Best Practices Guide to Multi-Channel Contact Centre Analytics

Customers today expect a unified contact experience across digital platforms

Multi-Channel Contact Centre Analytics
Data & Analytics

Published: March 25, 2021

Anwesha Roy - UC Today

Anwesha Roy

It isn’t enough to just explain your channel presence and train agents in channel blending.  

Customers today expect a unified contact experience across digital platforms, necessitating the intervention of multi-channel contact centre analytics. As per recent estimates, 66% of customers use 3 or more touchpoints for contact with their favourite brands. Multi-channel contact centre analytics can collect data from across touchpoints, run an aggregated analytics algorithm, surface a 360-degree view of the customer, and help you optimise contact on each channel.  

What is Multi-Channel Contact Centre Analytics?  

Multichannel contact centre analytics (also termed as omni-channel contact centre analytics) can be defined as a data processing solution that connects with telephony, email, SMS, instant messaging, social media, chat, and direct feedback to create a unified database and generate holistic insights on customer intent. Agents can leverage the results of multi-channel analytics to improve performance by enabling first contact resolution, conducting root cause analysis for customer problems, and engaging with customers on a channel of their choice.  

Best Practices for Multi-Channel Contact Centre Analytics 

The objective of multi-channel analytics is to monitor interactions across your entire channel presence and analyse them against parameters like specific keywords, tone of voice, interaction context, customer activity, etc. here are some of the things to keep in mind when using multi-channel analytics:  

  • Leverage real-time and historical analytics – Real-time analytics aids in short-term performance, like telling agents how to course correct during a problematic conversation by extracting data from another platform. Historical analytics reveals long-term trends to inform strategic planning
  • Use automated interaction classification – A properly classified interaction database enables searchability so that you can look up an interaction or refer to cross-channel insights on demand. A classification engine will also power drill-down reports where you can segment interactions based on classifiers to find deeper insights
  • Identify “hot topics” for self-service – Multi-channel analytics will reveal common themes, problems, and conversation points raised by customers across different platforms. You can tailor your self-service conversational bot to answer these hot topics
  • Allocate agents as per analytics recommendations – You can identify the most high-traffic channels and peak periods per channel using multi-channel analytics, realigning your workforce allocation accordingly, so that agent idle time is optimised 
  • Empower agents with multi-channel data – Once the insights from multi-channel analytics are operationalised, it is easy to leave agents in channel silos, giving only team leads/managers visibility into the full picture. Make sure to avoid this to unlock the full potential of multichannel analytics in your contact centre

How Does Analytics Work Across Multiple Channels? 

Multi-channel analytics has specific components for each channel – for example, speech analytics to detect emotion levels during calls, sentiment analysis to discover emotional connotes from email, interaction tagging to classify social media mentions, etc. These components exist at a channel level, with the resulting data insights feeding into an insights operationalisation engine. This engine gives you business metrics and unified, cross-channel information that you can use to train agents, create scripts, improve self-service, and uplift the overall customer experience. 

AutomationOmni-channelSentiment AnalysisUser ExperienceWorkforce Management

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