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.

What is generative CRM? Generative CRM combines the power of generative AI with CRM data to boost productivity and efficiency of teams. It has the power to execute limitless functions such as responding to queries, generating conversational text, suggesting next steps, drafting emails and more. The beauty of Generative AI is that the more people use it, the smarter and faster it will become. In the coming months and years, Generative CRM will effortlessly perform tedious everyday tasks, freeing up time of your teams so they can focus on more important tasks. With the ability to comb the internet for relevant data in a matter of seconds, it can help draft more meaningful responses thereby significantly boosting the efficiency of teams. How generative CRM can boost productivity, efficiency, and customer relationships People spend hours executing ordinary day to day tasks. They sift through data and information, wrack their brains to come up with new social media ad campaigns, iterate multiple times to create a perfect email pitch for a prospective customer, and engage in a fire fight to resolve issues of dissatisfied customers. What if they had a tool to streamline all of that, irrespective of the industry or department they work in?   Generative AI is on the brink of redefining CRM across companies in the coming years. Let us dive deeper to understand how this new age tech, when combined with your CRM, can help teams become more productive and deliver stunning customer experiences. The employee view If you are a new sales rep, and you have just been assigned a new account, it would take you many hours, perhaps even days to get an overview of the company, catch up on the latest company activities, discover the right contacts, and prepare an introductory email. With Generative AI, all this can be done in a matter of seconds by your CRM. So you can refine that email and connect with the right person sooner than ever. This is the potential of generative CRM. When the power of generative AI combines with your CRM data, it unlocks a never seen before power of your CRM. The view across teams Generative AI is poised to reshape how teams work across departments in the years to come. It will empower enterprises to quickly and effortlessly generate AI-driven content across multiple departments -sales, customer service, marketing, commerce, and IT. Service teams would have the power to create automated, smarter, more personalized chatbots that can engage with customers just the way a human rep would, but much faster.  They would have the ability to anticipate, comprehend and respond to customer requests faster than ever. For marketers, generative CRM can help in quickly creating accurate, compelling product descriptions that are optimized for web search. Here are some key benefits that generative CRM would deliver going forward. Reduce time to value AI has already been around for a while with Salesforce Einstein delivering over 200 billion predictions every day. Today, AI products like ChatGPT and Dall-E are empowering millions of people across industries to work more effectively. Generative AI is a deep tech that will filter out the noise that we encounter on the web. If you can ask the right questions contextually, generative CRM will be smart enough to know what to look for and how to present it to you. Free up humans for high-value work If you are a sales rep, imagine trying to acquire a potentially big new customer. You will have to spend hours sifting through data to strengthen your sales pitch, and by the time you do so, it may end up being archaic. You then comb your network and the prospect website and social media handles to find that perfect person to connect with, only to find that they moved on to another company recently. These repetitive, cyclical and routine tasks to acquire a new customer often waste precious time.  Generative AI can speed up these routine activities to make you far more productive. It will allow you to spend more time to do the real thing, which is building relationships with prospects and customers. AI that you can trust Security and privacy will be a critical aspect of generative CRM. Governed by guidelines that specifically address security and privacy concerns, generative AI will build on long standing principles for trusted AI. While publicly available generative AI tools depend only on publicly available data and information, generative CRM will be grounded on private and secure customer data, while also drawing on publicly available data and information such as social media and corporate websites. The ability to fuse public and private data is what makes generative AI driven CRM a trusted, and impactful experience for customers. Generative AI at Salesforce AI is already an integral part of the Salesforce Customer 360 platform, and its potential is limitless. Salesforce Einstein AI technology delivers over 200 billion predictions on a daily basis across multiple Salesforce’s business apps. This includes: Sales, which utilises AI powered insights, to establish the best next steps so reps can close deals faster. Service, which utilises AI to have bot-based natural conversations and provide the best fit answers, freeing up reps to work on more complex and important tasks. Marketing, which uses AI to better understand customer behaviour and personalize marketing campaigns to boost their efficacy.   Commerce, which utilises AI to deliver personalized buying experiences and smarter ecommerce. With generative AI, businesses can connect with their audiences in completely new, more engaging ways across every interaction. Guidelines for Trusted Generative AI Like they do with all their technology innovations, Salesforce is rooting ethical guidelines across all their products to assist businesses innovate rapidly and responsibly.  With the tremendous potential and challenges emerging in generative AI, Salesforce is building further on their Trusted AI Principles with a new set of guidelines to push for responsible development and deployment of generative AI. Here are 5 such guidelines. Accuracy: Use models to deliver verifiable results allowing customers to train models on their own data. Communicate when authenticity of the AI’s response cannot be established with certainty and enable users to ratify these responses. This can be achieved by citing sources, explaining why the AI gave those responses, underscoring areas to double-check such as stats, dates, and creating checks and balances that prevent certain tasks from being fully automated (like code review before deployment) Safety: Effort should be made to mitigate any bias or harmful output by conducting robustness assessments. The privacy of any personal private information should also be protected by creating guardrails. Honesty: When aggregating data to train and evaluate AI models, the source of data should be respected by ensuring their consent for use. Transparency in communication should be maintained by clearly stating that autonomously generated AI content has been delivered. Empowerment: While in some cases, a fully automated AI driven process may be the best option especially for non-critical, publicly available data, there are cases where AI should augment a human role, especially where human judgment is necessary. One needs to establish the right balance to turbo charge human capabilities and make generative AI solutions accessible to all. Sustainability: In our endeavour to establish more and more accuracy in our models, we should develop most appropriate-sized models wherever possible to reduce our carbon footprint. Summary If you are a Salesforce Consultant, this is an exciting time for you. Generative AI has the power to take CRM to the next level. By following the above guidelines, you can deliver never before seen value to your customers with the power of AI. Girikon is a Certified Salesforce Development Partner delivering value to customers across the globe. To know more about how Generative CRM can work for you, contact us today.

In March 2023, Salesforce launched Einstein GPT, the World’s First Generative AI for CRM. Einstein GPT uses the power of generative AI to deliver personalized content across every Salesforce product, thereby making teams more productive and delivering a better customer experience. Einstein GPT is open and scalable, and supports public and private AI models built specifically for CRM. Einstein GPT is trained on trusted, real-time data and seamlessly integrates the with the OpenAI framework to deliver out-of-the-box generative AI capabilities to Salesforce users. The new ChatGPT app for Slack integrates seamlessly with OpenAI’s deep AI technology to power instant conversations, and provide research and writing assistance. Whether it be sales, service, marketing or commerce, Einstein GPT for Salesforce will transform every customer experience at never seen before scale. In a sense, it opens a new door to the AI future for all Salesforce customers. Salesforce has fused its proprietary AI models with generative AI technology in Einstein GPT that unifies and synchronises all of a company’s customer data. Using Einstein GPT, customers can now easily connect that data to OpenAI’s advanced AI models, or use their own model and natural-language prompts directly within their Salesforce CRM to seamlessly generate content that self-adapts continuously to changing customer needs in real time. For instance, Einstein GPT can create personalized email drafts for sales reps, generate automated and tailored responses for service reps to respond to customer queries more quickly, generate appropriate content for marketers to augment campaign efficacy, and developers can get access to auto-generated code allowing them to build and deploy apps much faster. Going Deeper: Einstein GPT in CRM Salesforce Einstein is already delivering over than 200 billion AI-driven predictions everyday.  Einstein GPT is the next generation of Einstein, and by combining proprietary Salesforce AI models with OpenAI, customers can use natural-language prompts on CRM data to trigger powerful, time-saving automations, and create personalized, AI-generated content. Sales: Auto-generate routine tasks like drafting emails, preparing meeting schedules, and prepping for follow ups. Service: Auto generate articles from case note archives. Auto-generate conversational and personalised chat to smartly engage with customers. Fast track service interactions and enhance the customer experience. Marketing: Dynamically generate personalized and engaging content faster than ever to interact with potential and existing customers across channels. Slack : Get AI-driven customer insights in Slack for eg. smart summaries of sales opportunities and self updating knowledge articles. Developers: Developers can auto generate code by utilising Salesforce’s proprietary language model to ask contextual questions for languages like Apex through an AI chat assistant. Einstein GPT is in-built in Salesforce. Which means you can use your private data to tailor everything it generates suited to your unique business. And since Einstein GPT is available across the entire Salesforce platform, it can improve every single customer experience. Salesforce understands that generative AI encompasses more than just ChatGPT. Einstein GPT  has been designed to allow seamless integrations with other language models. This allows developers to bring their preferred model using normalized APIs and an open network of AI partners. Einstein GPT is designed to empower businesses with path breaking AI capabilities, using your own data and models to drive customer experiences. Embedding AI into the Salesforce platform has delivered huge operational efficiencies for partners and customers.  Generative AI technology has the potential to transform the way companies engage with their customers, deliver powerful experiences, and drive customer retention. Generative AI technology will drive the next generation of customer experience. Einstein GPT for Salesforce Developers As technology innovation progresses, so does the way developers write and analyze code. Generative Artificial Intelligence is perhaps the most exciting development in recent years for code generation and analysis.  This technology has the power to make development faster, more efficient and accurate. Let’s look at how Salesforce AI Research is powering Einstein GPT for developers across the globe and how it will change the way apps are deployed on Salesforce. Generative AI for code (Apex) Code generation involves using machine learning algorithms to analyse large amounts of existing code, and then generate new code based on that analysis. This is particularly useful for tedious tasks, such as drafting emails. One obvious and huge benefit of code generation is that it saves a lot of time for developers. Instead of writing every line of code from ground up, developers can use AI-powered tools to generate most of the required code automatically. Not only does this accelerate the entire development process, it also reduces the chances of human error. Code generation has many benefits, including: Code standardization: Automating generation of repetitive code blocks that guarantees consistency and standardization of code. Accelerated prototyping: Generative AI based code generation accelerates the prototyping process by quickly creating run of the mill code. Codebase becomes more scalable because of standardization. Simplified code: Generative AI automates the creation of repetitive code blocks thereby simplifying it. Code becomes easier to maintain and scale. Salesforce Consultants and Developers can now derive the benefits of Einstein GPT within the IDE experience. With inbuilt natural language processing capability, developers can have auto generated code created for them within the Code Builder as per their specific needs. The machine learning algos that drive the generative AI experience in Einstein GPT are based on Salesforce’s proprietary models and enhanced with best-in-class coding guidelines. Static and dynamic Apex analysis Code analysis is another field where AI is making significant progress. As software development become more and more complex, it becomes increasingly challenging for developers to precisely analyse and understand the code. Salesforce is piloting a new capability this year for Apex analysis. With this feature, developers can quickly and precisely analyse large amounts of Apex code, identify potential defects, runtime and other inefficiencies. This will save Salesforce Partners and developers a substantial amount of time and effort. They would no longer have to manually sift through each line of code to find potential problems. One of the key benefits of AI is that it can identify potential problems easily that developers may miss at runtime. AI-driven code analysis and code generation work synchronously. Using AI powered static and dynamic analysis, patterns in your code base will be fed back into the code generation process in run time, and vice versa. Conclusion AI-driven code generation and analysis is changing the entire development paradigm. And this is just the beginning. Going forward, Salesforce has plans to assist with automated test generation, intelligent code clarification, and more. If you are a Salesforce Consultant, this is an exciting time for you. Generative AI has the power to take CRM to the next level. With Einstein GPT you can get multi-dimensional insights of your CRM data and deliver never before seen value to your customers with the power of AI. Girikon is a Certified Salesforce Development Partner delivering value to customers across the globe. To know more about how Einstein GPT can work for you, contact us today.

Salesforce Lightning, put simply, is next generation CRM.  With Salesforce Lightning, developers can build stunning apps with unprecedented ROI. Lightning is super-fast, extremely powerful, very intuitive and has an abundance of features. It is a lightweight, component-based framework purpose built for accelerated app development. With Lightning, admins and developers can build and deploy apps faster than ever before, and makes business users more productive in every moment. Let’s look at some of the features of Salesforce Lightning that make it so powerful: Kanban View The Kanban view feature is available exclusively in the Salesforce Lightning experience. As was expected, it has become a natural go-to for most Salesforce users. Regular everyday users of Salesforce regard Kanban as their online workspace, and not just another view within Salesforce where data is presented based on selection of filters. For instance, when sales reps open the opportunity list view in Salesforce, they can view active sales opportunities organised by their respective stages in the timeline on a Kanban board. This view displays the opportunities within the selected filter. With this view, sales reps can get a clear view of the stage they are at in the sales process for each and every opportunity. Email Integration for Lightning Lightning does away with the need for shifting back and forth between your email app and Salesforce. Email can now be seamlessly and easily integrated with Salesforce in a single console, thanks to Lightning. Users can easily integrate Outlook, Gmail or other email services and bring together all connected Salesforce data such as call logs and tasks into the email UI. Lightning significantly improves productivity by allowing users to work directly from their email, with all the associated data they need from Salesforce. Salesforce Experience Cloud Communities Salesforce communities has been renamed Salesforce Experience Cloud. It includes tools used by businesses to connect with their customers and partners through shared portals on the Salesforce platform. With Salesforce Lightning, developers can easily create and manage branded community portals. This feature of Salesforce Lightning comes with an inbuilt powerful drag-and-drop interface. With this feature, users can easily add new functionality and content to the platform. With Salesforce Experience Cloud, it is easier than ever before to create content and feature rich community portals with the help of a library of pre-built templates. Salesforce Lightning Dialer Support reps and communication teams need to manage a high call volume to interact with prospects and customers on a regular basis. To be able to do this, their managers need to have information about the calls. In Salesforce Classic, this information is retrieved in real time. Having said that, managing calls would be far more efficient, if the phone functionality existed within Salesforce. The Lightning Dialer comes in-built in Salesforce Lightning, which reduces clicks and makes the whole process of calling and recording more efficient. This is made possible by integrating the Salesforce UI with calling systems. This allows reps to streamline calls and it automatically records the key performance indicators. Salesforce Lightning Paths When reps are working on complex processes in Salesforce, they often need guidance. Salesforce Lightning Paths is a feature specially built for Salesforce users to offer them that guidance to work with complex tasks or opportunities. With the help of this feature, Salesforce users are able to easily navigate through complex processes with the help of recommended pre-determined steps. An Advanced User Interface The Salesforce Lightning user interface is extremely robust and offers remarkably flexibility when compared to Salesforce Classic. The user interface offers easy interoperable code development across devices. It facilitates user productivity and drives work efficiency with much lesser code. Intelligent home pages built from template libraries allow users to track performance to goal, and get real time updates on key accounts. Reports & Dashboards The Salesforce Lightning Experience comes with a complete overhaul of reports and dashboards. They are now much more interactive, easier to navigate through and customise. Sales reps can get more insightful information from interactive charts without having to dive deep into reports. With the Lightning dashboard editor, dashboards can be fully customised with prebuilt components. Reports quality can be augmented and made more insightful without the need of the report builder. With the new Lightning UI, it’s much easier to find reports & dashboards via fully customisable home pages. Activity Timeline In Salesforce Classic, users have the option to view Open Activities which allows them to create tasks & record event. And with Activity History, users can log calls or send emails. In Lightning however, Salesforce has added a functionality that allows users to create the tasks and along with it, it gives you an activity timeline which shows the activities you have already created. With the activity timeline functionality users can view open tasks, scheduled meetings, and more in the activity timeline. Salesforce Einstein Salesforce users are familiar with Salesforce Einstein. This is one of the standout features of Lightning. Salesforce Einstein, an AI based digital data scientist, empowers businesses with the tools and resources to become more predictive about future trends and customer behaviour. Einstein significantly boosts the productivity of sales, service, marketing teams. Live Feeds This is another unique feature in the Salesforce Lightning Experience. When in a group in the Lightning Experience, the Chatter feed is on. Live feeds delivers posts and comments from other users in the group as and when they are posted, keeping you updated in real time about discussions within the group. Lightning App Builder The Lightning App Builder is a drag and drop tool using which you can easily create custom pages within the Lightning experience, giving you all that you need all in one place. With Lightning app builder, you can configure and customize lightning components in create business-specific apps. Lightning Snap-Ins Lightning snap-ins is a key component for Service Cloud users. Snap-ins allows businesses to quickly manage client support directly within their Lightning pages. With Lightning snap-ins service reps can reach out to customers in real time on any device, and deliver personalized service across every touchpoint in the customer journey. Steelbrick Feature Steelbrick is another unique feature built entirely on the Lightning infrastructure. With its  CPQ (Configure, Price, Quote) functionality, sales reps can easily create offers and contracts for brands. Also, they can easily get approvals and signatures without ever leaving Salesforce. AppExchange There are a plethora of Lightning-ready third-party apps on the AppExchange. And this number is growing by the day.  All these apps are vetted by Salesforce and are pre-integrated to work seamlessly with the Salesforce platform. With the help of these Lightning-ready apps, you can deliver consistent, personalised experiences to your customers, boost internal productivity and drive RoI. All you need to do is look for ‘Lightning Ready’ apps on AppExchange. The intelligent, intuitive, and future-ready design of Lightning is enabling businesses to improve efficiency and productivity significantly. The stunning UI, intelligent views, integrated reports and dashboards, pick-and-drop functionality, limitless customizability, backed by the power of AI, makes Lightning a compelling choice. As a Salesforce Gold Certified Salesforce Partner, Girikon has helped multiple businesses leverage the Lightning experience to achieve business success. Contact one of our experts today to know more about how we can help you get the most out of Lightning. 

  A summary of Unified Higher Education Marketing Higher Education marketing and communications takes place in a landscape where approximately 75% of campus staff associated with marketing and communications do not report centrally, but typically through a reporting head such as a college dean or department leader. Consequently, this can lead to cascading operational inefficiencies, brand misperception, and an incoherent experience for students, faculty, staff, and supporters. While colleges and universities can adopt several different marketing and communications strategies for various campus functions, one of the critical ones is to unify and centralize specific aspects of marketing and communications. Unfortunately, this approach comes with its inherent challenges and necessitates a high degree of collaboration. Some of those challenges include: Resistance to change: The common perception that the current state is adequate as is, can stall a more unified way of working. Siloed nature of departments: A legacy culture of decentralization makes collaboration and unification more difficult. Shadow technology: Marketing technology tools and solutions implemented without guidance from marketing or IT. Discounting central marketing and communications: When the central department isn’t viewed as a key partner in marketing and communications, departments depend on outside agencies. Moving too quickly: Trying to get too many departments aligned at once can complicate things rather than simplifying. The Benefits of Unified Marketing and Communications There are several benefits associated with a unified approach to higher education marketing and communications that go beyond just a connected experience for students, faculty and staff. These include some key operational efficiencies that save time, augment knowledge and absorption, and distributed costs among departments. As budgets change, marketing teams should be able to show the evident benefits of alignment. Shared Cost: Most colleges and universities have marketing and communications teams working with different technologies. This leads to cost redundancies that aren’t always apparent. Shared Knowledge: When using many different technologies, there are limitations in combining and understanding knowledge to drive innovation. Shared Data: Data is critical to understanding how institutions are engaging with stakeholders in a manner that makes sense for them. Shared Messaging: This constitutes messaging with an appropriate level of personalization. 5 Strategies for Higher Education Marketing and Communications The following five strategies should be embraced to achieve operational efficiencies across campus. 1. Consistent Messaging, Voice, and Tone A central messaging platform is vital. While centralised messaging, voice, and tone is important, it needs to be relevant for various departments across campus, for it to be widely adopted. For messages to resonate amongst the audience, it is essential to understand the overlap of audiences. Media: Media rules of engagement should be established clearly, to ensure consistent messaging across print, social media, web, and email. Brand: Most colleges and universities don’t have the bandwidth to support multiple brands.  It is important to adopt a “branded house” strategy and foster sharing of resources. 2. Segment Constituents Since the pandemic hit us, audience segmentation has been a key topic of discussion for higher education marketers and communicators. However, limited access to all data, especially constituent metadata such as descriptive information, makes audience segmentation and targeted personalization of messaging a challenge. Consequently, engagement is at times carried out using batch wise email blasts. The key points to consider are where the data is, its accessibility, can the preferred marketing and communications platform use it, and can it be segmented prior to launching outbound campaigns. Metadata for Constituents: Full name Address Major Last event attended When they last donated Expected graduation  date Research they engage with Forms submitted across the college/university 3. Support Services It is essential for marketers and communicators to be viewed as a strategic partner and not an someone who are meant to take orders for the rest of campus. Unfortunately, establishing this alignment doesn’t happen organically. It needs complete and mature support services that bring staff together from discrete departments. To begin with, best practises should be documented and made accessible online easily. Also, it must include ongoing training so that departments and staff can fully absorb the brand message, voice, tone. And understand how technology can help them to deliver it to the right audience. Higher Education Institutions that have successfully achieved alignment conduct monthly or quarterly meetings amongst all their campus partners where they share experiences and deliberate on ways to further engagement. Suggested Support Services Include: Brand Assets Library: Includes fonts and typesetting, color palette, graphic elements, email guidelines and templates, social media guidelines, and web standards. Training: Includes onboarding and regular training. Training methodology should balance courses for beginners and advanced ones, to keep all partners engaged. Campus Community: Regular meeting for the central department to share strategy with partners and encourage partners to share their perspectives. Governance Model: Establish the rules of engagement that partners need to follow. In an ideal scenario, the central department for marketing and communications is the owner. Center of Excellence (COE): This is a must when you are managing a central technology platform. It allows campus partners to ask questions and receive guidance and support. Innovation Workshops: Campus partners can learn about new features and functionality about the technology being used and understand how they will be used going forward. Best Practices Sharing: Regular feedback sessions to establish what’s working and what’s not. This opens up opportunities for partners to learn from each other’s mistakes and/or successes. 4. Have a Full Stack MarTech A unified, aligned higher education marketing and communications team is one that drives engagement with the right audience with a robust central MarTech strategy. Higher Educational institutions deploy a huge amount of marketing and communications messaging across multiple digital channels, such as email, SMS, web, ads and social media. While it is important to have a rich set of features and functionality for creating and launching campaigns across channels, the ability to have actionable data and deliver personalization is what is most important. This is where a best-in-class CRM platform for higher education becomes critical to aggregate constituent information, and use that information to segment audiences and deliver a personalized and relevant engagement. 5. Plan Big, Start Small Once you’ve identified your brand messaging platform, and established campus-wide technology, along with complete support systems, the next step is to get campus buy-in to set your plans in motion. Most colleges and universities however, operate on a decentralized model. Unless there’s a clear directive from the top leadership, bringing other departments along would require a consensus. If working with your central team is challenging, other departments may not see the value in aligning with them. Big changes don’t happen overnight, so start with small steps, one at a time. Identify and start with partners that are open to innovation. Do a test pilot, and fine-tune a unified approach. The learnings acquired from early partnerships are key since they form the blueprint that other constituents can follow. Steps To Get Started Identify a large, strategic partner who you think is key to success. Get them on board in the planning phase itself. Onboard one or two smaller departments with whom you have a good rapport. These departments should be aware of the value of alignment and are willing to innovate and learn with you. Keep the rest of the campus apprised of these partnerships. Some may be skeptical, but once they see value in what you have undertaken, they will get on board. Unified higher education marketing and communications can be quite a challenge. It requires a significant amount of effort to ensure alignment across many different departments, but it’s totally worth the effort. Higher Education institutions that follow these marketing and communications strategies can attain higher operational efficiency, a better understanding of the campus-wide marketing technology landscape, and higher engagement from their constituents. Behind every great strategy is a partner that you can trust. You need a certified expert. Learn more about how you can partner with Girikon, a Certified Salesforce Partner, to support your institution’s marketing and communications teams.

At Dreamforce 2022, Salesforce and WhatsApp announced a game changing strategic partnership that would allow Salesforce customers to connect with their customers seamlessly and empower them to deliver new messaging experiences on WhatsApp. With 2 billion users, WhatsApp is the most popular messaging app on the planet. And Salesforce is the world’s No 1 CRM platform. And when these two come together, the benefits of both will be amplified. WhatsApp integration with Salesforce boosts customer satisfaction and increases your brand loyalty. The Salesforce-WhatsApp integration is a solution from Salesforce that lays emphasis on delivering an integrated, connected omnichannel experience to its users. WhatsApp business messaging from Salesforce will bring Salesforce’s best-in-class CRM capabilities to deliver convenient, fresh, and personalized experiences to customers worldwide. This seamless integration will transform how brands engage with their customers across marketing, sales, and service interactions. The new integration will empower businesses to customize their experience effortlessly and connect with their customers in a fast, engaging and personal way to promote and sell products and provide support. This would in turn improve brand engagement and loyalty, improve convenience, boost interaction, and augment customer service.   How WhatsApp with Salesforce boosts customer engagement, loyalty and revenue Salesforce’s key Cloud offerings namely Marketing Cloud, Commerce Cloud and Service Cloud apps will integrate with WhatsApp to drive promotional and customer service messaging, and sometime soon into the future, integrate conversation based transactional commerce capabilities. This will allow businesses to transform their relationships with their customers across millions of conversations by personalizing the messaging experience for every customer, at scale. This will allow Salesforce customers to engage audiences on WhatsApp, fast track sales, and drive a far more effective and efficient customer service experience. Features of this integration include: Create an end-to-end customer journey: Using Journey Builder and WhatsApp, Salesforce customers can create, exchange, and manage interactions with customers throughout their journey to deliver a seamless customer experience. For instance, customers may receive a reminder message on WhatsApp about an upcoming order delivery in the coming week. As an upsell promo, the message could include a discount coupon of 25% for a related product. The user could then confirm in a single click if they wish to add this new product to their next order. Personalize every interaction with the Marketing Cloud Customer Data Platform (CDP): With Marketing Cloud Customer Data Platform, Salesforce customers will be able to personalize real time marketing interactions on WhatsApp with first-party customer data. Salesforce based Whatsapp messaging will leverage AI driven insights from across Salesforce and other sources to personalize customer engagement with smart promotions, and recommendations, across every interaction, at scale. Brands can also easily activate audiences directly through the Marketing Cloud CDP, to target high-value segments or new audiences with Click-to-WhatsApp ads on social media to drive customers to a one-to-one messaging experience. Enhance selling and service conversations with automation and AI: With the partnership between Salesforce and WhatsApp, businesses can significantly reduce support wait times and improve overall efficiency with automated personal interactions through messaging on WhatsApp. Salesforce customers using tools like automation and AI-powered chatbots have seen a significant increase in customer satisfaction, agent productivity, customer retention, and case resolution. Enrich customer conversations: Salesforce customers will be able to use customizable templates for messages that include brand media such as product videos and images, or display products and services with interactive textual content that allow consumers to view and buy products through WhatsApp. Customizable buttons allow users to take action with a single tap. Default Privacy and security: Privacy and security is at the heart of WhatsApp. Every WhatsApp message sent between businesses and their customers is protected by the best in class Signal encryption protocol that secures messages before they leave your device How to connect WhatsApp to Salesforce Before your customers can send you messages from WhatsApp, and you can reply from the Salesforce console, you need to have a Salesforce account and a verified Facebook Business Manager account. Along with that, there are some other requirements that you need to fulfill: Have Salesforce Classic or Lightning Experience. Have a Digital Engagement license Have a Service Cloud license Have a Chat user license. Have an approved WhatsApp Business account. To set up your WhatsApp account on Facebook Business Manager, send an email to WhatsAppEnablement@salesforce.com with the subject “WhatsApp Number Setup.” Include the information listed below in the body of your email: Salesforce Org ID. Facebook Business Manager ID. The name associated with your Facebook Business Manager ID. The WhatsApp number The Name and email address associated with the number The company name you want to display on WhatsApp Company description, logo, and website URL (optional) Going to your WhatsApp channel in Lightning Experience Go to Lightning Experience settings. Write “Messaging” in the search bar and select “Messaging Settings.” Go to “Channels,”  and you should automatically see WhatsApp. Automating Customer support with Einstein Bots With this new partnership between Salesforce and WhatsApp, WhatsApp will have access to Einstein Bots. Business teams can program or automate messages on WhatsApp just like on Facebook Messenger or with standard SMS messages.  Einstein Bots is an AI tool from Salesforce designed to create bots to assist customer service teams to manage customer queries and issues. Einstein bots allow you to answer questions on routine cases, while freeing up agents o that they can work on more complex customer issues. In Salesforce, you will need to install your company’s WhatsApp within Einstein Bots. Once you complete that setup, go to the main Bot Builder page and complete the following steps: In the Channel Menu click on “Add” Select WhatsApp in the channel options In the Deployment field select the channel name This functionality will make automating specific answers very straightforward to try to help customers before they even speak to an agent. Customers can make queries over WhatsApp while receiving automated responses in real-time, all leading to significant time saved for the customer support staff. Today customers spend more time than ever on their devices, and they want to interact with brands on the world’s most popular messaging app. Most self-employed professionals are already doing that. And it’s a matter of time before medium-sized and large corporations follow suit. Now brands and enterprises can communicate with their customers over WhatsApp with the following benefits: Implement in real-time. Implement in an organized way. Get access to official support from WhatsApp. Get real time AI driven performance analytics. Get the entire history of customer interactions. Easily map contact center agent traceability. Drive case resolution efficiency and boost productivity. Significantly improve SLAs Automate responses, alerts, and notifications with Einstein Bots. This partnership between Salesforce and WhatsApp is transforming how businesses and brands communicate with their customers to enhance the customer experience through personalization delivering a seamless experience privately and securely. Girikon is a Gold Certified Salesforce Consulting Partner and can help you transform your sales, marketing and customer service activities by integrating the World’s No1 CRM platform with the world’s most powerful messaging platform. Contact us to day to know more.