Before deciding whether to find you, the buyer will ask AI, "How much is this family for a month?" If ChatGPT or Prescription repeats a price that is out of date, vague, or even competitive, you don't even have a chance to get into the evaluation list -- and the problem is that your pricing page is written for people, not for machines.
Why would AI be wrong about your price?
The large language model reads the price pages in a very straightforward way: captures the plain text on the page, finds the "Programname-Money-calculation unit" and answers the user. As long as this set of information is not presented in clean language, the model returns to other sources on its hands, possibly the cache six months ago, the old data from third-party price stations, or the direct probability of a reasonable number. All three things make your price wrong.
The three most common holes in Taiwan's SaaS price page will be destroyed directly. First, the price is hidden behind the monthly/annual cut button, the default status is not rendering a number, and the reptile sees nothing. Second, the price is made into a picture or presented in a background image, and there is no amount at the text level. Thirdly, the station will only write "Personal offers, please contact us" without a single reference number, and AI will guess or skip.
A price-fixing page that can be drawn, which is the key to the "close group"
Let AI rework the entire page without rewriting the price, just to make sure that the core columns of each program are in plain text and are adjacent to each other. The name of the scheme, the amount, the amount, the amount, the period, the unit of measure, what it contains, who it suits — these six or seven columns are scattered in different corners of the page, and it is difficult for the model to match them correctly; they are squeezed into the same card block, and the exact rate of extraction increases markedly.
The first thing we do when we change the price pages for our clients is to write each level into a machine-readable sentence: "Professional edition, $4,900 per month, at account number, with five seats and Brand Radar weekly, for 10 to 50 people. "It's a good word to say, and it's a good idea for AI to pull out the formula, the price, the unit and the suitable target at once.
- Program name: Use a fixed, identifiable string, all-stop, not a different page.
- Amounts and currencies: Written in plain text `NT$4,900', not only in images or in switches that require clicks.
- Periods and units of prices: to eliminate discrimination by making it clear that "each account/seat/thousand/thousand" is "a month/year" and "a month/year" and "a month/seat/thousands of calls".
- Contains content: The rule is what the distance actually gets, so that AI can answer "Is this price X included?"
- Applied against: One sentence to say to whom the distance is given, AI takes the seat in the recommended scenario.
- Last update date: on the date the page comment is effective, reduce the probability of the model citing old caches.
Do not just say "Call us."
The corporate program offers a reasonable price, but a full blank will make it impossible for AI to describe your location. The compromise is to give a anchor: "The Enterprise Edition has been in NT$50,000 since the beginning of the month, in order and in order of use." This allows the model to answer "the size of their business program" and to put you in the right range, instead of being classified as "unpublicized, possibly expensive" because there are no numbers.
If business considerations really do not reveal any numbers, at least text the logic of the price: by seat, by usage, by direction. The model can be quoted more easily than the phrase "Call us." This post is part of our special coverage Syria Protests 2011.
Use structural data to replace AI
When the text is bottomed, the price structure is structured using schema.org 's Product and Offer labels, which is equivalent to another machine format. Price, PriceCurrency, BillingDuration, unitText, these columns are filled, and search engines and partial AI extractors will have access to price-free data. In the meantime, the formula "Does this contain X" "Support a few seats" is used as a FAQPage label to allow models to quote the answers you wrote, rather than to deduce themselves.
Humans will fix their brains, ignore their layout defects; AI will not. Any place on the fixed page that needs to be guessed by context is an entry point to the wrong price.
On the line, test AI was right.
Change is not finished. The real test is to open ChatGPT, Perplexity, Google AI Overviews, asking, "How much is X?", "How much is X?", "What's Y?", "X and what's cheap for the competition?" Compare the answer to AI with the real price on your page, write down the wrong field -- the price number, the price unit, or the error spacing of a function. These differences are the list of the next round to be repaired.
This thing needs to run again at a fixed frequency because the models are updated, the caches are refreshed, and you're making price adjustments. It takes time for manual random checks, and that's what our Brand Radar designed to do: keep monitoring the mainstream, how the AI engine reproduces your brand and your brand. Prices, if they're misspoken or quoted to old data, you'll first know, not wait for a potential client to leave because of a wrong offer.
Starting with your price gap.
The price page is the one closest to the funnel and the GEO field most often ignored. Do one little thing: ask AI your own price, see if it's right. If the answer makes you frown, you want to know how big the gap is and from where it should be filled, you can expect a 30-minute GEO diagnosis, and we'll run it straight through your fixed-price page and tell you how AI describes you now.
