Actionable uses customer and operational data to predict churn, satisfaction, complaints and repeat purchases, helping businesses identify the factors behind customer behaviour and act on them.
French customer intelligence startup Actionable has raised $10 million in funding to expand its platform for predicting customer behaviour and identifying the factors behind it. The round was led by Hi Inov, with participation from existing investor Axeleo Capital.
Founded in 2024 by co-CEOs Nicolas Rieul and Nans Thomas, Actionable works with large companies to predict customer churn, satisfaction, complaint risk and repeat purchases.
Actionable is addressing limitations in traditional customer satisfaction and predictive marketing tools. Satisfaction surveys typically capture responses from only a small proportion of customers, while predictive marketing systems often rely primarily on transaction and CRM data. Actionable combines this information with operational data from across the customer journey to generate predictions for individual customers and identify the factors influencing their behaviour.
The platform brings together transactions, CRM information, web analytics and operational data in a customer data model tailored to different industries. Depending on the sector, this can include information such as waiting times and order preparation in retail, load factors and delays in transport, or delivery times in e-commerce. Actionable currently supports businesses across retail, financial services, insurance, transport, energy, telecoms and automotive.
Using this data, the platform can identify operational factors that affect customer satisfaction and predict which customers are likely to be dissatisfied, enabling businesses to adjust services or intervene before complaints arise.
Clients provide raw tabular data, which the platform uses to reconstruct customer journeys and create a standardised model that incorporates the business context of the underlying information. Actionable says this process can reduce data engineering work that would typically take months to a matter of days.
Putting an LLM on top of a data warehouse is not enough: without business context, an AI reads raw tables very badly. The hard part is turning hundreds of tables and in-house definitions into a customer model a machine can use without getting it wrong. That is what we spent two years building, industry by industry,
said Nans Thomas, co-founder and co-CEO.
The company has also developed Actionable Intelligence, an AI agent that uses its customer data model to carry out analysis while retaining the business context attached to the underlying information.
The funding will be used to expand Actionable’s product, engineering and sales teams and support its international growth through reseller partners and expansion into the US market.