Contextualising Interaction Analysis in CX


Providing more intuitive and optimised customer support

Contextualising Interaction Analysis in CX

Enghouse’s omni-channel customer engagement software already provides leading AI-driven knowledge management features, to empower customers and agents with the right information at their fingertips in moments. But Product Director Steve Nattress is continually looking to the future and where the next breakthroughs will come, in providing ever more intuitive and optimised customer support — and driving back actionable insight to the enterprise delivering it.

The data is only getting bigger. But the intelligent aspect of it means zeroing in on the relevant phrases through the noise and providing contextually correct responses. “Being able to extract content from much larger documents and knowledge bases, and adapting that information for the audience at the right moment, is where the future lies,” he said. “It’s about extracting and formatting, for example, complicated product reference information needed by a new customer, from a technical paper into the answer they need right now,” he explained.

Mining your customer interactions for insight

That context will include metadata that will cumulate to inform the content of the knowledge base in the future — identifying trends, and unmet needs, as well as shifts in external circumstances.

“Interaction analytics is about analysing your inbound interactions, to inform what should be included in your knowledge base. What are your most frequently asked questions, what problems do you need to solve for your customers?”

“This is a really customer-centric model, where you don’t deflect their problems, you work on a way to fix them. And you embed it into your product development roadmap — it’s not just about support content, it’s how you create the features customers are asking for, that they actually need. Fix things that aren’t working well. Your business can fill in the gaps.”

Because this means you can upsell to your existing customers. Positive reinforcement is powerful. Greater satisfaction for them results from a better-aligned product that grows to fit their needs, and the business spends less on marketing and advertising for new customer acquisition, because of how well they’re selling to those they’ve already got. All the right numbers go up, including satisfaction metrics.

Better thinking, combining data and insight

This is only possible with an advanced AI that really understands the nuances and intent behind language, and can collate and surface genuinely unmet needs, in ways that customers may not directly express them individually. If you simply asked them (for example in a survey), you might end up with a roadmap for ‘faster horses’, or whatever your industry equivalent, incremental improvements rather than breakthroughs. Instead, interaction analytics can get at the real concerns they want to fix, then suggest the most intelligent solutions — crowdsourcing the need recognition, but proposing solutions with the latest smart technology.

“Our AI engine is a proprietary piece of technology that’s focused on the customer service vertical,” Nattress explained, “it understands the construction of language, but then it goes to the next level of detail. If someone gives you positive feedback, what exactly was positive about it, what behaviours are you trying to reinforce? What problems are you trying to address? What exactly is the issue with that product?”  It’s this level of detail that turns a reactive overview into an action plan. “You need to know the WHY, behind what people are saying, and what’s really happening,” he said.

Once the AI offers the why all developers will have to do is build what they know customers want — to ensure complete satisfaction.

 

 


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