Partnering with eClerx for contact center optimization, the client wanted to build a closed-loop insights engine across its consumer care ecosystem. Within 6-8 weeks, they were able to stand up their first live dashboards, and subsequently reduce inbound contacts by 85% over three years.

This sportswear brand is one of the world’s best-known sportswear manufacturers, serving millions of consumers through chatbot, self-service, and human-assisted support channels. As more customers move online, it needed better customer care intelligence to see and act on what was actually driving contact volume across a fragmented set of systems.

The challenge

The brand’s customer journey analytics showed significant measurement gaps at enterprise scale.

• Data on chatbot performance, case and contact activity, pre-chat clickstream behavior, and post-sales order triggers lived in disconnected systems like Cognigy, Salesforce Service Cloud, Adobe Analytics, and OMNI, with no unified view tying them together.
• Without visibility on customer experience analytics, the team lacked a defensible way to prioritize fixes or demonstrate ROI on support investments.

A lack of visibility makes it hard to answer basic but critical questions: Where is containment succeeding or failing? Which releases are actually reducing (or inadvertently increasing) contact volume? Are we looking at chatbot analytics the right way? Where is avoidable demand being created in the customer journey, and how can the team size it or prove impact once a fix ships?

Our strategy

eClerx designed a streamlined contact center optimization plan, deploying a closed-loop insights engine powered by agentic analytics, structured around four workstreams:

1. Observe: Built a unified KPI framework spanning chatbot analytics, case, clickstream, and order-lifecycle data, with release tagging and automated alerting to catch shifts in real time
2. Investigate: Ran root-cause diagnostics across the full customer journey to pinpoint exactly where and why avoidable contacts originated
3. Improve: Developed a ranked fix backlog, validated through A/B testing and pre/post readouts to confirm which changes actually moved the needle
4. Accelerate: Deployed AI-driven accelerators to speed up analysis and got the first live dashboards into stakeholders’ hands within 6–8 weeks

The engagement drew on a connected tool stack: Cognigy for chatbot analytics, Salesforce Service Cloud/SFSC for case and contact data, Clickstream/Adobe Analytics for pre-chat path analysis, and OMNI plus Order Lifecycle data for post-sales triggers — unified through eClerx’s InsightsOnDemand platform and CareOps AI accelerators.

The result

The engagement gave the client a measurable, release-by-release view of its customer journey analytics for the first time, driving sustained containment gains and demand reduction across its support channels.

• 85% reduction in inbound contacts over a 3-year partnership
• 6-8 weeks to first dashboard
• Higher bot containment and fewer escalations to human agents
• Reduced repeat contacts through targeted root-cause fixes
• A prioritized self-service roadmap ranked by effort vs. impact
• Proven demand reduction attributed by release, with cost avoidance quantified

The client continues its partnership with eClerx, 8+ years on, and is currently expanding the scope of the insights engine as new channels and markets come online.

How eClerx can help

eClerx’s Customer Experience solutions can help unify fragmented service, chatbot, and journey data into a single measurement layer. Our teams can also automate customer care intelligence or root-cause diagnosis, and accelerate fix prioritization to reduce avoidable contact volume. Contact us to learn how a similar closed-loop insights approach could work for your business.

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