This page describes the recommended steps to build a product recommendation within your app
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In the previous step, you already built a flow for image collection and ensured that all your end-users (both smartphone and desktop) receive the highest quality of image analysis.
Now, let's continue with product recommendations.
Product Recommendation Engine is a feature that tailors suggestions to each user's unique skin analysis results.
Integrating this feature into your app boosts sales by directing users to products they're more likely to purchase. In case your application does not provide any product/treatment recommendations (just image analysis), you can skip this guide page.
Check the Product Recommendation feature description for more details.
Product Recommendation in SaaS consists of three (3) parts:
Store and manage your product items list in the SaaS Platform.
Match product items with relevant product categories, tailored to skin concerns
for example, "makeup remover for oily skin type", or "night creme for anti-aging"
Match product categories with the skin concerns of the person, captured on Image.
The guide page below describes the utilization of all 3 parts. In some cases, your application might require an even higher degree of customization.
Check this section: Create a Custom Recommendation Logic
Import your Product Items into SaaS. Choose the method that is suitable for you:
Use bulk import in SaaS B2b interface, following this guide for import in CSV format;
Check the Product Inventory description for more details.
Product Shelf is a unique matrix that matches product items with relevant skin concerns. Several Product Shelves can be created from the same Product Inventory in order to organize product items in different recommendation setups.
This action is supported only in the SaaS B2B interface (no API methods). Follow this guide to do this: Create shelf and products via B2B SaaS UI
Check the Product Shelf description for more details.
Recommendation Engine Application is required to power the Product Recommendation feature.
Check the Recommendation Engine Application description for more details.
You should attach Recommendation Engine to your target Dataset, using the same method as here: Attach algorithms application to dataset.
Check the Recommendation Engine API for details about the relevant methods.
Recommendation Engine Algorithms Application requires Face Skin Metrics 2.0 Algorithms Application to be connected to your Dataset as well.
If you try to attach just a Recommendation Engine to the Dataset, you will get an error (see the docs about application attachment).
When both previous steps are successfully done ( Attach Recommendation Engine to the Dataset in SaaS, #configure-product-recommendations-in-saas) your application can now receive Recommended Products for the Image, uploaded to this Dataset.
Check the Products Recommendation API Overview for details on relevant methods.
The display of recommended products significantly depends on your marketing and sales strategies and may vary significantly. We recommend checking our Skin Consultant App Routine view for inspiration.
We recommend showing routines if:
You are selling skin care products that address all (or most) skincare concerns
Users expect a step-by-step guide on how to apply products.
We recommend to show a simple list of products in the following case if:
You are selling products that are focused on only a few skincare concerns (e.g., a moisturizer)
Your users are experienced and don't need any guidance
Products and services you sell can't be combined into a routine
In some cases, your product recommendation logic might require a higher degree of customization.
It might happen that product categories in the Product Shelf feature do not explicitly match your product categories.
It might happen that the Product Inventory does not match your requirements for storing product items.
In these cases, the best strategy would be to use only parts of the SaaS Product Recommendations feature that match your needs. For example:
Follow the step Attach Recommendation Engine to the Dataset in SaaS
Receive the recommended product categories in Webhook and map product categories to product items on your side.
At this moment, your app should be already well configured to start testing and bringing value to your end-users. Your application can now collect images of high quality, process them with AI image analysis and even recommend relevant products.
Follow our next guide Good Practices for Making Your App Trustworthy, Effective and Transparent for End-Users to learn about best practices to:
