Companies today have to be strongly focused on customer service if they want to succeed in competitive markets. There is nothing new about this, but the addition of the Internet of Things (IoT) has had a huge impact on customer service in recent years. Increased connectivity leads to higher customer expectations, thereby enhancing demand for companies to meet these expectations. According to research, 42% of industries are spending more than $3 million annually on average on IoT, for instance, Charter Communications has recently invested in IoT to ensure that Spectrum internet customer service provides a smoother experience for the users. Here are some impacts expected from the development of the Internet of Things on customer care.

Customer service will become more Complex

The Internet of things will complicate customer service, starting with those companies that offer services of the IoT. Telecoms will also need to understand how the customer will best make use of the service, how the customer uses other IoT devices at home or in the office, and how products of the company interact with other products overlap.

Here’s how the field could look. Assume a malfunction of a wired thermostat device. Not only does this malfunction influence the thermostat, but also the HVAC system of the consumer and eventually the safety of the consumer at home. This small problem of software leads to a major problem in customer service, showing the complexity of our interlinked world.

Making Customer Service Smarter

In the customer service world, data is one of the advantages of the IoT. The information on these customers is constantly being collected from all these connected devices and technologies. Using this knowledge correctly, you can use data more easily for your clients and give the business an overview of what the consumers desire and how they utilize the services that you offer. You will see higher consumer loyalty as you use this data to deliver value-added offerings on the goods and services, currently delivered to the consumers. Diego Tamburini calls this “the secret sauce” that could boost your marketing efforts.

This kind of data gathering and processing has tremendous possible implications as more items are linked or equipped with sensors. Companies will be able to draw lessons about their products’ past uses on the manufacturing side and plan to improve future product models, to meet better customer requirements. The system is just as good as this, when a component of a device fails, a supplier will use the sensors to submit a new portion, or arrange a repair procedure appointment, to fix this component before it fails. Customers should also maintain predictive servicing of operating goods and this goes far further to improving the perspective of the customer on the commodity offered by an undertaking.

Automation of some areas

Automation is one of the major advantages of the Internet of Things, which is the same in many ways as it applies to customer support. Systems that can track and control connected devices in an IoT system and provide a dashboard for problem-solving are created. This can remove a human intervention layer and protect the contact center in situations, in which genuine intervention is required.

Tim Wilson from near Shore America identifies this as a big benefit, but still a responsibility for new customer support. He says that computers will be the first move in problem-solving if people cannot solve the issue. He warns, “Agents must step up to the table.” This also means that customer care personnel will respond to the data gathered on the IoT devices and contact the consumer if automatic systems cannot fix the issue.

Improved Security

Consumers are, understandably, very defensive of their details and it is necessary for mass implementation of IoT to maintain the highest degree of protection. There have been cases in the past where hackers have also stolen wired toys and devices. Companies need to focus on protecting users from external users seeking unauthorized access to their IoT devices – otherwise, customer services and brand backlash will occur.

Conclusion

How is IoT evolution in the field of customer service going to change? It will bring more expectations that can be met with more automation. It will still complicate the environment a little bit, but it will still assist with the increased data. All and all, once such resources are utilized properly, businesses will have more opportunities to find meeting their consumer requirements. The Internet of Things has been a real game-changer for customer engagement – robust infrastructure and a progressively technically savvy populace, combined with heavy rivalry and that consumer demands in most markets indicate that IoT-enabled companies are expected to enjoy the benefits of improved customer satisfaction and higher profits.

The post HOW THE INTERNET OF THINGS IS CHANGING CUSTOMER SERVICE? appeared first on Artificial Intelligence.

Artificial Intelligence (AI) is creating a new frontline in information security. Systems that independently learn, reason and act will increasingly replicate human behavior. Like humans, they will be flawed, but also capable of achieving great things.

AI poses new information risks and makes some existing ones more dangerous. However, it can also be used for good and should become a key part of every organization’s defensive arsenal. Business and information security leaders alike must understand both the risks and opportunities before embracing technologies that will soon become a critically important part of everyday business.

Already, AI is finding its way into many mainstream business use cases. Organizations use variations of AI to support processes in areas including customer service, human resources, and bank fraud detection. However, the hype can lead to confusion and skepticism over what AI actually is and what it really means for business and security. It is difficult to separate wishful thinking from reality.

What Are the Information Risks Posed by AI?

As AI systems are adopted by organizations, they will become increasingly critical to day-to-day business operations. Some organizations already have, or will have, business models entirely dependent on AI technology. No matter the function for which an organization uses AI, such systems and the information that supports them have inherent vulnerabilities and are at risk from both accidental and adversarial threats. Compromised AI systems make poor decisions and produce unexpected outcomes.

Simultaneously, organizations are beginning to face sophisticated AI-enabled attacks – which have the potential to compromise information and cause severe business impact at a greater speed and scale than ever before. Taking steps both to secure internal AI systems and defend against external AI-enabled threats will become vitally important in reducing information risk.

While AI systems adopted by organizations present a tempting target, adversarial attackers are also beginning to use AI for their own purposes. AI is a powerful tool that can be used to enhance attack techniques or even create entirely new ones. Organizations must be ready to adapt their defenses in order to cope with the scale and sophistication of AI-enabled cyberattacks.

Defensive Opportunities Provided by AI

Security practitioners are always fighting to keep up with the methods used by attackers, and AI systems can provide at least a short-term boost by significantly enhancing a variety of defensive mechanisms. AI can automate numerous tasks, helping understaffed security departments to bridge the specialist skills gap and improve the efficiency of their human practitioners. Protecting against many existing threats, AI can put defenders a step ahead. However, adversaries are not standing still – as AI-enabled threats become more sophisticated, security practitioners will need to use AI-supported defenses simply to keep up.

The benefit of AI in terms of response to threats is that it can act independently, taking responsive measures without the need for human oversight and at a much greater speed than a human could. Given the presence of malware that can compromise whole systems almost instantaneously, this is a highly valuable capability.

The number of ways in which defensive mechanisms can be significantly enhanced by AI provide grounds for optimism, but as with any new type of technology, it is not a miracle cure. Security practitioners should be aware of the practical challenges involved when deploying defensive AI.

Questions and considerations before deploying defensive AI systems have narrow intelligence and are designed to fulfill one type of task. They require sufficient data and inputs in order to complete that task. One single defensive AI system will not be able to enhance all the defensive mechanisms outlined previously – an organization is likely to adopt multiple systems. Before purchasing and deploying defensive AI, security leaders should consider whether an AI system is required to solve the problem, or whether more conventional options would do a similar or better job.

Questions to ask include:

• Is the problem bounded? (i.e. can it be addressed with one dataset or type of input, or does it require a high understanding of context, which humans are usually better at providing?)
• Does the organization have the data required to run and optimize the AI system?

Security leaders also need to consider issues of governance around defensive AI, such as:

• How do defensive AI systems fit into organizational security governance structures?
• How can the organization provide security assurance for defensive AI systems?
• How can defensive AI systems be maintained, backed up, tested and patched?
• Does the organization have sufficiently skilled people to provide oversight for defensive AI systems?

AI will not replace the need for skilled security practitioners with technical expertise and an intuitive nose for risk. These security practitioners need to balance the need for human oversight with the confidence to allow AI-supported controls to act autonomously and effectively. Such confidence will take time to develop, especially as stories continue to emerge of AI proving unreliable or making poor or unexpected decisions.

AI systems will make mistakes – a beneficial aspect of human oversight is that human practitioners can provide feedback when things go wrong and incorporate it into the AI’s decision-making process. Of course, humans make mistakes too – organizations that adopt defensive AI need to devote time, training and support to help security practitioners learn to work with intelligent systems.

Given time to develop and learn together, the combination of Human and Artificial Intelligence should become a valuable component of an organization’s cyber defenses.

Planning for the Future Begins Now

Computer systems that can independently learn, reason and act herald a new technological era, full of both risk and opportunity. The advances already on display are only the tip of the iceberg – there is a lot more to come. The speed and scale at which AI systems ‘think’ will be increased by growing access to big data, greater computing power and continuous refinement of programming techniques. Such power will have the potential to both make and destroy a business.

AI tools and techniques that can be used in defense are also available to malicious actors including criminals, hacktivists and state-sponsored groups. Sooner rather than later these adversaries will find ways to use AI to create completely new threats such as intelligent malware – and at that point, defensive AI will not just be a ‘nice to have’. It will be a necessity. Security practitioners using traditional controls will not be able to cope with the speed, volume, and sophistication of attacks.

To thrive in the new era, organizations need to reduce the risks posed by AI and make the most of the opportunities it offers. That means securing their own intelligent systems and deploying their own intelligent defenses. AI is no longer a vision of the distant future: the time to start preparing is now.

The post Deciphering Artificial Intelligence in the Future of Information Security appeared first on Artificial Intelligence.

Emotional intelligence is an essential skill in the customer service functions with the productivity and efficiency of the role is directly tied to the quality of conversations. The personal dynamics of emotionally cognisant customer service agents play an important role in empathetically resolving any queries or concerns, impacting customer churn and increasing brand loyalty by leaving customers with a positive impression of an organisation.

However, rapid adoption of automation technology within customer-facing roles presents new challenges to organisations that want to harness its benefits, without impacting the service that it delivers to its customers.

A hybrid workforce

Already helping many companies increase customer service availability, reduce wait times and improve resolution rates, Gartner has predicted that a quarter of all customer service operations will use artificial intelligence (AI)-powered virtual assistants by 2020. In many organisations, this has resulted in the creation of a hybrid workforce of human and digital agents.

While some organisations are still using basic chatbots, businesses that prioritise giving high quality customer service are opting for more sophisticated digital colleagues, that can respond to more complex and nuanced requests. In addition to handling simple customer queries directly, they can also act as a whisper agent to human customer service representatives to help them provide faster and more accurate responses. For instance, at insurance firm Allstate, virtual colleague Amelia has collaborated with live agents on more than three million calls. Employees are able to access her knowledge to augment their own.

A common view of emotional intelligence

However, as digital colleagues play a greater role in customer-facing workplaces, a key challenge for businesses is to ensure that both human and digital agents are delivering the same emotionally cognisant service. While the leading digital colleagues can understand and react to customers’ emotional state, how can this be productively combined with human experience and intelligence to deliver a truly superior and consistent service?

To establish a common view between digital and human colleagues on emotional intelligence, we should consider how the symbiotic relationship can support the five components of emotional intelligence, as outlined by psychologist Daniel Goleman.

Self-awareness, self-regulation, motivation, empathy and social skills: these five original components of Goleman’s theory help us create a blueprint for emotional intelligence within the hybrid workforce. While human and digital agents don’t all excel in the same areas, it does show us the opportunity for augmenting human experience and expertise with AI, without losing the “human touch”.

So, how do the five components apply to hybrid customer services?

Self-awareness and self-regulation

When dealing with customers, it’s important to be aware of your own emotional state and how it will impact your actions. This is tough for human agents – especially when dealing with particularly frustrating or aggressive customers.

Working in symbiosis, digital colleagues can assist human agents in these situations by acting as a whisper agent. The intelligent system records and analyses the reactions to all responses previously given by customer service agents, enabling it to consider the emotional aspect of the dialogue when suggesting what the best response is for that customer in the given situation.

For instance, when working on a cancellation line, where the majority of interactions are negative, it can be tough for human agents to keep their cool. However, you’re less likely to get an emotional outburst from AI, because rules dictate how they act when seeing certain behaviour or emotions. This doesn’t make digital colleagues alone the best agent to deal with cancellations, as there is no opportunity for reconciliation and already-frustrated customers may feel fobbed off. However, they can provide the human agents the responses that best serve the customers’ emotional state, while helping them self-regulate their response.

 Motivation

As times have changed, the motivation for customer service roles has shifted from merely churning through calls as quickly as possible, to increasing the value of interactions and making sure that each has a positive outcome. While this is a welcome shift for customers, being engaging and personable for hours on end can be exhausting for customer service representatives, leading to lapses in concentration and less than ideal outcomes for customers.

A digital colleague is also focused on achieving the outcome and a positive emotional state for the customer when directly handling interactions, but, given its digital nature, doesn’t care if it spends five minutes or five hours handling one query and can scale to any number of conversations.

Even when working as a whisper agent, the digital colleague helps balance both the organisation’s need for a high amount of resolved queries and deliver a better service and outcome, as it is able to provide the agent with the best response and most relevant information in real time. This helps the agent provide that superior service at the high rate of resolution often welcomed in customer service roles.

Empathy

Having a customer service agent empathise with our issue and show dedication to resolving it is a key component of delivering emotionally cognisant engagements. Empathy is a key life skill for humans, however, it has been much harder for digital agents to authentically be empathetic, show listening skills and understanding.

Even when tracking and responding to a person’s emotional state, digital agents could still sound cold and robotic when using empathetic language. So, digital colleagues need an additional aspect of self-awareness, which recognises conversations that require empathy and sympathy, so they can pass them on to a human agent.

Social skills

That said, in their role as whisper agent, digital colleagues can help human customer service representatives build better relationships. Using their analysis of the emotional state and previous interactions, they can advise agents on how to best engage with any customer.

Augment the potential of customer services

The post 5 components of emotional intelligence in a human-AI customer service appeared first on Artificial Intelligence.

Insurance has become an extremely competitive market! While there are several players, customers are increasingly becoming aware about what they need. They are now seeking a more individualized experience and if you cannot provide it, rest assured they will find another company that does.

So, how do companies find ways to address the ever-increasing customer needs?
While the adoption of new technologies might have been slower than desired initially, the Indian insurance sector is certainly awakening to its benefits now. Several insurers are now deploying these processes to understand their customers better and for product innovation.
Of all these news processes, perhaps artificial intelligence and machine learning are proving to be the most potent!

Information overload
New data sources like third-party databases, social media activity, internet of things, and more are providing a steady stream of information. Machine learning is helping insurers create opportunities by computing this vast stream of data for hidden insights.
Using complex algorithms, insurers can find patterns in the available data to establish predictive and descriptive data models. However, the most important point to note here is that machine learning can make sense of dissimilar datasets – whether partly structured, unstructured or structured.
An uninterrupted flow of information from multiple sources – click stream, social media interaction, audio clips and more – is processed real time, enabling the systems to incrementally adapt and self-create a continuous learning curve.

Customer is king!
Given that insurance vastly requires customer interface, machine learning has helped companies to stay on their toes. They can quickly and effectively recognise dynamic customer needs to provide them with simple and intuitive experiences. The analytics can predict the future trends and behaviour patterns of the customers.
Using advanced machine learning techniques to analyze the behavioural and socioeconomic activities of a prospective customer, some insurance companies are successfully deriving precise and individualized product recommendations.
In fact, many companies are working towards creating chat bots to enrich customer interaction and looking at automating the issuance process.

Merits of machine learning
While many machine learning algorithms are not completely novel, applying complex mathematical calculations to big data – over and over, faster and faster – automatically is a fairly new. As discussed above in detail, new technologies will only better the way insurers conduct business and serve their customers.
So, here are the key merits of machine learning to show why machine learning is crucial:

a) Superiority in advisory
Virtual advisors based on machine learning algorithms could potentially change insurance advisory by eliminating human bias and improve trust, thereby boosting customers’ confidence. While India may not yet be completely ready for the machines to take over, insurers can certainly use these virtual advisors for tackling routine tasks and allow their consulting team to handle the more constructive and socially interactive challenges.
b) Enhanced operational efficiency
Machine learning can help insurers become more efficient operationally by reducing turnaround time and improving productivity.
c) Digital Marketing automation:
Insurers can automate and optimize digital marketing expenses by implementing machine learning algorithms in multiple areas including identification of optimum bidding price for each keyword, re-targeting as per propensity to purchase, and nurturing leads through right content at the right time using the right medium.
d) Dynamic pricing:
Given the enormous data at one’s disposal, insurers can create a dynamic pricing model as per the socio-economic factors of a prospective customer. For instance, by analyzing data from wearable devices that monitor physical activity and heart rate, policies can be recommended with discounts.
e) Claims management and underwriting
Considering machine learning depends heavily on a repository of data, it will help create a deeper understanding of a customer’s risk profile. This will make the underwriting process much more effective and accurate. It will also significantly reduce the turnaround time for processing of claims, and help identify any fraudulent incidences on a case to case basis. Both these factors are crucial is ensuring loss prevention.

The post Machine learning is the future of insurance and customer service appeared first on Artificial Intelligence.

Long gone are the days when customer service meant investing in a telephone system, hiring a team of contact center agents, and sitting by the phone waiting for the customer to call. The motto used to be, “If the customer is unhappy, they will call us.” Now it’s “What can we do today to make customers happy?”

Meeting customer needs in today’s demanding marketplace requires flexible and agile customer service solutions. Microservices are a way to increase speed and flexibility in getting new innovations to customers. With microservices, you can more easily adapt to the changing customer requests and demands, as well as offer services that create a competitive edge.

Microservices: Growing and building in real-time

Think about the process of moving into a new house. Instead of waiting for the whole house to be built, you might want to move in when the core components are up and running: The kitchen, bathroom, living room, and bedrooms. After some time passes, you decide to renovate. You add a new laundry room and a few extra windows to the living room. Later, you see that your neighbor has a beautiful balcony and you want to add a similar one to your home. In a few years, you realize you don’t need so many bedrooms after all, and decide to change one into a personal office.

This is how microservices work: The actual application consists of individual building blocks, each serving a specific purpose. These building blocks are combined to form a solution that meets your customer service needs.

In the whitepaper, “Microservices–Rewriting the Future Rules of Customer Service,” written by Esteban Kolsky from thinkJar, Kolsky investigates six ways that microservices can help deliver better customer service experiences:

1. Channel-agnostic

Microservices make it easier to add new contact channels as the need arises. You can start with the ones that are essential to your business today and add new ones when you and your customers need them.

2. Infinite elasticity

Microservices are built on cloud platforms, so you can start small and scale up as your business grows. You don’t need to worry about the technology limiting your future growth—it will grow with you.

3. Resolve once, share with many

Customers expect consistent customer service over time and through all channels. According to Kolsky, microservices make the needed levels of consistency easy, allowing central data stores of answers to propagate across whichever channels your customers like to use.

4. Performance increase

Powerful and rapidly scalable cloud platforms make it easier to maintain high performance, even during peak seasons. Kolsky says, “Microservices scale as needed using the cloud’s ability to quickly deliver additional capacity. This means you never fall short in busy periods.”

5. Access to resources

Kolsky calls it the “delight gap:” The needed information is somewhere within the organization, but is it not accessible for customer service when needed. Microservices use a common data store, which make it easier to access the relevant data whenever needed.

6. Cost savings

As Kolsky notes, customer service is finally recognized as a core business investment. With the scalable cloud platforms, companies can avoid excess investments and save costs. The increased speed of innovation also means the customer gets new features and enhancements faster.

The post Six Ways Microservices Will Change The Future Of Customer Service appeared first on Artificial Intelligence.

The creation and consumption of data continues to rapidly grow around the globe with large investment in big data analytics hardware, software, and services. The availability of large data sets is one of the core reasons that Deep Learning, a sub-set of artificial intelligence (AI), has recently emerged as the hottest tech trend. Huge giants such Google, Facebook, Baidu, Amazon, IBM, Intel, and Microsoft are heavily investing in big data, with the acquisition of talent hot on their agenda.

Big data is continuously creating new challenges and opportunities, all of which have been forged by the information revolution. This infographic takes a look at how those in the ecommerce industry are already using data sets to introduce a new level of strategic marketing and provide better customer service experiences.

Predicting trends, optimising pricing and forecasting demand, are just some of the ways that ecommerce businesses are using data to gain a competitive advantage. The guesswork has been removed, and now ecommerce businesses can accurately make strategic decisions on how to operate their online empires.

Big data is proving to be a game-changer when it comes to retail and ecommerce. If businesses can successfully implement effective big data strategies then they will reap the rewards of better customer experiences and bigger profits. This infographic explores practical ways to introduce data solutions with simple implementation.

The post 6 Ways to use Big Data in Ecommerce appeared first on Artificial Intelligence.