Pricing Strategy

A pricing plan built from what the market actually charges and how buyers actually search, not from a template. Every market finding comes from a page this skill reads or a call it runs, and every recommendation names the rule and the numbers behind it.

Pricing has three parts, and most teams only argue about the third:

1. Packaging: what each plan includes (features, limits, support, controls).
2. Pricing metric: the unit you charge for (seat, location, usage band, contacts, transactions, flat).
3. Price point: the amount, and how monthly and annual billing differ.

Before you start

You need the InsightfulPipe MCP connected. DataForSEO is the main data source (pricing pages and search demand). Search Console, GA4 and Stripe are optional and add your own signals. Without DataForSEO, see "Without a connection" at the end.

1. Call query_contexts with request="accounts" for platform="dataforseo", and for google-search-console, google-analytics and stripe if the user has them. Note workspace_id, brand_id, the Search Console site_url and the GA4 property_id.
2. Call query_contexts with request="actions_details" for onpage_content_parsing and kw_google_ads_search_volume (dataforseo), plus search_analytics, get_report, and get_products, get_prices, get_subscriptions, get_coupons (stripe) when those are connected. Follow the body shapes it returns.
3. Ask the user for these, and use the defaults if they don't know:
   • Product and buyer: what it does, who pays, self-serve or sales-led. Default: infer it from their site and say so.
   • Current pricing, or "new". Default: read their own pricing page in Step 1.
   • 3 to 5 rivals' pricing page URLs. Default: propose the category leaders and confirm them before reading anything (each page is a paid call).
   • Reference customer profiles: 2 or 3 sizes measured in the unit that drives cost in the category (staff, seats, contacts, locations, orders). Default: smallest buyer, typical buyer, large buyer; for example 1, 3 and 10 staff.
   • Goal: growth (more customers), revenue (more per customer) or margin. Default: growth for a new product, revenue for an existing one.
   • Value inputs (optional): what one customer gains per month (extra revenue, hours saved times hourly cost, loss avoided) and your monthly cost to serve one. Without them, the value check in D8 is not run.
   • Market (optional): country and language. Default: United States, English (location_code 2840, language_code "en").

State the date the pages were read, the market and the rivals at the top of the report.

How to run the queries

Every body goes through query_data with the platform it names. Rules:

• DataForSEO costs money. One call per pricing page plus one keyword call: about 5 to 7 paid calls for a run. Never loop or retry a paid call more than once.
• Pages localise. A page can come back in another language or currency depending on where it is fetched from. Always send "accept_language": "en-US" (or the user's market). Record the currency you got and keep other currencies out of the price math unless the user gives an exchange rate.
• If a call fails, keep going. Mark what it would have shown unknown, say which call failed and why, and never fill the gap with a guess.

Step 1. Market price map

Read each rival's pricing page, and the user's own if they have one:

{"platform": "dataforseo", "action": "onpage_content_parsing", "workspace_id": "<WORKSPACE_ID>", "brand_id": "<BRAND_ID>", "url": "<PRICING_URL>", "accept_language": "en-US"}

The text is in data[0].page_content.main_topic (and secondary_topic); structured offers, when the page has them, are in page_content.offers.

When prices are missing. Many pricing pages draw their prices with scripts, and the parsed text then has plan names but no amounts. If a plan has no price in the parsed text:

1. Read the page with your own web fetch tool, if you have one, and take the amounts from the visible text next to each plan name and its billing label.
2. Otherwise ask the user for the amounts.
3. Otherwise mark the price unknown. Never estimate a price.

Note the source of every price (parsed page, web fetch or user) in the map. "enable_javascript": true is accepted but did not recover script-drawn prices in testing, so don't pay for a second call to try it.

Record, per rival and plan, only what the page says:

Feature tables built from check-mark images parse as empty cells. Don't infer which plan has a feature from an empty cell; leave it out or ask.

Step 2. Price every rival at your reference profiles

For each profile, find each rival's cheapest plan whose stated limits fit it, and compute the monthly price on annual billing (seat price × seats, band price, or price per location). If a rival only shows monthly billing, use that and say so.

• A plan with no stated limit for the profile's unit fits.
• A free plan counts at $0. Say what it costs instead, if the page says so (card fees, usage fees).
• A rival in another currency, or one whose metric can't be mapped to the profile (for example included bookings when the profile is staff), stays in the map but out of this table. Say why.

This table shows how each metric scales: seat pricing climbs with team size, band pricing climbs in steps, location pricing and fee-funded models stay flat.

Step 3. Search demand and price sensitivity

One call with 20 to 30 keywords built from the category, the rivals and the segments:

{"platform": "dataforseo", "action": "kw_google_ads_search_volume", "workspace_id": "<WORKSPACE_ID>", "brand_id": "<BRAND_ID>", "keywords": ["<KEYWORD_1>", "<KEYWORD_2>"], "location_code": 2840, "language_code": "en"}

Each row gives search_volume (monthly average), cpc and competition. Build the list from these groups (C = category phrase, R = a rival, S = a segment):

Send both segment patterns for every segment the user names, and the whole list in one call. A keyword with no volume returns null: list it as "no data" and count it as 0 in the sums. Google Ads can merge close variants: two phrasings ("salon booking software", "booking software for salons") may come back with the same volume, CPC and monthly trend. List both, but count the pair once in a group's sum. Then compute:

• Free-seeking share = free-seeking volume ÷ (free-seeking + price-research volume). How strongly the market expects a no-cost way in.
• Leader free check = the leader's free-seeking volume ÷ "<leader> pricing" volume. How often buyers ask whether the leader is free before (or instead of) asking what it costs.
• Segment CPC index = a segment keyword's CPC ÷ the CPC of the highest-volume head keyword. What advertisers pay for that buyer relative to the average buyer, a proxy for how much that segment is worth.
• Switching volume per rival, and enterprise volume.

Step 4. Your own signals (optional, when connected)

4a. How searchers find your pricing (Search Console). Last 90 complete days:

{"platform": "google-search-console", "action": "search_analytics", "workspace_id": "<WORKSPACE_ID>", "brand_id": "<BRAND_ID>", "site_url": "<SITE_URL>", "dimensions": ["query"], "start_date": "<START>", "end_date": "<END>", "row_limit": 1000}

Keep queries with a price word: price, pricing, cost, free, cheap, discount, coupon, plan, subscription, trial, vs, alternative. Group them like Step 3 and report impressions and clicks per group. This pull stops at 1,000 rows; when it returns exactly 1,000, say the shares are of the visible rows.

4b. Does pricing traffic convert (GA4). Last 30 complete days:

{"platform": "google-analytics", "action": "get_report", "workspace_id": "<WORKSPACE_ID>", "brand_id": "<BRAND_ID>", "property_id": "properties/<PROPERTY_ID>", "dimensions": ["pagePath"], "metrics": ["screenPageViews", "activeUsers", "sessions", "keyEvents"], "start_date": "<START>", "end_date": "<END>", "dimensionFilter": {"filter": {"fieldName": "pagePath", "stringFilter": {"matchType": "CONTAINS", "value": "pric"}}}, "limit": 20}

{"platform": "google-analytics", "action": "get_report", "workspace_id": "<WORKSPACE_ID>", "brand_id": "<BRAND_ID>", "property_id": "properties/<PROPERTY_ID>", "dimensions": ["eventName"], "metrics": ["eventCount", "totalUsers", "keyEvents"], "start_date": "<START>", "end_date": "<END>"}

Report sign-up or purchase key events per 100 pricing page viewers. If no key event fired in the window, the rate is unknown: a defined event that never fires measures nothing. The page itself (layout, anchors, objections) belongs to the Pricing Page Auditor skill.

4c. What customers actually pay (Stripe). Untested until a Stripe account is connected; the bodies follow the helper schema:

{"platform": "stripe", "action": "get_products", "workspace_id": "<WORKSPACE_ID>", "brand_id": "<BRAND_ID>", "active": true, "auto_paginate": true}

{"platform": "stripe", "action": "get_prices", "workspace_id": "<WORKSPACE_ID>", "brand_id": "<BRAND_ID>", "active": true, "auto_paginate": true}

{"platform": "stripe", "action": "get_subscriptions", "workspace_id": "<WORKSPACE_ID>", "brand_id": "<BRAND_ID>", "status": "all", "auto_paginate": true}

{"platform": "stripe", "action": "get_coupons", "workspace_id": "<WORKSPACE_ID>", "brand_id": "<BRAND_ID>", "auto_paginate": true}

From these: plan mix (active subscriptions per price), average revenue per customer, share on annual billing, share with a discount, trialing count, and cancellations in the last 90 days per plan. Amounts are in the smallest currency unit (cents): divide by 100.

Signs you are underpriced (any two): most customers sit on the cheapest paid plan while using it heavily; prospects rarely push back on price; you are well under the paid median at the typical profile (Step 2); cancellations rarely cite price; rivals raised prices recently. Signs you are overpriced: cancellations cluster on one plan, heavy discount use (over a third of active subscriptions), and pricing traffic that doesn't convert while other pages do.

Step 5. Decisions

Apply each rule, show the numbers that triggered it, and say when the user's business makes a rule wrong. The thresholds are starting points.

Step 6. Value ladder and offer stack

Lay out the rungs a customer climbs, from the first free or cheap step to the most expensive plan, and the add-ons around them:

• Entry: removes the reason not to start (D1).
• Core: where most customers should land; the plan you badge.
• Growth: for the large profile; adds capacity and the controls bigger teams need.
• Band edges (when the metric is a band): each paid rung covers up to twice the profile it is priced for, so a growing customer has headroom before the next step. With profiles of 1, 3 and 10 staff: entry 1, core 2 to 6, growth 7 to 20.
• Segment package or custom rung: D7 and D5.
• Add-ons: only ones the market already sells (Step 1): usage credits, setup or migration services, extra capacity, assistants. Each add-on should answer "what does a customer who outgrows this plan buy next?"

Separate rungs with the levers buyers understand: usage limits (staff, bookings, contacts), features, support level, and access and control (API, single sign-on, audit logs, admin roles). Limits that track the value metric (D2) make the best upgrade triggers.

Flag gaps: adjacent rungs more than 3× apart, a rung with no reason to upgrade into it, or an add-on that costs more than the next plan.

Step 7. Perception and packaging review

From the pages (Step 1) and search (Steps 3 and 4a):

• Price endings: prices ending in 9 read as value; round prices read as premium and simple. Note what the category does.
• Badges and anchors: which plan rivals badge, and any "starts at" or high plan that makes the middle look reasonable (an anchor), or a weaker plan placed so the badged one looks like the obvious pick (a decoy).
• Billing framing: annual by default or monthly, and how the saving is shown (percent, months free, or per-month price). Small daily framing ("under $1 a day") makes a low price feel smaller; it doesn't help a premium position.
• Entry wording: how plainly rivals say "free" or "no card needed", and whether searchers are asking (leader free check).
• Plan names: names that say who the plan is for beat names that only say size.
• Readable prices: when a page draws its prices with scripts (Step 1 needed the fallback), search engines and AI assistants may not see them either. If the user's own page does this, recommend putting the prices in the page text.
• Your page (if you have one): the same checks on your plans, and the 4a query groups: many "free" or "cheap" queries landing on your pricing means the entry offer needs to be clearer, not cheaper.

Step 8. Validate before you ship

The market map tells you where you can sit; buyers tell you what they will pay. Recommend at least one of these:

• Van Westendorp price sensitivity survey: ask each respondent four prices (too cheap to trust, a bargain, getting expensive, too expensive to consider). Plot the cumulative answers; the crossings give an acceptable range and a point of least resistance. Read at least 100 answers per segment you want to price separately, and show the product description, not just the name.
• Gabor-Granger ladder: ask "would you buy at $X?" and move up after a yes, down after a no. Ladder: the lowest, median and highest paid rival price at the typical profile, plus one rung 25% above the highest.
• Conjoint: for a big repackaging; buyers pick between plan bundles, which prices each feature and limit. Use it when plans differ on many features.

Pricing experiments:

Test on new customers only, one change at a time, for at least one full billing cycle. If traffic is too thin for a split test, run the prices in sequence and compare like-for-like periods, or vary quotes in sales calls and track close rates.

Before launch: the pricing page and plan limits match, billing is set up for every plan and the annual option, sales and support know the new prices and the upgrade triggers, and the FAQ answers "what happens to my current plan?"

Changing prices you already charge

Use this when Step 4 shows you are underpriced, or the user asks to raise prices:

1. Pick the approach: new customers only (existing ones keep their price, simplest, leaves money behind); existing customers at renewal with 60 to 90 days' notice and an option to lock the old price by switching to annual; a new plan with new value at the new price, which existing customers may choose; or a full restructure that maps each customer to the nearest new plan.
2. Write the notice with: what is changing and from when, what was added since they joined, exactly what it means for this customer, what they can do before the date, and who to contact. No surprises in the invoice.
3. Watch paid conversion on the pricing page (4b) and cancellations per plan (4c) for two billing cycles.

Pricing and acquisition economics

Higher prices only help if retention holds. Lifetime value = monthly revenue per customer × gross margin ÷ monthly churn. Keep lifetime value at about 3× the cost to acquire a customer or better; a higher price gives more room to pay for ads, a lower price needs cheaper acquisition. For the full calculation on live ad spend, use the LTV:CAC Ratio Optimizer skill.

Advertise each rung to the buyer it fits: the entry offer to broad, problem-aware audiences; the core plan to high-intent searches ("<category> pricing", "<rival> alternative"); a segment package to the segment terms D7 found; the contact-us rung to named accounts.

Report format

1. Headline: the recommended metric, entry offer, plans and prices in three lines, with the market, the rivals and the date the pages were read.
2. Market price map: one row per rival and plan, with each price's source.
3. Price at your profiles: the Step 2 table, plus who was left out and why.
4. Demand signals: free-seeking share, leader free check, segment CPC indexes, switching and enterprise volume, with the keyword table.
5. Your own signals: 4a to 4c, or "not connected" / "not run" with the reason.
6. Decisions D1 to D8: each with the rule, the numbers and the verdict.
7. Recommended plans: rung, metric, price ceiling, who it's for, the limit that moves a buyer up.
8. Value ladder and gaps.
9. Perception review.
10. Validation plan and experiments.
11. Unknowns: every price, check or signal that couldn't be read, and why.

What this skill never changes

It changes nothing. It reads pages and data and returns a plan. It never edits prices, products or coupons in Stripe, and never edits your pricing page.

Without a connection

If DataForSEO isn't connected, ask the user to paste each rival's plan names, prices, limits and entry offer (or read the pages with your web fetch tool), and to share any keyword volumes they have. Run Steps 2 and 5 to 8 on that, label the demand checks unknown, and say the market map came from the user.

Go deeper

• Pricing Page Auditor: how the prices are presented and whether visitors act on them.
• Marketing Psychology: anchoring, decoys and price framing in more depth.
• LTV:CAC Ratio Optimizer: whether the new prices pay for acquisition.
• Signup Flow CRO: the free plan or trial start you chose in D1.

Rules

• Never invent a price, limit or volume. Unknown is an answer.
• Quote rivals' facts in your own words, with the source per price.
• Keep currencies apart; never mix amounts in different currencies in a median (percentages such as annual savings carry no currency).
• Every recommendation names the decision rule and numbers behind it.
• A recommendation to go above the market median needs a named reason and a test.
• No more than about 7 paid DataForSEO calls per run without asking.