Study the past to know the future – Confucius. While building product recommenders, the cold start is one of the biggest challenges that developers face, in addition to others such as data scarcity, scalability, and diversity. Read this blog to know more about Machine L
Use of Machine Learning In Marketing: Saving Cost of Customer Acquisition Marketers are increasingly finding machine learning (ML) an ally in their work. Although a new entrant here as compared to other fields, the use of commercial machine learning in digital marketing
The Rise of Commercial Artificial Intelligence in BI Even as global businesses continue to embrace big data and data analytics, the challenge many faces is: how to derive the most value from the big data. The latter refers to very large sets of data that cannot be handled
Using Artificial intelligence to Sense Buyer Intent – Customer Intent AI Some months ago, booking.com joined the ginger group of brands to combine artificial intelligence with mobile to get a heads-up in anticipating a customer’s purchase intent. Booking.com app use
Increasingly, more and more people and even companies have started to come around to the view that personal voice assistants have to move into an Enterprise environment. Some feel such business agents can be of great use in verticals, and of course, for specific roles wit
The introduction and adoption of Intelligent Personal Assistants (IPA) such as Google Assistant, Apple Siri, and Amazon Alexa have, to some degree, changed a customer`s journey. It`s over 7 years since the advent of the first IPA, so it would be the right time to ask thi
A minuscule percentage of those in the business of providing BI solutions have adopted NLP and adapted it to generate results for Enterprise clients. While the figure may be small today, advancements in the field are bound to push the number up.
Readers of this blog may have realized that Natural Language Processing (NLP) was missing from our ‘5 Data Analytical Trends To Watch For in 2018’ post. Our in-house team of predictive data analysts say it lost out to the other trends by a narrow margin. But that in no way takes away from the importance of NLP and its growing influence in the world of big data analytics. The loser by a whisker surely deserves an honorable mention, hence this 2-part post.
Customers leave behind an incomprehensible amount of data while they go about shopping. Making sense of that data and reacting in real time are the two things that will keep companies one-step ahead of their customers (and competition) in the present-day customer-centric world.
