Customers are significant assets to a brand, leading them to the top. Segmenting or grouping the vast customer base per different criteria eases marketing actions. Segmentation makes companies 60% more likely to comprehend customers' choices, thus molding their offerings for tremendous success. The segments can be based on wide factors like location, industry, or based on demographics. The market provides tools to assist businesses in effective communication and marketing via seamless customer segmentation.

This story is part of our special report, "Customer Experience in the Enterprise." Traditionally sales, marketing and service teams have operated independently. Over the next several years, however, the distinction between sales, marketing and service will begin to blur as customers demand more consistent experiences. As a result, businesses will need to pay attention to customer experience not only in customer service, but also in their digital or product experiences, as well as their physical touch points, to ensure one consistent experience across their company. Companies may also begin to consolidate customer-facing activities under a single leader, such as a chief customer officer or chief experience officer.

In this special guest feature, Aditya Bhamidipaty, Founder & CEO, FirstHive, discusses how businesses today face an expanding gap between the value their customers' data can potentially provide and the true value their CDPs can create. AI systems can help close this gap by enhancing the productivity of human workers, so long as those workers are trained how to use those systems effectively. Aditya has been a serial technology entrepreneur solving problems for marketers. His stewardship has helped FirstHive to carve a name for itself in the marketing technology industry, delivering ROI to some of the world's largest brands on their marketing initiatives. The role of marketing is synonymous with the notion of customer data.

The world is cautiously optimistic about using artificial intelligence (AI) from customer data due to privacy and AI-bias concerns. At the same time, AI is proven to boost sales for businesses. It saves the time it would get to get customer insights manually, can be used to tailor better customer experiences, and can work faster than any human could at finding anomalies in online behavior data. Businesses expect AI to impact their businesses. Esteemed industry analysts Gartner reports that AI is growing exponentially, with spending on Artificial Intelligence in eCommerce set to reach $7.3 billion per year by 2022- that's up by over 20%.

While many companies are still struggling to understand how artificial intelligence (AI) will ultimately enhance their menu of consumer products and services, one area where AI is already having a demonstrable impact is in the area of hyper-personalization. The timing for this couldn't be better. The pandemic has sent more consumers online to accomplish more tasks than ever before. What these consumers are looking for is the same quality of service they are used to receiving from traditional retail environments, typically from sales associates, in store promotions, loyalty programs, etc. Add to this the fact that consumers have also been spoiled by apps that serve their every whim no matter how niche, ecommerce tools like "customers also liked" pop-up suggestions, virtual dressing rooms, and responsive chat bots. Today's consumer expects more from their internet experience.

Even as global businesses continue to embrace big data and data analytics, the challenge many face is: how to derive the most value from the big data. The latter refers to very large sets of data that cannot be handled with traditional methods. With artificial intelligence (AI) & its subset machine learning (ML) becoming mainstream, i.e. moving from the laboratory to the commercial market, one option companies today have to handle their voluminous data is machine learning. Analytics has moved on from the traditional methods to automated solutions for better business intelligence. In fact, it's gone beyond the scope of a standalone human analyst.

As one of the leading trends in technology, Artificial Intelligence (AI) continues to gain in popularity for marketers and sales professionals, and has grown to be an essential tool for brands that wish to provide a hyper-personalized, exceptional customer experience. The availability of AI-enhanced customer relationship management (CRM) and customer data platform (CDP) software has brought AI to the enterprise without the high costs that were previously associated with the technology. A report on the Future of Work from RobertHalf indicated that 39% of IT leaders are currently using AI or machine learning, 33% said that they expect to use AI within the next three years, and 19% expect to use it within five years. AI has many applications for enterprise businesses, and in this article, we will discuss 4 ways that it can be used to improve the customer experience. The combination of AI and machine learning for gathering and analyzing social, historical and behavioral data enables brands to gain a much more accurate understanding of its customers.

The insurance industry is way past its time when timely response and a balanced price-quality relationship were enough to define customer experience. The advent of Artificial Intelligence, Machine Learning, and Advanced Analytics have disrupted the insurance industry and have reshaped the way it operates. Insurtech firms these days are using their AI and ML capabilities to drive high-quality customer experiences, increased loyalty, generate new revenue while simultaneously reducing the costs. The vision of the insurance firms today and for the future is where customers and customer experience comes first. The combination of AI and ML models built on top of the Customer Data Platform leads to improved customer experience through hyper-personalization.

But how do you turn that feedback into meaningful customer insights? In the past, companies used things like surveys to try to narrow down a general good/bad/neutral response to their recent marketing campaign or product. Still, there is so much more information in the form of unstructured data that could help companies better understand their customers. Whether they are using social media, blogs, forums, reviews, or online news commenting, customers are sharing their opinions in tons of different ways every single day. The only issue: many of these opinions are shared in nuanced ways that traditional AI hasn't been able to navigate.

But how do you turn that feedback into meaningful customer insights? In the past, companies used things like surveys to try to narrow down a general good/bad/neutral response to their recent marketing campaign or product. Still, there is so much more information in the form of unstructured data that could help companies better understand their customers. Whether they are using social media, blogs, forums, reviews, or online news commenting, customers are sharing their opinions in tons of different ways every single day. The only issue: many of these opinions are shared in nuanced ways that traditional AI hasn't been able to navigate.