Marketing and SalesMarketing Analytics

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• Transition from 1.0 to 2.0:
  Web Analytics 1.0 focused on log files and basic metrics such as page views and hits. Web Analytics 2.0 shifts to more sophisticated tools and metrics that focus on user behavior and engagement.
• Four Technical Aspects:
  • Clickstream Data: Quantitative data that reveals what users do on your site.
  • Multiple Outcomes Analysis: Understanding business outcomes and measuring business value.
  • Experimentation and Testing: Engaging in A/B testing and multivariate testing.
  • Voice of Customer (VOC): Collecting qualitative feedback to understand user needs better.

• Transition to using modern tools like Google Analytics to capture more detailed user interactions rather than relying on basic log files.
• Start incorporating multiple outcomes analysis by setting up goal tracking in your analytics tools to measure specific business outcomes like conversions, leads, and sales.

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• Interest Metrics:

  • Bounce Rate: Percentage of visitors who leave after viewing one page.
  • Average Time on Site: Indicates the time users spend engaging with your content.

• Engagement Metrics:

  • Page Depth: Number of pages viewed per session.
  • Visitor Loyalty and Recency: Frequency and time gap of repeat visits.

• To reduce bounce rates, experiment with different landing page content types and designs.
• Track visitor loyalty to gauge the effectiveness of your engagement strategies. Enhance content to encourage repeat visits.

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• Web Analytics Tools:

  • Google Analytics for comprehensive website analytics.
  • Crazy Egg for heat mapping.
  • Qualtrics or SurveyMonkey for VOC data.

• Techniques:

  • Segmentation: Divide website traffic into segments to understand distinct user behaviors.
  • Dashboarding: Create dashboards that reflect your KPIs at a glance.

• Start using Google Analytics to set up basic and advanced segments such as new vs. returning users or by geographic location.
• Create a custom dashboard tailored to your business goals to monitor daily performance efficiently.

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• Persona Development:

  • Use data to create user personas that represent common characteristics and behaviors of your customer base.

• Customer Journey Mapping:

  • Map out the entire customer journey from initial contact to conversion to understand user experience.

• Develop at least three user personas by analyzing demographic data, behavior patterns, and VOC insights.
• Perform a customer journey analysis by mapping interactions and touchpoints to identify potential drop-off points and optimize the user flow.

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• A/B Testing:

  • Test different versions of web pages to see which performs better concerning conversions and other metrics.
  • Example: Testing different CTA (Call to Action) buttons to determine which results in higher click-through rates.

• Multivariate Testing:

  • Test multiple variables simultaneously to understand their effect on user behavior.
  • Example: Testing different combinations of headlines, images, and CTA buttons.

• Implement A/B testing for key landing pages to optimize conversion rates.
• Use multivariate testing to optimize several elements of a page in parallel and achieve incremental improvements.

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• Data Accuracy:
  • Employ proper tagging and ensure all pages are accurately tracked to avoid data discrepancies.
  • Example: Regularly audit your analytics implementation to fix broken tags.

• Conduct regular audits of your analytics setup to ensure data cleanliness and accuracy.
• Establish a checklist for ongoing monitoring and validation of data collection processes.

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• Aligning Metrics with Goals:
  • Ensure that your KPIs reflect business objectives such as revenue growth, lead generation, or user engagement.
  • Example: Aligning web analytics goals with broader marketing campaigns for a cohesive strategy.

• Regularly review and align your analytics metrics with current business goals.
• Use data-driven insights to inform and adjust business strategies periodically.

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• Measuring Social Media Impact:
  • Track metrics like shares, likes, and engagement on social platforms.
  • Example: Measuring the referral traffic and conversions from social media campaigns.

• Set up UTM parameters for campaigns to accurately track social media traffic and conversions.
• Analyze social media metrics alongside web analytics data to understand the comprehensive impact of your campaigns.

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Marketing and SalesMarketing Analytics