For decades, salespeople have been taught that there is only one possible reason for lost sales: that salespeople have failed to defeat the customer’s status quo. Perhaps the customer doesn’t fully appreciate the problem that their solution is designed to solve. Or maybe they don’t yet see enough daylight between their company’s solution and that of the competition.

As “doer-sellers,” professional services partners are responsible for not just delivering services but also the entire business-development process. In this article, the authors identify five statistically determined profiles that professional services partners fall into, only one of which is correlated with positive performance, and they lay out the three key behaviors of a successful business-development approach

Most sellers struggle to cut through the noise with messages that actually resonate. Much of this is rooted in a lack of understanding of the buyer's business. New AI tools hold the promise to change this, especially when put directly in the hands of frontline teams. Our study details findings that help us all better understand how sellers can boost the relevance and resonance of their sales engagement efforts.

Particularly during the pandemic, when face-to-face visits with customers have been constrained, inbound selling in calls centers has become more important to company revenue. New research uses recordings of millions of such calls, analyzes the way salespeople drive the conversation, and record whether the call results in a sale.

To better understand the impact of this crisis on customer service departments, our team at Tethr (an AI and machine learning venture) recently completed a study of roughly 1 million customer-service calls involving more than 20 companies representing a broad cross-section of industries. All of these calls took place between March 11, when Covid-19 was declared a pandemic by the WHO, and March 26.

Using AI and Machine Learning to unearth surprising ways high performers respond to customer objections.

Ted McKenna introduces the market’s first predictive Customer Effort Score, a deep learning AI model that predicts customer sentiment based on conversations with customer service agents.

Ted explains the difference between traditional text analytics tools and advanced conversation analytics tools. How data is classified makes a bigger difference than you might think in terms of insight quality.

Change at the frontline is still a largely human endeavor yet the march towards automation contributes to poor employee experiences.

A lot has changed across the customer care and CX landscape since the customer effort score (CES) was invented more than 10 years ago. Now one of its original creators looks back to its foundational principles and forward to its future applications.

We examined the choices made by more than 1,000 boards and CEOs in North America and Europe. A study of the types of roles CEOs decided to take, the experiences they sought, and the opportunities they seized along the path to the CEO spot. The selection of these individuals reflect the choices made by thousands of board directors at many of the best-performing companies in the world.