Actionable raises $10M to turn customer data into behavioural predictions
French customerintelligence startup Actionable has raised $10 million in funding to expand itsplatform for predicting customer behaviour and identifying the factors behindit. The round was led by Hi I...
Customer intelligence firm Actionable has secured $10 million in funding to develop its platform for predicting customer behavior and identifying influencing factors. Led by Hi Inov, the round of investment also saw participation from existing investor Axeleo Capital. Established in 2024 by co-CEOs Nicolas Rieul and Nans Thomas, Actionable collaborates with large corporations to forecast customer churn, satisfaction, complaint risks, and repeat purchases.
Traditional customer satisfaction and predictive marketing tools lack thoroughness. Customer surveys often only gather opinions from a fraction of the customer base, while predictive marketing systems usually rely mainly on transaction and CRM data. Actionable aims to address these limitations by integrating operational data from across the customer journey to generate personalized predictions and pinpoint factors affecting customer behavior.
The platform amalgamates transactions, CRM details, web analytics, and operational data into a customized customer data model suitable for various industries. Depending on the sector, this may encompass data like waiting times and order preparation in retail, load factors and delays in transport, or delivery times in e-commerce. Actionable currently caters to businesses in retail, financial services, insurance, transport, energy, telecoms, and automotive.
By employing this data, Actionable can discern operational aspects impacting customer satisfaction and predict dissatisfied customers, allowing businesses to modify services or intervene before complaints escalate. Clients supply raw tabular data, which Actionable processes to recreate customer journeys and construct a standardized model incorporating the business context of the underlying information. This process can condense data engineering tasks from months to mere days.
According to Nans Thomas, co-founder and co-CEO, the crucial aspect lies in incorporating business context into an AI's understanding of raw tables, as an LLM on top of a data warehouse is insufficient. Thomas emphasizes that Actionable spent two years building an industry-specific customer model, ensuring its accuracy across sectors.
The funding will be allocated towards expanding Actionable's product, engineering, and sales teams, as well as supporting its international growth via reseller partners and the US market expansion.
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