Qemotion

Background

Q°emotion is a SaaS platform for semantic and emotional analysis that lets companies automatically analyze reviews, messages and feedback from their customers or employees using artificial intelligence.

The solution processes large volumes of verbatim feedback (customer reviews, surveys, support messages, comments) to identify the emotions expressed, such as joy, anger or frustration, and extract actionable insights to improve the customer experience.

With this technology, companies can:

• automatically analyze customer feedback
• identify pain points in user journeys
• track changes in NPS and customer satisfaction
• prioritize customer experience improvements.

As customer experience becomes a strategic differentiator, Q°emotion wanted to expand its B2B prospecting to identify more companies looking to use customer data to improve their customer relationships.

Objective

The goal of the partnership with Oliverlist was to generate sales opportunities with companies looking to analyze and improve their customer experience with artificial intelligence.

The priorities were to:

• book meetings with customer experience, marketing and data decision-makers
• target companies with a large volume of customer feedback
• position Q°emotion as an AI solution able to turn customer data into strategic insights

{{cta}}

Target audience

The prospecting strategy focused on the people responsible for customer experience and data:

• Customer experience (CX) directors
• Customer service / support managers
• Marketing managers
• Data or innovation managers
• CIOs

Target organizations included:

• e-commerce companies
• banks and fintechs
• SaaS companies
• organizations with large volumes of customer feedback or satisfaction surveys

What we put in place

Identifying companies with customer experience challenges

Oliverlist built audiences of companies that regularly collect customer feedback (NPS, reviews, customer support, surveys).

Targeting CX and data decision-makers

The campaigns targeted managers involved in improving the customer experience and analyzing user data.

Multichannel prospecting

We rolled out campaigns combining:

• outbound email
• cold calling
• automated follow-up sequences

to multiply touchpoints with decision-makers.

Messaging focused on customer insights

The messages highlighted:

• understanding customer emotions
• identifying pain points in the customer journey
• improving NPS and customer loyalty.

Continuous optimization

The campaigns were continuously optimized to improve:

• response rates
• audience relevance
• conversion into qualified sales meetings.