AI/ML

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.

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.

TensorFlow is an open-source library created by the Google Brain team to build enterprise-grade machine learning algoriths. TensorFlow bundles together a host of machine learning models and algorithms and with the use of common programming frameworks, makes them useful. TensorFlow uses Python and JavaScript to build user friendly APIs for connecting with apps, and uses core C++ to execute the app functionalities. While it is still early days for machine learning technology, it continues to evolve rapidly, introducing us to a new world of advanced algorithms and deep learning. Deep learning uses algorithms commonly referred to as Neural Networks. As the name suggests, they draw inspiration from our biological nervous systems, led by the brain, to process information. Deep learning algorithms enable computing devices to identify every single bit of data, establish what it means and learn patterns. TensorFlow is a tool to develop deep learning models. It is an open-source AI library that uses data flow graphs to build learning models. With TensorFlow, programmers can build large-scale, multi-layered neural networks. TensorFlow is primarily used to perceive, understand, classify data and create predictive models. Main Use Cases of TensorFlow While TensorFlow can be used for many applications, here are 5 commonly used applications in the world of artificial intelligence. Voice/Sound Recognition One of the most popular use cases of TensorFlow is audio signal based applications. When fed appropriate data, neural networks can perceive and understand audio signals. These can be: Voice recognition — primarily used in Internet of Things (IoT) applications, Automotive applications (Voice command based actions), Security (Authentication) Voice search — Commonly used in Telecom and by mobile phone manufacturers Sentiment Analysis — used in CRM applications Flaw Detection (noise analysis) —Automotive and Aviation applications The world is familiar with the common use case of voice-search and voice-activated assistants. This use case has been widely popularised by smartphone manufacturers and Mobile OS developers such as Apple’s Siri, Google Assistant and Microsoft Cortana. Understanding and analyzing language is another widely used use case for Voice Recognition. Speech-to-text applications are used to extract and understand sound bites in larger audio files, and convert it into text. CRM is another area were voice/sound based applications can be implemented to deliver a better and smarter customer service experience. Imagine a scenario where TensorFlow algorithms fill in for customer service reps, and guide customers to the right set of information much faster than an agent. Text Based Applications This is another commonly used application of TensorFlow. Text based applications for instance sentiment analysis can be used in CRM apps and Social Media for improving the customer or prospect experience, Threat Detection, used in Social Media and Government applications and Fraud Detection, used by Insurance, Finance companies are some common examples. Language Detection is another popular use case of TensorFlow for text based applications. We are quite familiar with Google Translate. More than 130 languages can be translated into each other using this service. An AI powered version of a translate engine can be used in common real world situations like translating heath diagnosis technical terminology or legal jargon in contracts into plain language. Text Summarization Google also came up with sequence-to-sequence learning, a technique for shorter text summarization. This can then be used to build headlines for news and articles. Another use case for TensorFlow popularised by Google is SmartReply. It automatically generates e-mail responses based on text recognition and contextual understanding. Image Recognition This use case is primarily used by Social Media and Smartphone Manufacturers. Facial Recognition, Image Search, Motion Detection, Computer Vision and Image Clustering are nowadays being deployed in Warehousing, Healthcare, Automotive, and Aviation Industries. Google Lens is another example where Image Recognition is being used to understand the content and context to help identify people and objects within images. TensorFlow object recognition algorithms have the ability to identify and classify random objects within larger images. This has found use in engineering modelling applications such as creating 3D spaces from 2D images. Facebook’s Deep face is another example of photo tagging using image recognition technology. Deep learning technology can identify an object in an image never seen before by analyzing thousands of images with similar objects. Healthcare Industry is also at the cusp of using Image Recognition for faster diagnosis.  TensorFlow algorithms can process information and recognize patterns much faster than the human eye to spot illnesses and detect health problems faster than ever. Time Series TensorFlow Time Series algorithms is another method used today to establish patterns and forecasting of time series data. Meaningful statistics can be derived by these algos along with recommended actions. TensorFlow Time Series algorithms allow forecasting of generic time periods apart from generating alternative versions of the predicted time series. A popular use case for Time Series algorithms is Recommendations. Time Series Recommendations has seen widespread usage amongst leading organizations such as Netflix, Google, Amazon, where they analyze and compare activity of millions of users to determine what a customer might wish to view or purchase. And with every interaction, while recording the activity of every action, these recommendations get even smarter. For instance, they throw up content what your family members or friends like or offer you a gift they might like. Finance, Insurance, Government, Security and Threat detection, Predictive Analysis, Resource Planning and forecasting are some of the other use case scenarios of TensorFlow Time Series algorithms. Video Detection TensorFlow deep learning algorithms can also be used on video data. This is used in Motion Detection in Automotive and Aviation, Role based Gaming, Security and Threat Detection. Today, universities are doing deep research on Video Classification at a large scale to perceive, analyze, understand, classify video data. NASA is using TensorFlow algorithms to build a system for orbit classification and object clustering of asteroids. Consequently, they will be able to classify and predict near earth objects. TensorFlow is an open-source framework, allowing developers the freedom to work on innovative and disruptive use cases, which will contribute further to Machine Learning technology. Amongst many things, TensorFlow’s popularity is primarily due to the computational graph concept, automatic differentiation, and the adaptability of its python API structure. This makes TensorFlow more accessible to developers to solve real problems. Here are some advantages of TensorFlow. 1. Scalable The TensorFlow library is well defined and structured. This means it works just as efficiently on a mobile device as on a powerful computer. 2. Open Source The TensorFlow library is available free of cost. Anyone, anywhere can work on it and use it to solve problems. 3. Graphs Tensorflow has a very powerful, inbuilt data visualization capability. This makes it easier for developers to work on neural networks. 4. Debugging Tensorboard, which is a part of TensorFlow, allows easy debugging of code blocks. This reduces the need for combing through the whole code. 5. Parallelism TensorFlow uses Central Processing Unit (CPU) and Graphics Processing Unit (GPU) for its functioning. Developers can use the architecture freely based on the problem they are trying to solve. A system uses GPU by default, which is why  TensorFlow is sometimes referred to as a hardware acceleration library because it reduces memory usage. 6. Compatible TensorFlow is compatible with popular programming languages like Python, C++, JavaScript, etc. This allows developers the freedom to work in an environment they are most comfortable with. 7. Architectural Support The TensorFlow architecture uses Tensor Processing Unit (TPU). This makes computation faster than what one would get when using CPU and GPU. TPU models can be easily deployed on the cloud and work faster than CPU and GPU. 8. Library management With the Google backing, TensorFlow is updated regularly with enhanced capability and flexibility with every release. AI is already an intrinsic part of Salesforce, the world’s leading CRM platform. 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.