Web analytics refers to the collection, measurement, analysis, and reporting of web data to understand and optimize web usage. It involves gathering data on user behavior on websites, such as pageviews, time spent on a page, clickthrough rates, and conversion rates, and analyzing this data to gain insights into user behavior and website performance. These insights can be used to make informed decisions about website design, content, and marketing strategies to improve user engagement, increase traffic, and drive conversions. Web analytics tools, such as Google Analytics, provide a range of metrics and reports to track and analyze website performance. In this blog entry I will explore how to do web analytics using a combinaiton of Google Analytics and AWS (Amazon Web Services).

What are Google Analytics and AWS, and what is the high level process for implementing a web analytics solution?

Before we dive into the article, lets talk about what is Google Analytics and AWS.

Google Analytics

It is a web analytics service offered by Google that tracks and reports website traffic. It provides insights into visitor behavior, including the number of visitors, their demographics, the pages they visit, and the actions they take on the website. With Google Analytics, website owners can monitor and analyze the performance of their website, gain insights to optimize their marketing strategies, and improve their website’s user experience.

AWS (Amazon Web Services)

It is a cloud computing platform that offers a wide range of services, including compute power, storage, databases, analytics, machine learning, security, and more. AWS allows businesses to operate their IT infrastructure in the cloud, providing them with flexibility, scalability, and reliability. AWS provides a pay-as-you-go pricing model, which allows businesses to pay only for the services they use, without any upfront costs or long-term commitments. AWS is one of the most popular cloud computing platforms, with millions of active customers worldwide.

High level outline of the process

To analyze website traffic using Google Analytics and AWS, you can follow these high-level steps:

  • Set up a Google Analytics account and obtain the tracking code for your website.
  • Set up an S3 bucket in AWS to store the data from Google Analytics.
  • Set up an AWS Lambda function to pull data from the Google Analytics API and store it in the S3 bucket.
  • Set up an AWS Glue crawler to crawl the S3 bucket and create a data catalog in the AWS Glue Data Catalog.
  • Set up an Amazon Athena query to analyze the data in the S3 bucket using SQL-like queries.

High level example code for implementing a small-scale web analytics solution

Here’s some sample code to get started:

Importing Required Packages
import boto3
import datetime
from google.oauth2 import service_account
from googleapiclient.discovery import build
from googleapiclient.errors import HttpError
Set up the Google Analytics API credentials
credentials = service_account.Credentials.from_service_account_file('/path/to/credentials.json')
Set up the S3 bucket
s3 = boto3.client('s3')
bucket_name = 'my-bucket-name'
Set up the Lambda function to pull data from the Google Analytics API and store it in the S3 bucket
def lambda_handler(event, context):
    try:
        service = build('analyticsreporting', 'v4', credentials=credentials)

        # Query the Google Analytics API for website traffic data
        response = service.reports().batchGet(
            body={
                'reportRequests': [
                    {
                        'viewId': '12345678',
                        'dateRanges': [{'startDate': '7daysAgo', 'endDate': 'today'}],
                        'metrics': [{'expression': 'ga:sessions'}],
                        'dimensions': [{'name': 'ga:date'}, {'name': 'ga:hour'}]
                    }
                ]
            }
        ).execute()

        # Store the website traffic data in the S3 bucket
        now = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
        filename = f'{now}-website-traffic.csv'
        data = response['reports'][0]['data']['rows']
        s3.put_object(Body=str(data), Bucket=bucket_name, Key=filename)

    except HttpError as error:
        print(f'An error occurred: {error}')
        data = None
Set up the Glue crawler to crawl the S3 bucket and create a data catalog
glue = boto3.client('glue')

response = glue.create_crawler(
    Name='my-crawler',
    Role='my-glue-role',
    DatabaseName='my-database',
    Targets={
        'S3Targets': [
            {
                'Path': f's3://{bucket_name}/'
            }
        ]
    }
)
Set up the Athena query to analyze the website traffic data in the S3 bucket
athena = boto3.client('athena')

response = athena.start_query_execution(
    QueryString='SELECT * FROM my_database.my_table WHERE sessions > 100',
    QueryExecutionContext={
        'Database': 'my_database'
    },
    ResultConfiguration={
        'OutputLocation': f's3://{bucket_name}/query_results/'
    }
)

Note that this is just a sample code to get started, and you will need to customize it to match your specific use case. Also note that there will be additional configuration and setup required, such as setting up the IAM roles and permissions for the Lambda function, Glue crawler, and Athena query, and configuring the Google Analytics API to allow access to your website data.

Benefits of implementing a web analytics solution

Web analytics provide many benefits for website owners, marketers, and business analysts, including:

  • Tracking website traffic: Web analytics tools allow you to track website traffic, including the number of visitors, unique visitors, page views, bounce rate, and session duration.
  • Understanding user behavior: Web analytics provide insights into user behavior, including where users are coming from, which pages they are visiting, how long they are staying on the site, and where they are dropping off.
  • Improving website performance: With web analytics, you can identify which pages are performing well and which ones need improvement. This helps you make data-driven decisions to optimize your website for better user experience and engagement.
  • Measuring marketing campaigns: Web analytics tools allow you to track the performance of your marketing campaigns, including the effectiveness of your ads, social media posts, and email campaigns.
  • Identifying business opportunities: By analyzing website data, you can identify new business opportunities, such as new markets, product or service offerings, and potential partnerships.

Overall, web analytics provide valuable insights into website performance and user behavior, enabling website owners and marketers to make data-driven decisions that can improve business outcomes.

Challenges in implementing a web analytics solution

Implementing a web analytics solution can present several challenges. Some of the most common challenges include:

  • Data Accuracy: Ensuring the accuracy of the data collected can be a major challenge. Issues can arise due to multiple domains, ad blockers, and third-party scripts. It is important to verify that the data collected is accurate, and identify and address any issues that arise.
  • Data Volume: The volume of data can be a significant challenge when implementing a web analytics solution. The data collected can be quite extensive, and processing and storing this data can be costly.
  • Data Privacy: Maintaining data privacy and complying with regulations such as GDPR can be a major challenge. It is important to be transparent with users about the data being collected and how it is being used, and to take steps to ensure the data is kept secure and used only for the intended purposes.
  • Technical Challenges: Implementing a web analytics solution can present technical challenges, particularly for organizations with limited technical resources. It is important to ensure the implementation is properly configured and optimized, and that the organization has the necessary resources to manage and maintain the system.
  • Analysis and Action: Collecting data is only the first step in the web analytics process. The real value comes from analyzing the data and taking action to improve the user experience and achieve business goals. This can be a significant challenge for organizations that lack the necessary resources or expertise.

Comments welcome!