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Consumer brands’ offers, as point-in-time data for investors.

Every offer email and text, the flows after a sign-up or a basket, discount depth and how often each brand sends. From any brand you name, each fact kept with the time it was seen.

Onboarding a limited number of teams. Name up to 5 companies to look up first.

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  • Harrowgate, a listed apparel retailer
  • 2 competitors
  • Email, texts and prices

  1. What you name

    The brands and countries

    • Brands, by country
    • Email and SMS
    • The flows after a sign-up or a basket
    • Public prices

    Any brand, not a fixed panel

  2. What comes back

    Every offer, as it was seen

    • Discount depth and type
    • How often each brand sends
    • Free delivery and new offers
    • The email or text behind each

    Each with the time it was seen

  3. Where it lands

    Into your research stack

    • The API or MCP
    • Supabase or a webhook
    • A weekly sheet

    History builds from day one

Name the brands. Every week from today becomes history.

A consumer analyst follows Harrowgate, a listed apparel retailer, and 2 competitors through the autumn.

Set it up

Which brands, in which countries, and where the data lands.

  • Any brand you name
  • UK and US
  • Email and SMS
  • Public prices

  1. Name the brands

    Every brand you cover, in each country where it sells.

    ExampleHarrowgate and 2 competitors, in the UK and the US.

  2. Pick what to read

    Offer emails and texts, the flows after a sign-up or a basket, and public prices.

    ExampleEvery email and text, the reminder after a basket, and the price of 20 lines on each site.

  3. Say where it lands

    The API, Supabase, a webhook or a sheet, in the shape your models read.

    ExampleA daily table in Supabase, and a webhook for any discount deeper than 30%.

Obsession collects it

Each fact keeps the time it was first seen, with the email, text or page behind it.

  • An inbox per brand
  • A number per brand
  • Timed on arrival
  • Kept as it arrived

  1. Discount depth by week

    Each offer read as it arrives: how deep, what kind and what it applies to.

    ExampleHarrowgate: 15% in September, 25% on 19 Oct, 40% on 2 Nov.

  2. How often each brand sends

    Sends per week by channel, so a change in pressure shows the week it starts.

    ExampleHarrowgate went from 3 emails a week to 6 in the fortnight before 2 Nov.

  3. Point-in-time, kept that way

    Every fact keeps the time it was first seen, so a backtest uses only what was knowable that day.

    ExampleThe 2 Nov email is stamped 07:12 that morning, and stays that way.

Panels show the brands they chose. History can’t be bought later.

Promotions data usually comes from a fixed list of brands. Offers sent only after a sign-up or a basket rarely reach it, and every week nobody collects is a week nobody can test.

  • Brands outside a fixed list

    The challenger or the private competitor your thesis depends on.

  • Flows after a sign-up or a basket

    The offer sent to finish an order, as well as the campaign email.

  • Texts beside emails

    Brands that send their deepest offers by text.

  • History you can test

    A clock that starts the day collection starts, so it pays to start it early.

Each fact is dated the moment it is seen and kept exactly as it arrived, with the email or text behind it.

What analysts, quant teams and private equity do with it.

  • Read a quarter before it reports

    Discounting and send pressure, week by week, ahead of results.

    ExampleHarrowgate’s discounts deepened 3 weeks before its competitors’, and its emails doubled.

  • Test a signal as history builds

    A feed with the time each fact was seen, for a backtest without look-ahead.

    ExampleEvery offer since 7 Sep, one row each, with its depth, channel, brand and time.

  • Diligence a target and its market

    What a target offers its customers, beside its competitors.

    ExampleAsked “Who discounted deepest before Black Friday?”, Insights answered with 3 brands, ranked.

  • Add a channel to a dataset

    Texts and the flows after a basket, beside the campaign emails a panel already holds.

    ExampleCompetitor A sent its free delivery offer by text a day before any email.

Guides for this job

Step-by-step guides behind this use case, each with what a pass looks like.

What funds and analysts ask.

Alternative data for investors includes what consumer companies offer their own customers: discount depth, free delivery, new offers and how often they send them. Obsession collects it from any brand you name as point-in-time data, with every email, text and price kept with the time it was seen. History builds from the day you start, delivered by API, MCP or a table.

To the day collection starts for each brand, and it grows every week after. Nothing is backfilled, because nobody can see last month’s email today. Discretionary funds, sell-side analysts and private equity read it from the first week; quant teams start the clock now for the history they test later.

Yes. Each fact keeps the time it was first seen and is kept exactly as it arrived, with the email, text or page behind it. A backtest uses only what was knowable on each date.

Point-in-time data, defined

From real sign-ups. Obsession’s agents subscribe to each brand’s emails and texts the way any customer can and keep what arrives. They use their own addresses and numbers, never a real customer’s, and never sign in to an account, ask for a quote or talk to staff.

The lines every agent keeps

Yes. Any brand you name, in the countries you pick. A brand not covered yet is added on request, and its history starts that day.

Which brands should we start the clock on?

Early access includes API and MCP access, lookups of every company already covered, and up to 5 companies added for you, set up with the founders on a call.

Onboarding a limited number of teams. Name up to 5 companies to look up first.

We keep what you tell us to set up your access and email you about Obsession. Privacy notice