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Customers are very demanding today, primarily because of the options available to them. When they reach out to a brand for support, they expect minimal wait times and fast resolution, regardless of the channel they use. Agents, however, have limited bandwidth and can only handle a limited number of cases at a time. So how does one scale customer support? Enter chatbots. But what exactly is a chatbot? That’s a significant question considering the fact that this technology is increasingly becoming a bigger part of our daily lives.  In fact, Gartner research predicts that soon the average person will spend more time interacting with chatbots than with their spouse. With round-the-clock digital support becoming a critical value proposition for brands, more and more businesses are using chatbots to engage with customers to deliver the SLA expected of them. At a technical level, a chatbot (derived from “chat robot”) is a piece of code (program) that simulates human interaction through text or voice communication. Today, chatbots can be customised and used in multiple ways such as: Chatbots that interact through smart speakers Chatbots deployed on smart home devices Chatbots that can be deployed on popular messaging platforms and web In addition to having a conversation with a service agent, customers can now interact with an intelligent software that helps them to find answers fast. Whether through text or voice, chatbots can communicate with customers and respond to requests faster. To put it simply, Chatbots are Artificial Intelligence (AI) powered digital assistants that answer common customer questions. They help customers quickly resolve simple and routine issues freeing up agent time to work on more complex issues that require human interactions. How does one create customer service chatbots Customer service chatbots resolve simple, repetitive tasks that don’t require interacting with a human customer service agent. For instance, if a customer wants to know how to reset a password or the estimated delivery time for a product they ordered, a customer service chatbot quickly accesses the relevant information and answers the question without keeping the customer waiting at the other end. And while this is happening, your service agents can focus on resolving more complex customer issues and build stronger customer relationships. If you are looking to invest in chatbot technology, your first goal is to establish the most common customer requests to identify what to automate. We suggest the following six tips that you should keep in mind when designing your first AI-powered chatbot: 1. Personalise every greeting Customer service agents are trained to be warm, greet customers by name, and recognise their service privilege status. A chatbot can do the same thing in the background, powered by AI. Chatbots can be programmed to retrieve their name to ensure chatbots greet them like a human agent would. 2. Move from static to conversational Customers hate the idea of filling out an online form and then having to wait for 12 -24 hours for a response. An AI powered service chatbot can dynamically ask a series of relevant questions based on customer inputs and make the interaction more engaging. It also helps resolve the customer issues faster. And in the case where agent intervention is necessary, they will already have all the relevant information logged by the chatbot available in their panel. 3. Create interactive FAQs Traditionally, customers are prompted to visit the FAQ section of a website or app to resolve issues in a self-service mode. Chatbots turn this process around its head. They bring the FAQ answers to customers. You can stack your common FAQs and their answers into your AI interface, including all related questions and their answers.  And with natural language processing (NLP) capability built into the AI engine, chatbots recognise everyday use language and respond to customer queries. Now your customers can find what they are looking for faster than before. 4. Deploy chatbots to additional channels Businesses today deploy customer service across multiple digital channels such as web, messaging, and social allowing customers to connect with your brand in the way they want.  Salesforce research indicates that an average customer today uses nine different channels to interact with brands. This variety of options creates multiple opportunities to deliver 360-degree customer service to meet their ever-changing behaviour. You can dive deeper into your analytics to identify the channel that gets the maximum traffic for your brand, and then identify the top customer service requests on that channel. Automate your chatbot to respond to these requests and save time for your agents. 5. Engage customers with formatted text and content Basic text is all right for answering simple questions, but professionally formatted text using a range of font styles, sizes and colors enhances the customer experience. You can even insert images and interactive menus into the chat. And because it is powered by AI, your chatbot can surface a product menu, a list of articles, or customer support options, based on wat the customer asked, all within the chat. 6. Embed process automation in chatbots With AI, you can empower customers to self-serve themselves by assisting them with guided, step-by-step instruction right within the chat console. Work with your teams to identify tasks that are easy for customers to complete on their own. Therese are typically tasks that can be easily automated without needing any human intervention like renewing an insurance policy. Once your team has identified these simple and common use tasks, you can program your chatbot to guide customers throughout the service journey. And for more complex issues, when the chatbot has to hand over the conversation to an agent, the agent is already empowered with all relevant information about the case so they can resolve it quickly. Scale customer service with chatbots Your customers will recognize the value your customer service chatbots bring to them with quick, efficient resolutions to their requests and concerns. And you agents will have more tie to focus on complex customer service requests instead of answering FAQs. With AI powered chatbots, you can easily scale support to handle any case overload as and when they come your way. AI-powered chatbot technology holds the promise to reinvent the customer experience. And high-performing service teams are leading the AI powered chatbot revolution to augment their existing human customer support teams. In today’s digital first context, where speed of service is king, chatbots are helping companies stay ahead of the curve. As a Gold Salesforce Consultant, Girikon can help you deploy AI powered chatbot based customer service at scale. Contact one of our experts to learn more.

Artificial intelligence (AI) is growing in stature in the marketing realm.  Marketers today rank AI as their #1 priority for investment, according to a recent State of Marketing Report published by Salesforce. The adoption of AI is staggering with about 84% of marketers reporting that they use AI in their customer acquisition and retention engines, that’s almost 300% growth in 2 years. What exactly are marketers doing with AI? The usage of AI is crossing all barriers, from improved segmentation and personalisation, deeper insight, forecasting and process automation. With the growth in advertising technology, riding on big-data driven AI, advertising companies reimagined the process of delivering digital ads. According to eMarketer,  online ad sales rose from $60 billion in 2019 to an estimated $97 billion in 2022. What do sales teams feel about the impact of AI on what they do? They would tell you-cautiously optimistic. According to latest research from Salesforce, 86% of sales reps view AI as having a positive impact on their future roles. However, 68% of the same reps had concerns as well. And as you would imagine, mostly concerning the very relevance of their existing jobs. 31% percent said technology might eventually negatively impact the art of selling, as AI driven optimisation of sales interactions replaces human-to-human relationships. Five reasons to be happy about AI in advertising While it is only natural to fear the unknown, there is reason to believe that AI will make the existing jobs of sales reps better. AI augments the sales process and leaves people to do what they do best which is to be human. AI has the potential to level the playing field for both advertisers and publishers. It empowers sales teams, who don’t have the budgets that large corporations or media companies may have, with the tools they would like. AI-driven ad technology can help sales teams boost both the effectiveness of their messaging and their efficiency. 1. Improved data unity and synchronization With the impending censure of third-party cookies in Apple and Chrome’s privacy policies, marketing professionals are increasingly seeking first-party data to power their marketing initiatives. Merkle’s 2021 Customer Engagement Report stated that first-party data was a strategic priority for a staggering 88% of the marketers. For publishers, first-party data is vital to building advertiser centric audiences; and for advertisers, first-party data is increasingly vital for targeted advertising. And that’s where the challenge is. First-party data is not only complex, more often than not, it is fragmented and ill organised. According to Salesforce research, 64% of customers start their purchase process on one device and finish on another. With the proliferation of smartphones and devices, marketers have to deal with an average of 12 primary sources of customer data, an increase of 20% from 2020. And as one would expect, most of the data residing in these sources has inconsistent identity information, expired or outdated information, and unconventional taxonomy. AI can be a great asset to a common Customer Data Platform to significantly improve identity matching. Algorithms can execute “fuzzy logic” on IDs and resolve or isolate discrepancies. AI can also ensure data consistency by mapping data from siloed systems to a common data model. 2. Better audience segmentation and discovery Customers today prefer digital first, and they want relevant experiences. They want the messaging to be useful and timely in their digital lives. Delivering a personalised experience mandates the need for organised data as well as smart algorithms to discover customer segments and reveal their needs that may be impossible to do manually in real-time. AI stands out at intelligent segmentation, discovering groups of customers and prospects with common attributes at a scale that is impossible for a human analyst to achieve. AI algos can sift through billions of records of customer data to identify meaningful patterns and segment audiences intelligently for more effective and targeted marketing programs. 3. Natural language shakes hands with technology Natural-language processing (NLP) and image recognition are 2 areas which are extremely promising when it comes to AI. Chatbots have taken customer service to the next level with conversational customer service. Voice assistants such as Siri and Alexa are already changing the user experience whether we want to book a ticket or are in the mood to have Chinese. As voice recognition technology approaches 95%+ accuracy, voice navigation will become an intrinsic part of customer engagement. Voice navigation is already built into call centre systems and used in analytics as well to some extent. However, AI based marketing technology is at the cusp of something never seen before. Imagine an ad sales rep never having to use a keypad again to make the right selection. With less and less human clicks efficiency will improve significantly, and so will the efficacy of the whole process. And this will lead to ad reps spending less time in searching and updating and have more time to do what they do best – selling. 4. Back-end processes efficiency We’ve already talked about how AI can help unify and harmonize data. It can also help sales teams become more efficient by prioritising their efforts, and allowing them to focus their time on key tasks while automating the mundane ones. Lead Scoring was one of the first areas to benefit from AI technology. While there was resistance from sales teams initially in adopting the technology, and rightly so, with the uncertainty of the what the unknown will do to their current jobs. Questions such as – How can a software qualify a good lead better than me, were commonplace. But with technology integration into existing systems, many sales reps now use algorithms regularly to augment their priority their prospects. The same is true for determining next best actions based on interaction history and scheduling meetings. AI can help ad sales teams match available inventory with “most likely to close” sales opportunities thereby significantly trimming the time spent on low-probability leads. 5. Improved measurement and optimisation In the post pandemic period, digital ad spend has seen a meteoric rise. Brands now spend more than half of their advertising budgets on digital platforms. AI can help aggregate and analyse all that data seamlessly to help advertisers establish the impact of campaigns viz a viz desired outcomes, such as sales. Simply put, AI can help ad sales teams separate the noise and identify what works. To measure the efficacy of multi-channel campaigns, one needs to harvest information from dozens of sources. And that’s not all. They also need to apply complex models to determine which aspects of the campaign, channels, devices, and tactics made an impact. AI-driven tools can help ad sales teams with automated recommendations to optimize campaigns based on the historical performance of ads. AI excels at automating tedious tasks, and is also fantastic at sifting through huge amounts of data at unimaginable speed. Something humans can’t do. By allowing AI technology to do what it does best, we make the entire process of ad sales less artificial and more intelligent. Girikon is a Gold Salesforce Consultant delivering value to customers across the globe for over a decade. Contact one of our experts to know how AI can help unlock the true value of your advertising sales.

CRM is reshaping customer service today and Salesforce Consultants are helping customers around the world remodel their customer service operations with the world’s leading Customer 360 platform. With rising customer demands and fickle brand loyalty, it is time to stop escalating customer issues and resolve them using a collaborative approach. With the help of the right Salesforce Partner, you can build an intelligent service swarming model to make your service teams become more efficient by bringing expertise to customers faster. Imagine a situation when a key customer reaches out to you with a complex issue. it’s the moment of truth. Does your agent escalate the problem or collaborate on it? If the process you follow is always to escalate then visualize this: a team of experts comes together quickly to help your service agent to resolve the problem. This is service swarming. Service swarming eliminates guesswork from customer service.  It allows service agents to share resources and expertise to resolve complicated customer problems faster. Let’s dive deeper into what service swarming is and how it can benefit your agents and therefore your customers. What is service swarming? Service swarming, often referred to as Intelligent Swarming, is a collaborative approach to customer service. A team of experts from across your organization collaborate with your service agents to resolve complex cases or larger incidents faster. These experts can be from any department such as sales, commerce, operations, legal, finance, or any other department, depending on the issue. This enables teams to leverage their expertise and collaborate on complex issues as and when they come to light. These experts share their knowledge and resources with service agents during the service swarming process. Once they arrive at a solution, the team documents the process and creates a knowledge article so other agents can reference it in the future when similar issues emerge. In today’s digitally connected world, businesses must be prepared to respond in real quick time to large incidents such as security attacks and service outages. The moment an incident like this occurs, the clock starts ticking. There is a barrage of customer calls. Service agents scramble to juggle between diagnosing the problem and dealing with the overwhelming number of calls. An SLA breach looms large which would lead to a PR nightmare. It’s critical for customer-facing teams to be able to quickly and seamlessly collaborate across departments to identify and resolve the problem. Swarming is particularly useful when there is a larger and complex issue facing a single customer like a security breach.  Swarming can also be scaled to address major incidents that affect multiple customers, like a Denial of Service (DoS). In either case, a collaborative approach that brings together multiple teams, departments, and in certain cases even external partners, is vital to finding a resolution. For instance, if a customer contacts a brand about goods showing up as delivered but not received, the agent can bring in the logistics partner to help.  The benefits of service swarming in customer support In a traditional customer service model, agents resolve most cases on their own. They search the knowledge base and seek the help of colleagues for issue resolution. But as more time passes, the customer starts to lose patience. The agent escalates the case to an agent at the next hierarchal level or connects with a supervisor, or in some cases transfers the case to an entirely department, which frustrates the customer even more. A swarming service model turns this entire process on its head. Agents collaborate with a team of experts and are able to arrive at a resolution faster. Not only that, in the process they also become more knowledgeable and efficient, which leads to cost savings for your business. Service Swarming leads to: Personalized customer engagement: According to Salesforce, 82% of customers expect resolution to their problem by interacting with just one person. Service swarming significantly reduces the complexity of larger problems because now the agent is their single point of contact for the customer throughout the case. This fosters a one-to-one relationship that builds trust and loyalty. Accelerated skills development: In any organization, knowledge spreads across many layers and sources. When a complex case is passed off by agent because of lack of knowledge, they lose out on an opportunity to gain valuable experience. However, when they collaborate with experts in a swarm, they learn something with every case resolution. The learning that comes over time with a swarm approach would otherwise take years to build. Scaled automation: According to Salesforce, 63% of agents say it’s extremely challenging to balance promptness and high-quality service. But isn’t that exactly what customers expect from you? With automation, agents can save time and lower operational costs by eliminating repetitive tasks, thereby boosting team efficiency at scale. Service teams more time to focus key activities like building strong, trusted customer relationships. Teams working together: Service Cloud has a unique feature called Expert Finder. The name says it all. Customer service agents no longer have to work in isolation. Service agents can quickly identify and access a support network of experts and resolve the issue. In fact, agents can be incentivized based on their participation and performance. When a case is resolved, supervisors can recognize those involved and award points which encourages greater participation. Evolved success metrics: Performance metrics such as average resolution time and first-contact resolutions are always valuable. In service swarming scenarios however, those metrics don’t always apply. Other key metrics such as lower customer wait times, escalation rates, and case handover take priority. Using these indicators, customer service managers can track agent productivity, expert utilization, customer satisfaction, and retention. Swarming is a new approach to customer service and gives you a fresh perspective of your service teams. There is a paradigm shift in the way your agents and experts work together to resolve customer issues. Now both have a customer centric approach. Collaboration becomes central to customer service; no one is working in isolation. A swarming support model requires a unified platform At Salesforce, the customer is at the centre of everything they do. With a unified platform, you can bring together automation and AI to drive productivity and efficiency. With automation and AI, building on a collaborative approach to problem solving, teams can do more with less, allowing you to focus on the most important thing – making customer delight the goal of every experience. A delightful experience leads to greater trust and lasting value. If you want to implement service swarming in your business to scale your service operations and make it more efficient, you need to invest in the right technology.  Empower your service reps a unified platform that is built for team success, allows for a high degree of automation, delivers insights with AI and helps you to deliver personalized customer experiences every time. With a unified platform, your teams can work together from anywhere and deliver the value that your brand stands for. Salesforce Service Cloud is the world’s leading customer service platform and can help your teams resolve issues and incidents seamlessly. With Slack, you can bring in cross functional swarm experts and easily navigate seamlessly across text, voice and video to deliver case resolution in quick time, thereby building on customer trust and loyalty. And while all this is happening, your service teams are being empowered with fresh knowledge that makes them future ready. Girikon is a Certified Salesforce Development Partner delivering value to customers across the globe. To know more about how we can help you deliver best in class SLAs in customer service with service swarming, contact us today.

PyTorch is an open source ML library used for developing and training deep learning models. The primary contributor to PyTorch is Facebook’s AI research group. PyTorch can be used with Python and C++. As one would expect, the Python interface is more sophisticated. PyTorch, backed by Facebook and supported by Amazon, Microsoft and Salesforce, is quite popular amongst developers and researchers. Unlike other more popular neural network based deep learning frameworks such as TensorFlow, which use static computation graphs, PyTorch uses dynamic computation, which allows for greater flexibility when one wants to build complex architectures. PyTorch works very well with Python, and uses its core concepts like classes, structures and loops, and is therefore more intuitive to understand. When compared with TensorFlow, which has its own programming style, PyTorch is simpler to work with. Why do we need PyTorch? The PyTorch framework can be viewed as the future of deep learning. There are many deep learning frameworks accessible to developers today, with the more preferred frameworks being TensorFlow and PyTorch. PyTorch however offers more flexibility and computing power. For machine learning and AI developers, PyTorch is easier to learn and work with. Here are some advantages of PyTorch: 1. Easy to Learn PyTorch has the same structure as traditional programming which makes it more accessible to developers and enthusiasts. It has been documented very well and the developer community is continuously improving the documentation and support. Thereby making it easy to learn for programmers and non-programmers alike. 2. Developer Productivity It works seamlessly with python, and with many powerful APIs can be easily deployed on Windows or Linux. Most of the tasks in PyTorch can be automated. Which means with just some basic programming skills, developers can easily boost their productivity. 3. Easy to Debug PyTorch can use debugging tools of python. Since PyTorch creates a computational graph at runtime, developers can use PyCharm, the IDE from Python, for debugging. 4. Data Parallelism PyTorch can assign computational tasks amongst multiple CPUs or GPUs. This is made possible with its data parallelism feature, which wraps around any module and allows parallel processing. 5. Useful Libraries PyTorch is supported by a large community of developers and researchers who have built tools and libraries to extend the accessibility of PyTorch. This developer community contributes actively in developing computer vision, reinforcement learning, Natural Language Processing (NLP) for research and production. GPyTorch, BoTorch, and Allen NLP are some of the libraries used by PyTorch. This provides access to a powerful set of APIs that further extends the PyTorch framework. Benefits of using PyTorch 1. Python-friendly. PyTorch was created keeping Python in mind (that’s why the prefix), as against other deep learning frameworks that were ported over to Python. PyTorch provides a hybrid front end enabling programmers to easily move most of the code from research to prototyping to execution for production. 2. Optimized for GPUs. PyTorch is optimized for GPUs to accelerate training cycles. PyTorch is supported by the largest cloud service providers: AWS currently supports the latest version of PyTorch. AWS includes its Deep Learning AMI (Amazon Machine Image) and is optimized for GPU.  Microsoft also has plans to support PyTorch in Azure – their cloud service platform. PyTorch has a built-in feature of data parallelism, that allows developers to leverage multiple GPUs on leading cloud platforms. 3. Plethora of tools and libraries. PyTorch comes with a rich ecosystem of tools and libraries for extending its availability and potential. For instance, Torchvision, PyTorch’s built-in set of tools allows developers to work on large and complex image datasets. The PyTorch community of researchers across academia and industry, programmer and ML developers have created a rich ecosystem that provides tools, models, and libraries to extend PyTorch. The objective of this community is to support programmers, engineers and data scientists to further the application of deep learning with PyTorch. 5 ways in which AI apps can use PyTorch With PyTorch, engineering teams can create deep learning predictive algorithms from data sets. For instance, developers can leverage historical housing data to predict future housing prices or use a manufacturing unit’s past production data to predict success rates of new parts. Other common uses of PyTorch include: Image classification: PyTorch can be used to build complex neural network architectures called Convolutional Neural Networks (CNNs). These multilayer CNNs are fed thousands of images of a specific object, say a tree, and much like how our brains works, once the CNN is fed a data set of tree images, it can identify a new image of a tree it has never seen before. This application can be particularly useful in healthcare to detect illnesses or spot patterns, much faster than what the human eye can do. Recently a CNN was used in a study to detect skin cancer. Handwriting recognition: Human handwriting has its inconsistencies as one moves across people and regions.  Handwriting recognition involves interpreting the inconsistencies in human handwriting across people and languages. Forecast time sequences: Another type of neural network is Recurrent Neural Networks (RNNs). They are designed for sequence modelling and are particularly useful for training an algorithm on past data. It can make predictions based on historical data, allowing it to make decisions based on the past. For instance, an airline operator can forecast the number of passengers it will have 3 months from now, based on the data from previous months.  Text generation: RNNs and PyTorch are also used for text generation. In text generation an AI model can be trained on a specific text to create its own output on its learning (for eg interpretation of poetry). Style transfer: One of the most exciting and popular applications of PyTorch is a style transfer. It uses a set of deep learning algorithms to manipulate images and use the visual style of that image on another image to create a new set of images, combining the data of one with the style of another. For example, you can use your vacation album images, apply a style transfer app and make it look like a painting by a famous artist. And as you would expect, it can do the reverse as well. Convert paintings to look like contemporary photos. AI is going to reshape many enterprise functions and how their respective teams work. And one of those areas is CRM. Salesforce, the world’s leading CRM platform is leading the way in embedding trusted AI into all their product offerings. As a Gold Salesforce Partner, Girikon is the preferred choice for many Salesforce customers across the globe. To know more about how AI can work for your business, contact us today.