Nory
Restaurant tech
London, UK
2025
GTM Engineering
Retainer
Case
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Nory
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GTM Engineering
A mapped market and an in-house data engine for Nory's sales team
Executive summary
Nory's sales team sold to restaurant groups without a shared view of which accounts were worth a call. Over a 14-month engagement, COM works built the data foundation, buying signals and market map behind its account-based selling, and handed the engine to the team.
Client
Nory
Website
nory.ai
Headquarters
London, UK
Industry
Restaurant management SaaS, B2B
Company type
Series B
Service
GTM Engineering
What was delivered
Market mapping, data foundation, gathering signals, lead scoring, sales team enablement
Teams in scope
Marketing, Sales (BDRs)
Model
Retainer
Quote
Pablo Sánchez
Head of Marketing, Nory
The situation
Nory's growth plan relied on account-based selling to restaurant groups, although the sales team had no clear view of its market. The CRM held a large account base with inconsistent data, and there was no shared understanding of which accounts to prioritise. BDRs researched accounts by hand across several data providers, which took time away from selling. Buying moments such as new restaurant openings or funding rounds were often spotted too late.
The approach
One reliable source of truth.
Several data providers were replaced by one enrichment pipeline, chosen after testing three approaches side by side on the same sample of accounts. Data accuracy rose from 78 % to over 90 %.
Sales works the accounts most likely to buy.
Funding rounds, new openings and leadership changes are detected with over 90 % accuracy and reach the BDRs in the tools they already use. A fit and engagement score tells each BDR which accounts to call now, so sales time goes to a focused share of the CRM.
The full market, mapped.
Systematic discovery across Nory's target markets added 42 % more qualified accounts than the CRM contained. Restaurants rarely show up on LinkedIn or in classic B2B databases, but most are active on Instagram and similar platforms, so standard B2B matching missed many of them. The matching logic was adapted to how restaurants actually present themselves online, and the rollout ran step by step to protect data quality.
Two key figures
42 %
more addressable market
90 %
data accuracy, up from 78 %
Nory owns a GTM data engine: one enrichment pipeline, signals flowing into the CRM, a scoring model the sales team works by and a documented process to keep mapping the market. Hands-on sessions on Nory's own lists made the team self-sufficient, with data spend focused on qualified accounts. Today Nory's team runs and extends the engine in-house.
Tools in use
Clay
HubSpot
Claude
Aplify
Slack
