What do we charge, and does the money work?

What is being priced

<the product being priced>

FINDING URLS. Several tools take a URL and cannot search for one — social_linkedin_posts, social_reddit_posts, social_reddit_comments, company_jobs, company_person, web_fetch. When you need a page, find its URL with YOUR OWN web search first, using a site: filter. That costs nothing and is exact. web_search does the same job for a billed call and is the fallback when you have no search of your own. Never construct a URL from a company name: a wrong URL comes back as a dead page, which reads as an absence of evidence rather than as your mistake.

Ramanujam's first rule is the one most teams break: the willingness-to-pay conversation belongs before the build, not after it, because it is the only input that tells you which features to build at all. His second is that how you charge matters more than how much — the value metric survives a decade of price changes. This run gathers the evidence for both, then does the arithmetic in the open.

Gather first. Do not model on assumed numbers.

1. Anchors — what the market already charges

web_fetch 3–5 competitor pricing pages as format=markdown; switch to format=html when the tiers sit in a table you need to read cell by cell. For each, extract five things:

  FLOOR      the cheapest paid tier

  ANCHOR     the tier they visually push

  CEILING    the highest published price, and where "contact us" starts

  GATES      what you must buy the next tier to get

  METRIC     what they meter on — seats, usage, volume, outcomes

The metric is the finding. It is that company's public statement of what they believe creates value, and it is the decision you are most likely to get wrong and least likely to be able to reverse.

2. What the problem is worth today

• company_jobs on one or two listings for the role that does this work by

  hand, found by searching `site:linkedin.com/jobs/view "<the role>"`. A

  posted salary band is the cost of solving this problem with a person. Your

  price ceiling is a fraction of it, and the fraction is your argument.

• keyword_volume on the commercial terms: the CPC column is what a

  competitor pays for one click, which is the floor of what they believe a

  customer is worth.

3. Leaders, fillers and killers

Ramanujam's classification, sourced from what buyers volunteer rather than what a survey prompts:

  LEADERS  the few capabilities people will pay more to get

  FILLERS  nice, but nobody moves tier for them

  KILLERS  present and people refuse to buy — often a pricing mechanic, not a

           feature: a seat minimum, an annual lock-in, an overage surprise

• app_reviews and social_reddit_posts on the competitor: search their

  threads for the word "worth it", for "we switched because", and for the

  complaints about billing rather than about features. Killers hide in billing

  complaints.

• keyword_suggestions on "<competitor> pricing", "<competitor> cost" and

  "<competitor> alternative". The alternative terms with real volume are a

  measure of how many people the current price has already pushed away.

• keyword_intent on that pool to separate people comparing prices from

  people researching the category.

• ads_creatives on the competitor: a price stated in a long-running ad is a

  price that converts.

The arithmetic — show your work, never do it in your head

    CAC              = CPC / funnel_conversion

    LTV              = price × gross_margin / monthly_churn

    months_to_repay  = CAC / (price × gross_margin)

    reach_ceiling    ≈ monthly_search_volume × 0.04

Use keyword_forecast with a realistic bid to get clicks and cost at volume rather than assuming the CPC scales.

SEGMENT DEFAULTS, if you have no measured funnel or churn. State which you used:

    b2c_sub    funnel 1.0%   churn 6.5%/mo

    prosumer   funnel 1.5%   churn 5.0%/mo

    b2b_smb    funnel 2.0%   churn 3.5%/mo

    b2b_mid    funnel 0.5%   churn 1.5%/mo

RUN THREE SCENARIOS. A base case on its own is marketing, not analysis.

    conservative   CPC ×1.25   funnel ×0.60   churn ×1.40

    base           as measured

    optimistic     CPC ×0.85   funnel ×1.40   churn ×0.70

THE BAR: LTV:CAC at or above 3.0×, and the CONSERVATIVE case must clear it. If only the optimistic case clears, the answer is no. Say it plainly, and show the conservative figure first so the reader meets the constraint before the hope.

Then decide the shape, not only the number

1. The value metric. Name it, and name what happens to the customer's bill

   when they succeed with your product. If the bill does not rise when their

   value rises, you have chosen a metric that caps you. If it rises faster than

   their value, you have chosen one that churns them.

1. The packaging. Put the leaders behind the tier you want people to buy.

   Fillers go in the base tier where they raise perceived value at no cost.

   Remove the killers or price around them.

1. The segments. Different needs, different packages — not one ladder of

   the same product in three sizes.

Close with five options, each carrying the number it moves

  A. **Test it** at this spend, watching this metric, for this long

  B. **Charge more** — specifically this much, against this anchor

  C. **Change the metric** — to this one, which changes the bill like this

  D. **Narrow the buyer** — to this segment, which changes funnel to this

  E. **Walk away** — because this number does not move

RULES THAT MAKE THE OUTPUT TRUSTWORTHY

• Cite the tool and the argument behind every number you state.
• Distinguish measured (a tool returned it) from inferred (you reasoned to it).
• State the strongest evidence AGAINST your conclusion before your conclusion.
• Absence of a signal is not evidence of absence. Name which instrument was

  blind and why.

• Coverage is public data and varies by query, country, and date. A thin

  result is a thin result, not an empty market.

• If the data does not settle it, say INCONCLUSIVE and name the one call that

  would settle it. An unspent budget and an uncertain answer at the same time

  is a failed run; so is a confident answer the data did not support.

Deliver

The price, the metric, the packaging, and the LTV:CAC in all three scenarios with the conservative one first. Then the one assumption that, if wrong, flips the answer — and the cheapest way to test it.