Large Suite of Web Based Machine Intelligence Products

Below is the continuation of the transcript of a Webinar hosted by InetSoft on the topic of Enabling the Intelligent Enterprise with Machine Learning. The presenter is Abhishek Gupta, Product Manager at InetSoft.

InetSoft's overall mission to help companies digitize. We see every company becoming a software-driven company, or software enabled company. Fundamentally this is about connecting things, connecting people, connecting businesses together with intelligence. InetSoft's offerings prove themselves along the spectrum with success factors. We have a large suite of web based machine intelligence products as well as superior collaboration in mobile tools.

The way we approach machine learning and machine intelligence, it's a holistic one. When it comes to enabling the intelligent enterprise, it is about business outcomes that we're targeting. It's not technology for technology's sake, but really about the main factors I talked about earlier: increasing revenue, optimizing processes by reimagining them with digital intelligence, having more quality with work and employee engagement, as well as increasing customer satisfaction and retention.

The way to do this is to have computers learn from data. We have the tool that's necessary to learn from data, and both models that add value to the enterprise by enabling digital intelligence. This is possible today because we're able to learn from the big data sets because we're leveraging technology advances like general purpose graphics processing units together with strong strategic technology partners like an Nvidia and Intel as well as deep learning algorithms where we partnered with Google and other advances in machine learning.

This is the reality behind artificial intelligence that you can consume today. The promise of enabling the intelligent enterprise nicely combines the human element of the I with the command line from traditional software applications. It symbolizes insight and foresight again with the I and a relentless drive forward into action thus closing the insight to action group.

Enabling the intelligent enterprise is about two main kinds of scenarios. It's about automation and augmentation of repetitive menial knowledge work and everything that happens in the shared service organization according to a process over and over again. These are prime candidates for machine learning. It can be like minor operations where all the peripheral digital information entering the enterprise gets processed by ML systems learning what generations of accountants have been doing with receivables and payables processing.

It can be in transforming HR shared services by learning from years of HR service tickets for the sick note or pay note or change of employers or change of tax bracket. You can find deep insights into what drives customer retention and what preventive measures, what customer satisfaction an enterprise must profitably be applied at a given point in time.

The other kind of scenario on the right hand side is about the impossible. When we've been able to provide a personal concierge to every mass market customer like never before, but now we've been able to take a picture of something and immediately order it or have it processed without any further intervention based on computer vision or mobile phone images like never before.

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Now we've been able to process the totality of travel providers and travel update emails. Never before have we been able to read them with a personalized attention to what constitutes the ideal trip to provide a personalized travel concierge or the perfect trip experience for travelers.

This is a scenario that was economically impossible before and by adding machine learning and digital intelligence into the mix today they can become a reality, and they can become embedded and pervasive in applications. If you'd like to learn more about this we have prepared an open online course on enterprise machine learning in a nutshell.

How Travel Agents Use Machine Learning

Travel agents have long been the trusted advisors for travelers seeking expert guidance in planning their trips. With the advent of machine learning, these agents have embraced advanced technologies to enhance their services and cater to the evolving needs of their clients. Machine learning algorithms enable travel agents to analyze vast amounts of data, including customer preferences, historical booking patterns, and real-time market trends, to offer personalized recommendations and tailor-made travel experiences.

One way travel agents utilize machine learning is through customer segmentation and profiling. By analyzing past booking behaviors and demographic information, machine learning algorithms can categorize travelers into different segments based on their preferences, budget constraints, travel habits, and other factors. This segmentation allows travel agents to target specific groups with personalized promotions, curated travel packages, and relevant recommendations, thus maximizing customer satisfaction and loyalty.

Another significant application of machine learning in the travel industry is dynamic pricing optimization. By leveraging algorithms that analyze various factors such as demand forecasting, competitor pricing, seasonality, and even external events, travel agents can dynamically adjust prices for flights, accommodations, and other travel services in real-time. This dynamic pricing strategy not only helps travel agents maximize revenue but also ensures that customers are offered competitive prices that align with their budget and preferences.

Furthermore, machine learning plays a crucial role in enhancing customer service and engagement for travel agents. Chatbots powered by natural language processing (NLP) algorithms can efficiently handle customer inquiries, provide instant responses, and even assist with bookings and itinerary customization. These chatbots can learn from previous interactions to improve their accuracy and effectiveness over time, offering a seamless and personalized experience for travelers while relieving the workload of human agents.

In addition to improving operational efficiency and customer satisfaction, machine learning also enables travel agents to stay ahead of the curve in terms of market trends and emerging destinations. By analyzing social media feeds, review platforms, and other online sources, machine learning algorithms can identify trending destinations, upcoming events, and niche travel experiences that appeal to specific segments of travelers. Armed with this insight, travel agents can proactively design and promote innovative travel packages, tapping into new markets and staying competitive in the ever-evolving travel industry.