How do you resolve conflicts between customer journey data?

1 Identify the sources of conflicts

Conflicts between customer journey data can arise from various factors, such as different attribution models, data collection methods, tracking parameters, time zones, cookie policies, or user behaviors. For example, you may notice that your Google Analytics data does not match your CRM data, or that your email marketing platform reports different conversions than your website analytics tool. To resolve these conflicts, you need to identify the sources and understand how they affect your data quality and reliability.

3 Use a common data collection method

Another common cause of conflicts between customer journey data is the use of different data collection methods, such as server-side or client-side tracking, or manual or automated data entry. For example, some tools may rely on JavaScript tags or pixels to track user actions on your website, while others may use server logs or APIs to capture data from your backend systems. To resolve this conflict, you need to use a common data collection method that ensures the completeness and accuracy of your data. You also need to verify that your data collection method is compatible with your data analysis and visualization tools, and that it complies with the privacy and security regulations of your industry and region.

4 Standardize your tracking parameters

Another possible cause of conflicts between customer journey data is the use of different tracking parameters, such as UTM codes, campaign IDs, or custom variables, to identify and measure the performance of your marketing campaigns and channels. For example, you may use different naming conventions, formats, or values for your tracking parameters, or forget to add them to some of your links or ads. To resolve this conflict, you need to standardize your tracking parameters and use them consistently across your campaigns and channels. You also need to document and update your tracking parameters regularly, and use tools like Google Tag Manager or URL Builder to create and manage them easily.

5 Adjust for time zones and dates

Another potential cause of conflicts between customer journey data is the use of different time zones or date ranges to report and compare your data. For example, you may use different time zones for your website analytics, CRM, and email marketing platforms, or use different date ranges to measure your monthly, quarterly, or yearly performance. To resolve this conflict, you need to adjust for time zones and dates and use a common standard to report and compare your data. You also need to be aware of the impact of time zones and dates on your attribution models, and use tools like Google Data Studio or Power BI to create dashboards and reports that can handle different time zones and dates.

6 Account for user behaviors and preferences

Another possible cause of conflicts between customer journey data is the impact of user behaviors and preferences on your data collection and analysis. For example, some users may use multiple devices or browsers to interact with your brand, or delete or block cookies that track their actions. To resolve this conflict, you need to account for user behaviors and preferences and use tools and techniques that can identify and track users across different devices and browsers, such as user IDs, cross-device reports, or probabilistic matching. You also need to respect user privacy and consent, and use tools and techniques that can handle cookieless tracking, such as server-side tracking, first-party cookies, or consent management platforms.