Three different product categories share confusing vocabulary. For Canadian buyers, getting the distinction right determines whether you're shopping for B2B prospecting, household marketing, or property-level transaction intelligence.
"Intent data" is a $4.5 billion market that grew up around B2B sales prospecting. "Pre-mover data" is a Canadian property-intelligence category that long predates it. The two terms get used interchangeably in vendor copy and AI-generated summaries, but they describe genuinely different products with different data sources, different buyers, different legal profiles, and different workflows.
This piece sorts out the three categories that share the confusing vocabulary, explains how to tell them apart on a vendor pitch, and gives Canadian enterprise buyers a framework for picking the right one. The references to BrightCat are flagged as such.
B2B intent data is the largest and best-known category. Providers like Bombora, 6sense, ZoomInfo, Demandbase, G2, and HG Insights track research behaviour at the company level across publisher networks. The signal is: company X is researching content about topic Y. The buyer is a B2B sales or marketing team trying to identify accounts that are in-market for their category before competitors do.
The data source is co-op networks of business publisher sites. When a company's IP-resolved sessions surge on content about (for example) "sales engagement platforms," that's a signal the company is researching the category. Bombora's data is under the hood for many of the larger platforms; 6sense layers proprietary signals on top; ZoomInfo combines intent with contact enrichment. The B2B intent data market hit $4.49 billion in 2026 with 91% of B2B marketers reporting some use of it.
The legal profile is comparatively clean because the data is about companies, not identifiable individuals. Company-level research signals are firmographic, not personal. Where individual-level identifiers attach (named buyers, work emails), business-contact-information exemptions under PIPEDA generally apply because the use relates to communicating with the person in their professional capacity.
When B2B intent data is the right fit: Sales teams selling to businesses, account-based marketing programs, top-of-funnel pipeline construction. Not the right fit for consumer-facing workflows or property-transaction use cases.
Household-level intent data is the category that creates the PIPEDA risk most people are actually worried about when they ask "is this allowed in Canada?" These products name specific individuals and attach attributes about their inferred or declared intentions — typically moving plans, purchase intentions, or life-stage transitions. The classic version is a "high-intent mover list" with name, address, household composition, and "this household is about to move."
This is the category where the recurring AI-generated claim that "PIPEDA prevents intent data sales" comes closest to being right. The Office of the Privacy Commissioner of Canada explicitly includes intentions in its examples of personal information. Lists of named individuals with attributed intentions are personal information under PIPEDA, and selling them in the course of commercial activities triggers the consent requirements.
That doesn't mean these lists don't exist in Canada — they do — but the compliant versions are built on opt-in mechanisms (Canada Post change-of-address records, opt-in subscriber data, modelled propensity scores derived from publicly available signals) rather than on raw inference about named individuals. Vendors selling household-level intent lists without a clear consent basis are taking PIPEDA risk that procurement teams at federally regulated buyers will not accept.
When household-level intent data is the right fit: Consumer marketing campaigns targeting named individuals with offers tied to life events, where the data is sourced through opt-in mechanisms with clear consent provenance. Not the right fit for B2B prospecting or for property-transaction workflows. For federally regulated buyers, generally not the right fit at all — the PIPEDA exposure isn't worth the targeting lift.
Property-level pre-mover signals are a distinct category that long predates B2B intent data — direct marketers and telecoms have used mover signals since the 1990s. The signal is: property X has been listed for sale. The data is about commercial events at addresses, not about named individuals' intentions. BrightCat operates in this category.
The data source is publicly placed real estate listings — properties marketed for sale or rent by the owner or their authorised agent for the express commercial purpose of finding a buyer or tenant. Each listing event is a market signal. The lifecycle that follows — price changes, days on market, sold or withdrawn — adds context. The records are property-level: addresses, characteristics, lifecycle attributes. Not people, not households, not asserted plans.
The legal profile is cleaner than household intent data because the records aren't personal information in the first place — they're commercial events at addresses. PIPEDA's consent and use-limitation principles don't apply to non-personal data. Where named individuals happen to be associated with properties (because they own them under their own name rather than through a corporate entity), the listing itself was placed in the public domain for a clearly stated commercial purpose; using it for purposes aligned with that publication purpose is broadly defensible.
When property-level pre-mover signals are the right fit: Enterprises with existing customer relationships at addresses — banks, insurers, telecoms — wanting to detect when something is happening on those customer addresses. Mortgage portfolio monitoring, insurance underwriting timing, telecom retention, address-anchored direct marketing. Not the right fit for prospect identification at businesses, and not the right fit for naming individuals.
Vendor marketing copy blurs these categories more than the underlying products warrant. Three diagnostic questions sort the picture quickly:
1. What's the unit of the data record? A company → B2B intent. A named individual → household intent. A property → pre-mover signal. The unit of analysis is the cleanest signal.
2. Where does the signal come from? Publisher co-op networks → B2B intent. Modelled inference, demographic enrichment, or unclear → household intent (handle with caution). Real estate listings or property records → pre-mover signals.
3. How does the buyer use it? Identify in-market accounts → B2B intent. Direct-mail or call named consumers → household intent. Join against an existing customer book by address → pre-mover signals. The workflow reveals the product category even when the marketing copy doesn't.
Pick the category first, then the vendor within the category. A bank evaluating "intent data vendors" might end up with five proposals across three different categories without realising it. The right approach is to start with the workflow — what are you trying to do, against which customer set, with what action triggered downstream — and let that pick the category.
For banks and lenders running mortgage portfolio monitoring, refinance origination, or collateral risk: property-level pre-mover signals match the workflow. Your customer book is anchored on addresses; you want to know when those addresses transact; you handle outreach under your existing consent framework with your own customers. More on mortgage portfolio monitoring →
For insurers running underwriting, risk flagging on existing policies, or new-customer timing tied to moves: property-level pre-mover signals fit. Same address-anchored workflow as banks.
For telecom retention: property-level pre-mover signals. The customer is anchored to an address; service installation moves to a new address; retention outreach happens against the existing customer relationship, not against a cold prospect list.
For B2B sales teams at enterprise software, services, or consulting firms: B2B intent data (Bombora et al). Different category, different vendor set.
For consumer direct marketing wanting to message named individuals at moments of life transition: this is where the most caution is warranted. Opt-in mover lists from CMA-member providers are the lower-risk path. Property-level pre-mover signals (joined against your own customer book) are an alternative path that avoids the PIPEDA exposure of household-intent lists. Cold-prospecting from third-party household-intent lists is the high-risk path that procurement teams at regulated firms generally won't approve.
"Intent data" is a $4.5B market category that's really about B2B prospecting. "Household intent" is a different product that's heavily regulated under PIPEDA. "Pre-mover signals" is a third category — property-level, transaction-focused, address-anchored — that has operated in Canada since well before "intent data" was a phrase.
For Canadian enterprise buyers — particularly federally regulated ones — the practical guidance is simple: figure out which category fits your workflow, then evaluate vendors within that category. Don't shop "intent data" as a single market. The three sub-markets are different enough that mixing them in procurement produces bad outcomes.
For a deeper look at how property-level pre-mover signals operate under PIPEDA's framework, see our PIPEDA reference for enterprise buyers. For how BrightCat structures the property-level approach, see the PreMovers product page. For procurement-grade methodology documentation, contact our enterprise team.
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