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How to segment data effectively in Google Analytics for improved customer experience

Data segmentation in Google analytics puts you in charge of your data by helping you analyse various layers of your customer database and get the most out of it.


Let’s say people visit your ecommerce website from multiple sources with different reasons that reflects in their navigational behaviour. What does that leave you with?

Averages of metrics that camouflage significant information misguiding you with inaccurate and incomplete data analytics with no actionable insights.

How do data segments in Google Analytics help?

In the world of website analytics, behavioural data segments are key for data personalisation. When you segment your customer database, you pull out the subsets from the data analytics pool from the rest of the website traffic and break it into relevant target groups. This helps in engaging your existing and potential customers in a personalised way with relevant & timely promotional email marketing/messaging.


How to effectively segment your data?

Right implementation is crucial.

  • Take inventory of the website analytics data you already have, and which data do you need hence forth.

    Dive into your data analytics to determine the kind of segmentation that can be applied. For example-If you know your target groups then you need to know which data needs to be applied to those target groups for an enhanced customer experience. This could mean setting up special campaigns, running surveys or sending personalised emails.

  • Use the data to engage your target group better.

    Once you’ve gathered the relevant data from your customer database, you’re ready to set up and run the targeted personalised email marketing campaigns to capture the interest of your target group.

  • Data segment your customer database to an advanced level.

    It means delving deeper into your data analytics for information you can use for data personalisation. For example, branching out the segmentation process beyond the obvious brackets of name, age, occupation etc. to applying it to a larger realm in terms of purchase history, brand preferences and browsing behaviour of the customer. You can also run specific campaigns like abandoned cart, loyalty email or preferred brand campaigns.

Tips to further improve the data segments.


You don’t want to leave any stone unturned do you when it comes to ROI.

  • Nurture each segment with reliable data –Fill and update each segment constantly with relevant offline and online data to reduce targeting errors and improve the ROI of your marketing campaigns.
  • Have an omnichannel approach in your marketing strategy –Customers use different channels and devices to shop. Hence it is crucial to converge scattered data from various sources into a unified customer database for an ideal customer experience. Need more details on how to analyse and unify customer data? We have it ready for you.
  • Identify the right criteria for segmentation –Maintain exclusivity (one person in one group), exhaustivity (each customer in included in one group) and homogeneity (customers with similar characteristics in one group) of data in each segment. You must have definitive segregations in terms of demographical, attitudinal and behavioural data for effective operational segments.
  • Real-time segmentation for data personalisation –Tap into every single action performed online for valuable information to help you personalise your content for your target groups. This real-time segmentation helps you chart a tailor-made journey for better ROI.
  • Prioritise your data segments –Rank your target groups based on their purchasing habits. This will enable you to differentiate between the hot & the cold prospects and value each group accordingly.

Segment your data from your customer database within google analytics. This data personalisation optimizes your customer experience and lets you deliver the right message to the right target group, at the right moment via the right channel.

Drive better results by understanding customer data