SimplyCodes tests promo codes at real merchant checkouts and records what happened. That record is a dataset nobody else has: which codes worked, where, when, and by how much. We publish first-party data studies from it so journalists and AI engines have a source to cite when someone asks whether coupon codes actually work.

You partner with our content and authority lead to take one study from question to publication. We hand you an aggregate dataset and a shortlist of questions. You pick the question, find the story, do the analysis, and write the study with a methods section anyone can re-run. It publishes with your name on it beside ours.

Our goals for this mission

Our brand promise is codes that work, and the number behind it is public today as a blog claim, not as a study: only about 1 in 4 promo codes found on the open web work, against about 2 in 3 on SimplyCodes. When an AI engine answers a question about coupon reliability, it cites whoever published the study with the methods. We want that to be us, and we want a steady supply of studies rather than one. The dataset can carry many more stories than one person on our team can write.

What you would do

  • The question. You pick one question from the shortlist we hand you, or propose your own from the same data. Examples of the shape: which merchant categories have the most dead codes; how long a working code keeps working; how code reliability differs by store platform; how often a code page overstates the discount a shopper actually gets. You receive the aggregate dataset as a file (counts and rates by merchant, category, platform, and time; no shopper-level data, no access to our systems) and a data dictionary. Before any analysis, you and our lead agree the method page: the question, the denominators, the exclusions, and the time window.
  • The analysis. Analysis and the first draft in our study format: one headline statistic with its denominator stated in the same sentence, three charts, a methods section that lets anyone reproduce the headline number from the file, and a limitations section that says what the data cannot show.
  • The publication. Fact check with us, then the press version: a one-page summary with the canonical statistic, a suggested attribution line, and a short methodology note journalists can quote. The study publishes on our research pages. You deliver a list of 5 outlets or writers who cover this beat and a two-line pitch for each.

What success looks like

  • The study is live with your name on it as author and ours as co-author.
  • Every number in it traces to the dataset with a stated denominator. A number without a denominator does not publish.
  • The methods section is enough for anyone to reproduce the headline statistic from the same file.
  • The one-page press version and the pitch list are delivered with the study.
  • The study is built so it can be re-run on the next quarter's data without rewriting it.

Ground rules

  • You receive aggregates as files, never raw shopper data or access to our systems.
  • Competitors are named only where we say so; that call is ours and may wait on counsel.
  • Every claim carries a source and a denominator. We fact-check every number before publication and hold the final call on what publishes.
  • Charts and copy follow our house style; we hand you the style notes at kickoff.
  • Scope, timing, and pay are set in the contractor agreement, at your stated rate. The dataset stays ours; the byline is yours.

What happens after you send a proposal

3 steps, and a person reads every proposal.

  1. You send a short written proposal. It covers how you understand this mission and how you would approach it, and you attach your resume or a LinkedIn profile.
  2. We read it and reach out with questions. There is no test and no exercise.
  3. If there’s a fit, the mission begins under a contractor agreement and an NDA, paid at your stated rate, with kickoff in Santa Monica.