Forward-thinking companies are already looking back at the AI-driven strategies they have been using for years to find ways to get even more out of smart technology. The fact is, customer experience is the top priority for 71% of B2B businesses. Not that you need any more pressure, but you aren't only racing against your competitors to harness the power of AI for enhancing customer experience. What AI can do for personalization, customer engagement, and wowing your customers with a great experience today is remarkably more sophisticated than what companies were using AI to do a few years ago. If you aren't using AI intelligently to build better customer relationships, how can you compete with the companies who have been leveraging this technology for years?
Machine Learning is (or should be) a core component of any marketing program now, especially in digital marketing campaigns. The following insightful quote by Dan Olley (EVP of Product Development and CTO at Elsevier) sums up the urgency and criticality of the situation: "If CIOs invested in machine learning three years ago, they would have wasted their money. But if they wait another three years, they will never catch up." I believe that this statement also applies to CMOs. Machine Learning-based personalization (SegOne Segment of One Marketing) is hotter than ever, especially when marketers select context-specific content to be presented to an individual consumer.
I was recently invited to participate as a panelist at Firebrand Talent's'Put it to the Panel' event on how Artificial Intelligence (A.I.) is changing the marketing profession. And as I am often asked about this topic, in the spirit of sharing, here are some of my thoughts. I am often asked about which APIs and A.I. technology, marketers should consider. Artificial Intelligence enables marketers to personalise and create more effective customer experiences, and improve ROI. At the core, artificial intelligence is all about technology, which enables humans to make better, more informed decisions.
Sentiment analysis is the automated process of understanding an opinion about a given subject from written or spoken language. In a world where we generate 2.5 quintillion bytes of data every day, sentiment analysis has become a key tool for making sense of that data. This has allowed companies to get key insights and automate all kind of processes. But… How does it work? What are the different approaches? What are its caveats and limitations? How can you use sentiment analysis in your business? Below, you'll find the answers to these questions and everything you need to know about sentiment analysis. No matter if you are an experienced data scientist a coder, a marketer, a product analyst, or if you're just getting started, this comprehensive guide is for you. How Does Sentiment Analysis Work? Sentiment Analysis also known as Opinion Mining is a field within Natural Language Processing (NLP) that builds systems that try to identify and extract opinions within text. Currently, sentiment analysis is a topic of great interest and development since it has many practical applications. Since publicly and privately available information over Internet is constantly growing, a large number of texts expressing opinions are available in review sites, forums, blogs, and social media. With the help of sentiment analysis systems, this unstructured information could be automatically transformed into structured data of public opinions about products, services, brands, politics, or any topic that people can express opinions about. This data can be very useful for commercial applications like marketing analysis, public relations, product reviews, net promoter scoring, product feedback, and customer service. Before going into further details, let's first give a definition of opinion. Text information can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about something. Opinions are usually subjective expressions that describe people's sentiments, appraisals, and feelings toward a subject or topic. In an opinion, the entity the text talks about can be an object, its components, its aspects, its attributes, or its features.