The latest generation of cognitive and analytics technology is capable of by far more than taking orders. It will make decisions and learn. By using cognitive computing – a technology that integrates data mining, pattern recognition and natural language processing to mimic the way the human brain works — new applications can process high volumes of information to support decision making. Digital analytics allow these systems to analyze structured as well as unstructured data (e.g. social media and internet data) and apply predictions in order to make the right business decisions.

Computing is coming of age

In the past, systems have relied on conditional programming statements (if/then/else). In 1950, computer pioneer Alan Turing already suggested that building a computer that “thinks” like a child by learning as it operates, would eventually produce technology with the maturity of an adult. In the 90s, technology took a first step in this direction with the introduction of computers that analyze text and mine for keywords and data.

Since the early 2000s, new capabilities for analyzing unstructured data, new data sets (e.g. streaming information) and even more reliable solutions have been in development.

These had been in parts spurred by the advent of the internet and went along with the subsequent need to analyze big data and therefore developing more refined algorithms. New applications and programs utilize natural language processing and machine learning to understand and reason, allowing researchers and businesses to make smarter decisions.

Cognitive computing now enables the enterprise to put the customer at the very center of their strategies. By giving these capabilities into the hands of marketers, a lot of valuable data is no longer hidden from their view. Companies can instantly gain more extensive insights into the most intimate side of the customer universe than ever before. They can discover new patterns, put these in context and get to know each customer with previously inaccessible detail. Subsequently, companies can become agile enough to execute campaigns on the fly.

These campaigns can be combined with data from multiple other sources (e.g. social media and weather), allowing enterprises to reach an entirely new level of interaction by putting customer information in context . Data have evolved from a possession that went unused by many companies into structured insights that are essential to compete, i.e. events and social media have become data in motion (sensor data, IoT, geo special).

How cognitive technology works today

Cognitive technology works in 3 phases:

1. Understanding
2. Reasoning
3. Learning

Cognitive software, combined with in memory technology reads and understands data and documents — structured as well as unstructured — on a huge scale. Through reading and analyzing this data, evidence-based recommendations are made. These are then augmented by leading expert knowledge. The cognitive engine learns and improves over time to enable independent decision making.

Dots are connected and questions that were not thought of before are identified and lead to new inspiration. Intentions can be better fulfilled and questions of customers and stakeholders more effectively answered. Decision making is supported by evaluating a condition and suggesting the right option.

Cognitive technologies are continually in development. IBM, for example, is teaming up with the MIT at their Laboratory for Brain-Inspired Multimedia Machine Comprehension to research and develop a machine’s ability to see and hear more effectively, thus improving machine learning capabilities.

Generating value: the future of healthcare is cognitive

The face of healthcare has already been changed forever on a number of fronts through cognitive technology. By using cognitive computing, researchers can access huge amounts of medical literature, combine it with patient information, and learn over time in order to create diagnoses or recognize patterns in medical information that can be used to improve patient care outcomes. Patients who are likely to develop a chronic health condition can be identified and offered programs to help prevent costly treatments.

As IBM reports, 80% of useful medical information is unstructured and thus cannot be recognized by non-cognitive computers. Once this knowledge has been compiled and machine learning allows computers to more intelligently process and analyze it, data can also be accessed globally at large scale. A doctor who sees a patient anywhere in the world could tap into this information and evaluate symptoms with the help of more data than any single human being could read in a lifetime to come up with the right treatment.

Life sciences also benefit significantly from cognitive solutions. The Watson Discovery Advisor for example, is offering genomics analytics to aid discovery and ensure safety. This area of development is of high value for the medical and life science industry. Real world evidence (RWE) and advanced analytics drive operational and clinical trial effectiveness, allowing for faster drug approval. This enables life science and pharmaceutical companies to develop new drugs faster with reduced costs.

Looking ahead

In the future, applications and increase in digitization will change the face of most industries. Healthcare and life sciences have already been significantly impacted. Information technology will continue to develop into a cognitive partner of industrial, scientific, and commercial organizations, helping to manage and make sense of data to increase knowledge, insight, and productivity.

The ability to learn and recognize patterns will change the way we do business and make decisions. Information can be processed and understood, in partnership with experts, on a scale and timeframe that humans alone cannot accomplish. Companies can use cognitive computing to create value. It has already been embedded in products today (e.g., Netflix and Amazon Prime) in order to deliver a customized and relevant end2end experience.

Amazon has already started to leverage artificial intelligence for the analysis of its individual customer. They know nearly everything about their shoppers: at which weather condition, on which days and at what time people buy certain items. Amazon applies algorithms to adapt prices, aiming at achieving the maximum amount a customer is willing to pay.

In order to compete in the future, companies need to embed cognitive computing and analytics along the value chain and connect data with company objectives, products and solutions. New ways of working and learning need to be developed. IT departments no longer simply support selected work processes, but have to be a key partner in developing and implementing new ways to use cognitive technology across the value chain of the entire enterprise.

As Audi’s CIO Mathias Ulbrich puts it, “We believe that the job of IT is to proactively support the ‘innovative transformation’ of the company as a whole.” Learning their way around cognitive computing, as well as strategizing ways to best apply in order to implement better business practices, should be at the top of the mind of every forward-looking company.

With its potential to change the way we understand and process information at every level, cognitive computing and machine learning are part of the progression towards more automated processes in business and research that began in the 1940s with Alan Turing.

Cognitive computing is the new revolution in the digital world, and businesses will need to harness this technology and use it intelligently to stay ahead of the game.

How did most of us learn about the advent of the “Internet of Things” (IoT)? Perhaps we read about fitness trackers you wear on your wrist? Or maybe about self-driving cars?

Today, everything seems to be digitally connected. Moreover, the expansion of the internet’s network of people and devices is changing the way the world operates and does business — in both B2C and B2B contexts. IoT encompasses a new era for business in the B2B sector known as Industry 4.0, in which devices within and across enterprises are connected. They communicate with each other through a versatile network of machines and production environments, resulting in increasingly automated processes.

As businesses want to be part of the Industry 4.0 wave, they need to embrace the shifting landscape that the IoT is about to bring. Moreover, they need innovation in order to make the most of it and deliver the respective business value.

In with the New, Out with the Old (Business Models)

The IoT can generate new value for businesses by utilizing Big Data, improving automation and cognitive analysis to ultimately enhance processes and products. These technologies can help to create advanced products and better customer experiences for both the B2B and the B2C sector. But in order to generate value from data and automation, new business models have to be developed and new processes need to be designed.

The development of smart devices alone cannot make this change happen. Instead, both the right strategy and an adapted way of working need to go hand in hand. The new value chain within the IoT is more complex than previous models as it needs to consider networked devices, connected products and new living services alike.

A completely new infrastructure is needed if companies want to fully leverage the possibilities of the IoT. An increased focus on partnerships with product and service providers is essential to fulfill all the new IoT functions. Companies have to plan ahead to utilize gathered information and new forms of automation to not only streamline processes, but also to deliver value to corporate clients and end customers.

Complex challenges with rewarding benefits

Let’s take the agriculture sector as an example: Today, there are many external factors that impact the agricultural industry. By supplying farmers and agricultural corporations with the respective information, they will be able to adjust their production and supply chains accordingly, enabling them to improve net profit.

Processes like fertilization are automated and performed more efficiently. Arrays of sensors used throughout the agricultural lifecycle communicate with each other. The gathered data is enriched by external information such as weather and GPS location based information, allowing to adjust schedules for fertilization. With fewer unknown influencing factors, agricultural businesses can forecast growth, processing, and distribution of products more effectively.

The value for farming businesses and their commercial partners comes in the form of fewer resources wasted, more efficient processes, and the ability to ultimately deliver better products. These improvements, in concert with partners in the crop producing industry that create highly effective fertilizers, extend the value chain and deliver new services such as the “intelligent farming”.

Ceaseless development and transformation

According to McKinsey Quarterly, new business models in the automotive industry involve devices and services that gather data about customer usage and continue to develop the product after it has been sold and delivered. For instance, if features like heated seats are shown to be underutilized by car owners, they can be given a more prominent and easy-to-use position on the dashboard’s touchscreen.

Adjustments like that will transform products into living and service-providing assets. And today’s customers are developing even higher expectations towards networked products. A recent study shows that 48% of car buyers would walk away from a car they liked if its tech features were difficult to use.

Intelligently designed products that are easy to use and continue to improve along its actual usage provide the highest value to the customer. Subsequently, products — whether they are cars, clothes, washing machines, or fitness trackers — will turn into enhanced services and assets!

The Value Impact of the IoT: Products Evolving into Services

The IoT transforms internal processes, as well as interactions among customers, partners, and businesses by the use of information. But how does this change the way we understand value?

Instead of just offering products with customer service support, companies can offer products that become services (e.g. Intelligent Farming) when embedded in an overall service experience. This business model is what Jennifer Fraser calls “a dynamic eco-system.” Instead of encounters with B2C and B2B customers that might lead to an actual purchase, companies follow customers on their everyday customer journeys [link to previous article on Bohé website], equally before and after the purchase, adjusting at each stage to better serve their individual needs and building valuable, long lasting relationships. This flexible and dynamic model will therefore have the potential to generate new and additional revenue streams while new needs are identified and met.

Value is measured not only in terms of products sold, but also in terms of creating and maintaining profitable customer relationships.. Whereas companies may have just sold products in the past, they will now need to go beyond product development and sales. They will need to plan which services they offer into which markets and what kind of partner eco-system can create the most value.

The Wild, Wild West of the Internet of Things and Industry 4.0

The many opportunities to generate value through the still-evolving IoT also pose risks for many enterprises. A lack of coordination between IT and operations, sales, and marketing may lead to lost potential benefits from new technologies. If the IT department has to change — it needs to do more than just specialize in managing software, infrastructure and platforms.

The IT expert’s work is now embedded and valuable part of the core strategies of a company. And it’s not enough to just have smart devices and amounts of information. Given the large and equally complex industrial systems, big data analytics and coordination between connected devices are just as important. Production and other processes have to be designed alongside the “new way of working” and subsequently automated. Once data has been gathered, it needs to be managed and used to develop and gain the most effective and efficient strategies.

Working with Personal Data is a Delicate Task

The requirements of data security and privacy have never been more crucial and need to be dealt with sensitively. As Ari Amster puts it, “Many companies have the attitude of creating and releasing the product first and foremost, then worrying about adding security features later through patches and updates.”

This is just another example of why IT needs to be a strategic partner within the IoT and Industry 4.0 scenarios. Along with this changing environment comes responsibility for businesses to be circumspect and ethical while at the same time staying on top of the latest developments in technology and security.

The Upshot: Innovate to Stay in the Game

IoT and Industry 4.0 are the new reality for businesses. As the world is becoming digitally connected, consumers, partners and customers expect to have access to responsive technology that makes using products and services easier. Businesses must join in on the IoT intelligently, otherwise they’ll lose rather than gain value.

It’s simply not enough to just adopt the technology. For example, although the oil drilling industry was an early innovator of the IoT technology, McKinsey found that only 1 percent of the data gathered by 30,000 sensors on a typical oil rig is used to adjust processes and strategy and generate value.

Making It All a Successful Reality: People and Culture

Digital culture is an essential part of successfully implementing IoT and Industry 4.0 business processes. Unfortunately this reality is often neglected. It is about blending values, leadership, behaviours and experiences to help an organization leverage all the opportunities that lie ahead. Creating this culture is inseparable from maximizing the value of your technology investments and realizing the full value of your new business models, products and operations.

Marketing to customers in the digital era can be dizzying. Every day, new communication channels or apps emerge along with constant changes in technology. With every new possibility comes the challenge of how to create new profitable marketing models.

Would you like to connect with customers in a relevant and individualized manner? Do you aim to improve customer experience to create profitable relationships? The most effective marketing strategies today are designed around a customer journey (which is the series of interactions a customer has with a brand). These strategies make use of analytics in context and real time.

Brief Encounters: The Customer Journey of the Past

Before the digital era, traditional marketing models conceived customer interaction as a single point of contact: the consumer coming face-to-face with a product in a store, or when listening to a radio advertisement while driving. Marketing strategies have grown out of these expected encounters with targeted consumer groups, using insights from separate “silos” of experts.

These silos could include marketing, product management, communications, customer data, and sales departments. Companies set objectives and strategized the ways they could reach consumers in order to meet those.

The Journey Matures: Customer Experience Today

With rising customer expectations, consumer interaction has become a broader, more nuanced concept. Creating the right customer experience is a key differentiator and should therefore be among every CEO’s top three priorities. Throughout a customer journey, he or she meets brands in a variety of digital and analog spaces, from smartphones and wearables to print media and retail stores. Mapping these events to customer journeys across time and across channels shifts the focus of marketing strategy from merely meeting sales objectives to improving customer experience. 86% of the consumers already willingly pay more for better customer experiences. Effective marketing today is more than meeting company objectives. It requires an understanding of the customer’s life cycle and needs while taking a wide variety of touch points and metrics into account. Customer journeys can conceptualize this information and bring it to life leveraging analytics.

“Why the #CustomerJourney is the perfect approach for brands in the #Digitalera and better #CustomerExperience. “

Twittern WhatsApp

The Evolution of Marketing in the Digital Era

The developments in digital technology, multi-channel integration, analytics and cognitive marketing strategies have made the evolution of the customer journey possible.

The new and evolving digital reality is more than an extension of the analog. It is a completely new basis for consumer interaction with brands, products and services. Today´s customers value convenience. They are looking to complete a specific journey in an easy and timely fashion. Companies can follow customers on their journeys and promote brands in a more targeted, community based and contextual manner using digital and analog touch points. Customers have more choice and are more connected. They need relationships with brands. And brands need to use analytics to anticipate and act on customer choices for increased ROI. All in all, it is not just about products, but living services and an individualized experience.

Steve Jobs

You‘ve got to start with the customer experience and work back toward the technology – not the other way around.

Take the example of the digital strategy by Nike, which has been expanding in recent years from selling shoes and sports clothing into creating an entire digitally-based fitness ecosystem, providing training programs and communities, fitness tracking apps and devices, and expert advice on gear. After forming a partnership with Apple in wearable technology, websites and smartphone apps, Nike is now able to meet a wide variety of customer needs. The company builds customer relationships by developing insight-based services, tailoring products to their customers’ fitness journeys and moving toward a service model generating continuous revenue streams.

Integrated Digital and Analog Strategies: Mapping the Future of Marketing

A customer’s journey constantly shifts between the digital and analog world: a customer orders a ride home from the airport with a driving service using an app, but meets the driver in person, having found the service by reading a print ad.

Given the complexity of customer journeys, companies can’t afford to isolate digital strategy from other facets of marketing. Therefore, the integration of digital strategies into the overall marketing and advertising strategy is crucial for customizing customer experience and journey design. In order to implement customer journeys successfully, they have to be target group specific, distinct and mapped appropriately. Analytics and cognitive marketing strategies are crucial to this mapping. Mapping the customer journey to products and services allows brands to make successful connections with today’s digitally savvy consumers.

Taking an integrated approach to digital and analog marketing drives innovation and ideas that create superior customer experience. Journeys that are digitized across all interactions lead to the greatest customer satisfaction and will therefore lead to the biggest business impact.

Summary where is the “so what”

In order to build and sustain profitable customer relationships, companies must take the realities of the digital world into account. The marketing strategy needs to be moved beyond traditional isolated encounters between customers and brands and deliver a consistent end2end experience.

The digitalization has enabled marketing strategies to actively use and drive customer journeys in order to generate significant business value and to stay relevant to both the consumer and the brand. According to a McKinsey research, a company can increase revenues by 2-3 % for every 10-percentage point uptick in customer satisfaction. Companies can use customer journey mapping to understand customer needs and perspectives. They can start to build relationships with customers in a consistent manner across all touch points and improve customer experience using analytics and cognitive marketing.

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