A study of more than 600 CRM users, cited in Spona's guide to what data enrichment is, found that 76% of organisations believe less than half of their customer data is accurate and complete. That figure is not about companies that have neglected their systems. It describes the normal state of a CRM that has been in use for a few years. The problem is rarely that the data is wrong. More often it is that the data is missing.
A stale record has a value that used to be true. A job title from three years ago, a phone number that now rings a different desk. An incomplete record never had the value at all. A name and an email address, captured from a web form, with no industry, no company size, no role. Both problems undermine sales and marketing, but they need different fixes. Data cleansing corrects what is already there. Data enrichment adds what was never captured. The two work in sequence, and the order matters: cleanse first, then enrich, because enrichment tools match far more accurately against records whose company names and domains have already been standardised.
The guide describes five types. Firmographic enrichment adds company-level facts such as industry, employee count, revenue and location, which is the foundation for deciding whether an account fits your ideal customer profile. Contact and demographic enrichment adds the person: job title, seniority, function and direct contact details, which is what makes it possible to route a lead to the right salesperson and to personalise a message. Technographic enrichment adds the technology a company runs, which matters most for technology vendors who need to know whether they are replacing a competitor or filling a gap. Behavioural and intent enrichment adds signals of active research, the most valuable and the fastest to expire. Geographic enrichment adds regional offices and territories for businesses that sell by region.No single source covers all five well. Companies that take enrichment seriously combine several sources and treat it as a layered process rather than a single subscription.
In markets where public company data is thin and business registries are incomplete, the gap between a web form and a usable record is wider than in mature markets. A lead with a name and a Gmail address tells a salesperson almost nothing. Enrichment is what turns that fragment into a decision: is this a fifty-person distributor in Lagos or a sole trader, and who at the company actually decides?
Clean the dataset first. Decide which missing fields would actually change a decision, rather than enriching everything and producing bloated records nobody reads. Choose sources, combining internal data such as CRM history and product usage with external providers. Match and append through an integration rather than by hand. Then validate before the enriched data feeds scoring, routing or outreach. That last step is the one most teams skip, and it is the one that matters most: a bad match does not just fail to help, it replaces an honest blank with a hidden error.
People change roles, companies merge, and contact details shift constantly. Enriched data decays at the same rate as unenriched data, so enrichment is a programme rather than a project. A quarterly refresh is a reasonable baseline for most B2B teams, and businesses in fast-moving markets need to refresh more often. Enrich once and walk away, and the investment quietly expires.
More accurate lead scoring, because the model finally has real values to weigh. Shorter forms that still produce complete records, because you ask for the essentials and enrich the rest. Reactivated dormant leads, when enrichment reveals that a contact has moved to a company with budget. And business signal monitoring: funding rounds, leadership changes and hiring surges that mark a buying window.Spona runs enrichment as an ongoing service with validation before delivery, and its guide to data decay explains why the refresh cycle never ends.