Notification Frequency Optimization

Email too much, and you annoy your customers. Email too little, and they forget about you. Find the right balance 👇 → Where Most Dispensaries Get It Wrong X Emailing Only When There’s a Sale: If the only time customers hear from you is during a discount, they’ll start expecting lower prices and stop buying at full price. X Blasting Every Customer With Every Email: Not every customer wants the same content at the same frequency. Sending too often to inactive customers can damage your email deliverability. X Not Testing Frequency at All: Many dispensaries guess at their send schedule instead of testing what actually works for different segments. → How to Optimize Your Email Frequency 1. Segment Customers by Engagement > High-engagement customers (open rate above 30%) can receive 2-3 emails per week without issue. > Moderate-engagement customers (10-30% open rate) should get 1-2 emails per week. > Low-engagement customers (less than 10% open rate) need win-back emails, not constant promotions. 2. Match Frequency to Buying Cycles > Daily shoppers might appreciate frequent updates on new arrivals. > Casual shoppers might prefer a weekly digest of deals and recommendations. > Lost customers need less frequent but high-impact emails with compelling reasons to return. 3. Monitor Unsubscribe & Spam Complaint Rates If unsubscribes spike after a specific email, that’s a sign you’re sending too often or to the wrong segment. If open rates drop below 15%, scale back or improve subject lines. 4. Test & Adjust Regularly Try sending one extra email per week and measure if engagement improves or drops. Compare sales data—are more emails leading to more revenue, or just more unsubscribes? → Try This & See the Difference Look at your email send frequency over the past month. Are you emailing different customer segments strategically, or just guessing? Test a small adjustment in frequency and track the impact on sales and engagement. If you want a data-driven email strategy, Tact Firm specializes in optimizing dispensary emails for maximum retention. Let’s get your frequency dialed in.

Still trying to manage your ever-increasing alert flow by hiring more analysts? That’s much like adding buckets to deal with a leaking roof. Invest in detection engineering and automation engineering to reduce the alert flow and prevent alert fatigue and unhappy analysts. Here are some best practices: - Apply an automation-first strategy: handle and/or accelerate all alerts through automation - Continuously tune and optimize detection rules - Let analysts and detection / automation engineers work closely together to increase the effectiveness of engineering efforts - Establish metrics for rule quality to identify candidates for tuning and automation - Test against defined quality criteria before putting any detection rules live - Increase the fidelity of your rules by alerting on more specific criteria - Aggregate and analyse batches of noisy alerts daily or weekly, instead of handling them individually in real-time - Consider your ideal ratio between analysts and engineers. Start out with 50-50, then decide what would best suit your needs - Make risk-based decisions on added value of rules compared to time investment, and drop time-consuming rules with little added value if they cannot be tuned properly This is by no means an easy thing to do. But by focussing on engineering and detection quality, you can transition to a state where you control of the alert flow instead of the other way around, so that analysts can focus on the alerts that truly matter. #soc #securityoperations #securityanalysis #detectionengineering #automationfirst

Delivery apps text me more than people… They notify me through their app, on whatsapp, text msgs.. and even knock on my door (but I ask for that when I make an order) We (the users) can feel when a product starts chasing us more than actually serving us (I mean will I grow an appetite to suddenly order something at 8:00am when your app texts me an offer?) This usually happens through notifications and msgs. I have seen this pattern in consumer apps that are under pressure to monetize fast. A user finishes one action, closes the app, and within minutes gets a nudge to come back, spend, recharge, upgrade, complete something. Then another one at 8 p.m. because that time “performs well.” For a while, the team sees movement and calls it engagement. But people are not reacting to value. They are reacting to interruption. The problem with push noise is not just annoyance. It changes the emotional contract. The product starts to feel less like a tool and more like a salesperson waiting by the door. That shift is expensive. Notification fatigue does not arrive dramatically. It builds quietly. First, people swipe away. Then they mute. Then they stop trusting that anything from you is worth opening. One bad week can train that habit faster than most teams expect. I think of it like airport security. A checkpoint can create safety. Too many checkpoints create stress, delay, and resentment. The system may be working exactly as designed, while the experience is breaking underneath it. The unseen issue is not message frequency alone. It is behavioral timing without emotional sensitivity. Every trigger teaches the user something about your product. About whether it respects their attention, or treats it as inventory. That lesson tends to stick longer than the campaign.

If you're just starting to dip your feet into in-app lifecycle stuff, I've got some handy tips you can steal 😚 Usually, the 3 types of in-app messaging you'll bump into are messages, SMS, and push. ⚡ Messages: use these when context matters, and you need users to take immediate action within the product. Example: "You're 2 steps away from completing your profile. Users with complete profiles see 3x better results." ⚡ Push notifications: these work best for time-sensitive updates that merit some interruption, even when users aren't in your app. Example: "Your scheduled export is complete. Tap to download before it expires in 24 hours." ⚡ SMS: I like to reserve these for truly urgent notifications that require immediate attention from a user. Example: "Your monthly subscription will renew tomorrow. Reply OK to confirm or PAUSE to stop this month's billing cycle." Rule of thumb: in-app messaging is for enhancing the current session; push is for bringing users back; SMS is for timely, need-to-know info. Here's an example of an in-app messaging track👇 For new users (first 14 days): - Focus on progressive guidance, not marketing - Limit to 1-2 messages per session - Tie directly to the next step/logical action 💡 Example: "Great job importing your first dataset! Ready to create your first visualization? Here's how.” For established users (steady-state usage): - Shift to value expansion and efficiency tips - Max 1 message per 2-3 sessions - Use behaviour triggers, not time-based interruptions 💡 Example: "We noticed you frequently export to CSV. Did you know you can schedule automated exports? Set it up in 2 mins." For power users: - Focus on advanced capabilities and beta access - Keep frequency very low (1 per week maximum) - Make dismissing easy and respect their preferences 💡 Example: "Based on your latest Workflow, you might find our new integration with Hubspot handy! Want early access? (Only 20 spots available)" If you're worried about being intrusive (or ruining a user's first experience with your product), you can think about avoiding in-app messaging: - During critical work moments (checkout, data entry, dashboard creation) - When users are troubleshooting or in help documentation - During their first 5 minutes in the product (ex. let them orient first) - When they've dismissed similar messages previously - When you're promoting something unrelated to their current activity You might also want to test some stuff, like: ⚡ The placement and timing of in-app messages ⚡ Starting with a minimal message and expanding only if users show interest ⚡ Tracking dismiss rates (ex. if dismiss rates exceed 70%) ⚡ Personalizing based on role, or JBTD (my personal fave) This isn't exhaustive, but it's a good place to start if you're new to in-app messaging! Hope it helps 🥰 #lifecyclemarketing #lifecyclemarketingtips #b2blifecycle #lifecyclemarketingstrategy #b2bmarketing #b2bmarketingtips

Transactional notifications, those automated messages triggered by specific user actions or system events, are a critical component of a positive user experience. They provide timely updates, confirmations, and important information, keeping users informed and engaged. However, poorly designed transactional notifications can be intrusive, confusing, or even ignored. This article explores best practices for crafting effective transactional notifications. Key Principles for Effective Transactional Notifications: Timeliness: Notifications should be delivered promptly, immediately after the triggering event. Delays can lead to confusion and frustration. Clarity: The message should be clear, concise, and easy to understand. Avoid jargon and use simple language. Relevance: The information provided should be directly relevant to the user and their recent interaction. Actionable: Include clear calls to action, guiding users on what steps to take next (e.g., "View Order," "Track Shipment," "Confirm Booking"). Personalized: Whenever possible, personalize the notification by including the user's name or other relevant details. Channel Optimisation: Choose the right channel for the notification (e.g., email, push notification, in-app message). Consider the urgency and context of the message. Frequency Control: Avoid overwhelming users with excessive notifications. Implement frequency capping and allow users to manage their notification preferences. By adhering to these principles, businesses can transform transactional notifications from mere updates into valuable touch points that enhance user engagement, build trust, and drive positive user experiences. #ux #userexperience #mobileux #designthinking

The biggest miss we're seeing with upper funnel execution is FREQUENCY. We just audited a business running awareness media on paid social: - Spending about 16% of budget on awareness (Ad recall optimized) - Running on automatic placements on Meta - 15 unique ads per ad set - Targeting Broad audiences (18+, US, Women) - Average weekly frequency across their awareness efforts was 0.90. We fortunately have a lot of research quantifying how frequency impacts effectiveness (example research shared below). Reach of course still matters a lot, with the first impression having the largest marginal impact. But proper frequency is a key driver of impact: - 0-1.5 weekly frequency is too low - particularly for lesser known brands or more complex products - 1.5 - 2 weekly frequency is optimal - especially when looking at purchase intent - 2+ weekly frequency is most critical for shorter term campaigns, like BTS, but the marginal return per impression is approaching 0 at this point. This is why I have previously harped on broad targeting for "new to awareness" advertisers on Meta. Broad audiences are not inherently bad, and in fact broad reach is important. But broad audiences at the expense of frequency is not a worthwhile tradeoff for most brands moving up funnel. Over emphasis on diverse creative only exacerbates this frequency problem. Performance-oriented teams are conditioned to needing a LOT of ads to "feed the algo" and keep performance up (ASC campaigns often feature 25+ ads). But excessive-creative proliferation dilutes your message and branding even further. So how can you tackle this problem? - Construct your audience size to allow for proper frequency at your budget - broad but not too broad - Test optimizing to reach with a target frequency - this will allow you to maintain a broader audience, but focus delivery on proper frequency - Filter out unproductive reach/impressions with placement exclusions - focus on feed, reels and stories, or just feed and reels. - Limit creative to 2-4 concepts - avoid running with an ASC 20+ creative strategy - Monitor & report on weekly frequency - it's ok to adapt mid flight if you are seeing too high or too low frequency And for those looking to go the extra mile, setup a lift experiment testing across different frequency levels (3-cell, 1, 2 and 3 weekly frequency - or something similar). The above are just guardrails - the best solution for you is to test for yourself. New Engen

🔕 Design Guidelines For Better Notifications UX (https://lnkd.in/ehgF7Taa), with practical techniques on how to make notifications more useful and less annoying — with snooze mode, by exploring how and when they are triggered and measuring their use (scroll down for the newsletter ↓). 🚫 High frequency of notifications is a very frequent complaint. ✅ Not all notifications are equal: some are more useful than others. ✅ Users value updates from close contacts, transactions, insights. 🤔 Users ignore automated, irrelevant, promotional notifications. ✅ Sending fewer messages can improve long-term product use. ✅ Let users choose notification modes (silent, regular, power). ✅ Suggest switching from push notification to email digests. ✅ Let users snooze, pause, mute if high volume is expected. ✅ Track how often notifications are ignored and acted upon. 🚫 Avoid disruption and notification fatigue by sending less. And most importantly: scrutinize the decision tree to find the right timing to send the right types of notifications. Experiment with wording, timing, grouping and frequency for different user segments. And when in doubt, postpone, rather than sending through. --- 👋🏼 I'm Vitaly Friedman, and you can find useful UX resources on my profile. I’m also running “Smart Interface Design Patterns” 🍣 (https://lnkd.in/d4CNaTxR) with a friendly video library and live UX training. 😊 #ux #design #notifications

We just saved a customer $200k/year with notification batching. 🤌 A new marketplace customer using Knock just implemented batching and reduced their Twilio spend by 30%. The best part is they did this while improving their user experience and increasing transaction volume. Here’s why batching works: 1️⃣ Fewer emails and SMS messages → Less inbox noise 2️⃣ Higher signal messaging → Users pay attention and stay subscribed 3️⃣ More bid activity → Higher transaction volume A simple (but technically complex) optimization that drives immediate cost savings and long-term revenue growth.

Let's unpack the research on OVER-ALERTING. My colleague Michele M Wood and I found three primary dimensions to describe over-alerting: 1. frequency 2. relevance 3. content Beginning with FREQUENCY, we found it was described in multiple ways. 1️⃣ To start, we need to consider how frequently a person's phone lights up, vibrates, or emits a noise to get your attention in general. We live in an "attention economy" and one study we cited said adults get upwards of 50 notifications each day on their phones. That's a LOT of messages to pay attention to. And an alert is competing for attention in that sea of messages. 2️⃣ Second, over-alerting frequency includes the number of times alerts are issued for a single threat/event. This becomes a problem when the second and third and fourth messages don't provide new information to help people understand how the threat has changed or what they need to do next. In the worst cases, this dimension of over-alerting frequency occurs due to technological malfunctions, such as when an alert "echoes" and is repeated in the alerting software-hardware system over time. 3️⃣ Third, over-alerting frequency includes the period of time over which those alerts are issued. If a person is "on alert" over a long period of time, with threat escalation happening only some of the time, then, similar to #2, there is a feeling of being over-alerted. Note that this dimension is very relevant to feelings of alert fatigued and we'll get to that in a later post. 4️⃣ Fourth, over-alerting frequency includes receiving redundant messages from multiple sources. We know that the best way to initiate attention and action is to issue consistent messages from varying sources, but it does contribute to a feeling of over-alerting. If you're like me and can think of examples from recent events that fit each of these dimensions, it becomes obvious that alert frequency is part of the over-alerting equation. In a future post I'll share about the second dimension - relevance. That one is JUICY, so you'll want to watch for it. To read the full paper, go here: https://lnkd.in/eZVJJGyt Or ask me for an open-access copy. Happy to share! For more insights into alerts and warnings, check out www.thewarnroom.com

Most IoT teams get notifications backwards. They start complex and wonder why users ignore them. Here's the 4-phase framework that actually works: ➞ Phase 1: Foundation (Essential Functionality) Start simple. Device status, error alerts, basic thresholds. Manual opt-ins, fixed delivery schedules, basic open rates. Nothing fancy, but it works reliably. ➞ Phase 2: Enhancement (Expanded Connectivity) Add depth. Hardware diagnostics, maintenance reminders, richer data inputs. Rich media notifications, time-sensitive alerts, offline queuing. Begin A/B testing preferences. ➞ Phase 3: Optimization (Contextual Intelligence) Now it gets interesting. Location-aware alerts, behavior-based timing, environmental context. AI starts prioritizing what matters most. Personalization based on actual usage patterns. ➞ Phase 4: Innovation (Self-Optimizing Systems) The pinnacle. Predictive alerts that prevent issues before they happen. Cross-device intelligence, edge-powered decisions, ML-driven delivery optimization. The system learns and adapts without human intervention. The mistake I see repeatedly? Teams trying to jump straight to Phase 4. Your notification system should mirror your product maturity. Start with reliable basics, then layer on intelligence as you understand your users better. The best IoT notification systems don't feel smart - they feel invisible. Users get exactly what they need, exactly when they need it, without thinking about it. What phase is your notification system in? Are you building on solid foundations or trying to skip ahead? ♻️ Repost if you believe great UX starts with simple, reliable notifications ➕ Follow me, Nick Tudor, for more practical insights on building intelligent IoT systems