Use Case #10: Using Datama Journey for Filter Optimization at JOTT

Context

How to Capitalize on Existing Filters and Make the Most of the Data Already Collected?

JOTT is a clothing brand from Marseille whose leading product is the popular Ultralight down jacket colorful, in easy-to-carry pouches.

JOTT’s website reflects their branding: A transgenerational, timeless and colorful brand that radiates the creative energy of the South in ingenious essentials.

For any e-commerce platform, filters play an essential role in helping users navigate through the extensive product offering, thus increasing the likelihood of conversions.
The main challenge is to find a way of analyzing and making sense of each filtering event, a task that might seem colossal and tedious… if you’re not using the right tool.
Good news: that’s exactly what Datama Journey does!

Approach: Using Datama Journey for Filter analysis

What is a Sunburst Chart?

JOTT was already collecting data in GA4, but this collection was carried out on events accumulating filter choices such as:

• event1 = size, size, collection, color, kind, type, color
• event2 = color, size, kind, color, type
• …

Upon closer examination, it became obvious that this data had great potential, since it had exactly the structure of a customer journey.
By querying GA4 data in BigQuery (and particularly leveraging STRING_AGG function), we ended up with a Dataset that fitted perfectly the required format for Datama Journey, i.e. the count of sessions and transactions related to each specific Filter Journey, split by Device
The data looks like this (see dataset – obviously this is fake anonymized data)

This dataset contains randomized and anonymized information for confidentiality reasons

Visualizing the Filter usage with Sunburst charts

A sunburst chart is a visualization of hierarchical data in the form of concentric segments. Each hierarchical level is represented by a ring. The center of the diagram represents the highest level of the hierarchy, and the segments subdivide into outer rings to represent lower levels of detail.
Sunburst charts are excellent for highlighting the proportions and relationships within hierarchical structures.
In the example below, we can see that ‘taille’ (in yellow) is the first selected filter. Next, “Couleur” is the second filter selected first by 15.55% of sessions. In the second step, this session selects right after ‘Capuche’ as filter. So the event => Couleur, Capuche is used by 1.97% of the sessions.

You can immediately identify your customer’s primary interest and other related elements. Here, we could see that 50% of the sessions are using only two filters.

Main takeaway for that first part was that filters should appear differently: so that the customer doesn’t have to read filter types sorted alphabetically, but by an order that reveals customer needs.

Understanding the contribution of each action through value attribution

Understanding the customer journey is essential, but a central question remains: 

Do the most frequently used filters have an effect on the purchasing act?

Thanks to attribution calculation and sizing of lost opportunity, Datama offers a clear roadmap for this analysis by plotting each action on those two metrics:

We can then immediately see which filter is the best driver and which needs to be optimized. It could be the position, the wording, the ease of selection (e.g., icons), or even its overall usefulness.

We could also compare performance on mobile vs. desktop or by content page group, raising a host of new questions just waiting to be answered.

Outcomes: DataMa Elevates JOTT’s Filter Performance

Thanks to the use of DataMa Journey, JOTT significantly improved the performance of its website filters.

Key Benefits:

• Customer Journey Visualization: Obtain a clear view of your customer’s navigation, highlighting critical interactions.
• Value Attribution: Evaluate and allocate value to each touchpoint, providing insights into high-impact areas.
• Improvement Prioritization: Know the order in which the filters are to be used to make it easier for customers to read them.

By leveraging Datama Journey, JOTT quickly identified the most frequently used filters and those needing optimization. For instance, the analysis revealed that 50% of sessions used only two main filters, underscoring the need to reorder the presentation of filters to better meet customer needs. Additionally, comparisons between mobile and desktop devices allowed for the detection of potential differences in user experience, leading to specific adjustments.

Datama Journey also enabled JOTT to understand the impact of each filter on the purchasing decision, revealing which filters are the most effective drivers and which may need adjustments, such as their position, wording, or ease of selection. This targeted approach resulted in increased conversion rates and an improved user experience.

Conclusion

Integrating DataMa Journey into JOTT’s analytical processes highlighted the importance of clear and relevant data visualization. Using visualizations like sunburst charts, JOTT transformed complex data into actionable insights, enhancing customer satisfaction and business performance. By capitalizing on these insights, JOTT continues to refine its filtering strategies, strengthening its market position and better meeting customer expectations.