From actions to insights: How event scoring reveals user behaviour

While a lot of ecommerce businesses rely on sales and singular micro-conversions to signal their marketing success, there’s a huge range of businesses where this just doesn’t suffice.

High-value or bespoke products often see a lower conversion rate and a longer consideration phase. This can leave business owners and agencies questioning whether their budgets are well placed, or if their marketing efforts are delivering a strong ROI.

Data leads decisions and rightfully so. Therefore, how can we maximise data when it appears fragmented, to establish campaign success?

Establishing scores for micro-events

When we’re working with data from multiple micro-events rather than macro-conversions such as sales or leads, it’s easy to get lost in the detail and not see the bigger picture.

What if we score individual users based on multiple micro-events? Each micro-event is assigned a score, and as users complete these actions, they build a score that represents their level of engagement.

For example, if a user uses a filter and then adds to their wishlist, their score would accumulate (800), based on the example scoring below:

Feeding user scores into GA4

Once scores have been established, we can begin collecting user scoring data, building custom reports to assign user scores to critical information, such as paid search campaigns, keywords, and on a broader scale, channels.

Suddenly, we’re seeing the true value of campaigns, simply by grouping micro-events with assigned scores. There is no longer a reliance on measuring campaigns against singular events, we can see how users driven by individual campaigns are engaging on-site.

So, when conversion rate is low, we’re still able to decipher campaign performance and ROI.

Utilising user scores to optimise campaigns

Taking this a step further, we can begin utilising this data to optimise our campaigns. We do this by creating user scoring thresholds. For example:

• Very High Intent: 2,500+
• High Intent: 1,000-2499
• Average intent: 500-999
• Low Intent: 0-499

As an example, the user from before who used the filter and added to their wishlist would be grouped into the ‘average intent’ category. If they then added to cart, their score would increase to 1800, putting them into the “High Intent” category.

We can see the amount of users falling into each category, which tells us how much value each will bring. For example, if only 5% of users fall into the ‘very high’ category, we know that these users are extremely high value.

To utilise this data, we create an event that triggers when a user reaches the ‘very high’ category. From here, we can import this into Google Ads, or set this up within Meta Ads, allowing us to optimise our campaigns towards these users.

When we’re utilising max. conversions bidding strategies, we know we can push towards these high-value users. Our bidding strategies can work harder for us as we feed in more conversions than the only the occasional sale or lead we were previously utilising.

In summary, not only does user scoring give us invaluable insight into the quality of traffic our campaigns are driving, but we can also utilise this data to push our campaigns harder, ensuring we’re delivering the highest of intent users.

If you think your business could benefit from user scoring, get in touch. Our data team are on-hand to revolutionise your tracking capabilities and deliver unrivalled insight, taking your marketing campaigns to the next level.