Many organizations struggle to capture the full range of data capabilities that exists in today’s business world. If this is something you identify yourself with, don’t worry! We brought a full deck of ideation cards into existence to help you get inspired. Download the first 10 cards here and/or contact the data team for a workshop to receive the full deck.
Have a look at some of the examples below:
ZipRecruiter is one of the largest online employment marketplaces in the United States. In 2018 the company launched a tool: Candidate Calibration. At The Reference, we think this is a text-book example of supervised Machine Learning. Most job sites match job seekers and job posters through keyword matching. ZipRecruiter took another approach: by explicitly asking organizations to select some types of profiles they are looking for, the algorithm automatically learns how to match demand to supply.
Churn prediction is the practice of forecasting which users are most likely to end their relationship with the organization. Because of the explosion of data sources such as clickstream data, transactional data and CRM data, churn prediction models have been much easier to implement and interpret. Not only are these models able to list users by their probability to leave the organization, proper churn modeling also gives insights into the main reasons that are causing customers to drop out.
Not all use cases have to involve artificial intelligence in order to create business value. For example: creative ways to integrate data can already provide an excellent service to customers. The reason Coolblue has become the go-to website to search for electronic devices, is the wide range of possibilities to filter and compare products on. Not everyone cares about battery life, fingerprint sensor or a state-of-the-art operating system. With Coolblue, customers know at least it's there. This makes sure that in the long-tail, customers who are looking for one particular feature are not left in the dust.