Measure what satisfies customers.
Improve what doesn't.

Because most platforms were built to manage conversations, not to generate insight from them. The result: basic ticket metrics that tell you how many conversations happened but not why, 24-hour data delays that force manual workarounds, and no way to connect CX data to business decisions outside the support team. Reporting is the #1 pain cited in Dixa's won deals — 49% of customers who switched mentioned it as a reason.

Zendesk's analytics work for operational tracking but break down when teams need real insight. Common complaints: 24-hour data delays that force manual ticket counting and third-party workarounds in Metabase or Geckoboard, inability to extract granular product-level data, and no native voice-of-customer analysis. Teams end up building shadow reporting systems just to answer questions their platform should answer automatically.

Voice-of-customer analytics automatically surfaces patterns in why customers are contacting you — not just how many contacts happened, but what they were about, which products generated complaints, which carriers caused delivery issues, which campaigns drove support volume. Dixa's Advanced Insights categorises contact reasons across all channels and surfaces these patterns to CX leaders. This is the capability cited most often in Dixa evaluations as not available in current platforms.

It changes what you can act on. A manager who sees queue length and SLA status in real time can shift agent availability during a spike before customers start waiting. A team lead who sees a specific issue trending in the last hour can brief the team and get ahead of it. With 24-hour delayed reporting, those decisions happen the next morning — after the damage is done. Dixa dashboards update live across every channel and every metric.

Auto QA uses AI to score every conversation against your quality criteria — tone, accuracy, resolution quality, compliance — automatically, without a human reviewing each one. Manual QA typically covers less than 5% of conversations, which means most quality problems and coaching opportunities go undetected. Auto QA covers 100% of conversations. You still do the coaching, but the AI surfaces what needs your attention rather than you sampling randomly. Deals involving QA close at 47% for Dixa — second-highest of any feature.

Yes, and this is one of the most valuable things it can do. CX data contains signals that product, logistics, and marketing teams need: which SKUs generate the most complaints, which carriers cause the most contacts, which campaigns drive support volume. Dixa's Advanced Insights is built to surface this cross-departmental intelligence, not just report internally. Pink Boutique described their previous platform as having "VoC data locked inside a system that can't surface it cross-departmentally."

Auto QA scores both. As Mim handles more of your contact volume, being able to evaluate the quality of AI-handled conversations — not just human ones — becomes essential. Dixa tracks CSAT, resolution rate, and escalation patterns on Mim conversations alongside human agent metrics. You get a unified performance view across your entire operation, not just the part handled by humans.