Recent developments in the field of artificial intelligence have blown people’s minds. Artificial intelligence is being incorporated in every industry, to the extent, it is impacting daily life.

People are moving adopting newer technologies with each passing day, making us more connected.

Significant advances  in sub-fields of Artificial Intelligence, especially Natural Language Processing (NLP),Machine Learning (ML) and Deep neural networks has led to   development of new tech solutions such as wearables and brought out other unique, innovative solutions.

In this blog, we will explore how such innovative solutions are being explored and the challenges they are solving.

What is machine learning and where is it used?

Machine Learning is a hot topic in tech circles these days. With multiple technologies being developed and incorporated, even the techies are finding it difficult to catch up. A lot of questions come up related to machine learning, especially, Is ML a technology?

Arthur Samuel, the scientist who came up with the name “Machine Learning”, defined it as “the subfield of computer science which employs huge datasets to provide computers the ability to learn without the need for it to be explicitly programmed”.

Though machine learning has been around for quite some time, it was shunned initially, as it required large computational requirements and the limitations of computing power. However, due to the information explosion which has taken place in the last few years, machine learning has revived.

Machine learning relies on algorithms to train models. Machine learning algorithms build a mathematical model according to the sample data in order to get the answers/predictions without explicitly being programmed to do so. Currently, applications of machine learning can be found in e-mail filtering, recommendations in streaming services, computer vision and is slowly being applied to other business.

Key Business Cases where Machine Learning methods have impacted

• User Acquisition:

A consumer acquisition funnel typically has three stages: segmenting customer base to understanding and addressing their needs, engaging with them at the right moment at the right time, and converting at the right time.

Amazon provides a great example for this business case. By implementing machine learning models, the right recommendation is shown to the customer at the right time, amplifying the likelihood of a purchase.

Aside from using it for recommendations, retailers are using machine learning methods to adjust branding, company and promotional prices on the go.

• Intelligent CRM:

Salesforce, another tech giant with top-notch customer service is another example to look at. Lead generation and scoring are one of the most difficult challenges any digital marketer faces.For these challenges, Salesforce has recently launched Einstein, a product that analyzes CRM data and provides detailed customized suggestions. Einstein, provides insights into every aspect of a customer’s relationship – right from the initial touchpoint to the current ones, helping analyze crucial moments in the process.

A couple of other bigger players are also looking at machine learning methods to help them improve customer experience. Ocado used Google’s machine learning API’s to build a custom system that can analyze the sentiment of the queries and move the one with the highest negative responses to the top of the cue, leading to Ocado responding four times faster to urgent messages, enhancing customer retention and brand loyalty.

• Managing Security and Fraud:

Machine learning has the power to intelligently analyze millions of transactions at the same time and still prevent fraud, preventing waste caused due to fraud. False positives are one of the key reasons for fraud wastage taking place.

PayPal is one of the most noteworthy leaders in this space. They have been utilizing open-source tools to build an artificial intelligence engine with the aim of reducing false positives.

Machine learning models built by them have managed to make an astounding impact by being able to cut the number of false positives by half.

• Human Resource:

Any business thrives due to the employees who are working over there and hence, hiring any candidate is a tedious process. Filtering and shortlisting suitable candidates from thousands of resumes is often a tedious job and this is where machine learning solutions can make the lives of human resource employees easier.

Companies like Restless Bandit are building candidate management systems with specific algorithm types of machine learning that can shortlist the proper candidates by evaluating their resumes on decisions made previously for similar candidates.

The algorithms can be modeled to augment the mentorship of great managers and help employees with unbiased career advice as well.

• Healthcare:

A lesser-known  but extremely important subject is how machine learning algorithms have impacted healthcare. A key case in point is IBM’s Watson. Not only has it proven itself in the game Jeopardy, but it has also proven to be useful in numerous cases by making highly accurate recommendations in terms of treatments required for cancers.

IBM Watson has been deployed in several healthcare centers, providing more detailed insights into the patient’s health. With better insights, better decisions can be made about a patient’s diagnosis and patient outcomes can be improved.

IBM Watson has the power to streamline data-driven decision making, improve patient outcomes, boost efficiencies while reducing costs of readmission and recidivism.

• Forecasting:

AI is being applied across industries in our daily life, so it should not come as a surprise that machine learning methods are being implemented across a wide variety of organizations for accurate forecasting models.

A number of industry leaders have been utilizing ML models for similar reasons. While Walmart has built ML models for accurate forecasting of sales by using its historical data, AIG has built a team to develop accurate forecasting models regarding insurance claims and predict outcomes.

Luxottica, a luxury eyewear brand is developing ML models to develop accurate predictions of sales.

Future of Machine Learning

With the impact of AI becoming more widespread, the importance of machine learning will definitely grow. With better data becoming more easily available, we can expect better insights on data, helping make better decisions. By incorporating machine learning in their business challenges businesses stand to gain more than they invest in the upcoming years besides the early mover advantage.