HIGHLIGHTS

• By offering personalisation, customisation, and enabling the use of virtual assistants, Machine Learning has become increasingly relevant for students more than before.
• ML can’t stay behind in the race considering the various benefits it offers and the wide scope it has.
• Machine Learning helps to upscale the e-learning process by upgrading the content and tailoring it as per the present requirement.

The education industry is one of the most thriving sectors in India. While the pandemic brought with it various challenges, it also helped in digital adoption across all businesses. This trend was especially witnessed in the education sector. The teachers and students had to cope up with the changing times and had to move the learning sessions to the online medium.

To ensure that the educational firms and the ed-tech platforms extend the best of the knowledgeable sessions to their students, they need to be armed with state-of-the-art technology. The key technological advancement that has brought about a transformation in learning for the students is Machine Learning. Having this technology in place enables efficient and swift processing of big data thereby extending superior quality education to the students.

Importance of machine learning in educational firms and ed-tech platforms

An extension of Artificial Intelligence (AI), Machine Learning (ML) provides the ability to the IT systems to interpret data at their end and self-educate basis the experience gained. Its primary objective is to grab hold of the required data, analyze it and accordingly create a problem and solution algorithm.

The best bit about this technology is that it does not require the assistance of humans or external coding. Every aspect of the process is covered by the machines only. A prominent example of this is when IT professionals communicate directly with their clients and provide them necessary solutions.

While machine learning is widely used in IT and e-commerce firms, it has acquired an important place in the education sector as well. It has now become the need of the hour rather than being a luxury. The most common ways in which we integrate machine learning with education are via learning analytics and educational data mining.

Siri, the virtual assistant is another soon to become essential part of the e-learning process.

As reported by Technavio, the global market share of online courses based on machine learning technology is expected to grow at a CAGR higher than 16% by 2021. This growth will be driven primarily by the affordable large data volume storage and its efficient processing. It will further help in making the e-learning process interactive and result-driven.

1. Customisation and personalisation

Machine Learning algorithms analyse the mannerism by which the students explore the information they are provided with. In case, the students have grasped the topic well enough, they move ahead. But if not, then ML takes them back and makes them go through relevant points again to make them understand the concept better.

They also keep track of the learning process of the students at an individual level. ML gives an edge to e-learning over classroom teaching by ensuring that the students learn effectively with the varied formats of content in an interactive, engaging, and techie manner.

2. Upgradation of content

Machine Learning helps to upscale the e-learning process by upgrading the content and tailoring it as per the present requirement. They analyse the quality of the content of the various courses offered and compare it with them. Then, they accordingly make changes in their courses to provide state of the art education curriculum to the students. This helps them give an edge over the players in the market as they pro-actively alter their courses to extend the best of the educational experiences to their students.

3. Ensures higher ROI

When ML is applied, students get personalised services. Additionally, Machine Learning provides real-time benefits to the students thereby leading to their increased adoption in the education system. The process of predictive analysis further helps keep track of the progress of every student and also enables their quick grading.

4. Provides instant solutions

When incorporated, Machine Learning enables the platforms to provide instant solutions to the queries of the students. This benefit of ML makes it increasingly accepted by the educational and ed-tech platforms.

5. Helps save time by taking up admin-related jobs

ML helps save the time of students by automating the admin-related jobs. This makes the students devote their time wholeheartedly to their studies and assignments and they won’t waste their time on the admin-related jobs.

Summing Up

By offering personalisation, customisation, and enabling the use of virtual assistants, Machine Learning has become increasingly relevant for students more than before. While it cannot replace the teachers, it is used as a system to support and assist the traditional teaching methods. Since, digitisation is present and proliferating, with it, the technological advancements will amplify too.

ML can’t stay behind in the race considering the various benefits it offers and the wide scope it has. It has indeed revolutionised the education sector and will lead to further upscaling of the industry thereby being a major contributor to its growth.

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For a provider of loyalty programs across some of the biggest brands in the world, it’s pretty important Aimia gets personalisation and customer engagement right.

Which is why the leader in loyalty marketing, services, technology, and data platforms recently turned to artificial intelligence (AI), to better utilise its vast amount of data.

Director of insights and analytics at Aimia, Scott Pete, told CMO one of the challenges it faces is it’s dealing with a lot of different types of industries including CPG, retail, travel and hospitality. The customer base, the way they interact, and purchasing behaviours vary tremendously, he said.

“And the data sets aren’t the same. They vary from hundreds of thousands of customers to smaller programs, tens of millions in the larger ones. So, what we needed to do is to improve our ability to understand the engagement levels in those audiences. But with the volumes of data that we’re dealing with, it can get a little daunting if you use traditional query tools and that type of thing,” Pete explained.

“We got into machine learning [ML] over the last six or so years, and we’ve been moving more and more into the ML side. And then we came across Driverless AI about a year ago and decided to see if that might help us improve our time to market and accuracy of the models, which is which is how we started.”

As Aimia was already using H2O elsewhere in the business, it decided to run a trial of Driverless AI, which uses automation to accomplish key machine learning tasks quickly by offering automatic feature engineering, model validation, model tuning, model selection and deployment and machine learning interpretation.

“Our development environment is Python, and the H2O open source project product was much, much faster. It allowed us to fit our models in about 25-30 per cent of the time it would take us using other methods,” Pete said. 

“When we tried Driverless, we were actually initially a little sceptical with the result because it seemed maybe a little too good, too easy and too fast. But we found Driverless was actually even more capable than the open source we were previously using. And we did evaluate other products before we made our purchase, but H2O came out on top.”

Since the trials, Aimia has developed multiple data models, including predictive churn, smart journeys, and fraud detection, with plans for more into the future.

“We’ve done a few predictive churn optimisations and either starting from scratch with a brand new model from H2O or taking existing models, and basically looking for ways to optimise miles and so we have had really good result with that,” he said. “The other one that really got our attention was fraud detection. And in some cases, some of the clients we deal with do have some incidents of fraud, and it’s difficult to detect because it’s really a very small percentage of the audience. And we found AI was a perfect tool for looking for a tiny signal in the data.

“The other application we are really excited about smart journey diagnostics. This allows us to look at the engagement levels in the customer base, and better understand and classify where each member is in their engagement, and also to predict where they’re likely to go next. We can actually quantify for our clients where the real opportunity is and where the risks are. We had really good results so far.”

In terms of measurement, Aimia is looking to accuracy to assess the results it is getting from AI, as well as speed and costs savings for clients.

“Accuracy is one of the top two, and our time to market. Time to insights is really an even better way to say it, because one of the one of the things marketers struggle with is trying to get insights out of their data, so that they can better prioritise marketing initiatives and marketing strategies,” Pete said. “And that’s something that we’re finding a lot of success with.

“Ultimately, once we get those insights, the accuracy in being able to predict a certain percentage of the audience likely to churn, showing a retention problem for example, is invaluable.”

As an example, one case study Aimia did recently broke the audience down into 20 different groups. The team was able to score all the members and then organise them into 20 different groups and identify the top six to eight groups where it could get the low hanging fruit and most impact from retention efforts.

“This saved the client about 700 per cent in campaign costs, because they weren’t spending money on offers in campaign communications unnecessarily,” Pete said.

Now the AI is working well across certain models, Aimia hopes to take it further over the next 12 months with a model it calls ‘next best experience’.

“Some of the research we’re doing right now we’re actually working on a model called ‘next best experience’. Marketers are familiar with next best action or recommendation engines, this model is going to take elements of those types of models and it’s going to take it to another level,” Pete said. 

“So if you if you think about our ability to better understand the audience and what engagement level they’re in, then do some behavioural segmentation, and some value segmentation so we can understand the potential value of those customers. Then ,we are better able to target our one-to-one communications and really achieve real personalisation.

“That is something we’re really excited about. Because what will happen as we achieve this is we will deliver a better user experience for the end customer. And we believe that will help deepen the relationship and improve the loyalty as well.

“I think the expectation of what we can do with technology is rapidly changing, as companies start to see the time is right to implement AI.”

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