Lead Enrichment, and Where the Waterfall Runs Dry

Lead enrichment is the act of adding missing fields to a record you already have. You start with a company name or a domain and finish with an industry, a headcount band, a technology list, a named contact and an email address. It is a data-completeness operation, and it is genuinely useful. It is also the wrong tool for the question most outbound teams actually have, which is not "what else is true about this company" but "is this company worth writing to this week".

Those two questions feel adjacent and are not. Completeness is a property of your database. A reason for contact is a property of the world, and it has a date on it.

The disclosure belongs here rather than at the bottom. We build Keaz Signals, which sells ecommerce leads with dated buying signals attached, and our contacts arrive with the store instead of being enriched onto it afterwards. We are a competitor to every enrichment vendor, and we benefit directly if you finish this page believing selection matters more than fill rate. There is a section below on where enrichment providers are the better purchase, and it is not a courtesy — it is where we lose.

A great deal, and the category earned its position honestly. Enrichment exists because the alternative was worse: sales teams typing company details into a CRM by hand, or buying a static file and discovering six months later which half had rotted. Four of its achievements are load-bearing, and nothing here overturns them.

• Normalisation is real work. Turning nine spellings of one company into one record, with a domain as the join key, is unglamorous and it makes everything downstream possible.
• Verification before sending matters more than coverage. An unchecked address that bounces is a domain-reputation problem, and the good providers treat it as one.
• On-demand beats bulk. Enriching a record when you need it, rather than buying a warehouse you never fully use, is the right shape for the problem.
• Breadth is a real advantage. A provider covering every industry in every country answers questions we cannot answer at all, because we watch one kind of business on two platforms.

There is an honest historical reason the discipline grew up field-first. For most of its existence the list was a fixed input, so the only improvement available was making each row more complete.

A waterfall queries several data providers in sequence for the same field and stops at the first one that answers. Ask for a contact email: provider one is checked, and if it returns nothing the request falls through to provider two, then three, until a value comes back or the list is exhausted. You pay per successful lookup rather than for one subscription that must be right about everything.

The logic is sound. No single provider is best across every geography and company size, so consulting several and taking the first hit raises fill rate above what any one delivers alone. That is a real gain, and it is why waterfall enrichment became the default architecture rather than a niche trick.

Two consequences follow from the design, and neither is a criticism of any vendor. First, the answer depends on which provider happened to respond, so identical records enriched a week apart can disagree. Second, a waterfall inherits the freshness of whichever source answered — the field arrives without telling you when it became true. Assembling this yourself versus buying a maintained pool is worked through in build a lead scraper, or buy the panel.

It runs dry at the point where you have every field and still cannot say why you are writing today. Enrichment answers who and what. It does not answer why now, and no quantity of additional fields converts one into the other, because the fields describe a condition rather than a change.

A job title reads identically whether the company changed direction yesterday or has been static for eighteen months. Headcount, industry and technology list are all like this: true, checkable, and silent about timing. Add twenty more attributes and you have a narrower list of businesses in the same unchanging condition. The reason for contact is still missing, so the copy is asked to invent one — which is how outreach arrives at the compliment about the branding and the "quick question" that is not a question.

There is a structural point underneath the tactical one, and it survives us being an interested party. An attribute is shared: everyone filtering for the same conditions gets the same rows, and the file is worn out the day it is exported. A dated event is perishable, so the number of people acting on it at any moment is small. Which categories of data can see change at all, rather than condition, is the subject of what each category of intent data can actually see.

Our answer is to make enrichment a property of the pool rather than a step you run afterwards. As of September 2026 we watch 4.1 million Shopify and WooCommerce stores across the EU and North America, re-scraped weekly — 1.7M in Europe, 2.4M in North America, 2.5M Shopify and 1.6M WooCommerce. About 41,200 carry a fresh buying signal in any seven-day window. Contacts, including founder addresses that are not publicly listed, are already attached, so there is no lookup step and no per-record fall-through. The pool is described on the ecommerce leads database page and the events in the buying signal catalogue.

Less than half of our records come from bought sources. The rest is our own collection: store pages including legal notices scraped and parsed, the Meta ad library scraped continuously, Instagram and TikTok for social growth, and founder addresses found by name-combination testing and verification rather than purchase. The honest downside is that this is expensive and maintenance-heavy — proxies and scrapers break constantly. That is also why it is hard to copy.

A post about lead enrichment is expected to state a match rate: how often the enrichment finds a working address. We are not going to give you one for ours. That is not modesty or a legal hedge — a single percentage is the wrong shape for the question, and quoting one here would imply a measurement this post did not take.

Any honest match rate is a rate for a specific segment, in a specific market, at a specific time. It varies with country, platform, store size and how the business publishes its own details, and a figure averaged across all of those tells you little about the slice you care about. The number worth having is the one for your segment, which you get by building it and looking.

Three further numbers are missing deliberately. There is no comparison of our coverage or freshness against any named vendor's, because we have not measured their data. There is no cost-per-record table set against other products, because what each product meters is not the same unit and a table pretending otherwise would be dishonest arithmetic. And there is no promise about inbox placement or reply volume — sending runs through Instantly in your own workspace, so the domains and the reputation are yours.

On data protection the position stays narrow on purpose. We are GDPR-conscious by design and will not claim more than that; responsibility for what you send stays with you. A list you enrich yourself and a list that arrives with contacts attached carry the same obligation in different packaging. That is a structural observation about where the duty sits, not a claim about anyone's compliance, and it is not legal advice.

Often, and in more cases than we would like. What we sell covers one kind of business on two platforms in two regions. Outside that, an enrichment provider is the correct answer and we are the wrong supplier. Six situations:

• Your buyers are not ecommerce stores. The pool is Shopify and WooCommerce, nothing else.
• Your market is outside the EU and North America. We do not cover it, and we would rather say so than describe a thin pool as a pool.
• You need to back-fill a CRM you already have. That is enrichment's home ground; we are not a lookup service for records you bring us.
• You need the records outside the system. We do not export — leads move into campaigns and stay there.
• LinkedIn is your channel. We have no LinkedIn signals and no LinkedIn sending.
• The field you need is firmographic. Registry triggers and hiring signals are marked planned as of 7 September 2026, and planned means they do not exist today.

Build the segment and read the count before planning around it. That single step answers, for your market rather than in general, both of the questions this post has separated: how many stores you can reach, and how many of them changed recently enough to be worth writing to this week.

Signing up gives 1,000 credits to spend inside segments you build yourself, as of September 2026, and what each tier includes is on the pricing page. How the per-store writing works is on the sales agent page. Access is capped per market, so the honest first step is the waitlist rather than a purchase.

• Own pool figures, page last updated 22 August 2026, retrieved 7 September 2026: 4.1M stores under watch, re-scraped weekly, 41,200 with a fresh signal in the last seven days; 1.7M Europe, 2.4M North America; 2.5M Shopify, 1.6M WooCommerce. Also the sourcing description and the 1,000 signup credits. These figures move, so the date is part of the number: /ecommerce-leads-database.
• Which events are live and which are planned, retrieved 7 September 2026 — hiring signals and registry triggers among the planned: the buying signal catalogue.
• Measurement disclosure: no enrichment match rate is quoted, no competitor coverage or freshness figure appears, no cost-per-record comparison appears, and no guarantee is made about inbox placement or reply volume. We did not take those measurements.