Reducing Customer Effort Scores

My API was dying. 500ms response times. Angry users. My manager asking "why is everything so slow?" I thought I was a decent developer. But my API performance? It was embarrassing. That's when my senior taught me something that changed everything: "𝗦𝗽𝗲𝗲𝗱 𝗶𝘀𝗻'𝘁 𝗷𝘂𝘀𝘁 𝗮𝗯𝗼𝘂𝘁 𝘆𝗼𝘂𝗿 𝗰𝗼𝗱𝗲. 𝗜𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝘁𝗵𝗲 𝗲𝗻𝘁𝗶𝗿𝗲 𝗷𝗼𝘂𝗿𝗻𝗲𝘆 𝗼𝗳 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮." Here are the 7 game-changing strategies that took my APIs from embarrassing to lightning-fast: 𝟭. 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗶𝘀 𝘆𝗼𝘂𝗿 𝘀𝗲𝗰𝗿𝗲𝘁 𝘄𝗲𝗮𝗽𝗼𝗻 The day I added Redis, my database queries dropped by 80%. Suddenly, the same data wasn't being fetched 1000 times a minute. 𝟮. 𝗬𝗼𝘂𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗶𝘀 𝗽𝗿𝗼𝗯𝗮𝗯𝗹𝘆 𝘁𝗵𝗲 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸 One missing index was costing me 300ms per query. One `EXPLAIN` command saved my career. (Pro tip: If you're doing N+1 queries, you're doing it wrong) 𝟯. 𝗘𝘃𝗲𝗿𝘆 𝗯𝘆𝘁𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗮𝘆𝗹𝗼𝗮𝗱 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 Switched from JSON to Protocol Buffers. 40% smaller responses. Users stopped complaining about loading times. 𝟰. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗶𝘀 𝗳𝗿𝗲𝗲 𝘀𝗽𝗲𝗲𝗱 Enabled Gzip compression. Boom. 70% smaller responses. Literally a one-line config change. 𝟱. 𝗔𝘀𝘆𝗻𝗰 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝘀𝗮𝘃𝗲𝗱 𝗺𝘆 𝘀𝗮𝗻𝗶𝘁𝘆 Stopped making users wait for background tasks. Email sending? Background job. Image processing? Background job. Response times went from 2s to 200ms. 𝟲. 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝗮𝗹 Returning 10,000 records in one response? Recipe for disaster. Paginate. Filter. Sort. Your servers will thank you. 𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗹𝗶𝗸𝗲 𝘆𝗼𝘂𝗿 𝗷𝗼𝗯 𝗱𝗲𝗽𝗲𝗻𝗱𝘀 𝗼𝗻 𝗶𝘁 (Because it does) Set up alerts. Profile slow endpoints. Fix problems before users notice them. The brutal truth? Most developers write code first, optimize later. But the best APIs are designed for performance from day one. Your users don't care about your elegant code structure. They care about speed. My API now: - 50ms average response time - 99.9% uptime - Happy users - Happier manager Your turn: What's the one API performance mistake you wish you could warn your younger self about? Drop it below 👇 Let's help each other avoid these painful lessons.

A sluggish API isn't just a technical hiccup – it's the difference between retaining and losing users to competitors. Let me share some battle-tested strategies that have helped many  achieve 10x performance improvements: 1. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Not just any caching – but strategic implementation. Think Redis or Memcached for frequently accessed data. The key is identifying what to cache and for how long. We've seen response times drop from seconds to milliseconds by implementing smart cache invalidation patterns and cache-aside strategies. 2. 𝗦𝗺𝗮𝗿𝘁 𝗣𝗮𝗴𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 Large datasets need careful handling. Whether you're using cursor-based or offset pagination, the secret lies in optimizing page sizes and implementing infinite scroll efficiently. Pro tip: Always include total count and metadata in your pagination response for better frontend handling. 3. 𝗝𝗦𝗢𝗡 𝗦𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 This is often overlooked, but crucial. Using efficient serializers (like MessagePack or Protocol Buffers as alternatives), removing unnecessary fields, and implementing partial response patterns can significantly reduce payload size. I've seen API response sizes shrink by 60% through careful serialization optimization. 4. 𝗧𝗵𝗲 𝗡+𝟭 𝗤𝘂𝗲𝗿𝘆 𝗞𝗶𝗹𝗹𝗲𝗿 This is the silent performance killer in many APIs. Using eager loading, implementing GraphQL for flexible data fetching, or utilizing batch loading techniques (like DataLoader pattern) can transform your API's database interaction patterns. 5. 𝗖𝗼𝗺𝗽𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 GZIP or Brotli compression isn't just about smaller payloads – it's about finding the right balance between CPU usage and transfer size. Modern compression algorithms can reduce payload size by up to 70% with minimal CPU overhead. 6. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗼𝗼𝗹 A well-configured connection pool is your API's best friend. Whether it's database connections or HTTP clients, maintaining an optimal pool size based on your infrastructure capabilities can prevent connection bottlenecks and reduce latency spikes. 7. 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝗟𝗼𝗮𝗱 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 Beyond simple round-robin – implement adaptive load balancing that considers server health, current load, and geographical proximity. Tools like Kubernetes horizontal pod autoscaling can help automatically adjust resources based on real-time demand. In my experience, implementing these techniques reduces average response times from 800ms to under 100ms and helps handle 10x more traffic with the same infrastructure. Which of these techniques made the most significant impact on your API optimization journey?

A new study involving 10,000 calls shows the right greeting can improve first contact resolution by 12%. Imagine you're a customer calling to get help with an issue. Which greeting gives you more optimism about getting your issue resolved? 📞 Example 1: "Thank you for calling Acme Megacorp, where every day is a great and wonderful day. My name is Joy. How can I make your day great and wonderful today?" 📞Example 2: "Hi, this is Becky in Boise. Who do I have the pleasure of speaking with?" The second example wins by a mile. It's more human. The first greeting feels like it's been written by someone who's never used a phone. (Yes, both examples are real.) I partnered with Prosodica, a voice analytics firm, to test various types of greetings. Prosodica used its proprietary AI software to analyze a pool of 10,005 inbound customer service calls from four clients in different industries. 🔎 Results: Asking a customer for their name at the start of the call correlated with better outcomes: First contact resolution: +12% Call escalations: -11% lower Unresolved issues: -33% 💬 Discussion: A good phone greeting is the starting point for building rapport. Asking for someone's name puts them at ease and makes the call more cooperative. 💡Next steps: Try asking customers for their name as part of your phone greeting. If you run a team, ask your agents to try different approaches to find one that works for them.

If you want to transform adult social care, health, or public services more generally, stop obsessing about ‘demand management’. Start with citizen contact. Not as a call centre. Not as a cost to minimise. Not as a front door you try to keep shut. Citizen contact is the system’s main sensing organ. It’s where people tell you, in their own words, what’s going wrong. It’s also where you either build capability – or manufacture dependency. Here’s the ugly bit: most ‘managing demand’ is just delay, deflection, denial. It doesn’t reduce demand. It distorts it, shifts it around the system, and then congratulates itself for ‘handling volume’. That’s how you create repeat contact, failure demand, and collapsing trust. The better framing is simple and operational. Make the front door the place where demand turns into one of three things, quickly and well: 1 resolution (problem solved), 2 enablement (person leaves more able than when they arrived), 3 or legitimate escalation (specialist work only when it’s genuinely needed). Everything else is pinball. People bounced between teams, forms, scripts, channels, referrals, ‘signposting’. And we call it success because a spreadsheet says ‘referred onward’. A form is a conversation in slow motion. A script is a conversation under pressure. If you design either for speed, you get speed and rework. If you design for first contact resolution, you start fixing the actual system defects: confusing forms, poor signposting, broken handoffs, downstream processes that can’t deliver. And the most counterintuitive point is cybernetic: measures create purpose. - Measure average handle time and you will get short calls and repeat demand. - Measure problems solved and people enabled and you’ll get learning, redesign, and demand genuinely falling rather than being pushed elsewhere. That’s #SystemsThinking in practice, not slogans. So yes, this is ‘customer contact’. But it’s also strategy. It’s service design. It’s #publicservicereform. And it’s the most practical route I know to doing #adultsocialcare and #health differently, because it forces you to design against real demand rather than imagined processes. If your front door suddenly became your main learning system – not your main cost centre – what would you have to stop measuring, even if it made this month’s performance report look worse?

🔥 I reduced our API response time from 850ms to 47ms. Here's what actually moved the needle. 📉 850ms → 47ms: How I Actually Fixed Our Slow API (Not How You'd Expect) Spent 3 weeks hunting performance issues in a production API serving 2M+ requests daily. The wins didn't come from where I expected.   The false starts: Enhanced caching → Negligible impact (already at 94% hit rate) Vertical scaling → Burned budget, minimal gains Refactoring algorithms → 2 days for 2ms improvement   The actual game-changers: 1. Killed the N+1 monster 47 database queries per request. Consolidated to 3. Result: 650ms → 180ms 2. Switched to streaming responses Replaced eager loading with IAsyncEnumerable<T>. Started sending data before collecting everything. Result: 73% less memory, 50% faster responses 3. Fixed connection pooling We were spinning up fresh DB connections for every single request. Result: 180ms → 89ms 4. Ditched reflection in JSON serialization Source generators replaced runtime reflection. Result: 89ms → 47ms   The actual takeaway: Performance optimization isn't a bag of tricks. It's a process: Instrument before you investigate Profile real traffic, not synthetic benchmarks Architecture problems beat code problems Load test with production patterns I burned week one "fixing" non-issues. BenchmarkDotNet + dotTrace finally showed me what actually mattered.   Measure → Identify → Fix → Verify Everything else is guesswork. What performance problem did profiling reveal in your systems that surprised you? 💬 Write a comment below 👇 💬 اكتبلي في التعليقات 👇 💬 If this post helped you, give it a #repost so others can benefit too 👇 💬 لو البوست ده فادك اعمله #repost علشان غيرك يستفيد 👇 #dotnet #csharp #performance #softwareengineering #backend

In the customer experience (CX) industry, Average Handle Time (AHT) and repeat calls are two critical metrics that often have a direct impact on each other, but they represent different aspects of customer service quality. Average Handle Time (AHT): Definition:AHT measures the average time an agent spends on a customer interaction, from the moment the customer initiates contact until the issue is resolved, including time spent on hold and after-call work. Goal:Many companies aim to reduce AHT because shorter handling times often translate to operational efficiency and cost savings. However, focusing solely on reducing AHT can negatively affect service quality. Impact on CX:If agents rush through interactions to lower AHT, they may not fully resolve the customer's issue, leading to frustration and dissatisfaction. Repeat Calls: Definition:Repeat calls refer to instances where a customer has to contact support multiple times for the same issue because the problem wasn’t resolved during the initial interaction. Goal:The objective is to minimize repeat calls to improve first-call resolution (FCR), a key indicator of customer satisfaction. High repeat call rates often signal poor problem-solving or a lack of empowerment for agents to resolve complex issues. Impact on CX:A high repeat call rate can damage customer trust and satisfaction. Customers expect efficient solutions in one interaction, and having to call back increases their effort, making the overall experience less favorable. Balancing AHT and Repeat Calls: The key challenge in the CX industry is balancing AHT with minimizing repeat calls. Here’s how businesses can approach it: Focus on First-Call Resolution (FCR):Prioritise resolving the customer’s issue on the first call, even if it means allowing for longer call times. This might increase AHT in the short term but will reduce repeat calls and increase customer satisfaction. Empower Agents:Equip agents with the tools and authority to address issues more effectively. Well-trained agents can resolve issues quickly without escalating or requiring follow-up calls, improving both AHT and FCR. Quality over Quantity:While AHT is an important metric for operational efficiency, it should not come at the expense of service quality. Agents should focus on addressing the root cause of the issue, which often requires taking the necessary time to ensure a thorough resolution. Use of Technology:AI and automation tools can assist in handling repetitive tasks or simple queries, which can reduce AHT without impacting the customer’s need to call back. For more complex cases, technology can guide agents to provide better solutions. CX organizations should avoid fixating on AHT alone and instead look at it in conjunction with metrics like repeat calls and FCR. A balanced approach that prioritizes resolving customer issues efficiently and thoroughly can lead to higher customer satisfaction and retention in the long run.

Fast Contacts = More Repeat Contacts? Many contact centers focus on reducing Average Handle Time (AHT), especially when teams are short staffed and queues are growing. But when agents rush through conversations, customers often call back because their issue was not fully resolved. The result is more repeat contacts, lower customer satisfaction, and even more pressure on the team. You may save 90 seconds on a call but create another call from the same customer after 15 minutes. When resources are stretched, prioritize better. Let experienced agents focus on complex issues that need more time and attention. Use self-service, IVRs, and chatbots for simple requests. Improve forecasting and scheduling to reduce pressure before it becomes a problem. Most importantly, focus on First Contact Resolution as one call handled properly is always better than three calls handled quickly. The best measure of success is not how fast a call ends. It is the experience the customer had during that call. Did they feel heard? Was their issue fully resolved? Did they leave the interaction with confidence? Because speed without resolution is not efficiency. AHT is important, but resolution is what customers remember.

It was a small thing. So small. We almost missed it. During a routine CX review, a team member flagged something. It had been sitting in the data for months. ✓ Customers who contacted us again. ✓ About the same issue. ✓ Even after we had marked it as resolved. Were churning at nearly three times the rate of everyone else. Three times. We had been measuring resolution. → But not whether the resolution lasted. To us: ✓ A closed ticket meant a solved problem. To the customer: ✓ A closed ticket sometimes meant we had stopped listening. That one discovery changed everything. We stopped measuring how fast we closed issues. → We started measuring whether the issue came back. We introduced a 72-hour follow-up. For every resolved complaint. ✓ Not a survey. ✓ Not an automated message. ✓ A real check-in. "We resolved your issue three days ago. Is everything still working as it should?" The impact? ✓ Churn in that segment dropped. ✓ Customer satisfaction improved. But something else stood out. Customers started saying: "You actually followed up. I did not expect that." That sentence said a lot. It showed us the bar we had been setting. The biggest CX improvements rarely come from big overhauls. → They come from paying attention. → And noticing the small thing that changes everything. So, ask yourself: Are you measuring resolution? Or are you measuring whether the problem actually went away? There is a difference. And it matters more than most teams realise. Repost and share. Ann Murungi Helping businesses create clear, human customer experiences that build trust and retention.

We finally trust our voice agent with real customers.” Operations Manager, Founders & Co. Realty " “100% calls answered. 34% more first‑call bookings. That’s when the team said, ‘Okay we trust it with real customers " Let’s keep this real. Before we stepped in, their phone lines were a bottleneck. Missed calls after hours. Long holds at lunch. Agents juggling showings and voicemails. Trust was low because the system kept dropping the ball. Problem → approach → outcomes Problem: Missed calls, inconsistent answers, and too many escalations to humans for simple questions. Approach: We rebuilt the agent with 5 clear states (intro, intent, Q&A, qualify, book/transfer), added simple speaking rules, set exact function triggers (only check calendar after name + number + reason), and put a reliability test plan in place. Outcomes (last 30 days): 100% calls answered (including evenings/weekends) 28% fewer escalations to humans 34% more appointments scheduled on the first call Average handle time down 21% More first-call bookings = more showings on the calendar. The team didn’t hire. The agent didn’t burn out. Pipeline moved faster without adding headcount. What “trust” looks like (for them): Calls don’t vanish into voicemail Readouts are clear (time, email, phone) Urgent cases route to a human fast Weekly QA catches small issues before they grow What would “trust” look like for your team fewer escalations or higher first-contact resolution? - Sahil Verma