Releases in May, 2024

Major features:

Unleash the Power of Funnels 🚀

We are thrilled to introduce Funnels, a powerful no-code analytical feature designed to track and analyze customers' journeys from start to finish.

Visualise your customer journey: Effortlessly track the path your customers take through a sequence for use cases such as completing a purchase, signing up for a service, or navigating through a multi-step process.

Filter and compare behaviours of different customer segments: Create charts on various attributes to see flexible and precise analysis of your target that allows you to investigate and pinpoint changes in the website or customer journey that impact conversion rates.

Gain insights into how customers interact with your brand: Investigate the ways in which customers engage with your brand across various devices and touchpoints, including their behaviours, preferences, and engagement patterns.

Manage access control: Set the right people to see and edit the right charts. Give permission for viewing or editing charts, so your team can work together safely and effectively.

Learn more about Funnels and their possibilities in our documentation. 

 

Enhancing data precision with regular expression filtering over events' identifiers

The regular expression filtering empowers you to define specific patterns that identifiers must adhere to to be considered for stitching, ensuring enhanced data quality and precision. You can establish rules that dictate the format or structure an identifier should follow by utilizing regular expressions. Whether it's email addresses, phone numbers, unique codes, or any other data format, you can now define the desired pattern using regex.Regex.

For more information on utilizing regular expressions to filter events' identifiers and practical examples, visit our documentation.

Special thanks to our talented developers who brought this feature to life: Allan Konecny and Marek Langer!

 

Context Queries: unlocking the power of centralized data

Introducing the new Context Queries tab. The main objective of this feature is to facilitate data access and analysis by pre-calculating key statistics that require data from the entire customer base. This includes metrics such as averages, medians, and percentiles across all customers rather than for individual customers.

Some examples of use cases for Context Queries include tracking customer engagement metrics like purchase frequency, average order value, or product affinities relative to the entire customer base. You can also group customer profiles into different segments using techniques like RFM (Recency, Frequency, Monetary) analysis.

We believe that Context Queries will significantly enhance your data analysis capabilities within CDP. For more information, refer to the article.

Special thanks to our talented developers who brought this feature to life: Allan Konecny and Jan Smrz!

Bug fixes:

• It is impossible to have '%' in event filter in autoload;
• Add "event_value" between rotated appsflayer v2 columns