Top 5 reasons to implement Machine Learning
There are lots of bright inventions around us and new ones happen daily. However, while some new technologies come and go barely noticed, the others cause real hype and become the true
disruption in their field. One of such technologies is machine learning, which — if applied right — has the potential to improve the business practices, increase the ROI and result in higher customer satisfaction with your products and services. That is why in this article we list top 5 reasons to implement machine learning today in order to reap the benefits in the nearest future.
Machine Learning (ML) is the recently developed technology of using adapting algorithms deployed in the cloud to analyse Big Data and find emerging patterns without actually knowing what to look for.
This allows for more precise analytics of the ongoing processes, finding room for improvement and avoiding possible failures proactively, instead of facing the consequences, thus improving the bottom-line, customer satisfaction and employee engagement. This is a huge market, with $5 billion in venture capital invested in machine learning startups in 2016. As a matter of fact, a fresh report on digital IQ from PWC, one of the world leading consultancy and analytics providers, showed that 30% of respondents expect machine learning to disrupt their field of business within the next 5 years!
Below are the reasons to implement machine learning every company can have:
- Personalized service for prospects and customers. Call centers are a dying form of service, as Millennials prefer self-service portals with chatbots. As a matter of fact, 44% of US customers preferred chatbots over real customer service reps, if the experience was right and the bots were able to quickly help the customer solve the issue. ML algorithms help automate the service and even if a real customer service has to intervene — the algorithm will analyse their actions to be able to solve such issues in the future.
- Better applicant screening for HR. Due to simplicity of access to information, there can be hundreds or even thousands of applications for every job posting. Shortlisting these candidates for an interview can be a really daunting task. A specialized ML algorithm can use preset filters and sort out the applicants that lack required technical skills or other needed features, thus greatly decreasing the time and effort spent on applicant screening.
- Improved financial interactions for billing department. Customers can send payments with some details lacking, and finding out where the sum should be applied can be a real pain. A specialized machine learning algorithm can analyse such payments and predict the missing details, making processing daily financial operations much less stressful for financial department.
- Better employee engagement and retention. A good paycheck is not the most important part of the job reward any more. As a matter of fact, 35% of respondents to PWC survey on Millennials at work stated corporate training and professional development programmes to be the most convincing feature when choosing a new job. Quite opposite, routine tasks can cause boredom and frustration, which might result in talent loss.
Applying machine learning can help analyse the data like history of social interactions and browser searches to provide valuable insights on the employee’s emotional state. This helps understand if everything is fine, or is the talent thinking of a better haven and some additional motivation is needed to keep them engaged. This is especially beneficial for L&D departments that should provide personnel with the opportunities to learn, grow as professionals and improve their satisfaction with the job.
- Market predictions and business opportunities. There are lots of possibilities to apply machine learning in business practices. From stock exchange and bitcoin trading to analysing the industry-related news feeds, ability to gain insights on important events before they gain notion can provide a competitive edge. For example, learning of a fire at the competitor’s factory that will disrupt their operations can help a business form a targeted special offer package to win the customers that will be inevitably dissatisfied with unfulfilled obligations of the competitor.
The era of dinosaurs is long gone and we are not talking of archeology here. Being competitive often means being swift in nowadays business world. The one who is well informed is well armed, and machine learning, if implemented right, can become exactly the tool to help analyse all the richness of Big Data and come up with influential insights on a variety of business processes.
Correct implementation is the key here, as deploying and maintaining machine learning in Big Data analysis is quite a hard task. Thus said, choosing the right provider today can tip the scales tomorrow and create value for any business. We stand ready to help companies and organizations harness the power of machine learning in Big Data analysis, so get in contact and we will provide superior solutions for your ideas!
Feel free to browse through the latest insights and hints on the DevOps, Big Data, Machine Learning and Blockchain from IT Svit!
What is the cloud pyramid: the layers of DevOps services
We use cloud computing services quite a lot nowadays, yet many people don’t have a clear picture of the cloud structure in mind. This is what the cloud pyramid looks like:
DevOps Culture: A Huge Step for Mankind
In order to perform well, the company should be comprised of departments concentrated on performing their tasks. Effective interaction between these departments is what drives the DevOps culture. How to achieve this goal?
How CryptoKitties help the Blockchain technology evolve
The game centered on collecting, breeding and trading virtual felines for ethers has taken the Ethereum network by storm. Here are certain important outcomes for the Blockchain technology as a whole.
Google distrust of Symantec SSL certificates. Why is it important?
As soon as in Google 66, which is scheduled to be released on April 17, 2018, Google plans to distrust all Symantec-issued SSL certificates issued prior to June 1, 2016. What should be done about this?