We are all humans: customers and employees, patients and doctors, consumers and shoppers, students and teachers. Why use a difference lens to measure our experience - Customer Experience (CX), Employee Experience (EX), People Analytics, Patient Experience? It's all about humans, so let's call it "the human experience" (HX). Welcome to Heartbeat HX™
Making the irrational, predictable.
We understand that becoming truly human centric might require a seismic shift in your organization’s purposes and outcomes. Heartbeat AI is part of the migration from where you are to where you want to be.
We want to take you on a journey to understand your people and to connect their emotions with their behavior.
Lean into emotions that matter to attract and retain your people.
Why measure emotions?
1. Emotions drive loyalty
Emotion holds the key to achieving CX leadership. Forrester showed that elite brands provided an average of 17 emotionally positive experiences for each negative experience. The lowest-performing brands provided only two emotionally positive experiences for each negative experience.
Telecom companies have some of the lowest customer satisfaction scores of all industries. To understand and improve satisfaction - and ultimately customer loyalty - some of the leading telecom companies use Heartbeat to analyze open-ended survey responses on on-going basis. They set benchmarks by emotion, not just by NPS, and build actionable strategies to address customer frustration, resentment, criticism or anxiety.
2. Emotions drive decisions
Emotions are an important part of how we make decisions. If you are measuring customer satisfaction (like Net Promoter Score or CSAT), but not measuring your customer emotions, you can't fully understand the "why" behind their decisions. This case study shows how love, joy and trust drive high satisfactions scores for US banks.
3. Emotions explain & predict behaviour
Unlike basic sentiment analysis, Heartbeat fine-grained emotion tools are shown to improve predictive models such as prediction of churn, advocacy, brand equity, purchase intent, and even political elections. We utilize multiple data sources - survey open-ends, social media, customer reviews, call center transcripts - to build accurate predictive models that connect with key business outcomes.