Insider Tips to Improve Deliverability Today: Insider Tip #1: Send to less technical audiences. This isn't my favorite advice to give. However, less technical audiences hit the report as spam button less. Insider Tip #2: Send to companies without Proofpoint, Cisco, and Mimecast MX Records. Prioritize companies invested in email security systems lower than ones who don't. Use LeadMagic to figure out what the company uses in the email finder. Insider Tip #3: Use LeadMagic's New Features on MX Detection & Valid_Catch_All Status to prioritize who to send to first. Prioritize valid (mail server checked) > catch_all. Use valid_catch_all status from LeadMagic which detects if the email has been found other ways. Prioritize Google or Microsoft email servers higher than Proofpoint, Cisco, and Mimecast email servers. This will lead to better delivery & reply rates. p.s. open tracking is not dead for email marketing, but that's not what I am talking about.

PSA 1: Gmail did not kill Email Open Tracking. PSA 2: Email Open Tracking is dead for years. Let’s unpack this. Recently, a screenshot of Gmail blocking images has been circulating on LinkedIn, accompanied by alarmist claims that this spells the end for open tracking. But here’s the truth: Yes, email open tracking relies on images being loaded — typically, by detecting whether a tracking pixel (a tiny, transparent 1x1 pixel unique to each email recipient) has been downloaded. No, Gmail did not just start blocking these pixels. The screenshot actually shows a specific scenario: Gmail blocks images when it identifies an email as likely spam or a scam. Google does this to protect you from being tracked by malicious senders, and it’s been working this way for years. So, does this mean your open tracking is safe and sound? Not really. While Gmail hasn’t started blocking all your tracking pixels, other Email Service Providers already do. Open tracking is frequently blocked by B2B email server admins, often inaccurate due to security bots, and impacted by privacy settings and browser extensions. So, is Email Open Tracking useless? Well… maybe. If you’re using it as a high-level trend marker for opens, it might still offer some value. But if you’re relying on it for behavioral decision-making or key performance indicators (KPIs), especially in the B2B market, it’s largely ineffective. What should you do instead? Click tracking is a better option — although still not perfect, especially due to security bots in the B2B market. Ultimately, the best approach is to focus on the final goal of your email. Why are you sending it? If it’s to sell a product, track product purchases instead. More to come, so keep on analysing #MarketingCloud #SalesforceOhana and #MarketingChampions!

AB testing can easily manipulate decisions under the guise of being "data-driven" if they're not used correctly. Sometimes AB tests are used to go through the motions to validate predetermined decisions and signal to leadership that the company is "data-driven" more than they're used to actually determine the right decision. After all, it's tough to argue with "we ran an AB test!" It's ⚡️data science⚡️... It sounds good, right? But what's under the hood? Here are a few things that could be under the hood of a shiny, sparkly AB test that lacks statistics and substance: 1. Primary metrics not determined before starting the experiment. If you're choosing metrics that look good and support your argument after starting the experiment... 🚩 2. Not waiting for stat sig and making an impulsive decision🚩 AB tests can look pretty wild in the first few days... wait it out until you reach stat sig or the test stalls. A watched pot never boils. 3. Users not being split up randomly. This introduces bias in the experiment and can lead to Sample Mismatch Ratio which invalidates the results🚩 4. Not isolating changes. If you're changing a button color, adding a new feature, and adding a new product offering, how do you know which variable to attribute to the metric outcome?🚩 You don't. 5. User contamination. If a user sees both the control and the treatment or other experiments, they become contaminated and it becomes harder to interpret the results clearly. 🚩 6. Paying too much attention to secondary metrics. The more metrics you analyze, the more likely one will be stat sig by chance 🚩 If you determined them as secondary, treat them that way! 7. Choosing metrics not likely to reach a stat sig difference. This happens with metrics that likely won't change a lot from small changes (like expecting a small change to increase bottom funnel metrics, ex. conversion rates in SaaS companies)🚩 8. Not choosing metrics aligned with the change you're making and the business goal. If you're changing a button color, should you be measuring conversion or revenue 10 steps down the funnel?🚩 AB testing is really powerful when done well, but it can also be like a hamster on a wheel-- running but not getting anywhere new. Do you wanna run an AB test to make a decision or to look good in front of leadership?

I use this simple 3-step logs flow that helps me debug almost anything in Kubernetes under 30 minutes. 𝗦𝘁𝗲𝗽 1 → kubectl logs <pod> Ask: “Did the app fail inside the container?” If the pod is up, this is your first stop. Look for stack traces, startup errors, misconfigs. But if logs show nothing (or the pod never started), move on fast. 𝗦𝘁𝗲𝗽 2 → kubectl describe pod <pod> Ask: “Did Kubernetes kill the pod?” This one’s underrated. It shows you probe failures, CrashLoops, image pull issues, and mount errors. Basically, if K8s is mad at your pod, this will tell you why. 𝗦𝘁𝗲𝗽 3 → kubectl get events --sort-by=.metadata.creationTimestamp Ask: “What else is breaking in the cluster?” This is your timeline. It shows broader issues: node pressure, CNI problems, preemptions. If the problem isn’t in logs or describe, this one usually holds the clue. This is the exact flow we use inside incident war rooms. ➤ If the pod is running → check logs. ➤ If it’s crashing or pending → check describe. ➤ If you’re still lost → check events. Don’t waste 45 minutes staring at Grafana hoping something makes sense. Start with the logs. Ask better questions. Fix faster. I built a 1-page cheatsheet of this debugging flow. It’s part of our SRE onboarding at Infra360. Want it? Drop a “LOGS” in the comments and I’ll send it to you.

Quishing”: The Latest QR Code Phishing Threat You Need to Know About “Quishing” is emerging as the latest threat in the phishing landscape, exploiting the widespread trust in QR codes. Despite their seemingly harmless appearance and convenience—popping up in restaurants, bars, and city streets—QR codes are becoming a tool for cybercriminals. These scannable codes, once solely a quick access tool to websites and information, are now being used in scams to direct users to malicious websites that could steal or compromise personal information. Understanding the Dangers of Quishing Scams Quishing scams exploit QR codes to lead victims to unsafe websites, posing potential risks to personal and financial information. While QR codes are commonly found in physical locations, their presence in emails should raise red flags, as it is unconventional for companies to use them over direct links. This anomaly serves as a warning sign for potential quishing attempts. Despite the growing concern, there’s no immediate need for widespread panic. The creation of malicious QR codes and the sites they link to requires significant effort and resources, making it unlikely for every QR code encountered to be malicious. Additionally, scam websites can often be shut down quickly, reducing the practicality of this method for scammers who would need to continuously create and distribute new QR materials. Stay Informed and Protect Yourself from Quishing As QR codes become an integral part of our digital lives, it’s essential to stay informed about the potential risks they pose. By understanding the signs of quishing and exercising caution, especially with QR codes received via email, individuals can better protect themselves from falling victim to these sophisticated scams. #cybersecurity #quishing #cybersecuritytips #onlinesecurity #protectyourdata #womenincybersecurity

When Loads Move Faster Than the Grid Can Think NERC’s latest white paper doesn’t speculate. It documents. Emerging large loads, data centres, AI clusters, hydrogen, crypto, aren’t just big. They’re fast, invisible, and operating on their own timelines. ➤ A 450 MW data centre ramped down to 40 MW in 36 seconds. No fault. No command. No visibility. Just software doing what it was programmed to do. ➤ A 1,500 MW load drop in the Eastern Interconnection wasn’t a breaker trip. It was data centres transferring to backup after multiple voltage dips. The substations didn’t trip. The load simply left the grid. NERC’s Language Is Clear: • “System operators cannot account for the load response or create accurate forecasts.” • “Ramp rates of 1.9 p.u./sec over 250 ms.” • “Load ramping now challenges frequency regulation and reserve sufficiency.” Beyond Planning: The Real Risk Is Loss of Control This isn’t just about planning. It’s about control. And right now, control is slipping. The grid still assumes load is passive. It’s not. It’s power electronic, programmable, and often strategically opaque. The consequence? • Frequency spikes from loss of load, not generation. • Oscillations triggered by AI training cycles. • Generator instability from sudden reactive changes. • Load behaviour that mimics uncoordinated inverter-based generation. • UFLS failing, not because it tripped too late, but because the load was already gone. And We Haven’t Even Mentioned Restoration: Blackstart strategies now face an unmodeled threat 1) Large loads that reconnect too fast, or demand more than the island can handle. 2) Restoration isn’t just harder, it’s being shaped by load behaviour no one controls. Why the Old Interconnection Framework Doesn’t Hold Up: We’ve built interconnection frameworks around static MW thresholds. But none of them account for ramp speed, backup transfer logic hidden behind the meter, or autonomous disconnection outside system visibility. Yet these are now determining how the system fails, and how it recovers. Planning Means Nothing If Visibility Comes Too Late: i) Planning adequacy means nothing if a 300 MW electrolyser ramps to zero in 2 seconds because its own logic deems the voltage “unstable.” ii) Frequency control is irrelevant if the load that tripped wasn’t visible to begin with. iii) Restoration is compromised if blackstart islands can’t segment large loads in time. This is not a future scenario. It’s happening now. Quietly. Repeatedly. Systemically. #GridResilience #LargeLoads #NERC #DataCenters #AIInfrastructure #Hydrogen #FrequencyControl #VoltageStability #RampRates #DynamicLoads #InverterDominatedGrids #PowerSystemStability 

Grid-forming inverters don't have a fixed fault current profile. That's what makes protection engineers uncomfortable. With a grid-following inverter, the fault response is predictable: the PLL loses lock, the current limiter activates, and you get roughly 1.1–1.5x rated current for a few cycles. It's deterministic enough that relay engineers can work with it. Grid-forming inverters don't work that way. Their fault response depends on the current limiting strategy in the control design. Virtual impedance gives you one profile. Hard current saturation gives you another. Mode switching from voltage-source to current-source behavior during the fault gives you a third, and the transition dynamics are non-trivial. This matters because protection coordination relies on predictable fault current magnitudes and timing. If the inverter's fault response is software-defined, the protection engineer needs to know exactly what the control does under every fault condition, across the full range of grid impedances at the POC. That detail is rarely in OEM datasheets. It's often not tested systematically during commissioning. And grid codes don't yet require it in a standardized form. The capability argument for grid-forming is solid — better weak-grid stability, synthetic inertia, voltage support without a stiff reference. But those benefits come with a longer list of study deliverables and commissioning requirements that most project schedules don't account for. What are you seeing from protection engineers on GFO projects? Are they asking for more detailed fault characterization, or is it still being handled the same way as GFL?

We've fixed exactly what clients asked for, executed well, delivered clean work, and still, the results felt underwhelming. Not because the client was wrong but because they were just too close to see it. Most clients don't come with a problem. They come with a symptom. "We need more leads." "Our LinkedIn isn't working." "Sales calls aren't converting." But when you're inside the system every day, you diagnose based on pain, not patterns. You assume the last visible failure is the root cause. But the real issue usually sits one or two layers deeper. "We need more leads" is often unclear ICP. "Content isn't converting" is often weak positioning. "Sales isn't closing" is often misaligned expectations set by marketing. So, before touching anything, I ask:  What decisions led you to believe this is the problem?  What changed recently that made this feel urgent? Then I work backwards. If a client says, "We want more inbound leads," I'm not thinking about content calendars. I'm asking: Who exactly are your ideal clients? What would make them hesitate before reaching out? Most of the time, the client realizes it themselves: "Oh... maybe this isn't a leads problem." Because the best work doesn't start with agreement. It starts with asking if we're solving the right problem. PS: Are you fixing what's broken, or just treating what's painful? #StrategicThinking #B2BConsulting #PositioningStrategy #ProblemSolving #BusinessGrowth

Why do marketers test the wrong things? Here's what I typically see: • Testing button colors • Testing form length • Testing hero image placement • Testing slight headline variations The problem isn't the volume of tests. It's optimising details while missing the variables that actually drive behaviour change. A good test begins with "If we change X, then Y will happen" and is grounded in qualitative insights. This helps you articulate what core customer need (or friction point) you’re really addressing. Even small tests can uncover unexpected insights when guided by a clear hypothesis. It isn’t about test size alone in terms of impact; it’s about test relevance to your target prospect. Why do folk get this wrong? V͟i͟s͟u͟a͟l͟ ͟b͟i͟a͟s͟ It's easier to test a button color than to challenge our fundamental assumptions about what prospects actually care about. F͟a͟l͟s͟e͟ ͟e͟q͟u͟i͟v͟a͟l͟e͟n͟c͟y͟ "Company X tested their hero image and saw a 20% lift." Great. But Company X sells shoes to teenagers while you're selling enterprise software to CIOs. T͟h͟e͟ ͟a͟c͟t͟i͟v͟i͟t͟y͟ ͟t͟r͟a͟p͟ Running lots of tests feels like progress. But activity ≠ impact. T͟h͟e͟ ͟B͟2͟B͟ ͟b͟u͟y͟e͟r͟ ͟j͟o͟u͟r͟n͟e͟y͟ ͟i͟s͟ ͟m͟u͟l͟t͟i͟f͟a͟c͟e͟t͟e͟d͟.͟ ͟ Simple A/B tests often fail to capture this complexity. You need multi-touch, multi-variable experiments that mirror how your prospects actually buy. Two frameworks worth considering: J͟o͟b͟s͟ ͟T͟o͟ ͟B͟e͟ ͟D͟o͟n͟e͟ ͟(͟J͟T͟B͟D͟)͟:͟ ͟ Your prospects aren't buying your product - they're hiring it to solve a specific problem. Understanding this job guides better test design. T͟h͟e͟o͟r͟y͟ ͟o͟f͟ ͟C͟h͟a͟n͟g͟e͟:͟ Map out how your product drives the transformation your customer seeks. This reveals critical touch-points worth testing. To get moving in the right direction start with customer research. Not surveys, but actual conversations. The variables worth testing: • Different ways of articulating your core value proposition • Various approaches to demonstrating credibility • Different interpretations of the root problems Focus your testing on the variables that could 10X your results, not 10%. Real growth comes from challenging core assumptions, not tweaking pixels. -- p.s. If you're a SaaS CEO, check out my newsletter on growth & GTM on my profile.

One of the patterns I see most often in transformation work is this: By the time organisations talk about “execution problems”, the real issues are already baked in. The ambition is usually sound. The leadership team is capable. The intent is genuine. But people are starting from different places, even if they don’t realise it. Different views of what’s actually broken. Different assumptions about how much change the system can absorb. Different ideas of what success will really look like once the dust settles. From the outside, things look aligned. There’s a strategy. There’s a plan. There’s momentum. Under the surface, the organisation is already carrying tension. That tension doesn’t show up immediately. It emerges later as slowed decisions, quiet workarounds, rework, or delivery issues that feel surprising at the time. When you trace those problems back, they rarely come down to effort or capability. They come back to the same thing: the organisation never really established a shared starting point. Not a headline vision. Not a set of slogans. But a clear, honest view of: where the organisation actually is today, what constraints are real (not aspirational). and what kind of change is genuinely feasible now This is why serious transformation starts with diagnosis. Not as a formality, and not as reassurance, but as a way of creating clarity that everyone can work from, even when it’s uncomfortable. If you don’t do that early, the organisation ends up trying to execute on different problems at the same time. And no amount of delivery discipline fixes that later.