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Google Trends: Understanding the data.

How to interpret Trends results.

Sourcing Google Trends data.

Google Trends analyses a sample of Google web searches to determine how many searches were done over a certain period of time.

For example, if you’re doing a story about the Zika virus and you want to see if there was a recent uptick in searches on the topic, select Past 90 days. Trends analyses a sample of all searches for Zika virus within those parameters.

Reading the Interest Over Time graph.

When you search for a term on Trends, you’ll see a graph showing the term’s popularity over time in (nearly) real time. Hovering your mouse over the graph reveals a number, which reflects how many searches have been done for the particular term relative to the total number of searches done on Google.

Numbers on the graph don’t represent absolute search volume numbers, because the data is normalised and presented on a scale from 0-100, where each point on the graph is divided by the highest point, or 100. The numbers next to the search terms at the top of the graph are sums, or totals.

A line trending downward means that a search term’s relative popularity is decreasing—not necessarily that the total number of searches for that term is decreasing, but that its popularity compared to other searches is shrinking.

Finding the most searched topic in every region or country.

When you search for multiple terms on Trends, you’ll see a comparative map showing which term or topic is most searched in each region. 

Step 1
Interest over time comparison. Let’s compare the search terms Zika virus and malaria. You’ll find that over time, malaria experiences a steady query rate  while Zika was barely searched for until a huge spike in January 2016.

Step 2
Compared breakdown by subregion: The colour intensity of each region represents the percentage of searches of the leading search term in that region. This example shows that Zika virus was a more popular search term in the Americas while malaria was relatively more popular in Asia. 

Rising data.

At the bottom of your results page, the Related queries chart can show you the Top and Rising terms associated with any topic or trending story. 

The Rising tab represents terms that were searched for with the term you entered and had the most significant growth in volume over the selected time period. You’ll see a percentage of the Rising term’s growth compared to the previous time period. If you see “Breakout” instead of a percentage, it means that the search term grew by more than 5000%.

The percentages are based on the percent increase in search interest for the selected time frame. If you’re looking at the last 7 days, the benchmark for the rise in searches  would be 7 days prior; if it was the last 30 days, the benchmark would be for the 30 days prior. The only exception is when viewing the full history (2004-Present), when the percentages are benchmarked at 2004.

Reading the Related searches chart.

Step 1
Click the dropdown to see Top terms. 

Step 2
This table shows terms that are most frequently searched with the term you entered, in the same search session, with the same chosen category, country or region. If you didn’t choose a search term (and just chose a category or region), overall searches are displayed.

Data that is excluded.

 Trends excludes certain data from your searches:

  • Searches made by very few people: Trends only analyses data for popular terms, so search terms with low volume appear as 0 for a given time period.
  • Duplicate searches: Trends eliminates repeated searches from the same user over a short period of time for better overall accuracy.
  • Special characters: Trends filters out queries with apostrophes and other special characters.

Utilizing Big Data From Google Trends to Map Population Depression in the United States: Exploratory Infodemiology Study


Background: The epidemiology of mental health disorders has important theoretical and practical implications for health care service and planning. The recent increase in big data storage and subsequent development of analytical tools suggest that mining search databases may yield important trends on mental health, which can be used to support existing population health studies.

Objective: This study aimed to map depression search intent in the United States based on internet-based mental health queries.

Methods: Weekly data on mental health searches were extracted from Google Trends for an 11-year period (2010-2021) and separated by US state for the following terms: “feeling sad,” “depressed,” “depression,” “empty,” “insomnia,” “fatigue,” “guilty,” “feeling guilty,” and “suicide.” Multivariable regression models were created based on geographic and environmental factors and normalized to the following control terms: “sports,” “news,” “google,” “youtube,” “facebook,” and “netflix.” Heat maps of population depression were generated based on search intent.

Results: Depression search intent grew 67% from January 2010 to March 2021. Depression search intent showed significant seasonal patterns with peak intensity during winter (adjusted P<.001) and early spring months (adjusted P<.001), relative to summer months. Geographic location correlated with depression search intent with states in the Northeast (adjusted P=.01) having higher search intent than states in the South.

Conclusions: The trends extrapolated from Google Trends successfully correlate with known risk factors for depression, such as seasonality and increasing latitude. These findings suggest that Google Trends may be a valid novel epidemiological tool to map depression prevalence in the United States.


Over the past few decades, the amount of data stored, transferred, and analyzed has grown extensively, with the big data market reaching a value of US $139 billion in 2020 . The term “big data” was coined in 2005 in reference to a large set of data that was essentially impossible to manage and process using traditional methods and tools . As industries and companies have developed analytic tools targeted toward big data, information that was once inaccessible is now obtainable. One of the most important applications of big data in medicine is extrapolating trends and using them to support health care groups and organizations seeking to understand population health changes and predict the future.

Google Trends is a free online tool developed by Google LLC in 2008 that allows users from anywhere in the world to analyze big data . It tracks search content across various countries and languages and compares relative search intent between 2 or more terms. The usefulness of Google Trends was demonstrated in 2009: Ginsberg et al published a groundbreaking study predicting the spread of influenza earlier than the Centers for Disease Control and Prevention (CDC). Google Trends was subsequently utilized to predict the outbreaks of many viruses, including the West Nile virus, norovirus, varicella, influenza, and HIV . More recently, Google Trends has been frequently used to study a variety of health care domains, including the COVID-19 pandemic .

Depression is the most common psychiatric disorder in the United States, with 18.5% of adults experiencing symptoms of depression in 2019 . Since the start of the COVID-19 pandemic, the prevalence of depression symptoms has increased to 27.8%, affecting an estimated 91.2 million Americans. Epidemiological data for depression have traditionally been collected through surveys. Major organizations such as the National Institute of Mental Health (NIMH), Anxiety & Depression Association of America (ADAA), and CDC provide only limited data specific to the time and population being studied from their surveys . In response to the COVID-19 pandemic, the CDC and US Census Bureau collaborated to track mental health in the United States .

In this study, we provide estimates of depression search intent across the United States using big data from Google Trends. Our analysis fills the gap in current depression epidemiology, which is mainly derived from voluntary surveys, by extrapolating trends from big data across time and space. We provide an analysis of how internet search intent can be used to map population depression and how this can be compared in relation to depression risk factors. This model serves as a proof of concept that analyzing big data in association with environmental and geographic factors can be used as an epidemiological tool for psychiatric disease surveillance models. In terms of population health, analysis of Google Trends depression search intent represents a digital epidemiological tool that may one day be used for real-time surveillance of high-risk and underserved populations. The trends accessed through internet data may one day guide public policies, workforce supply decisions, and allocation of resources.


Google Trends

The following methodologies were designed based on published methods . All search queries entered into Google’s search engine become anonymized and grouped based on both the general query topic and the specific keywords entered. Google Trends interprets the information and normalizes the data into an index between 0 and 100. The numbers represent the search interest relative to the highest point based on the given location and time frame within the query. A value of 100 represents highest search popularity for a term, and a value of 50 represents half the search popularity for a term .

To examine the US population’s interest in depression, we completed a series of search queries in Google Trends between January 1, 2010 and March 1, 2021. Data sets were downloaded for symptoms and terms listed by the American Psychiatric Association for major depressive disorder: “feeling sad,” “depressed,” “depression,” “empty,” “insomnia,” “fatigue,” “guilty,” “feeling guilty,” and “suicide” . To account for random variance and overall increases in search queries, data sets were also downloaded across similar time periods for control terms based on previously published studies and popular internet search terms: “sports,” “news,” “google,” “youtube,” “facebook,” and “netflix” . The values of depression search intent were summed and normalized relative to the control terms for the given region and time and are represented on a scale of 0 to 100 arbitrary units (AU).

Two separate data sets were extracted from Google Trends. The first data set represents the entire US public interest in depression over time with a data frequency of monthly averages from January 1, 2010 to March 1, 2021. The second data set represents public interest in depression on a statewide level collected as a single value per state averaged from January 1, 2010 to March 1, 2021.

Environmental and Geographic Risk Factors

Given the known phenomenon of seasonal affective disorder, we obtained the annual temperature, humidity, and sunshine percentage from 1971 to 2000 from the National Climatic Center to assess for environmental and geographic risk factors of depression . The sunshine percentage represents the percentage of time between sunrise and sunset that the sun reaches the earth’s surface. For the Air Quality Index (AQI), we obtained data from the 2010 to 2014 American Community Survey . Values from the AQI were calculated for 4 major air pollutants regulated by the Clean Air Act . Lastly, data for urban percentage were obtained from the 2010 US Census .

Statistical Analysis

Multiple linear regression models were conducted to analyze the relationship between depression search queries and environmental factors and geographic factors. Confounding variables were identified using a correlation matrix and appropriately removed. The P values for each variable were adjusted according to the Bonferroni correction for multiple comparisons, with statistical significance determined at an adjusted P<.05. For predictive analysis, the multivariable regression models were constructed to generate quadratic forecasts to predict depression search intent and control search intent. The multiple regression models allowed us to account for confounding variables and prevent ecological fallacies according to previously published methods . The values for normalized depression search intent were categorized into 4 regions according to the US Census Bureau: Northeast, Midwest, South, and West . Geographic heat maps were generated in Microsoft Excel 2018 (Microsoft Corporation, Redmond, WA) to visualize the relationship between state temperature and state depression search intent.


Multivariable Regression Model and Predictive Analysis in Relation to Time and Seasonality

The Google Trends data from January 2010 to March 2021 demonstrated an upward trend such that depression search intent grew 67% from 58.7 AU to 92.9 AU (n=135), while control search intent grew 24% to 67.1 AU (n=135). Based on the quadratic forecasts, depression search intent is predicted to increase an additional 7.4% to 99.8 AU in 2025 (95% CI 96.6 to 102.9 AU; n=135), while control search intent is predicted to increase 3.5% to 64.7 AU (95% CI 63.7 to 65.7 AU; n=135). A significant pattern of seasonality can be observed in Figure 1 with a peak in depression searches in the spring (March, April, May) and a trough in depression searches during the summer (June, July, August).

Figure 1. Time series plot of search intent for depression and control terms in the United States from 2010 to 2021 with predictive forecasts to 2025; demonstrates significant upward trend and seasonal pattern in depression search intent over time. AU: arbitrary unit.
View this figure

Table 1 presents the multivariable regression model using time and seasonality to predict depression search intent over time. The variables that were significant predictors of search intent were time (r=0.69, adjusted P<.001; n=135), time2 (r=0.91, adjusted P<.001; n=135), winter (r=0.03, adjusted P<.001; n=135), spring (r=0.12, adjusted P<.001; n=135), and fall (r=0.06, adjusted P<.001; n=135). Applying the regression model, there was a 0.5 AU (95% CI 0.42 to 0.57 AU; n=135) month-over month increase in depression search intent from 2010 to 2021. Depression search intent in the spring, fall, and winter were 7.0 AU (95% CI 5.3 to 8.7 AU; n=135), 4.6 AU (95% CI 2.9 to 6.4 AU; n=135), and 4.5 AU (95% CI 2.8 to 6.2 AU; n=135) higher than in summer, respectively.

Table 1. Multivariable regression model using time variables and season to predict seasonal depression search intent (R2=0.91).

VariablesCoefficientsStandard errort statisticP valueAdjusted P valuear

aBonferroni correction for 4 independent analyses on the dependent variable (alpha=.05).

bNot applicable.

cRelative to summer.

Multivariable Regression Model in Relation to Environmental and Geographic Risk Factors

Table 2 presents the multivariable regression model of depression search intent based on state-specific environmental and geographic factors and has a predictive value of R2=0.57. In this model, variables that were significant predictors of depression search intent were AQI (r=0.30, adjusted P=.01; n=50) and the South (r=–0.2, adjusted P=.01; n=50). Applying the regression model, as AQI increased by 1, the depression search intent increased by 0.4 AU (95% CI 0.14 to 0.61 AU; n=50). Examining the depression search intent relative to US census regions, the South had a decrease of 6.3 AU (95% CI –10.2 to –2.3, adjusted P=.01; n=50) relative to the Northeast. Figure 2 visually demonstrates the regional differences such that states in the South such as Florida and Texas had lower depression search intent in comparison with states in the Northeast such as Maine and Pennsylvania. No relationships existed between depression search intent and temperature (r=–0.5, adjusted P=.99; n=50), humidity (r=0.2, adjusted P=.99; n=50), urban percentage (r=0.3, adjusted P=.06; n=50), or sunshine percentage (r=–0.5, adjusted P=.99; n=50).

Table 2. Multivariable regression model of depression search intent in relation to environmental and geographic risk factors (R2=0.57).

VariablesaCoefficientsStandard errort statisticP valueAdjusted P valuebr
Air Quality Index0.
Urban %–0.10.0–
Sunshine %–9.013.1––0.5

aMultivariable regression model using environmental and geographic risk variables to predict depression search intent. Environmental and geographic data sets were collected as an average from 1971 to 2000 and 2008 to 2019, respectively (n=50). This model predicts depression search intent for each state based on the state\’s average annual temperature, humidity, air quality, urban %, sunshine %, and US census region.

bBonferroni correction for 6 independent analyses on the dependent variable (alpha=.05).

cNot applicable.

dRelative to the Northeast.

Figure 2. Geographic heat maps of the United States visualizing depression search intent on (A) Google Trends, (B) Air Quality Index, and (C) average annual temperature (° F) by state.
View this figure


Principal Findings

To our knowledge, this is the first study to geographically map depression search intent across the United States in relation to environmental and geographic risk factors by using statistical analysis of big data through Google Trends. Traditionally, prevalence data for mental health and depression have been collected through surveys that require an intensive amount of time and resources to conduct . These surveys are limited not only by human and monetary resources but also by participants’ willingness to be included in research. According to the National Survey on Drug Use and Health (SAMHSA), 32.9% of the selected sample did not complete the interview because of refusal to participate, absence from their home, language barriers, or other reasons such as physical or mental incompetence . Response bias is a known recurring issue with epidemiological surveys and has been difficult to overcome as patients with severe mental health and the homeless population are continuously marginalized by society .

The solution to this problem may be utilization of big data found through the internet. In 2020, roughly 86% of the total US population had access to the internet . A US study in greater Los Angeles that examined digital technology use in homeless populations discovered that 94% owned a cell phone . Currently where digital technology is a requirement for survival, internet data can be used to track populations from all over the world over any period. The use of real-time monitoring of internet data to track trends and diseases overcomes the issues of resources, time, and physical location. Analyzing big data through Google Trends is free to researchers and provides information and predictive insight that may one day surpass national or local surveillance systems.

Comparison With Prior Work

The validity of using big data for epidemiology was demonstrated during the influenza outbreak of 2009. At the time, Google Trends was an experimental tool used by researchers for real-time monitoring of influenza outbreaks . By analyzing health care info-seeking behavior on the Google search engine, Google Trends was able to detect regional outbreaks of influenza 7-10 days before the CDC. Google Trends has been successfully used to track viral outbreaks and is currently being used to monitor COVID-19 outbreaks across the world .

Depression is a major public health concern and one of the most prevalent mental health illnesses in the United States . In 2010, the estimated annual economic consequence of depression was upwards of US $200 billion . Considering depression also leads to diminished productivity, poor quality of life, and negative psychological impacts on well-being, the true costs of depression on society are much higher . Worsening mental health and an increasing prevalence of depression, especially during the COVID-19 pandemic, signify the increasing importance of monitoring and treating patients with depression. Based on our analysis, Google search intent for depression in the United States has grown by 67% from 2010 to 2021 and is projected to grow another 7.4% by 2025. This increase reflects the epidemiological trends reported by US national surveys, with an increase in depression prevalence by 61% from 2008 to 2018 (6.6% to 10.4%) . This corroborates the concept that, as depression prevalence in the United States continues to grow, so does the information-seeking behavior on Google Trends. Furthermore, depression search intent in the United States demonstrated a significant seasonal pattern, such that depression search intent was lowest in the summer. Relative to the summer, the fall, winter, and spring seasons had an increase in depression search intent by 4.6 AU, 4.5 AU, and 7.0 AU, respectively. This increase in depression search intent reflects the seasonal pattern of seasonal affective disorder (SAD) which has been shown to have higher prevalence in the fall and winter seasons and a decrease in the summer . Although SAD has been shown to begin remission in the spring, the increase in depression search intent in the spring may reflect population interest in depression in the early stages of a patient’s recovery.

In relation to environmental and geographic risk factors, the state’s air quality and geographic location had significant predictive values for depression search intent. States that had a 1-unit higher AQI had an increase in depression search intent by 0.4 AU In other words, states with worse air pollution had higher levels of depression search intent than states with cleaner air. These results reflect previously published findings that air pollution is linked to depression . Our results comparing the 4 US census regions demonstrated that the South had less search intent, by 6.3 AU, relative to the Northeast. The West and Midwest also demonstrated decreased levels of depression search intent, by 4.4 AU and 3.8 AU, respectively, though their adjusted P values were insignificant. These results reflect the findings that depression is correlated with latitude, with regions further from the equator having a higher prevalence of depression . Although the season and location of a state cannot fully predict the depression search intent at a given time, the trends extrapolated from Google Trends have demonstrated their validity in relation to known risks of depression.

Although mining for epidemiological trends within big data is a fascinating prospect, it should not be assumed to replace the work of national and public health organizations. Instead, researchers should consider comparing their results with big data and using big data to support their findings. Our study has demonstrated that depression search intent increased over time following a seasonal pattern and was higher in states with higher air pollution and states with northern latitudes. This supports the trends found in US epidemiological surveys on mental health and supports published results of known risk factors for depression.

Future studies should build upon the results demonstrated here by examining other risk factors for depression such as socioeconomic, demographic, or lifestyle variables. More specifically, whether age, income, marital status, race/ethnicity, or gender are predictive variables of depression search intent, both on national and state levels. Considering the COVID-19 pandemic, future studies should analyze the data based on advanced time series modeling to analyze the effects of the pandemic on mental health. In the future, public organizations such as the CDC or regional hospitals may be able to monitor depression prevalence in real time based on the search intent of their communities through publicly available internet data. The clinical applications of big data in the medical field are limitless and will continue to become more useful as technology software improves.


Several limitations are present in our study. First, interpreting the trends extrapolated from Google Trends is challenging without supporting clinical information normally collected by traditional surveys such as medical comorbidities or symptom severity. Second, the data in Google Trends may be influenced by various factors such as trending television shows or bots. For example, in 2017, the internet search intent for suicide queries increased by 19% over a 19-day span after the release of popular Netflix series, 13 Reasons Why, which elevated suicide awareness . Third, our data may overrepresent people that search terms in English as Google Trends does not combine search intent of the same word in another language. Fourth, the geographic and environmental data sets were consolidated into a single data point for each state regardless of varying climates and heterogenous landscapes. Lastly, patients with severe or debilitating depression may not have the capacity to search for depression or have the access if they are hospitalized. These limitations illustrate that overreliance on big data, much like on epidemiological studies, may inadvertently exclude certain populations.


Our study is the first to demonstrate that big data in Google Trends can be successfully utilized as a novel epidemiological tool to geographically map out population depression in the United States. This method of mapping allows for easier visualization of areas with higher depression search intent, which were mostly states with higher air pollution and those further from the equator. The interest in depression has grown tremendously in the past decade, with an upward trend that follows a seasonal variation pattern similarly seen in SAD. AQI and geographic location were stronger predictors of depression search intent than temperature, humidity, urban percentage, or sunshine percentage. Further investigation is needed to determine whether the factors significant in our study hold true to depression trends across the world. From a clinical perspective, narrowing the scope of depression search intent to specific cities or high-risk populations should be the next goal of researchers.

How to Use Google Trends: 10 Mind-Blowing Tricks for Entrepreneurs

Google Trends isn’t your average SEO tool. For those of us in ecommerce and dropshipping, it’s pretty handy at letting you know the seasonal trends of certain products – or your niche. You can even use it to edge out competitors by monitoring their positions. In this article, we’ll share how to monitor everything from YouTube stats to the Google Trends compare feature. But most importantly, we’ll share how to use Google Trends to maximize your business. So, let’s get down to it.

Google Trends is trends search feature that shows the popularity of a search term in Google. You can view whether a trend is on the rise or declining. You can also find demographic insights, related topics, and related queries to help you better understand the Google trends.

Google Trends is a great tool to find a skyrocketing niche. Whenever looking for a new niche, you’ll want to make sure you change your range from “Past 12 months” to “2004-present.” Doing this helps you see clearly whether the search volume is increasing or declining. But it also allows you to see seasonal trends in one clear-cut shot.

Here’s an example of a skyrocketing product in Google Trends: posture corrector.

You can see quite clearly that over the past several months there’s been skyrocketing growth. In January we saw a sudden peak with a slight dip in February. However, that doesn’t mean you can’t still capitalize on sales. So this trending product would need to be monitored for a while longer.

Here’s an example of a stable niche in Google Trends: men’s fashion.

You can clearly see in the graph that there are slight dips. However, for the most part, the search volume for this niche is pretty stable. Over the span of several years you’ll see some slight dips or increases, which is normal. But for the most part, Google Trends shows that men’s fashion is a pretty stable niche. You might be wondering what the dips and increases mean. Those show you the seasonal trends of the searches. October through December sees an increase in searches with a decline starting in January. That doesn’t mean you don’t want to start a men’s fashion store in January, it just means you might see a lower amount of website traffic at that time of year.

Wondering what a fad looks like on Google Trends? Well, you might want to check out this data on fidget spinners.

There were virtually zero searches for “fidget spinners” until February 2017. Three months later in May, the product hit its peak. It’s pretty clear that there was a drastic and sharp increase in attention in those first few months. However, the sharp decline following the peak shows that this is no longer a good business idea to consider starting.

Say you’ve created a niche store focusing on fake eyelashes. After owning your niche, you might be interested in expanding into other verticals. So instead of only selling fake eyelashes on your store, you want to sell other product categories that people may also be interested in.

After typing “fake eyelashes” into Google Trends, scroll down to the bottom where you’ll find “Related topics.”

What’s interesting is that two of the examples, “Nail” and “Eye shadow,” are a bit unrelated to fake eyelashes but could make sense as product categories on your store. A person who searches for or wears fake eyelashes is likely also interested in nail products or eye shadow. So if you’re looking to expand the product collections on your store, checking out the related topics might be helpful. As for the adhesive, that product could also be sold on your store, as most fake eyelashes require eyelash glue.

Keep in mind that as you scroll through some of the related topics, you might find some don’t make sense for your business. For example, Kim Kardashian is listed as a Google Trends related topic for fake eyelashes. But hey, you could always write a blog post about Kim Kardashian’s eyelashes.

Now say you’re selling women’s blouses on your store. Google Trends shows that searches for this is trending upward, which is a good sign. But now you want to figure out which keywords to go after, how to name your product categories, and how to optimize a blog post on the topic of women’s blouses. A little trick you can do is take a quick look at “Related queries,” which is on the right side of “Related topics” section we just talked about.

Throughout the 25 queries, you can consistently see a callout to color. In the graphic above you see two listings for the color black. On other pages, you’ll find white, blue, pink, and green. For these examples, you might choose to create a product category based on color such as “black blouses.” However, you can also include those keywords in your product page and in the name of the product. “Women’s shirts” or “blouses for women” could also be listed as a product category since they have search volume and make sense for this clothing type.

Seasonal trends play an important role in the success of your business. Throughout the year there’ll be peaks and dips that will impact your monthly sales. During peak season, competition and sales will increase in full force. During the dips, you might start selling seasonal products. Let’s break this down with a “summer” product: padded bikinis.

What’s interesting is that the data shows us that padded bikinis aren’t just for summer but for winter, too. The first peak of the year starts in January and it continues to climb until June. Then there’s a drastic dip in November. Now, say it’s November and you decide to start a swimwear store, you might be feeling pretty discouraged about that dip. But that’s actually a great time to get started. It gives you a couple months to build your swimwear business so that when January rolls around you’re ready to go!

Now you might be wondering what to sell during the off season. Think of products that naturally complement the products on your store but would be a good match for the off season. Since swimwear is often sold by lingerie brands, selling pajamas might be area to focus on during the winter months. Let’s take a look:

Between September to December, pajamas hit their peak, making it the ideal product to sell during the bikini off season by filling in the seasonal gaps.

Content marketing is helping top online retailers own more traffic to increase brand awareness and to get more customers than ever before. So creating blog content for your website can help grow your business. One way to drive sudden spikes in search traffic is to do “content freshness.” What’s that? It’s when you remove outdated content, add fresh new details, and republish the content on your blog.

How does Google Trends fit into this? Let’s examine seasonality again but for a different purpose. Say you own a cycling store, you might have an article on your blog for the keyword “how to fix a bike.” Throw in those keywords into Google Trends and it’ll look like this:

What this shows is that peak season for this search tends to happen in June and July each year. Now, of course you need to do content freshness whenever you start to lose your position in Google. That’s a given. But if you want to step up your game, you can also coordinate your content freshness around the search term’s peak season. So if you’re that cycling store owner, you might update your “how to fix a bike post” at the end of May. And by doing that, you’ll skyrocket to the top of search results for that keyword. Do this strategy on your top performing SEO articles, and you’ll grow your website traffic in no time.

On the homepage of Google Trends, you’ll find a section for trending searches. Trending searches are the hottest topics of the moment. You can browse daily trending searches, realtime search trends, and search by country.

While most of the trending searches are regarding celebrity news, you will find some buzz-worthy stories that tie into certain niches. For example, on February 27, 2019, the most searched daily trend was regarding the “Momo Challenge,” which amassed over 5 million searches. You can see keywords such as “kids” and “parents” in the “Related news” section. If you own an online store that targets parents of young children, you could’ve written a newsworthy article on the topic on your blog.  

Right now, the second most popular daily trend in the UK is about Tesla. If you own an automotive accessories store (and target customers in the United Kingdom), you might choose to cover car news about top automotive manufacturers. Why? Because people who are interested in automotive news might be interested in buying your automotive accessories. And by bringing relevant audiences back to your website, you increase the effectiveness of retargeting ads.

By occasionally covering newsworthy stories on your store’s blog, you can drive high volumes of traffic back to your website. You can also jump on trending hashtags on Twitter to share your newsworthy article to get more Twitter followers and social media engagement.

7. Find Niche Topics by Region

One of the most interesting elements of Google Trends is how you can find niche topics by region. When it comes to advertising, we often target an audience based on their country. There are 325 million people in the United States alone. Surely the audience in New York isn’t the same as the audience in Louisiana. So let’s break down what these two states think about gold earrings.

In Louisiana, interest in gold earrings isn’t very high. In November 2004, there was a sharp increase in search volume, but that level of interest has yet to return.

Now, let’s take a look at New York’s search interest on Google Trends.

Right now, the search volume for “gold earrings” is on the rise in New York. So what does this mean? Well, if you create an ad for a pair of gold earrings that you’re selling, you’re better off targeting individual states like New York. Since there’s growing interest there, you’ll be able to capitalize on some sales whether you promote it on Facebook or via Google Adwords. You can cross-reference all the states to determine which states have been seeing the trend rise. That way, you increase your chances of landing sales without wasting money by targeting places where interest is lagging.

On Google Trends, you can even monitor your competitors and see how well they’re performing against your brand. With the Captain Marvel movie out, let’s do a Marvel Comics vs. DC Comics comparison to see how the brands have been performing over the years.

What’s interesting here is that in 2004, the two brands were performing at about the same level, with Marvel having a slight advantage. However, after 2013, we see the clear rise of Marvel coming out on top.

With this feature, you can compare up to five search terms or competitors. As your brand gains a bigger search audience, you can use Google Trends compare to ensure that you’re always a step ahead of competitors. And if you find that certain competitors are growing a faster rate than you, you know to start analyzing their marketing channels to understand how you can improve.

While Google Trends is typically used to improve your website’s performance, you can also use it to improve your reach in social media as well – in particular, YouTube. After heading to YouTube and looking for “fashion” videos, we noticed that the top videos used the keyword “fashion trends 2019.” So, let’s plug that into Google Trends and see what we get.

In January, this search term skyrocketed in interest. Of course, the year added to the keyword will make it a popular search term at the beginning of the year. However, after going back to YouTube and looking up “fashion trends 2019” in the search bar, we noticed something interesting. Let’s take a look:

Each of the top videos were published in 2019. Why’s this interesting? Because often times, vloggers (and bloggers, too) create content prior to the new year so they can get a head start on traffic. But we find in the top results is that 2019 content ranks quite well when it’s published in 2019.

So, if you plan to create a video centered around fashion trends in 2019, your best bet is to publish the video sometime in January to capitalize on the data you’d find in Google Trends.

But let’s rewind a second. Since we won’t know what the data is for the rest of 2019, let’s take a look at the data for “fashion trends 2018” so we can know what to expect over the next year.

So, we see that at the end of 2017, Youtube users start browsing the keyword “fashion trends 2018.” And then we see that January spike that we saw in our 2019 image. But what’s interesting is that there are more spikes in March and September – right before summer, and right before winter.

How can you capitalize on traffic during those periods? Well, if you have an email list, you might decide to send an email during March and September to boost your video’s popularity again. Google will see that you’re promoting your older video content and will likely reward you with a higher position for your video so you can get even more views to it. You can also use this strategy if you find that the views on popular evergreen videos are declining too.

You can also use Google Trends to help you determine the best time to create Google Shopping ads. Say you’re a fashion retailer trying to promote a brand new white dress on your store. By taking a look at Google Trends’ Google Shopping feature you can determine the best months for your ads. Let’s take a look at the data below:

Between February and June, there are high volumes of searches for white dresses in Google Shopping. Despite a small dip in August, the next two months, September and October, see a rise in Google Shopping searches. So, if you’re a retailer, you might start selling and promoting your white dresses between February and June and September and October. Well, provided that you’re selling to an American audience. You’ll need to check the data for other countries if you plan to target outside the U.S.

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