SalesForce

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.

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

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

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.

  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.

What is a subscription-based business model? Subscription business models are those where customers pay recurringly for continuous access to a product or service. Subscription business models can accelerate growth by virtue of creating recurring revenue. This means as a business owner you get revenue again and again over predictable intervals such as monthly or annually as and when your customers renew their subscriptions. What are the benefits of subscription-based business models? Subscription business models protect you during circumstances created by economic uncertainties. They create a stream of predictable revenue from a stable base of returning customers allowing you to plan for longer into the future. Here is some further detailing of the benefits. Fast Tracked revenue. As your business attracts more customers, recurring revenue from subscriptions grow exponentially. It’s always more convenient to manage existing customer than to attract and retain new ones. Subscription results in better retention drives revenue growth more efficiently and easily. Predictable revenue. You don’t need to plan quarter to quarter. Businesses who offer subscriptions, more often than not, start each quarter with a predictable expected revenue closer to the preceding quarter and then build on top of that. Business agility. Once you establish long-term relationships with your customers, you get access to valuable continuous data that will help you to get a better understanding of your customers, and consequently empower you to server them better as you go. Armed with aggregated customer data of their past interactions with you, you can learn about them faster and respond with new appropriate offerings. And the more this happens, the more efficient and unique your offerings will become. What are the different kinds of subscription business models? There are several kinds of subscription business models, but essentially, they all mean the same thing which is, you charge for access to products instead of charging for the products themselves. Here’s a look at three of the most popular ones: Subscription model: Revenue is set for each subscription cycle which could be weekly, monthly or annual. The charges payable are predetermined and based on a fixed date cycle. For example: a fixed monthly subscription fee for a music or video service. Consumption model: In this case, revenue is variable. The amount that is chargeable and when it is due is based on usage. For example, a cab service. Hybrid model: In this model, customers are offered a mix of subscription and usage. In this case revenue has both fixed as well as variable components. For example: over usage data fees on your internet usage on top of a fixed monthly service fee for a fixed data usage cap. How can I implement a subscription-based model for my business? To implement a subscription model, you need to adopt tools and processes that allow you to create a consistent buying journey, while knowing that customers move through different channels and make modifications to their subscriptions with each of them over time. Once you create a consistent journey, track new metrics to ensure you are on the right path. Here’s how you can get there: 1. Empower customers to subscribe and pay over any channel With a subscription-based revenue model, you are constantly interacting with your customers, and they are not always available for a conversation. And in today’s pandemic reality, they increasingly want to engage on their own term. In fact, recent research suggests that they now prefer digital self-service and remote customer service rather than face-to-face interactions. Businesses need to make it possible for customers to renew and/or upgrade their subscriptions and pay instantly on the channels of their choice. This could translate to having a website or a mobile app, to enable that service. It’s a known fact in today’s digital business context, customers don’t just want to choose digital channels for interaction. They want to be able to cross them.   As a business, you want to know how you can deliver a seamless and consistent journey across channels. For instance, a customer might initiate a purchase on your online store, but then being routed to a sales rep when they stall or hesitate at checkout. A good subscription management mechanism can help. It combines customer relationship management tools, and along with the power of self-service, provides access to the same data to all teams. Once they have that, they can pick up customer conversations from where they left off. As a business, you want every touchpoint with the customer to be amazing. And in order to deliver an exceptional journey, you need to integrate all customer data for a compete view of your customer. Subscription management provides a mechanism way to connect customer data that delivers a seamless journey and provides a great customer experience at every touchpoint. 2. Deliver value to customers Subscriptions don’t mean much if they are delivering value to your customers at every step. You’re consistently asking them to renew and pay for their subscriptions. IF there is no value in the offering, customer won’t renew their subscriptions. Consequently, they’ll fall through the cracks and your growth will hit a roadblock. There needs to be a paradigm shift in your thinking from delivering products to delivering value. How can you consistently deliver value-as-a-service to your customers? For instance, Spotify doesn’t sell music. They offer entertainment-as-a-service. Likewise, an e-retailer doesn’t just deliver great products. They deliver an exceptional buying and customer service experience-as-a-service. Value is unlocked as customer’s access and use the product. This brings customer service teams into focus, who will need to ensure sure adoption and customer satisfaction is high to the point of being delightful. A customer relationship management system that provides complete visibility of data and a certain level of automation is vital. Visibility helps you keep an eye on and act on adoption and usage patterns. And you can jump is to support and assist a customer to fix an issue early before it becomes a nuisance. For instance, with automation you can make it easy and fast for customers to start using new products or services they’ve purchased. 3. Understand recurring revenue with new metrics Customer acquisition is a thing of the past. It doesn’t hold the key anymore. Customer value, which leads to customer retention, which eventually drives recurring revenue, are the key metrics now. How will you improve customer retention without measuring it? Let us first look at customer value metrics. You can measure customer value by tracking exactly how much your customers are using your subscription offering. One of the key metrics is to track is average revenue per user, which is as simple as it sounds. Gross revenue divided by number of users. As your business evolves, with more processes and automation in place, you will become more and more effective at targeting customers with the appropriate offerings, all while retaining them and getting them to renew their subscriptions. And once that happens you will see an improvement in the average revenue per customer. To measure growth in top-line revenue, you need to monitor monthly and annual recurring revenue. These are significant since increasing them translates to your revenue streams becoming more and more predictable and that allows you to plan ahead. Ready to open new paths to revenue growth? A subscription-based business model is today’s new reality of a digital-first approach for business. However, it can only succeed if its authentic. Customers are smart. And you can’t fake value to them.  They will renew only when they get real, consistent value for their money. It’s both a challenge and an opportunity to get close to your customers, not just know them better but understand them better, and offer what they want in the way they want it. Girikon is a Certified Salesforce Consulting Partner and can help you to re-imagine your offerings and envision a whole new customer journey. Contact us today to learn how a subscription-based revenue model can deliver value to your customers and drive revenue and agility for your business.