What ML and NLP use cases are game-changers for customer engagement?
Leveraging ML and NLP to run customer intelligence through all your CX systems shifts teams from the mode of blind reactivity, where everyone is working on the basis of the last thing that happened, to informed proactivity, where you can get to that holy grail of “what customers need, when they need it.” That’s a phrase that’s been used a lot in relation to personalization, but the difference now is how much context is available— how much deeper we can go on organic conversations than click data.
While many companies have already adopted AI in its most obvious form—chatbots and other labor-replacement tools— augmentation is the next level up in productivity for ML and NLP, i.e. customer engagement use cases which go beyond automation to empower teams with better access to data. The next tier up, and perhaps most exciting, is intelligent infrastructure, where AI connects disparate data streams across an organization to give unprecedented context and timeliness to all the systems that use customer data.
How do we measure the ROI on insights?
This can be tricky because the value comes from what you do with insights, i.e. how efficiently they improve the systems that use them. It can be difficult to trace the impact of an insight on the outcome of the process it’s working inside of. That’s why organizations should focus on aligning AI with existing KPIs and processes rather than creating new metrics. By plugging AI into systems already tracking critical outcomes, companies can clearly see the added value of AI without overcomplicating measurement. This approach allows enterprises to directly observe how AI-driven insights improve outcomes they already care about, whether it’s enhancing customer satisfaction, reducing costs, or increasing revenue.
Leveraging ML and NLP to run customer intelligence through all your CX systems shifts teams from the mode of blind reactivity, where everyone is working on the basis of the last thing that happened, to informed proactivity, where you can get to that holy grail of “what customers need, when they need it.”
How should enterprises think about build vs. buy?
Somewhere between building AI systems in-house and buying off-the-shelf solutions is composing a mix of both. To figure out what to build and what to buy, enterprises must evaluate the value of their data. Pre-built solutions will likely fall short for really unique data streams, and the risks of using third-party products is non-trivial. But building everything internally comes with its own risks, often leading to the “uncanny valley” between a demo and a production system. To assess which systems make sense to build in-house and where external solutions offer scalable, reliable benefits, enterprises should look to engineering patterns that provide observability and control without overwhelming internal teams.
Thanks to all the panelists for a rousing conversation and stay tuned for Part 2! Curious to see how you can power your CX systems with real-time customer intelligence? Learn more about Frame AI for CX.
