Global Digital Skills Index from Salesforce research conducted in 2022 indicates a growing digital skills crisis. The in-depth research about digital skills is based on a survey with 23,000+ respondents (existing and prospective workers) across 19 countries. It includes areas such as their impact on the future of work, their job readiness concerns, and the importance of continuous up-skilling.

COVID has changed the way we live. Today, we spend most of our time online, looking for products and services. For marketing professionals, this translates into a great opportunity to stay connected with their audience in today’s pandemic context. But there is a catch. Customers are overwhelmed with digital information, and they are interacting with brands across multiple channels with so many options available. Which means not every interaction is attracting engagement. Today, engaging with customers is a whole new ball game. It requires faster and precise decision making about what needs to be communicated, how it needs to be communicated, when and where. The need of the hour is to deliver relevant messaging that conveys a deep understanding of the customer context. AI-driven solutions, with their ability to make intelligent recommendations, can help marketing teams make the right decisions, in quick time. They can also help marketing teams to launch contextual campaigns with personalised communication with their audience, achieving better engagement levels and enhanced customer experiences. So what exactly is AI Marketing? AI marketing is based on technology that uses machine learning algorithms to make automated decisions based on data aggregation and its analysis, along with analysing market trends and data that may impact marketing initiatives. Typically, AI technology is used in digital marketing activities where speed is critical. AI-powered marketing solutions use data to understand the best fit ways to communicate with your audience, and deliver personalised messaging at the right time, on the right channel without intervention from marketers, thereby ensuring a high level of efficiency. Digital marketers across the world today use AI to augment the efficacy of their efforts or to perform more complex tasks that would otherwise consume huge amount of time or resources. Here’s a look at how AI-powered solutions are transforming the marketing game: 1.  Smarter segmentation for enhanced audience discovery Understanding the customer is the foundation of today’s digital marketing strategy. AI-powered solutions can intelligently segment your audience. By scouring hundreds of data points across multiple marketing campaigns, AI-powered solutions can help marketers discover new sub-segments. Let’s look at an example. An outdoor hiking gear brand has an audience segment of “trekkers”. AI reveals to marketers that there are two categories in this segment – leisure trekkers and tech-savvy trekkers. With these insights, they customize their product recommendations based on the segment they are targeting. For example, leisure trekkers are shown apparel and accessories, and tech-savvy trekkers are shown the latest navigation devices or solar chargers. Identifying the right segment helps marketing teams deliver hyper-personalised marketing messages. Marketing solutions powered by AI technology can reveal unique customer traits within a segment. This can then be used by marketers to customize the marketing content and drive more engaging interactions. 2.  Accurate predictions to improve lead conversion Before the digital revolution, marketers relied on their experience and “gut-feel” to arrive at marketing decisions. AI-powered marketing solutions eliminate the guesswork by surfacing accurate, data-driven predictions and recommendations. These predictions allow marketing teams to personalise every customer journey and improving lead conversion through the marketing funnel. Let’s look at Einstein Engagement Scoring. This AI-powered feature of Marketing Cloud applies machine learning algorithms on customer data to arrive at a score for a company's email subscribers. The score tell you how likely each subscriber is to engage with your email campaigns, and eventually, to convert. The feature can also tell you the likelihood of each subscriber opening an email, click the links within the email body, or to un-subscribe. Marketers can use these AI powered predictions to build more tailored customer journeys. For instance, customers with a low likelihood of opening emails can be targeted through social media and mobile messaging. 3.  Personalised messaging across channels to drive engagement AI algorithms can use data such as browsing history, age, and recent interaction history with a brand to serve up a campaign landing page that is most likely to resonate with the subscriber. The same applies in advertising. The algorithm can use that data and serve up the right content for an ad in real time based on the user’s profile. This allows marketing teams run tailored ad campaigns that are relevant and better targeted at users, thus boosting ROI. With the evolution of technology, today’s AI-powered marketing solutions can be further targeted to help marketing teams deliver just the right amount of content. For example, Marketing Cloud Einstein has a feature called Engagement Frequency. It tells you the just the right number of emails to send out to customers and prospects for brand recall without being perceived as spam. Likewise, it also tells you which subscribers are being left out or being contacted too often. Based on this intelligence, marketing teams can customize their messaging strategy for improved customer engagement. In fact, AI-powered solutions can also tell you if a social media strategy would be a better bet than an email marketing campaign. Customers value the experience as much as the product and service, and brands will need to deliver personalised messaging across channels to stay ahead of the curve. AI can take your marketing team's understanding of your customers to the next level. Conclusion Marketing teams across industries are rapidly adopting intelligent technology solutions to improve overall operational efficiency and the customer experience and drive growth. This need for customer intelligence has ushered in a new era of Artificial Intelligence (AI) marketing solutions. With these AI-powered marketing solutions, marketing teams can get a deeper and nuanced understanding of their audience. The AI-powered insights can help marketing teams to drive conversions at scale. Regardless of the size of your marketing team, AI-powered marketing technology can help improve productivity, boost ROI, improve organizational efficiency, all while processing heaps of data your team may not have the bandwidth to deal with. If you are new to AI, even your first small step into AI-marketing like using a machine learning program to draft an email subject line and a greeting for your upcoming marketing campaign, can keep your brand ahead of the curve. It’s a small but significant step towards an AI-powered future. As a Gold Salesforce Consulting Partner, Girikon is in a great position to help you leverage the powerful technology of the World’s No1 CRM platform. To know more about how your marketing teams can use Einstein for Marketing Cloud to deliver personalised, contextual marketing campaigns, contact us today.

Imagine a scenario where a customer calls customer support only to navigate through multiple options and then being put on hold for several minutes before getting through to an agent. Only to be put on hold again as they look for answers to your problem. We’ve all had to deal with this at some point. And this is only half the story. Imagine being asked the same question over a hundred times a day by different customers. And not knowing answers to most of those questions. That’s what customer service teams have to deal with on a daily basis. In a world of fickle customer loyalty, how do businesses deliver excellent customer service? Disruptive technology like Artificial Intelligence (AI) may have the answer. Let us look at 10 ways in which AI can enhance the customer experience. 1. Chatbots Customer service reps today have to deal with a large number of calls on any given day. And on top of that there’s performance pressure to reduce the average resolution time. Enter Chatbots. Not only can chatbots provide quick answers in real-time, they can also reduce the case load on human agents by resolving common customer queries quickly. 2. Cost reduction Chatbots can help businesses trim customer service costs significantly by accelerating response times, freeing up agents to work on more complex cases, and resolving a very high % of routine customer queries automatically. A great example of this is call automation, which combines machine learning and voice recognition to augment existing IVR systems while delivering a significant cost reduction as compared to human agent assisted set ups. 3. Round-the-clock support Customers want service delivered at the time and on the channel of their choice. Businesses must be available at all times to customers to support them. Automated customer service makes that possible. It allows enterprises to deliver 24/7 customer service and resolve cases as soon as they come to light. This means customers don’t have to wait for long periods for a response. Prompt case resolution improves customer satisfaction and builds trust, loyalty and brand reputation. 4. Improved human interactions with customers AI can play a key role in supporting human interactions with customers. Two of the most common ways in which AI is supporting customer service is through AI-driven messaging and email tagging. AI-driven messaging allows service reps to handle a big chunk of cases with chatbot assistants. With AI-driven email tagging, service reps don’t have to read every customer email. AI-powered tools can scan and tag emails, and direct them to the right inbox. This frees up time for service reps so they can work on more complex tasks that necessitate human intervention. 5. Personalized experiences According to Salesforce, 72% of customers want to be able to solve service issues by themselves. AI technology can play a significant role in enabling customers to find what they are looking for more efficiently. AI analyzes customer data and key metrics, and makes intelligent recommendations on products or services to customers.  AI is always working in the background, analyzing every incoming piece of data, and suggests best fit content to customers. AI enables service reps to have a better understanding of customers, so they can send relevant content to them at the right time on their preferred channel. As a result, customers are able to find what they are looking for without having to call customer service. 6. Gathering data AI-powered technology simplifies data aggregation and serves a unified customer snapshot. Earlier, AI relied on existing customer data that was fed manually. Today however, things are far more advanced. Today’s AI-powered solutions proactively request data automatically. They can easily analyze patterns in behaviour, understand customer sentiment and quickly respond to their needs. 7. Predictive insights It is critical for businesses to deliver engaging and personalised experiences to customers.  AI powered personalization makes it easy for businesses to serve up tailored products or services to customers. Many businesses around the world that have integrated AI technology into their systems to deliver relevant information to customer, have seen significant improvement in their customer satisfaction scores. This improves brand reputation and builds loyalty. 8. Deeper insights from customer data In the early days, data mining was tedious and time-consuming. Today with AI-powered tools and solutions, huge amounts of data can be captured and analysed faster than ever, to get deeper customer insight, opening up new market segments and opportunities for brands. With AI, businesses can capture every customer action, uncover their interests, and apply these insights to drive targeted campaigns. AI can help businesses get faster results, get deeper insight, and eliminate human error and bias. And freed up human resources can be utilized for more complex tasks. 9. Assisting customers to drive decision making In today’s COVID 19 context, customers spend a lot more time online. They engage with brands across devices, and personalization across every touchpoint becomes all the more critical to assist customers in making the right decision. AI-powered assistants respond to customer queries in real time, and with a deeper insight on customers, are able to serve up intelligent recommendations to accelerate decision making. This frees up agent time and they can focus on more pressing tasks. In case of service requests when the conversation between a chatbot and a customer becomes complex, the interactions is automatically handed over to a human agent with a snapshot of the entire interaction history. AI powered solutions can sense behaviour patterns based on which they can make smart predictions. 10. Simplified task management One huge advantage of customer service chatbots is that you need only one chatbot to handle literally thousands of concurrent customers. Imagine the amount of agent time that can be freed up to resolve routine issues like serving up expected delivery date of a product they ordered or when is their insurance renewal due. This has transformed the relationship between brands and customers. How many times have you hung up on the customer support line because you lost your patience wating for an answer? And how many times have your support calls been left unresolved? You are not alone. Brands around the world are proactively investigating the use of AI into their business to interact directly with customers. While human agents can get overwhelmed performing tasks when they have to deal with mountains of data, AI can deliver answers without breaking a sweat. AI can easily sift through piles of data, analyzing, searching and serving up relevant information to customers in real time. AI can analyze unstructured data at lightning speed, something a human cannot do. AI analyses data and identifies patterns, which can be easily overlooked by a human. AI has in-built Natural Language Processing (NLP) capability. It can read a support ticket and instantly direct it to the right team. Customers are growing increasingly digital today.  It is becoming imperative for businesses to integrate AI into their existing systems to acquire new customers and retain them, at scale. AI has the potential to take the customer experience to whole new level. By making the customer journey more engaging, it can help you stay a step ahead of your competition. And it also eases the lives of service reps. In many ways they are the flag bearers of your brand. Automated responses, personalization, case routing, data analysis and intelligent recommendations, predictive insights, case prioritization are some of the things AI can do without breaking a sweat.   As a Gold Salesforce Consulting Partner, Girikon has been helping organizations around the world leverage the world’s most powerful CRM platform to drive productivity and growth. We recognise that improved efficiency and quality of your customer support will lead to happier customers. To know more about how AI can help your customer service teams improve your CSAT scores, contact us today.

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 deep learning framework that offers flexibility and speed, enabling developers and researchers to design, train, and deploy AI models with ease. It is backed by the Linux Foundation and supported by AWS, Meta Platforms, Microsoft Corp, and Nvidia. PyTorch has become the go-to framework for cutting-edge AI research and enterprise-scale applications. Its popularity stems from a Pythonic, intuitive design, dynamic computation graphs that make experimentation seamless, and strong ecosystem support. What sets PyTorch apart from other popular neural network-based deep learning frameworks, such as TensorFlow, is that it uses static computation graphs. On the contrary, PyTorch uses dynamic computation graphs, allowing greater flexibility when building complex architectures. So, let’s dive deep into the nitty-gritty of PyTorch, understand its features, benefits, and major applications, where it enables faster experimentation and prototyping. What is PyTorch? PyTorch is an open-source deep learning framework that supports Python, C++, and Java. It is often used for building machine learning models for computer vision, Natural Language Processing (NLP), and other neural network tasks. It was developed by Facebook AI Research (now Meta), but since 2022, it has been under the stewardship of the PyTorch Foundation (part of the Linux Foundation). Despite being a relatively young deep learning framework, PyTorch has become a developer favourite for its ease of use, dynamic computational graph, and efficient memory usage. Understanding How PyTorch Works Two core components of PyTorch are tensors and graphs. Let’s understand them: Tensors Tensors are an essential PyTorch data type that are similar to multidimensional arrays. They are used to store and manipulate a model’s inputs, outputs, and other parameters. In addition, tensors resemble NumPy’s ndarrays, but unlike them, tensors can run on GPUs to accelerate computing for large workloads. Graphs Graphs are data structures consisting of connected nodes (vertices) and edges that help the framework track computations and calculate gradients during training. With two processes: forward propagation, where the neural network carries the input and delivers a confidence score to the nodes in the next layer, until the output layer, where the ‘error’ of the score is calculated. Whereas, in the backpropagation process, gradients of the loss function are computed and sent backward to update parameters, PyTorch keeps a record of tensors and executed operations in a directed acyclic graph (DAG) consisting of Function objects. In this DAG, the leaves are the input tensors, and the roots are the output tensors. Unlike static frameworks like TensorFlow, PyTorch builds its graph at runtime (i.e., dynamically as the code runs). This makes the framework more flexible, easier to debug, and an ideal choice for accelerating innovation while supporting enterprise-grade deployment. 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. Why Do We Need PyTorch? PyTorch works very well with Python, and uses its core concepts like classes, structures, and loops, and is therefore more intuitive to understand. The PyTorch framework is seen as the future of deep learning. There are many deep learning frameworks available to developers today, with TensorFlow and PyTorch among the most preferred. PyTorch, however, offers more flexibility and computing power. For machine learning and AI developers, PyTorch is easier to learn and work with. 5 Key Features of PyTorch Here are some capabilities that make PyTorch suitable for research, prototyping, and dynamic projects. 1. Easy to Learn PyTorch follows a traditional programming structure, making it more accessible to developers and enthusiasts. It has been well documented, and the developer community is continuously improving the documentation and support. This makes it easy for programmers and non-programmers alike to learn. 2. Developer Productivity It works seamlessly with Python, and with many powerful APIs, can be easily deployed on Windows or Linux. Most PyTorch tasks can be automated. Which means with just some basic programming skills, developers can easily boost their productivity. 3. Easy to Debug PyTorch can use Python’s debugging tools. Since PyTorch creates a computational graph at runtime, developers can use PyCharm, the IDE for Python, for debugging. 4. Data Parallelism PyTorch can assign computational tasks amongst multiple CPUs or GPUs. This is made possible by its data-parallelism feature, which wraps any module and enables parallel processing. 5. Useful Libraries PyTorch is supported by a large community of developers and researchers who have built tools and libraries to expand PyTorch’s accessibility. This developer community actively contributes to developing computer vision, reinforcement learning, and NLP for research and production. GPyTorch, BoTorch, and AllenNLP are some of the libraries used with PyTorch. This provides access to a robust set of APIs that further extend the PyTorch framework. Top Benefits of PyTorch Python-friendly: PyTorch was created with Python in mind (hence the prefix), unlike other deep learning frameworks that were ported to Python. PyTorch provides a hybrid front end that enables programmers to easily move most of the code from research to prototyping to production. Optimized for GPUs: PyTorch is optimized for GPUs to accelerate training cycles. PyTorch is supported by the largest cloud service providers, including AWS, which currently supports the latest version. AWS includes its Deep Learning AMI (Amazon Machine Image), which is optimized for GPUs. Microsoft also plans to support PyTorch on Azure, its cloud platform. PyTorch includes built-in data parallelism, enabling developers to leverage multiple GPUs on leading cloud platforms. Plethora of tools and libraries: PyTorch comes with a rich ecosystem of tools and libraries that extend its capabilities and availability. For instance, Torchvision, PyTorch’s built-in set of tools, allows developers to work on large and complex image datasets. Large Network & Community Support: The PyTorch community, comprising researchers across academia and industry, programmers, and ML developers, has created a rich ecosystem of 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. Production & Deployment Challenges with PyTorch Scaling Models Efficiently: Sometimes, scaling applications at production requires optimization to support a large user base. System Integration: PyTorch models don’t usually integrate with the overall enterprise workflow, so APIs or conversion tools are frequently required. Performance Optimization: To reduce latency and boost throughput in real-time applications requires tuning and, in some instances, specialized hardware. Lifecycle Management: Model drift may occur, so PyTorch models need monitoring, retraining, and version control to ensure reliability. 5 Ways in Which AI Apps Can Use PyTorch With PyTorch, engineering teams can build deep learning predictive algorithms from datasets. For instance, developers can leverage historical housing data to predict future housing prices or use a manufacturing unit’s past production data to predict the success rates of new parts. Other common uses of PyTorch include: Image Classification PyTorch can be used to build complex neural network architectures, such as 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 work, once trained on a data set of tree images, they can identify a new picture of a tree they have never seen before. This application can be particularly useful in healthcare for detecting illnesses or spotting patterns much faster than the human eye can. Recently, a CNN was used in a study to detect skin cancer. Handwriting Recognition PyTorch makes it easier to build systems that can recognize human writing across people and regions. These systems can even account for inconsistencies in human handwriting across people and languages by leveraging flexible tools for image processing and neural networks. Developers can train these models to learn and understand the shapes and strokes of handwritten characters, enabling apps to convert notes or forms into digital text automatically. It is a useful capability for digitized entries, smart pens, or educational tools. Forecast Time Series Another type of neural network is a Recurrent Neural Network (RNN). 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 in 3 months, based on data from previous months. Text Generation RNNs and PyTorch are also used to generate human-like text, from simple word structures to advanced chatbot responses. In text generation, by training RNNS and AI models on large datasets, developers can build AI applications that produce content, answer questions, or provide customized communication. For instance, AI assistants such as Siri, Google Assistant, and other AI-powered chatbots rely on similar systems to enhance and personalize user interaction. Style Transfer One of the most exciting and popular applications of PyTorch. It uses a set of deep learning algorithms to manipulate images and transfer their visual styles onto other images, creating new images that combine 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 them look like paintings by a famous artist. And as you would expect, it can do the reverse as well. Convert paintings into contemporary photos. Closing Statement Undoubtedly, PyTorch has made deep learning more accessible than ever before. Since its architecture is uniquely suited to support both rapid research experimentation and the scalability required for production development, it’s becoming a trusted framework in the research and development industry. Therefore, its growing influence reflects how advances built on PyTorch are moving beyond research to shape enterprise platforms. Salesforce, the world’s leading CRM platform, is one such example. The CRM platform embeds trusted AI across all its product offerings, from predictive analytics and SMS apps to Voice Agents that enhance customer engagement. As a Gold Salesforce Partner, Girikon helps organizations leverage these advances in Salesforce CRM and bring AI innovation and tangible business outcomes. Get in touch today to discover how our salesforce ai services can help enterprises embed trusted AI into CRM workflows and transform the way you interact with customers.