There are several steps involved, from identifying and acquiring the data points needed in the first instance, through to leveraging the CDP to enrich the existing customer profiles in the next instance.

The first step is around Data Enrichment and Segmentation. You need to utilise the CDP's capabilities to enrich customer profiles with the additional data, such as demographics, preferences, and browsing behaviour. You can then segment those behaviours and used them as leverage within the CDP’s segmentation features to group customers based on common characteristics and behaviour patterns.

Be sure to integrate GTM (or any TMS) with the CDP for additional onsite data points. Using the capabilities within GTM or any other TMS, you can capture and transmit relevant customer data, such as user interactions, product views, and purchases to the CDP to further enhance the data available for audience segmentation purposes in realtime. As visitors interact with the website, GTM triggers events and sends data to the CDP, which processes it and applies segmentation rules to determine personalised recommendations.

It’s a useful time and resource save!

Don’t overlook Dynamic Product Recommendations. With the data harvested and segmented in your audience and segmentation builds, you can then utilise the personalised customer data collected in the CDP (including via the GTM event handlers) to help generate dynamic product recommendations, which is curated by your [customer] martketing team, on the website. Those recommendations can then be displayed in the usual places and across various pages, such as the homepage, product pages, shopping cart, or through targeted pop-ups.

Consider using Contextual and Behavioural Triggers within GTM. Your CDP or dedicated Personalisation Engine can then be used to trigger specific events and display recommendations based on user behaviour, such as viewed items, abandoned carts, or previous purchases. Tailor the recommendations to match the user's interests and preferences, increasing the likelihood of engagement and conversions.

Always remember to measure the changes via A/B Testing and Optimisation. The one thing you will want and need to continuously do is monitor and analyse the performance of the personalised recommendations (some TMS platforms provide a built-in reporting feature for this purpose, or you can achieve the same results by integrating with your analytics tool). Remember, you cannot manage what you’re not measuring!

Be sure to conduct regular A/B tests to refine the recommendation algorithms, messaging, or placement for optimal results.

Tracking key performance indicators (KPIs) such as click-through rates, conversion rates, and revenue generated from the personalised recommendations through performance measurement and iteration. Use the insights gained to iterate and improve the recommendation strategy, adjusting segmentation rules and content as needed.