HOW ARE COMPANIES USING IT
Have you ever wondered that pricing a product or a service in order to maximise revenue and conversion is more of an art than science? Yes, thought so.
An art form
In the last few years, the use of Machine Learning for price optimisation has become a very attractive approach since it allows organisations to minimise guess-work using predictive models to determine the best price for each product or service while minimising churn. Human decision process, even though very powerful, has its limitations in digesting lots of data points hence most pricing exercises leave revenue on the table or drive out prospects. Machine Learning models are capable of identifying hidden patterns from a broad set of data points and then can continuously integrate new information and detect emerging trends or new market demands.
For example, a big dilemma retailers face is; How can I accurately price this product? When they attempt to tackle this question, the common considerations are:
All these questions are incredibly hard to answer at a human-centric level because these factors are constantly changing. When the complexity of the problem and number of parameters increase, optimisation will take longer to find the optimum solution. Parallel computing using libraries (such as Dark) on top of powerful computing machines provisioned on AWS SageMaker will speed the computation considerably.
How can we help
Typically, optimisation problems are difficult mathematical and statistical problems to solve. At Intellify, we pride ourselves with attracting world class data scientists who have years of experience in the field across a wide variety of industries and verticals. They are well versed in handling big data modelling as well as capturing domain knowledge in a short period of time. Our top talent combined with our proven scientific and engineering delivery methodologies shorten the time to achieve value for our customers.
See the illustration below for a view of human-centric and ML driven decision-making pricing.