Product recommendations is an efficient and proven way to boost your WooCommerce sales.
The downside is that it could be a very time-consuming activity to bundle products manually, as well as the fact that it is not always the logical “pairing” that will generate the most sales.
The way forward is to utilize AI (Artificial Intelligence) and let analytical data models do the work for you.
Meet Engage, an AI-powered product recommendation engine. The type of recommendation vary a bit depending on the page your visitor is viewing. This is mainly because the recommendation model needs input to function properly (e.g. the first time a new visitor lands on your homepage, the model doesn’t know anything about their behavior and therefore it can’t provide recommendations.
But as the user interacts with the website the model picks up on behavioral patterns and is then able to provide better recommendations.
How Engage works
The following illustrates how information is added to a user in order to provide recommendations relevant to each step of the journey.
The initial visit to the website allows only for use of high level variables such as time of visit or geographic region etc. These indicators are usually considered weak and don’t generally provide enough information to recommend relevant products for an individual user. They may however still outperform the options of not recommending any products.
When the user starts interacting with the website, like browsing products or adding products to cart, the model ingest information that can be used to compare this visitors pattern to earlier visitors and thereby extract possible products of interest for the user based on that pattern.
Once the user reaches the checkout page, the model has a pretty good set of information about the user that are used to recommend upgrades or additional products.
Since checkout often requires some kind of identification of the user, the visitors historical purchases can also be utilized here if any purchases were made prior to this one.
Following the order completion, the user can be re-targeted with product recommendations via email or advertisements based on specific customer segments.
Product recommendation output design
This feature gives store admins the ability to design their own output for product recommendation so that it aligns with the WooCommerce theme.
The engine is designed from the perspective that the admin should not need any web design knowledge/experience, meaning that it offers a “drag and drop” functionality with a one click deploy to WooCommerce.
The tool will take the admin through a 5 step workflow:
- Select a template
- Select previously saved designs
- Design the output with “drag and drop” functionality
- Set the display options, e.g. the number of products to recommend and its “backfill” products (products to be displayed if no recommendation is available)
- Select heading and deploy to WooCommerce
Why do recommendation engines work so well?
There are a few reasons why recommendation engines generally outperform manual selections of recommendations at scale.
The first one is simply the scale and speed of which a recommendation engine can produce relevant recommendations for all products in store, not only a select few. And it can maintain it in real-time updating it as trends changes or seasons shift.
Secondly, the model introduces less bias on what should be recommended or what “goes” well together. The model simply looks at what’s actually been sold together and the patterns and behaviors likely to be sold together next time.
Furthermore, the model can learn from their prior recommendations and adjust the next recommendation for a particular product based on the historic outcome. All of which happens automatically each time the model is retrained.
Data driven customer segmentation by Engage
Engage also automates and simplifies the process of creating and exploring customer segments.
The store admin can either define their own segments to explore, or use one of the pre-built templates. Segments are based on various customer traits such as returning customers or top spending customers. As of now the following pre-built segments are available in engage to get you started:
- Top Spending Customers – Segment to be used to find who your most valuable customers and their behavior within different periods of time.
- Most frequent Customers – Segment to be used to identify who your most active customers are and their behavior within different periods of time.
- Returning Customers – Segment to find your most loyal customers and their behavior within different periods of time.
- One-time Customers – Segment to be used to identify who your disloyal customers are and their behavior within different periods of time.
- Most recent Customers – Segment to be used to investigate difference in number of customers, value of sales, top selling products, etc within different periods of time.
Each segment has its own dashboard where sales and performance data can be analyzed in detail over time.
Since these segments are intended to be used for different marketing activities, the export functionality makes it easy for the store owner to export the selected audience to e.g. Facebook and Google custom audiences.
Besides the pre-built segments, Engage offers each store owner an easy to use toolbox for generating and saving their own “Custom Segments”.
Each created segment will have their own dashboard and data can be updated by user request or just act as a snapshot at the time of creation.
Engage is a powerful WooCommerce extension when it comes to advanced analytics. It gives store owners the ability to increase revenue, analyze and understand their customers and reduce the time previously spent on analysis and manual product bundling.
It is right now released in beta and free of charge. Get started today, and get the full potential out of your WooCommerce store.
The work on Engage has just started and we have an extensive roadmap with new functionality to be added that will further improve the customer experience as well as boost your revenue.
The main theme will of course continue to be around data, and how to best utilize the asset of information that every store possesses. Some examples of functionality in the later stages of development are:
- Integration with Facebook and Instagram, that will give the store owner the ability to publish customer segments as audiences for marketing
- Advanced analytics reports concerning Customer Lifetime Value (CLV)
- More granular statistics around the product recommendations
Stay tuned for future updates regarding Engage on zubi.ai