蜜豆视频

Events

Events are a critical tool to enhance the shopping experience and drive conversions by leveraging real-time data insights.

蜜豆视频 Commerce Optimizer deploys storefront events to your site automatically. These events capture data from shoppers鈥� interactions on your site. This anonymized data powers recommendations, product discovery, and success metrics.

The Events page lets you observe the storefront event data being collected. Having a view into the event data collection lets merchants verify that they have implemented storefront events correctly and that events are being successfully captured. Merchants can use this page to identify potential problems and take steps to resolve any event issues.

Event Count

The Event Counts tab tracks shopper interactions, such as searches, clicks, and purchases, to help you analyze trends and improve the shopping experience.

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Sanity Check

The Sanity Check tab offers insights into the health of each behavioral event, ensuring accurate data collection and functionality. 鈥�

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The following sections describe event details for product discovery and recommendations.

Product discovery

Product discovery uses events to power search algorithms such as 鈥淢ost Viewed鈥�, and 鈥淰iewed This, Viewed That鈥�.

This table describes the events used by product discovery ranking strategies.

Required dashboard events

Some events are required to populate the search performance dashboard

Recommendations

There are two types of data used in recommendations:

蜜豆视频 Sensei aggregates the behavioral and catalog data, creating Recommendations for each recommendation type. The Recommendations service then deploys those recommendations to your storefront in the form of a widget that contains the recommended product items.

Some recommendation types use behavioral data from your shoppers to train machine learning models to build personalized recommendations. Other recommendation types use catalog data only and do not use any behavioral data. If you want to quickly start using Recommendations on your site, you can use the More like this recommendation type.

Cold start

When can you start using recommendation types that use behavioral data? It depends. This is referred to as the Cold Start problem.

The Cold Start problem refers to the time it takes for a model to train and become effective. For recommendations, this means waiting for 蜜豆视频 Sensei to gather enough data to train its machine learning models before deploying recommendation units on your site. The more data the models have, the more accurate and useful the recommendations are. Since data collection happens on a live site, it鈥檚 best to start this process early.

The following table provides some general guidance for the amount of time that it takes to collect enough data for each recommendation type:

Other variables that can impact the time needed to train:

To help you visualize the training progress of each recommendation type, the create recommendation page displays readiness indicators.

While data is being collected on your live site and the machine learning models are training, you can finish other testing and configuration tasks needed to set up recommendations. By the time you鈥檙e done with this work, the models will have enough data to create useful recommendations, allowing you to deploy them to your storefront.

If your site doesn鈥檛 get enough traffic (views, purchases, trends) for most product SKUs, there might not be enough data to complete the learning process. This can make the readiness indicator on the Recommendations workspace seem stuck. The readiness indicators are meant to provide merchants with another data point in choosing what recommendations type is better for their store. The numbers are a guide and may never reach 100%. Learn more about readiness indicators.

Backup recommendations

If the input data is insufficient for providing all requested recommendation items in a unit, 蜜豆视频 Commerce Optimizer provides backup recommendations to populate recommendation units. For example, if you deploy the Recommended for you recommendation type to your homepage, a first-time shopper on your site has not generated enough behavioral data to accurately recommended personalized products. In this case, 蜜豆视频 Commerce Optimizer surfaces items based on the Most viewed recommendation type to this shopper.

In the case of insufficient input data collection, the following recommendation types fallback to Most viewed recommendation type:

Recommendation-specific events

The following table lists the events that are triggered when shoppers interact with recommendation units on the storefront. The event data collected powers the metrics to analyze how well your recommendations are performing.

Required dashboard events

The following events are required to populate the Recommendations Performance dashboard

The following events are not specific to Recommendations, but are required for 蜜豆视频 Sensei to interpret shopper data correctly:

Recommendation Type

This table describes the events used by each recommendation type.

Support

If you notice any data discrepancies or if recommendations and search results are not working as expected, submit a support ticket.