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Machine-learning decision software helps retailers improve pricing, marketing, and inventory choices.
now just over 5M in booked ARR · 40 customers · 60 employees
Enterprise software can start as paid consulting when the project earns data access and exposes the real workflow.
Fewer bars = easier, cheaper, or faster for an AI-assisted solo builder. Editorial judgments based on the case details.
7Learnings builds decision-optimization software for larger retailers and brands. Its product helps teams decide how to price products, allocate marketing, and plan orders using machine-learning forecasts and optimization. Cofounder and CEO Felix Hoffmann spent six years doing pricing projects at the consulting firm Kearney, then two years at Zalando, where he saw predictive decision-making used for pricing. The company began with a consulting project that provided both revenue and access to a useful dataset, then built product capabilities using that data. The first live pricing upload performed badly, showing that model quality and operational fit must be proven in the customer environment. The interview says the company reached its first $1 million ARR with ten customers, all sold by Felix, and later reported 5 million in booked ARR, 40 customers, and 60 employees. Enterprise sales and implementation shaped its path from bespoke work to product.
For enterprise pricing software, first customers usually come through founder credibility and trusted relationships, not broad self-serve marketing. Felix had worked in pricing consulting and retail, so he could approach decision-makers who understood the cost of weak pricing decisions.
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Data credibility: Cofounder and CEO Felix Hoffmann states the 5M booked ARR, 40 customers, 60 employees and the roughly 13% profit uplift in a SaaS Club interview; the host states the first $1M ARR came from 10 customers. All figures are founder-reported, with no dashboard or filings shown.
The first money arrived as a consulting contract, before 7Learnings had a SaaS product. That engagement funded work and exposed the team to a retailer dataset, which it used to develop forecasting and optimization capabilities.
The growth loop depends on proving value in demanding accounts, then using customer confidence to lower risk for the next retailer. 7Learnings’ first ten customers were founder-sold.
Enterprise products can spend months in data preparation, integration, and evaluation before a customer judges results. 7Learnings had to secure a large dataset, build forecasting and optimization systems, and work through a failed first live upload.
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