You know what they bought. You do not know who they are.
Your order table has a name, a phone number and an address. Age, profession, income band and affluence tier come back against it — so the segments you build and the audiences you buy are made of people, not order history.
Businesses already running on the graph.
Consumer businesses and platforms building segments on ZipLabs data.
Marketing to behaviour instead of to people.
Order history tells you what happened. It does not tell you who it happened to, which is the part that predicts the next one.
Your best customer is a row in a table
Four orders, one address, an average basket. Nothing in that record says whether they are twenty-four and stretching or forty and barely noticing the price.
Lookalikes built on a pixel
The platform models people who behave like your buyers on its own surface. That is a proxy for a proxy, it degrades every time tracking gets tighter, and you cannot inspect it.
The same win-back to everybody
One lapsed-customer campaign for a person who moved city, a person who outgrew the product and a person who is simply between purchases. Two of those three were reachable.
Segments made of people.
Each of these starts from the customer list you already own and ends somewhere the budget goes further — in a file, an API, or the CRM your team already uses.
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one audienceby who they arethe fifth that carries the revenue
The base, split by who they actually are
The same customers, separated by age, profession and affluence rather than by how much they last spent. The tier that repeats stops being hidden inside the average.
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# order 40118 4 orders · ₹7,400 M. Sen · 31 · designer a row a personmatching the identifier… building the profile… age, profession, band, affluence
An order row becomes a person
The phone number on the order is enough. What comes back is who placed it — which turns a purchase log into a customer base you can actually describe to a creative team.
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PG P. Ghosh same basket · 2 yrs moved · own home new categorylife stage moved band moved up
The life event that changes the basket
A move, a marriage, a promotion. People buy different things in those months, and a brand that notices gets to make the first offer in a category the customer has not shopped yet.
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seed: top 5,000 customersdescribing them…1 28–38 · salaried 62%2 affluent · metro 44%an audience you can read
Lookalikes built from people, not pixels
Describe your best customers in attributes you can see, then buy against those attributes. The audience survives a tracking change, and you can explain to the board what is in it.
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RK R. Kaurno order, 9 mo change observed…relocated still in bandRKTSAB 3 lapsed for a fixable reason
A lapsed customer who simply moved
Churn and a change of address look identical in an order table. Separating them means the win-back goes to people who can still buy, and stops going to people who never could again.
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affluent28–38 metro · salariedwriting it once…journeys · ads · scoring one definition✓ every system agrees who this is
One segmentation, inherited downstream
The same attributes feed the email journey, the paid audience and the loyalty tier. When the definition of an affluent repeat buyer lives in one place, the numbers stop disagreeing.
Send the list, or wire it up.
The same profile, delivered as an enriched file you can act on today or as an API into whatever you have built.
Consumer Intelligence
An identifier in, the person out — demographics and profession globally, with income band and financial health on top for India. Most brands start by sending a customer list and getting it back enriched.
Integrations
The same intelligence inside Zoho CRM or TeleCRM, enriching every lead as it lands, so the segment a campaign uses is the segment the sales and support teams see.
Your customer list stays yours.
Matching runs on hashed identifiers, so a customer file can be enriched without the raw list leaving your side. Purpose, scope and retention are agreed before anything moves, and every attribute traces to where it came from.
Security & Trust Centre
EU & UK
California
Audited
What a growth team
asks first
Do you push audiences into Meta and Google for us?
No — we hand back the enriched segment and you upload it wherever you buy. That is deliberate: the account, the consent basis and the bidding stay with you, and nothing about the segment is locked to one platform's definition of it.
How is this different from the lookalike the platform builds?
A platform lookalike is behaviour on that platform, and you cannot see inside it. This is attributes on a person — age, profession, income band, affluence tier — so you can read the audience, argue with it, and rebuild the same one somewhere else next quarter.
Which attributes are India-only?
Income band, credit health and existing obligations are India coverage. Demographics and professional data run globally on the same 1B+ person graph, so affluence signalling outside India comes from the professional layer. Consumer Intelligence sets out both packages.
We only have phone numbers, no emails.
That is the normal case here and it is enough. A phone number resolves to the person the same way an email does; the package you get back is identical.
How do we know the enrichment is right?
Start with a segment where you already know the answer — your top repeat buyers, or the cohort your team can describe from memory. If the returned profile does not match the customers you know, nothing further is worth discussing, and that check takes a day.
Find out who your best customers are.
Send the list you already have. We will hand it back described, and you can check it against the customers your team already knows by name.