Data Science algorithms help in uncovering the key attributes of high value customers so they are nurtured from early stages.
Data Science, Machine Learning, Artificial Intelligence are the new buzz words in the world of technology. For businesses that have only recently started their journey, its not the lack of options but rather too many options that cause a confusion. The journey of a business from traditional reporting to advanced data science projects is often looked upon as a data transformation project handled by the IT team. However, it is the business people who are responsible for and benefit from the success of this transformation. At FOYI, we place people at the center of this transformation.
At FOYI, we don’t just work with the data team. We work closely with the business and the data teams to ensure that the business teams are part of the exciting journey from traditional reporting to advanced data science projects.
Types of Projects
Mapping relevant items together into meaningful groups is segmentation. For example, grouping customers into groups based on the value that they bring to the business is customer segmentation.
We overlay simple business rules based grouping with a Machine Learning (ML) based grouping.
This ensures that the segmentation not only works well but can be easily understood by the business.
Machine Learning & AI
Just like the business teams have learnt from past experiences, machines learn from past data. This process is called Machine Learning (ML). For example, training an ML algorithm to identify what behaviours the high value customers exhibit leads us to predict if a new customer would become a high value customer.
This helps the business to take extra care in nurturing these customers into high value customers for the business.
As the number of data science projects increase, so does the need for larger data science teams for regular upkeep and maintenance tasks. This regular maintenance and management is generally referred to as Machine Learning Operations (ML Ops). We help in automating most of the maintenance with the right tools.
This enables the business to add more data science projects while sustaining the existing data science team size.