Using AI and machine learning to create highly-personalized product recommendations
The behavioral profiles mentioned above can lead to effective automation, as machine learning algorithms calculate the optimal products to suggest based on any number of behaviors – blogs read, pages visited, videos viewed, internal searches, and so on… As personalization increases, the results are better recommendations, which leads to increased click-through, add-to-basket, and conversion rates.
With AI and ML, you can also run tests to calculate the optimal metrics to base your recommendations on, by looking at the relative click-through rates.
Another interesting ML use case is to calculate the ‘relevance period’ for a recommendation, as there is an inevitable decay in levels of interest in a product as time goes on (this can be created with fixed logic, but ML can be more effective).
How to build a product recommendation engine
In terms of basic requirements, a recommender system should:
- Have access to historical data – behavioral, transactional, and demographic data
- Create and store a table so analysts can query this data and create automations
- Expose an API to receive and respond to requests from the web server.
The technical architecture might look something like this:
Learn more about building recommendation systems – this is for content recommendations, but the technical principles are the same.
Step-by-step instructions to build an e-commerce data product accelerator (DPA)
You can now build an e-commerce Data Product Accelerator (DPA) for free. This fast-tracks you on your way to making decisions from your data warehouse.
A DPA gives you a data model which generates tables on your product views, cart interactions, transactions, and more. This gives you room to mature your data collection through customization, which is not possible with a pre-packaged product recommendation solution.
Ultimately, you can generate better product recommendations and increase revenue.
Common challenges with product recommendations
As the personalization of your recommendations increases, so too does the level of complexity. Without firm foundations, such as well-organized and understood data sets, this is not achievable.
Furthermore, in order to create flexible and well-understood data for advanced analytics, a warehouse-first approach is generally required. This is both an opportunity and a challenge for organizations, as it requires adopting several tools and increasing the sophistication of your data capture, processing, and activation; but the rewards can be massive, as you gain the flexibility to slice and dice data into powerful data models which are customized to your business.
In order to serve relevant recommendations, near real-time data is often required.
Unfortunately, a significant number of data tools cannot process your data in real-time, while providing the rich data models required for AI and advanced analytics. This can limit a company’s aspirations when it comes to increasing the effectiveness of product recommendations.
How Snowplow helps you create better product recommendations
Snowplow collects event data from your website, mobile app, or other digital properties. Data is then sent to your warehouse or lake where you can build a complete picture of your customers’ behavior, preferences, and interests.
Once this data is modeled, you can automate the delivery of personalized product recommendations to each customer, in real-time, across all touchpoints, based on a detailed usage history (check out a modeled data set or our e-commerce documentation to learn more).
To test this for yourself, try Snowplow for free