Home Global-ICTGlobal-ICT 2014 Putting context into content to enhance brand loyalty

Putting context into content to enhance brand loyalty

by Administrator
Luc BurgelmanIssue:Global-ICT 2014
Article no.:15
Topic:Putting context into content to enhance brand loyalty
Author:Luc Burgelman
PDF size:213KB

About author

Luc Burgelman is a co-founder and the CEO of NGDATA. Prior to NGDATA, he co-founded Porthus (EU, 2000), which delivered cloud-based solutions that IPO’d in 2006 and was acquired by Descartes Systems Group in 2010 (US/CA). At that time, Luc became the EVP global marketing, product strategy and chairman of corporate planning office (US/CA).

After leaving Descartes, Luc observed what was becoming a massive expansion of useful consumer data and realized that consumer oriented companies would need a solution to store, organize, sift and gain customer insights with what would become known as Big Data. Through his experience in artificial intelligence (neural networks, large data processing and rule based systems) he decided to join forces to create NGDATA.

Luc holds a Master in Engineering and Information technology (UGent, BE) and Executive MBA (Antwerp, Kellog, IMD).

Article abstract

Companies that will succeed need to understand their customers and know all aspects of that customer – behavior, context, interests and preferences—in order to properly ‘wow’ them and turn them into loyal customers. 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.

Full Article

No matter which company they interact with, consumers today have far greater expectations from their experiences – especially from their favored vendors. Service is king and the customer is always right have been thrown on their heads and are being taken to a new level when it comes to wowing and keeping customers. Add to this that content is being consumed through a growing number of channels including mobile devices, social media, web, mail, email, television, etc.

Companies can no longer treat/view 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 who they know well and serve like no other.

Companies that will succeed need to understand their customers and know all aspects of that customer – behavior, context, interests and preferences—in order to properly ‘wow’ them and turn them into loyal customers. 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 content and better customer experience
Companies have so much information at their fingertips, but unless they can use this information quickly and effectively, it’s useless.

For example, based on [customer] John’s available information (i.e. location, engagement, transactional, CRM, website history, social…), you might have learned that he recently purchased a house. If you had merely looked at his age and income level, you might have sent content to John – via offers through email, social, mail, etc.—pertaining to loan options and mortgage-related products. How is this of any value to a new home owner? Without understanding this particular customer’s context, you have not delivered a timely nor appropriate content/offer for him. These offers would have been a lot more effective had your company taken action and delivered these offers to John before he purchased a home, or while in the buying cycle.

As a result, John’s relationship with your brand diminishes, as he recognizes that you don’t truly understand his needs.

Real-time personalization
As customers consume content via the various channels mentioned above, 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, one 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 assets. 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.

Subscription-based companies must leverage the enormous amount of existing user data they have stored and are constantly receiving to create user experiences that are so much more personalized than those created by technology companies that count merely 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 offers that add convenience to their lives.

This presents an exciting opportunity for businesses to leverage their data to better personalize user experiences. Businesses used to have to rely solely on their customers’ intent, and product considerations and purchasing decisions were entirely customer-driven. 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 they need them.

This customer-centric strategy requires the anticipation of future needs—looking at behavioral patterns, market trends, and user experiences for proactive measures to secure a personalized, unique and memorable experience across multiple channels. This, in turn, enables the customer to feel understood and valued, and likely to develop a loyalty that will be a good basis for customer retention, up-selling and cross-selling.

It also requires that companies go beyond placing customers in aggregate categories and create what NGDATA likes to call individual Customer DNA to specifically target content at the individual level, based on preferences derived from all available data sources. Customer DNA must include up-to-date, well-organized data points of each individual customer, prepared and ready to deliver content at the most appropriate time and place. No more hunting in pools of raw interaction data, no more batch processing or broad, static segmentation exercises—companies now have access to thousands of relevant metrics for immediate action.

By creating Customer DNA, companies can access all the data on a customer to predict the propensity they might have for a [new or existing] product, a service or a particular content offering. This propensity is calculated based on a number of machine learning algorithms, and also updated in real-time, using all incoming interactions.

Data-driven applications and machine learning for customer satisfaction…and privacy
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 applications that help employees do their daily jobs better. More importantly, these data-driven applications must be 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. Machine learning encompasses the algorithms, optimization and learning tools that interact with the data, thereby eliminating any human interaction/intervention between the data being generated and the offers or services being delivered to the customers—ensuring customer data remains secure and private.

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 the need to quickly make something of it. 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 mention 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 for the problems of the business. There are a lot of solutions available to work with big data, and now they are not only allowing the ability to search many of the databases that hold big 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, understand their lifestyle to deliver products, services and content that are pertinent to them, via the right channel, at the right time.

The end result is a happy customer and happy business!


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