New ASAPP research introduces an orthogonal technique that augments existing data to train better out of scope detectors operating in low-data regimes. Read more

Are your ML models learning the right things? Here’s what we discovered when we took a closer look at what agents do vs. what they should do in one situation. Read more

Does the customer need a live agent or is automation the right way to handle it? Many companies think it’s a binary question. The best answer is more nuanced. Read more

Are you measuring what matters in your contact center? AHT – or average handle time – can’t really measure agent efficiency in an asynchronous, digital world. Here’s thought on more modern metrics. Read more

Containment rate is not a good measure of success, used alone. A good metric will also consider if the customer’s need was met. Here’s how we measure. Read more

How do you measure efficiency and effectiveness in your contact center—for individual agents and for the team as a whole? Gain insight from our CX pros working with large consumer brands. Read more

How do you identify the right workflows to automate in the contact center? And quickly implement them? ASAPP was awarded a patent for the technology to meet this challenge. Read more

On the path to real-time speech sentiment analysis, new ASAPP research achieves training efficiency gains with transfer learning between spoken and written language domains. Read more

Rather than maintaining multiple ASR models that work under varying time constraints or conditions, new ASAPP research introduces a single multi-mode model that can dynamically adjust to different scenarios. Read more

Here’s the data science behind getting the best conversation summaries (call notes) for your contact center. Read more