{"id":114789,"date":"2018-02-20T16:08:00","date_gmt":"2018-02-20T21:08:00","guid":{"rendered":"https:\/\/www.dacgroup.com\/building-customer-journey-google-analytics-new-features\/"},"modified":"2024-12-23T00:37:02","modified_gmt":"2024-12-23T05:37:02","slug":"building-customer-journey-google-analytics-new-features","status":"publish","type":"post","link":"https:\/\/www.dacgroup.com\/en-gb\/insights\/blog\/analytics\/building-customer-journey-google-analytics-new-features\/","title":{"rendered":"Solving the customer journey puzzle: Understanding Google Analytics\u2019 new features"},"content":{"rendered":"

Nowadays, more and more brands are embracing data-driven decision-making to put the customer at the core of their marketing activities. This is the\u00a0so-called customer journey mapping. A recent eMarketer study revealed that 61% of worldwide digital marketers expect personalisation to be the leading tactic used to attract new customers.<\/p>\n

To aid in gathering more information to personalise customer experience, brands can make use of a wide variety of tools available. These range from web analytics platforms such as Google Analytics and Adobe Analytics as well as CRM platforms such as Salesforce. Which to use depends on several factors: depth of analysis, budget, or expertise required. Further, these tools are continuously updated to allow for improvement in the level of analysis about customer behaviour, interaction and intent. In this blog post we will be looking at several of the recent Google Analytics updates.<\/p>\n

\"analytics\"<\/p>\n

Google Analytics New Features review<\/strong><\/h2>\n

Google Analytics<\/a> have rolled out a series of interesting new features<\/a> that expand the current level of analysis.<\/p>\n

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  1. Conversion probability<\/strong>: This new metric uses machine learning and shows you the probability that a given user will convert in the future. Spending time with this feature interrogating your data and understanding your customer can create powerful audience groups indicating the probability a user in that audience will convert based on machine learning. Further, using those audiences for remarketing lists can help target efficiently only those visitors with a high likelihood to purchase. This feature is available for those properties with >1000 transactions per month. A new report is generated on a monthly basis.\"conversion<\/a><\/li>\n
  2. Audiences reporting<\/strong>: This new form of reporting allows users to see a cross-channel view for any of the audiences they have created within google analytics. Audiences can be set up in GA through Admin tab > Custom Audiences.\"audiences\"<\/a><\/li>\n<\/ol>\n

    These two functionalities have been developed in addition to the new user explorer feature, which has been around for a year. It offers the possibility of shifting the analysis in GA from session level to user level, raising the understanding of their engagement across different sessions and multiple days.<\/p>\n

    These new features from GA are a clear step in the right direction. However, they are not valuable without a structured approach. Currently, it is still somewhat a challenge to use these insights to plot an in-depth view of the customer journey. For example, see below the difference between the GA data and a bespoke consumer segmentation report produced by combining several data sources. Data shown is by age, gender and interest. Same data \u2013 different insights.<\/p>\n

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