Your customers and prospects will spend a limited amount of time searching through all the content on your website. If you don’t help them find content that matches their interests, they’ll settle for second-best, or they’ll lose interest and go to your competitor’s site. Either way, you’ve lost the chance to delight them.
A recommendation engine is designed to categorize users based on the way they navigate your website and their common patterns of engagement. When the engine detects similar behavior in other users, it recommends relevant, previously chosen content.
Instead of using expensive products like Sitecore or Optimizely to automate the complex, back-end processes of recommendation, use an end-to-end analytics platform to create your own engine.
With Alteryx you can:
- Directly connect your recommendation engine to more data sources than just your website
- Fully blend user data from across touchpoints to develop interest profile
- Keep users engaged in Alteryx by providing suggestions based on each user's unique historical activity
- Learn more about each user's specific interests in content through passive feedback
- Connect existing Alteryx workflows to a master recommendation engine workflow using Macros, like the example below
Content Recommendation Workflow with Macros
1 - Simplify with Macros
2 - Prep and Blend
3 - Advanced Analytics
4 - Data Export