Feature Requests

Shopify's analytics shows the retention percentage of the customers (new/returning) but without telling source of those returning customers (direct / organic / meta / google / snapchat / tiktok / email... etc.) A "Retention indictor per channel" is needed for managing the budget allocation and re-allocation.

Your customer data can do a lot more for you! Now you have every customer's entire journey, it's time to empower your store's growth with the data. Attribuly allows you to discover the data with Gemini in Bigquery About Gemini in BigQuery Gemini in BigQuery provides AI assistance in the following ways: ### Explore and understand your data with data insights. Data insights offers an automated, intuitive way to uncover patterns, assess data quality, and perform statistical analysis by using insightful queries generated from the metadata of your tables. This feature is especially helpful in addressing the cold-start challenges of early data exploration. For more information, see Generate data insights in BigQuery. ### Discover, transform, query, and visualize data in a natural language-based data canvas. (Preview) Using natural language, you can find, join, and query table assets, visualize results, and seamlessly collaborate with others throughout the entire process. For more information, see Analyze with data canvas. ### Get assisted SQL and Python data analysis. You can use Gemini in BigQuery to generate or suggest code in SQL or Python, and to explain an existing SQL query. You can also use natural language queries to begin data analysis. To learn how to generate, complete, and summarize code, see the following documentation: ### Optimize your data infrastructure with partitioning, clustering, and materialized view recommendations. You can let BigQuery monitor your SQL workloads for opportunities to improve performance and reduce costs. For more information, see the following documentation: ### Autotune and troubleshoot serverless Apache Spark workloads. (Preview) Autotuning can automatically optimize Spark jobs by applying configuration settings to a recurring Spark workload based on best practices and an analysis of prior workload runs. Advanced troubleshooting with Gemini can explain and surface job errors, and it can offer actionable recommendations to fix slow or failed jobs. For more information, see Autotuning Spark workloads and Advanced troubleshooting. ### Customize your SQL translations with translation rules. (Preview) Create Gemini-enhanced translation rules to customize your SQL translations when using the interactive SQL translator. You can describe changes to the SQL translation output using natural language prompts or specify SQL patterns to find and replace. For more information, see Create a translation rule. Use cases Personalize sign-up emails. Trigger different product recommendations, based on the user's previous events. Customer segmentation and targeting. How it works Attribuly syncs different customer datasets to BigQuery, so you can analyze and take further actions via Gemini in BigQuery. The BigQuery database is owned by you. The dataset The initial dataset includes customer, product, and customer events. The datasets have been process to a standardized structure. The dataset can be updated in a near real-time frequency, so you can use it to personalize customer experience. What's next? Welcome to comment your use cases so we can build them for you. :)

Last-day ROAS can be an unstable metric. Would it be possible to add the last seven-day ROAS to give our marketing team a longer time metric to make decisions?

The Limitations of Klaviyo’s Default Web Pixel While Klaviyo’s default web pixel is a powerful tool for tracking user interactions, it has its limitations. The default pixel might not capture all user activities, leading to gaps in the data collected. This can result in missed opportunities for engagement and revenue generation. Moreover, the default pixel might struggle with accurately connecting events back to existing Klaviyo profiles, especially in cases where users switch devices or clear their cookies. This can hinder your ability to create personalized and timely marketing campaigns. How First-Party Pixel Data Enhances Klaviyo's Capabilities Attribuly's first-party pixel data can significantly enhance Klaviyo's capabilities by filling the gaps left by the default web pixel. By leveraging Attribuly's identity resolution technique and connecting more enriched events back to existing Klaviyo profiles, you can gain a more comprehensive view of your users' behaviors and preferences. This enriched data allows you to create more effective email marketing flows, such as 'Abandon Browse' and 'Abandon Cart' emails, which target users who might have otherwise slipped through the cracks. As a result, you can capture more revenue and optimize your email marketing strategies. Implementing First-Party Pixel Data in Your Klaviyo Strategy To implement first-party pixel data in your Klaviyo strategy, you need to integrate Attribuly's Conversion Feed. This involves setting up Attribuly's first-party pixel on your website and configuring it to send event data to Klaviyo via the Events API. Once integrated, you can start using the custom events generated by Attribuly to set up additional flows in Klaviyo. These flows can target users based on more specific behaviors and actions, allowing for more personalized and effective marketing campaigns. There are 5 events enriched by Attribuly Active on Site Viewed Product Added to Cart Checkout started Checkout complete

This insight helps me understand: Usage of different devices. The journey across different devices.

So the automation metrics will follow the default attribution model. The model should include a view-through based as well.

Please see the attached image

I use Shopify Marketplace Connector to sync orders from different marketplaces. In Attribuly, it makes these orders as referrals which is incorrect. Please recognize these orders and give me the option to exclude them from the profit calculation. Also attach other vendor's method for your information.

Showing the percentage of attribution overlapping differences between how many actual orders reported on Shopify and how many total orders (attributed) reported by all connected ads channels for a selected date range period.