Intro

This reports goal is to analyze which AI platforms are being used the most over the last five years. Google Trends data are reported on a relative scale from 0 to 100, where 100 represents the highest level of search interest during the selected time period.

Hypothesis for Peak Interest: ChatGPT’s massive spikes in search volume are largely driven by its significant first-mover advantage in the generative AI space. Following its launch in November 2022, it became one of the fastest-growing consumer applications in history, reaching an estimated 100 million monthly active users by January 2023. The highest peaks on the trend line typically correlate with major, highly publicized version releases (such as the GPT-4 updates) and cyclical academic usage, where search interest naturally surges during university semesters.

While competitors have made recent gains—for example, Gemini experienced substantial traffic growth in late 2025 following the rollout of the Gemini 3 models—ChatGPT’s deeply entrenched brand recognition continues to generate the highest volume of sustained web search interest.

Required Packages

# Run once to install
install.packages(c("gtrendsR", "tidyverse", "ggplot2", "scales",
                   "lubridate", "knitr", "kableExtra"))

library(gtrendsR)     # Pull data from Google Trends
library(tidyverse)    # Data manipulation and plotting
library(ggplot2)      # Visualization
library(scales)       # Axis formatting
library(lubridate)    # Date handling
library(knitr)        # Tables
library(kableExtra)   # Enhanced table formatting

Understanding the gtrends() Function

"now 1-H"        → Past hour
"now 4-H"        → Past 4 hours
"today 1-m"      → Past 30 days
"today 3-m"      → Past 90 days
"today 12-m"     → Past 12 months
"today+5-y"      → Past 5 years
"all"            → Since 2004

Tutorial 1: Single Keyword — Brand Search Interest

Query Google Trends

ai_trends <- gtrends(
  keyword = c("Chat GPT", "Gemini", "Claude"),
  geo = "US",
  time = "today+5-y"
)

# Extract interest over time
iot_raw <- ai_trends$interest_over_time

# Clean the data to convert any "<1" strings to numeric 0 values
iot <- iot_raw %>%
  mutate(hits = as.numeric(ifelse(hits == "<1", "0", hits)))

ggplot(iot, aes(x = date, y = hits)) +
  geom_line(color = "#00704A", linewidth = 1) +
  geom_smooth(method = "loess", se = TRUE, color = "#1E3932",
              fill = "#D4E9E2", alpha = 0.3) +
  scale_x_datetime(date_labels = "%b %Y", date_breaks = "2 months") +
  labs(
    title    = "Google Search Interest: Starbucks",
    subtitle = "United States | Past 5 years | Web Search",
    x        = NULL,
    y        = "Relative Interest (0–100)",
    caption  = "Source: Google Trends via gtrendsR | github.com/utjimmyx"
  ) +
  theme_minimal(base_size = 12) +
  theme(
    plot.title    = element_text(face = "bold", size = 14),
    plot.subtitle = element_text(color = "gray50"),
    axis.text.x   = element_text(angle = 30, hjust = 1)
  )

Summary of Findings

Analysis of the 5-year U.S. web search data pulled via the gtrendsR package reveals that Chat GPT clearly dominates the comparative trend lines, reaching the absolute peak of 100 on the relative search interest scale.

References

Google Trends. (2024). Google Trends Tutorials [YouTube series]. Google Search Central. https://developers.google.com/search/blog/2024/09/google-trends-tutorials Massicotte, P., & Eddelbuettel, D. (2023). gtrendsR: Perform and Display Google Trends Queries [R package]. https://cran.r-project.org/package=gtrendsR Chan, M. (2019). Vignette: Google Trends with the gtrendsR package. https://martinctc.github.io/blog/vignette-google-trends-with-gtrendsr/

Google Trends for Marketing Analytics

Jimmy

2026-06-17