Electronic Retail Sector Marketing: Google Trends Analysis Report

This report analyzes the use of Google Trends for understanding and predicting trends in the electronic retail sector. It begins by outlining the current trends in the sector, emphasizing the growing reliance on online search engines like Google for customer information, and the importance of tools such as Google Trends for tracking consumer interests and preferences. The report then delves into the data available through Google Trends, including query volumes, geographic locations, and time series data, and how this data can be used to forecast future trends. The analysis includes a discussion of how businesses can acquire retail sales data, track customer behavior, and identify competitor information. The report provides predictions for the electronic retail sector for the next one, two, and three years, based on the analysis of current trends and the potential impact of technological advancements, such as machine learning and big data. The report concludes by highlighting the increasing importance of internet retail marketing and the value of tools like Google Trends for SEO specialists and businesses seeking data-driven insights. The report also references several academic sources to support its findings.

RUNNING HEADER: Marketing
Marketing
Name
Institution
Professor
Course
Date
1

RUNNING HEADER: Marketing
Current trends
As the customers in the car dealing retail sector become more depended on the internet to acquire
motor information, their reliance on search engines like the Google trend grows. Some of the
current trends include opening a channel to track new shifts in the customer's interests. It is
customer intelligence tool that helped the retail sector to identify several terms that clients have
typed in the search box thus keeping information regarding vehicle volume indexes. This retail
can receive constant automatic SEO audit report in it smart tool (Boone, Ganeshan, Hicks &
Sanders,2018,p.99). The automotive industry indicates increased chances for reaching customers
who prefer ride sharing. Electrical sales market has grown rapidly as a result of Google trend
initiative where customers get a chance to test drive and see features of vehicles on the online
platform. It classifies the vehicles in accordance with the client tastes and preferences. The
Google trend tools link the customers contact enabling them to get notification regarding the
activities and products of this particular retail sector.
Data
Google trends literally offer an index of the volume of various Google queries through the use of
geographic location and category. The data usually report any specific query index. Some of the
data got are numbers of query shares and dates of the total query volume showed as a percentage
deviation from the existing query share (Hu, Du & Damangir,2014, P.318). It helps individuals
to acquire data from past years and evaluate patterns in customer behavior annually. It helps
people search for current and future traffic volumes. The Google data also utilizes prediction of
forecasting private consumption
2

RUNNING HEADER: Marketing
Google trends help businesses to acquire the available retail sales data, for instance, the motor
vehicle and parts dealers as key indicators of existing economic conditions.it is also possible to
acquire sales promotion data.
It helps track customer data in order to understand them more and acquire information of
products for commerce website. It also identifies competitor's information.
The following data is obtained;
 Qualitative and quantitative data.
 Time series data such as annual Google profits, day to day IBM stock prices.
 Quarterly sales results from the retail.
 Electronic demand data.
Another type of data that can be determined is travel planning in getting information about a
destination that is helpful in predicting visits to several world destinations (Vosen & Schmidt,
2011, P.567). In this way, car dealer retail will get information in order to establish showrooms
so that those visitors get a chance to view their products. This data will be important to track
various numbers of expected customers.Some of the forecasting models utilized are seasonal
autoregressive (AR) model.
Prediction
It is evident that the world of internet retail marketing is rapidly changing thus we can predict
what is store in a few years to come. Technology and internet savvy organizations will
consequently produce various programs and tools that will make work easier for SEO specialist
and anybody who relies on the internet for data search for services.
3

RUNNING HEADER: Marketing
Year 1: The continuous influx of search tools like Google trends will help business development
in online market careers. There is no doubt that "chore" shopping will be much easier and
existing demand level for cherishing will be even stronger (Wu, & Brynjolfsson, 2015, P.101-
102). Market players like Amazon will make this particular part of retail simple by providing
offers such as auto-renewals. Google trends will offer an opportunity for customers to design and
customize the retail products at the online platform. Machine learning and utilization of big data
will become applicable to the Google trends.
Year 2: Increase of utilization of year-on-year growth rates. More Google trend features will
emerge in the sector.
Year 3: improved survey –based indicators predictive power on the web. Increased forecasters of
client spending
4

RUNNING HEADER: Marketing
References
Boone, T., Ganeshan, R., Hicks, R.L. and Sanders, N.R., 2018. Can Google Trends Improve
Your Sales Forecast?. Production and Operations Management.
Hu, Y., Du, R.Y. and Damangir, S., 2014. Decomposing the impact of advertising: Augmenting
sales with online search data. Journal of Marketing Research, 51(3), pp.300-319.
Vosen, S. and Schmidt, T., 2011. Forecasting private consumption: survey‐based indicators vs.
Google trends. Journal of Forecasting, 30(6), pp.565-578.
Wu, L., & Brynjolfsson, E. (2015). The future of prediction: How Google searches foreshadow
housing prices and sales. Economic analysis of the digital economy (pp. 89-118). University of
Chicago Press.
5