Using Data Science to Win and Retain the Right Customers More Efficiently
Data Science relies on programming, data mining, statistics, analytics and tools like data visualization to extract meaningful insights and predictions from big data to solve business problems. There are many use cases for data science, but some of the most widely cited examples are in Marketing and can be best explained in terms of the Sales funnel.
There are basically 5 stages of the buying process:
- Qualified Prospect
- Purchase (Customer)
With so much structured and unstructured consumer data available online and offline, companies can now get a 360-degree view of their prospects and / or customers. Moreover, data science can be used in each phase to automate tasks and improve decision making.
For example, if a company wants to build a model to identify who its best prospects are in order to score or prioritize leads for their sales team, they could look at transactional, demographic, behavioral and geographic data from their existing clients as a proxy. Once those leads are identified, another model can be created to determine those prospects most likely to respond to a certain promotional offer.
Let’s say you’re a bank and you’re concerned with limiting fraud and risk. You may want to consider including credit attributes in your model to isolate consumers who are more likely to default, so you don’t include them in the mailing. On the other hand, you’ll want to tailor your message and sampling methodology to target those with better credit, and once someone becomes a customer, you can continue to revisit and train the model with actual data to make it even more accurate over time.
In short, when you have too much data to use conventional methods and you want to improve processes, data science can be an excellent option.
By: Jennifer Cooper