Hear Muthu Krishnan from Infosys talk about AI and interaction data and how AI is revolutionizing the way we handle low-value interactions, transforming them into efficient machine-to-machine interactions. Discover how Scout helps find and fix these interactions, allowing enterprises to focus on high-value engagements. This shift enables customization at scale across various personas, driving value and customer loyalty.
In this video, Muthu showcases a real-world example with a financial services firm. He demonstrates how the approach not only enhances customer experience but also translates into tangible financial benefits, ultimately boosting EBITDA. Their methodology includes:
𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: By identifying over 100 unique approval processes, they discovered that nearly 80-90% of interactions were redundant, stemming from acquisitions and geographic expansions.
𝐕𝐚𝐫𝐢𝐚𝐧𝐜𝐞 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: They found significant variations in processes across agents, geographies, and providers, highlighting the need for standardized measurements.
𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲𝐢𝐧𝐠 𝐁𝐨𝐭𝐭𝐥𝐞𝐧𝐞𝐜𝐤𝐬: Through detailed measurement, they identified inefficiencies in the approval process. For instance, while the average cost per approval was $2, some processes cost up to $8 per approval due to high rejection rates.
Their efforts led to the rationalization and harmonization of these processes, reducing unnecessary service providers and integrating generative AI and digital agents in contact centers.
Watch the video to learn how these insights can 𝐬𝐭𝐫𝐞𝐚𝐦𝐥𝐢𝐧𝐞 𝐲𝐨𝐮𝐫 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬, 𝐫𝐞𝐝𝐮𝐜𝐞 𝐜𝐨𝐬𝐭𝐬, 𝐚𝐧𝐝 𝐞𝐧𝐡𝐚𝐧𝐜𝐞 𝐜𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐬𝐚𝐭𝐢𝐬𝐟𝐚𝐜𝐭𝐢𝐨𝐧.
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