Google Analytics data can be used to forecast user traffic and alter marketing plans. To effectively plan your marketing strategy, a marketer or business owner must have a thorough understanding of the traffic patterns to their website. Google Analytics is a powerful tool that provides insightful data on user engagement and website behavior. Utilizing forecasting models and data analysis, you can predict how many users there will be in the following month and adjust your marketing strategies.

This article will show you how to forecast the number of users in the upcoming month and change your marketing strategy using Python programming and data from Google Analytics.

You can use Google Analytics data to forecast the number of users in the upcoming month and adjust your marketing strategy as necessary.

Step 1: Gather and prepare the data

• Log into your Google Analytics account and go to the Audience > Overview section.
• Select a time frame for the previous six months.
• To export the data in CSV format, select the Export button at the page’s top.
• Remove any extra columns or data points by opening the CSV file in a spreadsheet program like Microsoft Excel or Google Sheets.
• The remaining columns should be renamed to reflect the format needed for Python code.

The data must be in the pandas DataFrame format, with the following columns:

• Date: The time the data point was collected.
• users: The total user count on that particular day.

Step 2: Enter data into Python

• Launch a Python editor, such as Spyder or Jupyter Notebook.
• Use the following code to import the Panda library:

import pandas as pd 

The following code will load the CSV file into a pandas DataFrame:

data = pd.read_csv('filename.csv')

Step 3: Explore and visualize data

Using the following code, check the first few rows of the DataFrame:

data.head()

The following code will tell you what the columns’ data types are:

data.dtypes

Visualize the data using a line chart using the following code:

import matplotlib.pyplot as plt

plt.plot(data['date'], data['users'])

plt.show()  

Step 4: Preprocess data

You can convert the date column to a pandas datetime format by using the following code:

data['date'] = pd.to_datetime(data['date'])

The following code sets up the DataFrame’s index according to the date column:

data.set_index('date', inplace=True)

Resample the DataFrame to a monthly frequency using the following code:

data_monthly = data.resample('M').sum()

Step 5: Build and evaluate model

The following code will split the training data into testing and training sets:

train_data = data_monthly[:-1]

test_data = data_monthly[-1:]

You can use the Facebook Prophet library to build a forecasting model by following these steps:

from fbprophet import Prophet

model = Prophet()

model.fit(train_data.reset_index().rename(columns={'date': 'ds', 'users': 'y'}))

Here is a code for generating predictions for the upcoming month:

future = model.make_future_dataframe(periods=1, freq='M')

forecast = model.predict(future)

The following code visualizes the predictions:

model.plot(forecast)

plt.show()

Step 6: Adjust your marketing plan as necessary.
Use the following code to compare the anticipated monthly user count with the actual user count in the test data:

actual = test_data['users'].values[0]

predicted = forecast['yhat'].values[-1]

if predicted > actual:

    print('Increase marketing efforts.')

else:

    print('Maintain current marketing efforts.')

Based on the anticipated and actual number of users, this will print a message suggesting either an increase in marketing efforts or their maintenance.

Please take note that the preceding code presupposes that the Google Analytics data CSV file has already been downloaded and saved to the same directory as the Python script. If the file is saved in a different directory, the file path will need to be modified accordingly.

Combine Script

import pandas as pd

data = pd.read_csv('filename.csv')

data.head()

data.dtypes

import matplotlib.pyplot as plt

plt.plot(data['date'], data['users'])
plt.show()

data['date'] = pd.to_datetime(data['date'])

data.set_index('date', inplace=True)

data_monthly = data.resample('M').sum()

train_data = data_monthly[:-1]
test_data = data_monthly[-1:]

from fbprophet import Prophet

model = Prophet()
model.fit(train_data.reset_index().rename(columns={'date': 'ds', 'users': 'y'}))

future = model.make_future_dataframe(periods=1, freq='M')
forecast = model.predict(future)

model.plot(forecast)
plt.show()

actual = test_data['users'].values[0]
predicted = forecast['yhat'].values[-1]

if predicted > actual:
    print('Increase marketing efforts.')
else:
    print('Maintain current marketing efforts.')