Consumers today are more conscious about their choices and decision they make. They have far greater expectations from their experiences. “Service is king” and “The customer is always right” have been forming the customer agenda, while companies are doing their best to take customer engagement to a new level. Add to this, content is being consumed through a growing number of channels including mobile devices, social media, web, mail, email, television, etc.

Companies can no longer view and treat customers in aggregate, demographic categories— making broad offers through fixed channels - if they want to stay on top. They need to see customers as individuals whom they know well and serve like no other.

Organizations that will succeed need to understand their customers, which include– behavior, context, interests and preferences—to properly ‘wow’ them and turn them into loyal customers. For this, they have to reach their customers via the right channel, with the right content, at the right time to improve the customer experience, enhance brand loyalty, and increase the customer value.

Data + Context + Action = Better Connected Experiences
Companies have so much information at their fingertips, but unless that is channelized and used effectively, it’s useless. This is true when you consider customer experience. If the customer context and the content shared are not relevant at that time, the exercise becomes a complete waste and your brand relationship with the customer diminishes, as he recognizes that you don’t truly understand his needs.

Real-time Personalization
As customers consume content on the go via various channels, something critical is happening—that customer is becoming a significant factor in a company’s distribution or referral program. After all,it’s the customer that ends up referring content on social platforms and word of mouth. It’s critical, therefore, to build a personal relationship with your customers, whereby you truly understand their needs and preferences.

Technology companies built from the ground up to be data-driven are good examples of how customer experience management is a true asset. Take for instance, Google. Google gathers massive amounts of data about its users’ activities, locations, interests and more – merely from its web activities. As a result, your experience with Google is more personalized than that of say, your bank. Google now goes so far as to tell you today’s weather before

you start your day, how much traffic to expect before you leave for work, when the next train will arrive as you are standing on the platform or your favorite team’s score while they’re playing. And the best part? All of this happens automatically.

Data-driven applications create true business value because they provide users with actionable tasks in real time

scription-based companies must leverage the enormous amount of existing user data they receive by constantly creating highly personalized and connected user experiences; as compared to those created by technology companies dependent on web traffic activity and information. They must resemble a virtual, personal assistant. Because end users’ expectations of their vendors are increasing, and consumers have more of an affinity for those vendors that offer more pertinent information, instruction, and add convenience to their lives.

This presents an exciting opportunity for businesses to leverage their data to better personalize user experiences. Businesses used to rely solely on their customers’ intent, and product considerations, with little need for conversations or interactions. Now, however, productively utilizing customer data allows businesses to determine what a customer is most interested in and create a personalized experience where content, products and/or services are presented to customers before they even realize their need.

Data-driven Applications and Machine Learning for Customer Satisfaction
Thousands of companies are using big data and analytics to gain insight into their data. And while visualizing data can be helpful, graphs alone don’t cut it. What businesses need are data-driven insights that help employees perform their daily jobs better by being actionable. For instance, they should alert a marketing or salesperson each morning with a notification such as: “Here are the 50 customers that might churn in the next 30 days.”

This is how big data processing can create real business value—by providing finite and actionable insights for employees that allow them to better serve their existing and prospective customers immediately.

Data-driven applications create true business value because they provide users with actionable tasks in real time, are scaled for the enterprise, and remove human subjectivity via machine learning.

The sheer mass of data on customers is not possible to process in one data scientist’s human brain. Machine learning must be used to analyze and deliver instruction on what should be done to better the business. So, instead of a data scientist looking deeply at a section of the data, the systems are looking at and devising outcomes from all the data—mainly due to the ever-growing volume of data. And as more data is fed into the system, machine learning continues to get smarter to deliver the best, most relevant content to customers.

But, as I mentioned earlier, it doesn’t make any sense to keep making graphs about data and big data—companies need to focus on the business problem, have clear goals, and introduce data-driven applications based on machine learning to deliver more automated and actionable results. There are a lot of solutions available to work with data, and now they are not only allowing the ability to search many of the databases that hold data, but also aggregate, analyze and visualize that data.

At the end of the day, the more content and data companies have on their customers, the better their ability to quickly drive actionable results and deliver greater revenue to the business, while ensuring privacy and convenience for the customer. But, remember, the key to delivering superior customer experience is to contextualize their data, and get personal—understand your customer at the individual level, via the right channel, at the right time.

The end results are happy customers and a happy business!