Not far from now, you will have hard time in figuring out whether this blog is written by an machine or a human! Machine learning, the recent buzz word in the tech industry have started closing the intelligence gap between humans and machine.
Developers are often interested in learning about the software industry’s best practices, so that they can improve the robustness and efficiency of their code. The best way to learn is by reading the source code of programs that are in production and running heavy workloads.
Consider a simple problem Insurance sector problem of recommending a product to a potential customer. The recommendation is based on certain customer attributes, similar to predictive analytics in target marketing.
Brands, no matter how big or small they may be, a common concern they all share is maximizing their campaign effectiveness. For the sake of illustration, imagine a Mercedes Benz Ad targeting a female aged 22-27, will it be effective in any sense?
The opening line of Charles Dickens’ “The Tale of Two Cities” said: “It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, …
With continued expansion of advanced analytical techniques and practices, one aspect often overlooked is the power of data representation. Visualizations, when done correctly, can re-emphasize strong trends and provoke deeper thought on the subtler ones.
Open source has successfully penetrated into many of the commercial companies. Many industries have released libraries and tools in open source that have reduced development effort. Analytics industry has also benefited from open source software.
For classifying or clustering data in the context of a machine learning problem, the first step is to create a representation of data, usually called the Feature Vector. Datasets consisting of images or audio files have feature vectors that are already in numeric form.
It takes a lot of effort from diverse teams across the organization to get $4M revenue into the system but it just takes one Sales Manager to lose the same amount of money! CEB recently reported that a failed Sales Manager would cost the company $4M. (CEB Sales & Service Data).
The advent of ‘big data’ has completely changed the way businesses can harness the information about customers to make powerful business decisions. Data could be of any type – campaign information, customer demographics, individual transaction behavior, interactions on social networks, web usage, or satisfaction surveys etc. In this infographic, we highlight the impact of customer experience on business outcomes and BRIDGEi2i’s customer intelligence solutions.