Improving Customer Experience

We set up a single dashboard with key metrics: first response time, CSAT, NPS, and first-contact resolution rate. You see the load on each channel in real time, not three days later in a report. Metrics refresh every 15 minutes, and anomalies arrive in your messenger.

Every conversation gets a quality score across twenty parameters: answer accuracy, compliance with guidelines, courtesy, and resolution speed. Once a week we audit a random sample of a hundred dialogues and fix the gaps we find in scenarios within two business days. The results are visible in the report.

We test scenario variants through A/B testing on real traffic: one group of users gets one answer, the other gets a different one. Over two weeks we collect statistics and pick the variant with the highest CSAT. You do not have to guess which tone or wording works better, the numbers decide for you.

We set SLA targets for each channel and request type, defining the maximum response time for chat, calls, and email. The overdue trigger fires a minute before the deadline, and the manager sees a red marker in the queue. Within a quarter you cut SLA violations by 30-50% without growing the support team.

We deliver reports every week and every month with trends across each direction and channel. You understand which channel is growing fastest, where processing is slowing down, and which scenarios need rework. The team gets a concrete action plan, not abstract recommendations buried in a slide deck.

Dashboards are built on the first day of the pilot, but the full picture takes shape over three to four weeks, once enough data accumulates for trend analysis. This is not a flaw but the reality: metrics without history show you only the current moment. We warn you about this upfront, so early numbers are not treated as final.

We track churn signals: a sharp drop in contact frequency, negative phrasing in chat, and a rise in complaints. One week before a customer leaves you receive a list of at-risk accounts scored from ten to a hundred points. A timely save costs five times less than acquiring a new customer, and the score is recalculated every night.

We launch proactive campaigns for customers with high churn risk. The bot offers a discount on the next order, a free consultation, or priority support, without waiting for a complaint. Retention conversion through a proactive offer reaches 25-35%, and the contact cost is ten times lower than a cold call. The first campaign starts in week three.

We restore loyalty after a bad experience through personalized compensation. The system automatically offers a discount, subscription extension, or refund depending on the severity of the situation. The customer gets a resolution within an hour instead of waiting three days for a responsible manager to reply. Compensations without approval work up to a preset amount.

We run regular CSAT surveys after every completed interaction. You get statistics by channel and by agent, you see where customers are unhappy, and you take action before the dissatisfaction turns into churn. Closing the feedback loop takes two business days, and the questions take the customer less than a minute.

We build a loyalty program based on behavioral data: purchase frequency, average order value, number of support contacts. Customers with personalized offers return 40% more often than those who receive generic promotions. However, building the profile requires at least three months of history, and bonuses accrue automatically after every order.

Proactive retention delivers strong results, but not every customer is ready for unexpected offers. Some audiences perceive a reminder bot as intrusive. We tune the contact frequency and channel for each segment, but mistakes happen, and unsubscribe rates from outreach campaigns rise by 5-8%. A pilot on a small sample reduces the risk.