Google Shopping trend data for AI assistants

Google Shopping trend data on Trends MCP is a weekly 0-100 index of product search volume with about 5 years of history. As of August 15, 2026, running shoes had an index value of 6, down 94.0% over 3M. The free plan includes 100 requests per month. Starter is $19 per month.

What the data showed in August 2026

Running shoes had a Google Shopping index value of 6 on August 15, 2026, down 94.0% from a peak of 100 on May 16, 2026 and down 70.0% from 20 on August 16, 2025. Bluetooth headphones had an index value of 9 on August 15, 2026, down 91.0% from 100 on May 16, 2026 and down 77.5% from 40 on August 16, 2025.

Air fryer had an index value of 5 on August 8, 2026, down 94.32% from 88 on May 9, 2026 and down 64.29% from 14 on August 9, 2025. All 3 product phrases show the same seasonal shape: a sharp spring peak in shopping search interest, then a steep summer decline. The 1Y window separates seasonality from trend. Running shoes at -70.0% and bluetooth headphones at -77.5% are weaker than a year ago, not just weaker than May.

The exact calls

get_trends returns the history. get_growth returns period percentages. get_top_trends with type Google Trends returns the live breakout list with no keyword.

get_trends(keyword='running shoes', source='google shopping', data_mode='weekly')

get_growth(keyword='running shoes', source='google shopping', percent_growth=['3M', '1Y'])

How Google Shopping differs from Google Search and Amazon

Google Shopping measures product search interest. Google Search measures general web search interest. A phrase can trend on Google Search while the Shopping series stays flat, if the intent is informational rather than transactional. The Google Search data page covers web search interest in more depth.

Amazon search is a buying-adjacent action. A person who types air fryer into Amazon is closer to a cart than a person who types the same words into Google Shopping. The air fryer contrast shows the gap: on Amazon the phrase had an index value of 51.5 on July 31, 2026, up 5.75% over 3M with 5,577,980 estimated searches, while the Google Shopping series fell 94.32% over the same window. The Amazon search trends page covers that series in more depth.

Who uses Google Shopping data

Ecommerce teams watch Shopping curves to decide whether a product category is still rising or has peaked, and to time inventory and ad spend against demand. Investors watch Shopping volume as a demand signal that sits earlier in the funnel than Amazon search. Content teams watch a product's Shopping series to decide whether a review or comparison article is still timely.

A practical workflow: call get_growth with 3M and 1Y on a list of product phrases, keep the ones with positive Shopping growth, then compare with Amazon on the same phrase to separate research from shopping. A desk tracking 30 phrases can run that loop inside 100 requests per month on the free plan.

Seasonal categories need the 1Y window, not just 3M. A phrase down 90% from a May peak can still be up against the prior August. The 2-window read is what separates a dying category from a normal summer trough.

Limits and caveats

Low-volume phrases return weak quality scores or misses. The 0-100 index is relative to that keyword's own peak in the window, so two phrases at 50 are not equal in absolute searches. Shopping volume does not measure units sold, revenue, or ad impressions, and seasonal categories will swing hard between peak and trough.

Quota is 100 requests per month on the free plan, Starter $19 per month. A crawl of 10,000 keywords overnight is a different product. Deduplicating the list and caching weekly snapshots is how research teams stay inside the month.

Getting started

Sign up on the free tier, copy the API key, and make the first call with any HTTP client. The setup guide covers Claude, Cursor, and plain REST in 4 copy-paste blocks. The Google Trends page covers web search interest in more depth.