Product Recommendations
The Product Recommendations model predicts a set of products likely to interest
a specific user.
- For a given user, this model recommends a set of products likely to interest
the user.
- For a given product, this model recommends a set of users who are most likely
to be interested in the product.
CDP can apply post processing business rules to promote a specific type of
products.
Use cases
Automation and personalization
Marketing communication is personalized and automated from the content
personalization within CDP. The marketing team focuses on building
creatives and the marketing strategy. The Acquia recommender system mines data
to find the right product for the right person.
Timing of email campaigns
An individual’s recommendations remain the same unless the browse or
purchase event occurs. Therefore, Acquia’s use cases are event-based:
- Post purchase follow up (typically 14-30 days)
- Alternative to Abandoned Browse (2-4 days)
- Lapsed Customer (8 months inactivity)
How does the model work?
CDP’s recommender system is based on collaborative filtering techniques. The
recommender system:
- Is a frequency-based algorithm based on the purchase and browse history of
most users.
- Mines the order history for highly correlated products.
- Considers that the frequency of product pairs or groupings exceed a minimum
confidence threshold.
- Is biased toward recent purchases.
- Removes outliers such as top 1% buyers and top 1% transactions.
- Performs regular computations as needed. For example, a daily or weekly
computation.
- Excludes items recently purchased by the customer in personal
recommendations.
Using the Product Recommendations model
CDP displays the output from the Product Recommendations model in:
- 360 > 360 Profiles
- Actions > Campaigns and Actions > Campaigns+
To learn more about how the Product Recommendations model can enhance
your marketing workflow, contact us.
