Getting stuck in customer service purgatory is one of the most miserable experiences a person can have.
But bad service isn’t just a customer issue – it also reflects poorly on businesses and leads to higher churn.
Actionable, an AI-driven predictive customer experience engine headquartered in Paris, France, aims to catch those unhappy customers and turn their experience around before they pivot to a competitor.
On Wednesday, Actionable announced a $10 million funding round, led by European tech investment firms Hi inov and Axeleo Capital. The cash raised will go toward funding Actionable’s direct sales expansion in the US, as well as toward hiring researchers and data scientists to further develop its product road map.
Forward thinking
Actionable had previously raised $2.2 million in pre-seed funding back in 2024, which helped it develop its predictive modeling platform and add agentic capabilities to its user interface.
Actionable’s tech processes a client’s existing data and determines which elements of the brand’s service have the greatest impact on customer churn and repurchase habits.
The first step is ingesting all of a client’s data from external platforms, like the brand’s CRM and CDP, and using it to map the customer journey – and how to keep them satisfied over the course of that journey.
Actionable uses AI to analyze all that data. But its real differentiator is in understanding the nuances of what clients in different vertical markets expect from their tech providers, the company’s Co-Founder and Co-CEO Nicolas Rieul told AdExchanger.
LLMs “just don’t understand” raw data without an additional semantic layer and industry-specific context, like why, exactly, customers are churning, said Rieul. And without that context, the models can hallucinate.
Actionable’s solution for avoiding such hallucinations was to develop an AI agent that maps the raw data into a “common model” for each industry that Actionable works with (including transit, energy and financial services).
Basically, the common model pinpoints the most important factors that make or break the customer experience. Some, like the quality of customer service, are consistent across industries, said Rieul, while others are industry-specific, like delay times and seating options for transit.
Will they stay or will they go?
The model is built on data that Actionable has gathered from clients over the past two years, but no client-specific data is “pooled or shared between accounts,” Rieul emphasized.
Instead of relying on any individual customer’s data for training, the model pays more attention to overarching trends and behaviors in the market.
Clients can uncover these trends by querying their own data via Actionable’s data analyst agent – a new product launched since the company’s prior funding round.
For instance, store cleanliness is a major concern for retail customers, said Rieul. A retailer could ask Actionable’s agent to compile all of the people who complained about an unclean store and note any impact that had on their repurchase habits.
The result is a 360-degree view of the client’s individual customers, including their experience with client service, operations and waiting times. Those stats help Actionable’s model predict the customer’s state of mind and future behavior – and gives the brand a chance to correct any negative experiences before it’s too late.
The technology allows companies to be more proactive about finding better ways to serve their customers. It’s more effective than only responding to negative feedback, said Rieul, because the majority of unhappy customers don’t go out of their way to reach out to the brand and complain.
For instance, European railway company Ouigo only saw a 5% response rate to its customer satisfaction surveys. But from those surveys, Actionable was able to determine that a 13-minute wait time was the breaking point for most customers between feeling satisfied with their transit experience versus feeling frustrated.
From there, Ouigo was able to target customers that were likely dissatisfied in an attempt to sway their opinion of the transit brand. For instance, Ouigo launched its “Black Cat Initiative,” which targeted customers who had two consecutive negative experiences with ads that included an apology and an offer or discount.
Cause and effect
The next step for Actionable is to bring its platform to a wider pool of potential clients – hence its planned US expansion.
Actionable will spend a portion of its new funding on hiring direct sales support in the US. Right now, said Rieul, the company has reseller partners in the US, but not a standalone business.
But commercial development is just one part of Actionable’s future plans. Most of the $10 million will be used to develop new features for its platform.
Although Actionable uses AI to predict potential customer pain points on behalf of its clients, the company is relying on good old-fashioned human expertise to predict emerging product opportunities and uncover areas where certain markets are currently underserved.
The bulk of the funding won’t go toward investing in Actionable’s product team itself, said Rieuel, but rather toward hiring research engineers and data scientists who can advise on what changes need to be made down the road.
For instance, causal AI is a “hot topic” right now, he said, as AI is being trained to become deterministic, rather than predictive. Right now, he added, determinism is a major issue for AI models.
“If you crack that,” he said, “you predict the future.”
