Tag: AI

Today’s consumers are tech-savvy, demand consistency and know they can switch brands at any time. Therefore, Customer Experience (CX) is extremely important now more than ever. Customers aren’t just buying a product. They are looking to gain a good experience throughout the customer journey – before, during and after their purchase.

Imagine the pressure of not slipping up in any instance! If it wasn’t for AI and robotics, this could be a daunting affair. Thankfully though, the application of robotics and AI has streamlined the entire customer journey. As a CTO, leading the company towards this technological transformation is a great responsibility. This blog will take you through 4 ways in which robotics and AI can improve customer experience.

1. Personalization

Hyperconnected customers of this digital age highly value personalization. This personalization is expected at all touch points and marketers are employing various strategies to achieve this. In the basic sense, this includes presenting personalized recommendations using algorithms and predictive analytics. Algorithms mine transaction data, browsing patterns and purchasing behaviors to gain customer insight. This information then enables to design personalized offers and create cross-sell opportunities.

Machine Learning is going one step further with AI-Augmented Contextual Analytics. Contextual Awareness is made available through machine learning and human curation. This awareness is implemented across the entire data and analytics workflow of the company. Thus, personalization is made possible throughout the operations and can be implemented at any touch point.

Sentiment Analysis is the next big thing that companies are implementing in order to improve customer experience. Voice, text and visual engagement can be analyzed by technologies to gauge sentiment and emotion. Voice biometrics over phone calls, parsing of facial expressions during video chats, and texts through chatbots can be used to understand emotion and intent. This insight helps companies measure and deliver customer satisfaction more effectively.

Read More: Predictive Analytics: The Key to Effective Marketing and Personalization

2. Improving Internal Processes

As we have discussed, customers expect a satisfactory experience in all touch points. This includes the way the company is run, the backend and the corporate image. Efficient processes and effective decision-making filters down to a profitable company and happy customers. AI and robotics can be used to achieve this.

Mark Robinson, co-founder of the world leading SaaS solution Kimble talks about how augmented intelligence helps businesses grow. “Using augmented intelligence to make suggestions to staff, and to record their response, means that humans and the machines can work together to come up with the best solutions,” he says. “There is less need to worry that people are going to miss important actions or take wrong decisions because of the visibility of what’s going on.”

It is believed that AI has the power to increase productivity by 40% or more by 2035. By automating processes and supporting innovation, AI can improve manufacturing and business processes. One example is smart product ordering systems, which use the power of AI to streamline order fulfillment. Our recent blog brought out the opportunities in this: “From ensuring that the product is personalized and available where the customer wants it, to streamlining the order shipment and delivery process, the customer experience is enhanced by the intelligent product ordering system.”

3. Effective Pricing 

“What I ‘charge’ today has nothing to do with yesterday or tomorrow. It has to do with ‘now’!”   

– David Wayne Wilson 

This has profound significance today, where pricing drops or increases by the second. However great your marketing, your technology and other systems are, it all comes down to pricing. If a consumer gets the same benefits and quality at a lesser cost, he will opt for a cheaper option. Pricing is therefore a vital component in competitive advantage. This means that businesses must keep a close eye on competitors’ pricing behavior.

That is where Dynamic Pricing Science holds significance. With the power of Artificial Intelligence, cognitive algorithms, big data and machine learning, businesses can now effectively adjust their pricing strategies in real-time. Dynamic Pricing Science enables tailor-made pricing solutions to customers at the right time and consistent across channels. It helps understand buying patterns, identify effective correlations, compare competitor prices, match it across channels and create a customized offer – all in real time.

4. Improving Response Time 

Response time is crucial in business. This is true in leads conversion as well as customer service. The importance of response time in lead conversion was brought out by Dave Elkington, the CEO and founder of InsideSales.com. Quoting a study he was involved in he says: “The study revealed that the odds of making contact with a new lead are extremely high if you call within the first 5 minutes of submission … a rep is 100x less likely to make contact if the first call is made 30 minutes after submission. The odds of making contact drop by 3000x if the first call is made 5 hours after lead submission.” Converting a lead into a customer is the first customer touch point and it is vital to get it right.

This is important in customer service as well. The State of Multichannel Customer Service report found that:

• 68% of people stop doing business with a brand due to a poor customer service experience.
• 34% of people feel that quick resolution of their problems was the most important feature of customer service.
• 61% of people on social media expect a response from a brand in less than 24 hours.

A bad response time could break a business as the numbers show. However, AI and machine learning have helped immensely in this regard. Chatbots are proving to be a great boon, especially when it comes to swift response on first-level queries. This ensures that there is an immediate response and minimizes time spent by personnel on repetitive queries. Agents can then handle deeper conversations and conversions with the assistance of AI.

China Merchants Bank is an excellent case study on the effectiveness of real-time chatbot interaction. Interactions through these bots had reached a record of 41 million by the end of 2015, with the chatbot handling 99.5% of the questions with an accuracy of 99.8%!

Power Your Customer Service With AI

As you can see, the power of AI and robotics in customer service cannot be overemphasized. Fingent works with clients across industries all over the globe. We would be happy to help you with any questions you might have. Drop us a message and let’s get this rolling.

Artificial Intelligence (AI) is the simulation of human intelligence processes with the aid of machines. These machines primarily include computer systems and tools that carry out learning, problem-solving, reasoning and perception.

Customer service is one area where AI tools and technology can be used efficiently. As per a recent study, it is estimated that nearly 85% of the customer interactions will be handled without the presence of a human agent in next three years. There are approximately 38% of the enterprises that are using AI and the number will grow to 62% by the end of 2018. AI has the capability to accomplish the following set of tasks and activities in customer service and interactions.

Pre-emptive Action

AI embedded monitors can provide the customer service team with the ability to analyze the primary customer issues. The systems can offer real-time support to the customer for the resolution of such issues. The huge clusters of web applications can be gathered and studied to resolve the issues even before their occurrence. This empowers the business organizations to reduce the customer abandonment rates and enhance the level of customer engagement.

Messaging Applications

The use of messaging applications is not restricted to connecting with friends, family, and colleagues. The brands are using these applications for customer interaction and the usage is set to increase further. The use of social media applications and in-house messaging applications allows real-time interaction with the customers and thus resolve their queries and issues.

One-time Training

The expenditure on hiring the customer service agent and training the member is quite high. It is also necessary to organize training sessions at regular intervals. However, with the use of AI platforms, a one-time training is sufficient which in turn would bring down the costs to a huge margin.

Non-stop Services

Customer service agents cannot be made available for a non-stop period of time. The human resources work for specific hours in a day and may remain unavailable at the time of an incident. These limitations can be resolved by the use of automated support and customer service tools which interact with the customers at any hour of the day. The queries and incidents reported by the customers are automatically recorded and a suitable response is also provided that matches the reported query.

Self-service Options

A recent customer service study revealed that over 72% customers do not prefer phone calls for the resolution of their query or issue. Self-service options for customer service are on a rise and the use of chat-bots and forums have rapidly increased. AI has enabled the easy development and usage of such options. The companies that are still not utilizing such platforms will be required to incorporate the same in the future for enhanced customer relationship management and service.

Reliability & Scalability

Customer service and interaction have a great role to play in customer engagement and relationship management. According to a survey, 42% of the customers increased their purchase after a good customer service response. On the other hand, 52% customers reduced their engagement with the organization after a poor experience. AI platforms can enhance the reliability of service with their non-stop availability and unbiased customer interaction. The AI tools can also be scaled up or down as per the requirements. These applications can simultaneously handle a wide number of customers without any impact on the speed and performance.

Cost-Savings

There are advanced customer services that can be provided by artificial intelligence. The use of automated applications can reduce the costs. Resource and training costs along with the cost of the infrastructure are eliminated with the incorporation of AI tools.

It is often assumed that Artificial Intelligence will replace the jobs currently being handled by the human resources. There are over 10 million jobs that will be replaced by AI tools and applications in next ten years. However, in the case of customer service, automated assistance can only resolve 10-35% of the customer queries. Human assistance and capabilities will be required in the rest of the cases. The business organizations must research the AI applications and platforms that can be incorporated into their customer service interactions. 

The past decade has been a game-changer for the way businesses operate in the realm of retail. The advent of e-commerce and its subsequent boom has compelled brick and mortar outlets to undertake a paradigm shift from a profits-first to a consumer-centric approach. Failure in conforming to new consumer demands fueled the retail apocalypse that toppled the brick and mortar landscape. Thus, we see retail giants like Bon-Ton Stores Inc., Sears and Macy’s filing for bankruptcy and liquidate their holdings.

Implementation of targeted mobile promotions, loyalty benefits, e-payment gateways is just some of the strategies adopted by retail outlets to maintain a competitive advantage in the face of fierce technological overhauls. With disruptive innovation gaining a strong footing now more than ever, the need to constantly reinvent and augment is more pressing than before. Here are five key disruptive technology trends that you need to sync your business model with, to offer consumers retail experience par excellence:

Related Reading: 5 Ways to Enrich Customer Experience at Your Retail Store

1. The advent of Artificial Intelligence

Robots and AI bots capable of not just learning but also executing real smartness are the new focus in tech innovations. Retail giants are already experimenting with ways to implement these AI bots in their business operations. A strong case in point would be Amazon’s no-checkout cashier-less convenience stores, Amazon Go being tested across different states in North America.

Then there are self-driving grocery stores and automated trucks making home deliveries that are still undergoing trials. Of course, that is not to say that AI dominating retail operations will become the new normal tomorrow, but it is in the offing. Businesses that are in the retail sector for the long haul will stand to gain from their preparedness to embrace this change.

2. The Internet of Things

The ability of devices to interact with humans, understand commands and execute them is passé. The Internet of Things (IoT) puts the limelight on the ability of machines to interact with one another. The slow but consistent development of IoT is shaping up a new ecosystem where our gadgets will be able to operate without human intervention. Besides, the global market size for IoT in retail is expected to grow around 94.44 billion by 2025.

The emergence of IoT will inevitably alter the dynamics of the way consumers interact with retail business and the way businesses interact with distribution networks and supply chain partners. More importantly, it will usher in a connected customer model by relying on smart-store applications like smart shelves, beacons, and customer service robots. Making room for these swift connections powered by the internet will help you build a business model that is future ready.

3. Striking the Online-Offline Balance

It is the age of digital customers where the lines between online and offline existences are forever blurring. Brick and mortar businesses need an online extension to sustain themselves. Now, the spotlight is on understanding the dynamics of virtual and augmented reality and creating a marketing strategy that caters to the customers’ dual persona – considering their social media image and real identity – to encourage continued interactions and conversions.

The result – a complete overhaul of the shopping experience by bringing in a consistent omnichannel approach built around a convenient digital backend. For instance, Oasis, the UK-based fashion retailer is closing the gap between in-store and online purchasing by merging shopping experiences across its mobile app, website, and brick-and-mortar stores.

4. Personalized Shopping Experience

Take a look at how e-commerce websites function – bringing customers exactly what they need, every time, on every device, without fail. This carefully curated shopping experience eliminates the need for buyers to browse through the inventory of online stores to find what they need. Over time, this approach toward shopping has been normalized to an extent that customers expect the same out of their retail shopping experience too. Installing smart screens, tablets etc. is one way of using technology to recreate the same sense of personalization in your retail business.

5. Banking on Data

Big data is the next big thing in terms of business operations. Multinational corporations are pumping in billions of dollars to assimilate and organize this seamless information to create the right kind of marketing strategies. While big data may be out of your reach as a standalone business entity, you can create your own pool of data and use it to offer improved retail experiences for your customers.

Fun quizzes, for instances, are a great way to gather insights into your customers’ buying preferences, which can then be used to offer personalized product recommendations. You can take it a step further by tracking these recommendations to know if they are appealing to your customers and tweak them accordingly.

Related Reading: How Big Data and Analytics are Evolving Customer Experience in Retail

[Courtesy : European Bank for Reconstruction and Development]

Meanwhile, other technologies like virtual and augmented reality will continue to grow in popularity and efficiency. As a retailer, the onus of using these disruptive innovations to offer a seamless customer experience falls on you. Pairing with the right technology partner is the first step. Get in touch with our experts today to uplift your retail experience with cutting-edge software solutions.

Artificial Intelligence (AI), the next big thing in the technology space, is all set to unleash big scale disruptions. The potential impact is more profound in the financial sector, which lives and breathes data.

Artificial Intelligence (AI) powered “smart” machines do not just crunch data. It indulges in self-learning to solve cognitive tasks, until now the forte of the human brain. A traditional analytical engine or software robot requires someone to feed in clear set of rules set in advance. AI-endowed self-learning machines create the rules and frame the logic by itself, as it crunches through millions and millions of rows of historic data, identifying. It learns to perform the required task based on the decisions humans have made previously. AI powered algorithms also learn from mistakes, meaning the predictive analytics in spews becomes more and more accurate with every recorded transaction.

In the financial sector, such AI-powered systems delve into many years of banking, insurance, mortgages and financial trading history, apply deep learning principles, and self-construct algorithms to automate routine tasks, unlocks insights, improve decisions, mitigate risk, and prevent frauds. AI’s natural language processing system can read through regulations, reassembling words into a set of computer-understandable rules.

The CBInsights Conference on the Future of Fintech predicts a rapid pace of disruption in the financial sector, requiring incumbent stakeholders to adapt or risk being submerged in the coming tidal wave of predictive analytics. Apart from conventional tools, Artificial Intelligence-inspired technologies such as blockchains, insurance tech, robo-advising, and other latest cutting-edge innovative tools promise to take analytics to a whole new level.

The following are the potential applications of Artificial Intelligence, which would take over financial services in the near future:

• Improved efficiency through greater automation and better insights
• New models for several traditional functions, especially in stock analysis and wealth management advisory services
• Customization and personalization of financial products, leveraging the services of analytics-driven recommendation engines;
• Improved cyber-security monitoring and responsive systems, and automated fraud detection

Improved Efficiency

AI and robotics powered automated solutions are on the verge of subverting the traditional business models of banks and other financial institutions.

AI-based algorithms automate much of the routine tasks that require extensive workforce now, leading to considerable savings on overheads, accelerated processes, and overall improved efficiency. Being lean and mean is the mantra for success in today’s highly competitive environment, and AI will help financial institutions discover new meaning to lean and mean.

AI-based technology simplifies and automates a host of routine tasks related to money management processes, such as identity authentication, know-your-customer checks, sanctions list monitoring, billing fraud oversight, anti-money laundering monitoring, risk control processes, and more.

AI facilitates several tasks, such as bot messaging, document discovery and more which require extensive manual work now.

Such efficiency improvement interventions have the potential to offer huge ROI for the financial service industry. Banks adopting ROI already report about 40% increase in productivity.

New Trading Models

AI powered predictive analysis has the potential to create entirely new business models in equity, forex and other trades.

Algorithms are already in widespread use to manage risk and exposure. With AI getting better, the scope of intervention will change from refining existing models to becoming the bedrock of newer innovative models. AI-powered algorithms offer brokers and investors access to charts and trends at a much more sophisticated level than before, enabling them to chase short-lived opportunities cutting across venues, asset classes, and geographies. Trading algorithms assess the best liquidity providers during execution. The possibilities are endless.

Thomson Reuters predict algorithmic trading systems to handle 75% of the global trade volumes in the near future. Almost all hedge fund in the world already have a huge data-science team, and deploy sophisticated filters to screen investment ideas.

Tightening regulation mandating extensive record keeping and tracking of all trades, and the need for increased speed in correlating multiple variables, to keep pace with a fast paced and highly competitive environment plays into the eventual dominance of AI.

Deep Personalization and Customization

One of the innovative possibilities with AI in the financial sector is in the realm of marketing. Already, marketers are analyzing behavioral data captured from online activities in a big way, to customize and personalize offerings based on spending habits, social-demographic trends, location, and other preferences.

With AI capable of understanding human language and emotion, marketers of financial products and services would soon take personalization to a whole new level. For instance, AI-powered marketing database could suggest language that elicits certain emotional responses to advertising and email subject lines, and trigger cultural sensitivities to certain words and timing of campaigns. Biggies such as Citi and AmEx already use such tools to good effect, to fine tune their social media and marketing engagement.

Security and Fraud Prevention

Cyber-attacks have brought down many high-profile financial institutions, and continue to be a nightmare for the industry stakeholders. AI promises a fresh breath of hope.

For instance,

• AI based analytic engine capture multivariate time series patterns, to predict anomalies.
• AI based predictive analytics make explicit customer behavior, to flag potential fraud or breach in real-time
• AI can easily correlate and link multivariate transaction data, facilitating easy recovery of layers of bank documentation and data to meet regulatory policies, and establish a money trail to bust financial frauds.
• Sophisticated machine learning and algorithm that monitor market movements in real time allow regulators to prevent major accidental market movements.

Regulations influencing financial markets, such as the European Union Markets in Financial Instruments Directive II (MiFID II) push for greater automation of trades, further boost the prospects of AI in the niche.

Experts estimate AI to surpass human intelligence by 2040. However, the financial services sector need not wait that long for predictive analysis to take over and relegate human brains to a role of supporting AI functions. It nevertheless takes great skill in developing actionable apps and products that leverage AI to unlock the many possibilities on offer. Your best bet is to partner with us and leverage our highly talented and experienced skill set for the task. We are in the thick of AI inspired things, having access to the latest technology and developments, and offer you the unique value proposition of technical competency with a considerable track record in every industry to understand your specific needs and develop highly customized products.