Introducing Custom Recommendations
Updated: Jan 26, 2020
With our latest release of Process Tempo version 3.1 we are excited to announce the availability of our new Custom Recommendation Engine.
Developing a custom recommendation such as a "Next best offer" or a "Likely to be interested in" formula is often very difficult. It requires a skilled data scientist team, a lot of code, access to historical data, and a means to make these recommendations accessible to those that can benefit from them. In other words, a huge investment.
With Process Tempo 2.2, we make it easy for organizations to create their own custom recommendations and deliver this information to non-technical users in an easy to use, web-based interface.
This new capability allows us to inject Artificial Intelligence into our customer's data so that they can leverage this information to create new opportunities for revenue growth, operational efficiency and employee productivity.
Let's discuss some possible scenarios using the Northwind database as an example. The Northwind database was originally produced by Microsoft and contains sample information about products, customers, orders and suppliers. It mimics data that a retailer would produce during the course of business.
With this data loaded into Process Tempo, we can now look at historical product sales and customer activity to produce interesting recommendations.
For example, if I am looking at a product record such as the product "Chai" Process Tempo can tell me which products are most often sold with it. I can now make smarter product recommendations to my customers who may have once purchased or are considering to purchase this product.
If I am looking at a customer record such as "Save-a-lot Markets" I might want to see a list of products that this customer might be interested in. Process Tempo could provide me with a recommended list of products based on the buying patterns of similar customers. The next time I speak or interact with Save-a-lot Markets I will have something interesting to pitch to them.
These types of recommendations can help customer-facing employees create new opportunities to generate revenue. If you have ever shopped on Amazon.com you know how helpful recommendations can be. Imagine being able to provide intelligent recommendations to your customers and the impact this could have on revenue generation!
Beyond retail, there are plenty of other scenarios in which recommendations can produce valuable insight. Some additional examples:
Preventative Maintenance - which error codes are most likely to produce equipment downtime?
Healthcare - which combination of medicines are most likely to cause negative side effects?
Marketing - which offers would this prospect be most interested in?
Insurance - which client activity is most likely to indicate fraud?
Finance - which financial products will this client be most receptive to?
The number of ways our Custom Recommendation Engine can be leveraged are immeasurable and we are really excited to be able to offer it.
To learn more, please contact us to schedule a quick chat.