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Savvy data specialists will treat 2020 differently<\/h2>\n
Massive shifts in online purchasing, remote working, and more made 2020 the most anomalous year in recent memory. Yet, paradoxically, the trillions of data points collected may cloud our ability to see our customers clearly.<\/p>\n
The wrong way forward<\/span> \u274c<\/h3>\n\n- Failing to account for increased device sharing during lockdowns, which may have polluted individual customer profiles (think homeschooled child clicking on a YouTube pre-roll ad for a toy while logged into a parent’s profile)<\/li>\n
- Combining 2019 and 2020 data in your models<\/li>\n
- Lacking regional adjustments in your data to account for local variability in lockdown protocols<\/li>\n<\/ul>\n
The right way forward<\/span> \u2714\ufe0f<\/h3>\n\n- Ensure your models rely on 2020, and not years before<\/li>\n
- Overcome model fragility<\/a> by putting a robust anomaly-detection protocol in place<\/li>\n
- Go granular to understand how behavior changes in regions following a change in restrictions<\/li>\n<\/ul>\n<\/li>\n
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Google Analytics 4: The game-changer is here<\/h2>\n
The rollout of Google Analytics 4 promises marketers a major lift in the intelligence offered by the platform. Applying machine learning to generate insights into customer behavior, GA4 could unlock potential of customer web analytics data to drive performance.<\/p>\n
![\"Churn](\"\/wpe-content\/uploads\/2021\/01\/ga4.jpg\")
<\/p>\n
The wrong way forward<\/span> \u274c<\/h3>\n\n- Using out-of-the-box tagging and tracking of common goals without investing in creating a proper measurement strategy<\/li>\n
- Allowing anomalous 2020 data to skew GA4’s machine learning algorithms<\/li>\n
- Sticking to your site’s custom tagging because of the sunk costs\u2014or, conversely, making the leap to GA4 without established analytics support<\/a><\/li>\n<\/ul>\n
The right way forward<\/span> \u2714\ufe0f<\/h3>\n\n- Your app is gaining traction, so deploy GA4 specifically to gain better visibility into these users<\/li>\n
- Shift your business to a “user-based” framework, where app and website data combine to focus more on the visitor than the channel<\/li>\n
- Give your analytics team access to raw data using BigQuery for deeper analyses, all for free<\/li>\n<\/ul>\n<\/li>\n
\n \n \n \n
The right way forward<\/span> \u2714\ufe0f<\/h3>\n\n- Ensure your models rely on 2020, and not years before<\/li>\n
- Overcome model fragility<\/a> by putting a robust anomaly-detection protocol in place<\/li>\n
- Go granular to understand how behavior changes in regions following a change in restrictions<\/li>\n<\/ul>\n<\/li>\n
- \n
Google Analytics 4: The game-changer is here<\/h2>\n
The rollout of Google Analytics 4 promises marketers a major lift in the intelligence offered by the platform. Applying machine learning to generate insights into customer behavior, GA4 could unlock potential of customer web analytics data to drive performance.<\/p>\n
<\/p>\n
The wrong way forward<\/span> \u274c<\/h3>\n\n- Using out-of-the-box tagging and tracking of common goals without investing in creating a proper measurement strategy<\/li>\n
- Allowing anomalous 2020 data to skew GA4’s machine learning algorithms<\/li>\n
- Sticking to your site’s custom tagging because of the sunk costs\u2014or, conversely, making the leap to GA4 without established analytics support<\/a><\/li>\n<\/ul>\n
The right way forward<\/span> \u2714\ufe0f<\/h3>\n\n- Your app is gaining traction, so deploy GA4 specifically to gain better visibility into these users<\/li>\n
- Shift your business to a “user-based” framework, where app and website data combine to focus more on the visitor than the channel<\/li>\n
- Give your analytics team access to raw data using BigQuery for deeper analyses, all for free<\/li>\n<\/ul>\n<\/li>\n
\n \n \n \n
Google Analytics 4: The game-changer is here<\/h2>\n
The rollout of Google Analytics 4 promises marketers a major lift in the intelligence offered by the platform. Applying machine learning to generate insights into customer behavior, GA4 could unlock potential of customer web analytics data to drive performance.<\/p>\n
<\/p>\n
The wrong way forward<\/span> \u274c<\/h3>\n\n- Using out-of-the-box tagging and tracking of common goals without investing in creating a proper measurement strategy<\/li>\n
- Allowing anomalous 2020 data to skew GA4’s machine learning algorithms<\/li>\n
- Sticking to your site’s custom tagging because of the sunk costs\u2014or, conversely, making the leap to GA4 without established analytics support<\/a><\/li>\n<\/ul>\n
The right way forward<\/span> \u2714\ufe0f<\/h3>\n\n- Your app is gaining traction, so deploy GA4 specifically to gain better visibility into these users<\/li>\n
- Shift your business to a “user-based” framework, where app and website data combine to focus more on the visitor than the channel<\/li>\n
- Give your analytics team access to raw data using BigQuery for deeper analyses, all for free<\/li>\n<\/ul>\n<\/li>\n
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The right way forward<\/span> \u2714\ufe0f<\/h3>\n\n- Your app is gaining traction, so deploy GA4 specifically to gain better visibility into these users<\/li>\n
- Shift your business to a “user-based” framework, where app and website data combine to focus more on the visitor than the channel<\/li>\n
- Give your analytics team access to raw data using BigQuery for deeper analyses, all for free<\/li>\n<\/ul>\n<\/li>\n
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