In the age of convergence, customer churn is a concern for service providers, challenging most retention techniques...
There a slew of companies that are providing cloud based or SaaS based services in the areas of advanced analytics and more so as the most used jargon goes ‘predictive analytics’ (it sometimes amuses me how the concept of predictive analytics is abused by companies to get google’s attention but that is for another blog). It is interesting to read about so many companies providing cloud based predictive analytics solution in fields as diverse as insurance claim management to sales forecasting and customer churn.
What further interested me is that most of the companies if not all, make claims that their proprietary algorithms adapt and learn with data and of course time. Which primarily means that the more data you feed the algorithm the better it becomes? That being said what is not clear is whose data.
There is no debate that advance analytics is a data science which means to build good algorithms we need to fulfil two major criteria associated with data.
When we talk about quality we are talking about the process and technology used to capture data as well as internal organisation discipline and hygiene.
When we talk about quantity we are definitely not talking about big data we are talking about sufficient amount of data that will be required to build a decent model.
What is more important is the correlation between quality and quantity; if the quality of the data is bad then quantity is of no use and vice versa. The other part is both quality and quantity is depended on the client and not the vendor.
So there is a good chance that the vendor will use client A data to learn and make a good model and provide the service to client B who never spent a dime on building a good data acquisition platform and now has a great algorithm working for them.
There will be counter arguments to my line of thought but one thing that we all agree upon is that every organisation is struggling to build a competitive advantage and it really hurts when one realises that somebody else is gaining from your hard work.