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Billions of Dollars invested-Still Indian E-Retailers not able to figure out AI and Big Data

Indian E-commerce companies are still new and working on Artificial Intelligence and big data. They are not shying away from investing big on AI. They think that AI can help them to offer a better user experience.

In 2017, Indian E-commerce firm announces the launch of an initiative known as AI for India where the company would develop solutions for the issues like decoding the complex addresses and catching the address fraud. Flipkart owned fashion brand Myntra runs two AI-powered brands Moda Rapido and Here and Now.

Paytm is another Indian origin brand that has personalized and reordered its homepage for each of its 225 million users. The platform makes 20,000 recommendations per second each of them under 20 milliseconds.Despite the investment, the companies have yet not been able to make a major breakthrough in providing the customized shopping experience. Everybody is doing it but there is a lot of variations in the quality of work. Easy applications like chatbots are plenty but using the data science to provide better resolution is still nascent in India.

What Exactly is wrong?

If you go and search for “blue sequin top” on an Indian E-commerce portal, there is a high chance that the website will show you tops that are blue or just covered in sequins or maybe something irrelevant. And if you find what you are looking for, there is a likelihood that you may be shown fake reviews and end up buying the wrong product.

 

The reason behind this is because e-commerce companies’ approach to AI is now just at the elementary level. Most of the companies are not able to boost the data points and come up with the solution that can delight the customer.

In addition to this, the quality of data gathered by these portals is also sub-par. The data used does not have a quality that can fetch accurate results. The AI algorithm is only good as the amount and quality of data they have access to.

For E-commerce giants in India, it is impossible to find large data sets that are publicly available from private entities or even the government. The ecosystem is too much scattered and lacking the harmony.

Most of the companies work with their own data sets. If they use external data sets too, it can help in collating a wide range of information about the end customer from what they eat to what are their music preferences, to whether they love to travel etc.

Even after putting the technology into play, teaching a machine to connect the dots is a difficult task. The process behind figuring out the person on Facebook is the same as transacting at Big bazar still need to sharpen.

Many E-commerce companies in India are trying to improve AI capabilities in that direction.

Where to get started

At Flipkart owned fashion portals Myntra and Jabong, big data analytics is used to derive insights about almost every aspect of the business. They use AI for everything from demand forecasting, supply chain optimization, personalization, and recommendation systems. AI is not only used to design the in-house brands but also to price the products accurately.

To convert the people browsing in their sites to customers, e-commerce companies are making use of recommendation engines. Machine learning, deep learning, and natural language processing play a big role in the recommendation process.

Visual search is another personalization tool. It works like a virtual shop assistant that can display the products that look similar to the products in which the customers are interested.

In order to create a train and maintain efficient machine learning processes, India’s e-commerce firms also need more AI talent. And there is a serious manpower crisis that will become a hurdle in the path.

Not Enough Talent

As per the United States reports, India accounts for the highest share of STEM (Science, Technology, Engineering, and Mathematics) graduates in the world. But still, there is a huge shortage of AI talent in the country.

India has an extreme shortage of experienced people in this field and most of our universities are also not equipped to solve it in the upcoming years. Silicon Valley has the required talent at their disposal and they can easily hire fresh graduates from local universities who have studied analytics and ML in depth. This is a big factor and hard to resolve.

The standard of candidates is subpar when available. Most people working in data related fields wrongfully proclaim to be the data scientist professional. Over half of India’s data science community holds a bachelor degree. In comparison to two third such professionals in the US that hold a Master degree or Ph.D.

It is difficult to find out the good talent in artificial intelligence. A part of Flipkart’s AI staff is outsourced and works from Palo Alto. Paytm has outsourced its analytics and fraud detection to its team in Canada.

Moreover, trained data scientists who fail to keep evolving with the advent of new technology risk becoming irrelevant quickly. Especially in sectors like retail where the speed of innovation is quick and many of the jobs around AI and big data didn’t even exist five years ago.

Besides training data and employees, e-commerce websites also need to spend big computational resources to store and analyze a large amount of data.

Conclusion

Greater and deeper industry and academic collaborations can be one area where the larger ecosystems can invest in. The area of artificial intelligence and machine learning is pure research-based and the results can take months if not years to materialize. If a steady approach is followed, companies can easily build the capabilities in these areas which have a high potential.