Introduction

This report uses Google Trends data to study seasonal search interest for “tax software” in the United States. The purpose is to identify the annual seasonal pattern, compute average interest by month, and recommend a campaign launch window that begins before the expected peak in demand.

Google Trends reports normalized search interest rather than raw search volume. Values range from 0 to 100, where 100 represents the highest relative search interest during the selected time period and geography. The data are collected in R using the gtrendsR package, which provides an interface for retrieving Google Trends results over time and by geography.

Packages

packages <- c(
  "gtrendsR", "tidyverse", "lubridate", "openxlsx", "scales", "readxl"
)

installed <- rownames(installed.packages())
for (p in packages) {
  if (!(p %in% installed)) install.packages(p)
}

library(gtrendsR)
library(tidyverse)
library(lubridate)
library(openxlsx)
library(scales)

Export Scraped Data to Excel

The scraped Google Trends data are exported as an Excel spreadsheet for submission with this report.

write.xlsx(
  interest,
  file = "tax_software_google_trends_data.xlsx",
  overwrite = TRUE
)

Seasonal Pattern

ggplot(interest, aes(x = date, y = hits)) +
  geom_line(linewidth = 0.8) +
  geom_point(size = 1.6) +
  scale_x_date(date_breaks = "3 months", date_labels = "%b %Y") +
  labs(
    title = "Google Trends Interest for Tax Software in the U.S.",
    subtitle = "Three-year web search trend",
    x = "Date",
    y = "Search interest, normalized 0–100"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Average Interest by Month

monthly_avg <- interest %>%
  group_by(month_num, month) %>%
  summarize(avg_interest = mean(hits, na.rm = TRUE), .groups = "drop") %>%
  arrange(month_num)

monthly_avg
## # A tibble: 12 × 3
##    month_num month     avg_interest
##        <dbl> <chr>            <dbl>
##  1         1 January           26.3
##  2         2 February          32.6
##  3         3 March             33.0
##  4         4 April             29.2
##  5         5 May               16.8
##  6         6 June              11.3
##  7         7 July              11.0
##  8         8 August            10.7
##  9         9 September         12.5
## 10        10 October           11.3
## 11        11 November          13.5
## 12        12 December          13.4
ggplot(monthly_avg, aes(x = reorder(month, month_num), y = avg_interest)) +
  geom_col() +
  labs(
    title = "Average Google Trends Interest by Month",
    subtitle = "Keyword: tax software",
    x = "Month",
    y = "Average search interest"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Peak Month and Campaign Timing

peak_month <- monthly_avg %>%
  filter(avg_interest == max(avg_interest, na.rm = TRUE))

peak_month
## # A tibble: 1 × 3
##   month_num month avg_interest
##       <dbl> <chr>        <dbl>
## 1         3 March         33.0

Preliminary Findings

The Google Trends analysis shows a strong seasonal pattern for the keyword “tax software” in the United States. Average monthly interest peaks in March (33.04) and remains high in February (32.65) and April (29.19). Search interest drops substantially after the tax filing season, reaching its lowest levels during the summer months.

The time-series chart also shows a dramatic spike during the 2026 tax season, where search interest reached the normalized value of 100. This indicates that consumers actively seek tax preparation software immediately before filing deadlines.

Based on these findings, marketers should begin advertising campaigns in late January or early February so that promotional efforts are visible before peak search activity occurs in March.

Campaign Recommendation

The highest average monthly search interest occurred in March. Therefore, the optimal campaign launch window is between late January and early February (approximately 2–4 weeks before the peak search period). Launching campaigns during this period allows marketers to reach consumers while search activity is rising but before competition becomes most intense.

Limitations

Google Trends data are normalized, not raw search counts. A value of 100 represents the highest relative interest within the selected query, region, and time period. Therefore, the results are best interpreted as relative demand patterns rather than exact market size. In addition, search interest may be affected by tax deadlines, policy changes, advertising, news coverage, and broader economic conditions.

References