AI-powered CRM

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.

The growing scope and complexity of businesses have forced organizations lookout for a robust solution that can empower sales reps to close more deals quickly and effectively. Salesforce CPQ is one such software that has the potential to streamline processes, speed up quoting, while generate quick return on investment. However, for effective Salesforce implementation of this software, organizations have to leverage innovative technologies like AI and ML.

The retail industry is dealing with increasing costs, declining sales, disgruntled customers, and stiff competition. This has encouraged retailers to adopt technology tools and solutions to enhance operational efficiency and improve customer interactions. In fact, the retail industry offers a lot of scope for technological transformation in areas of supply chain management, inventory management, sales, marketing and more thereby empowering retailers to increase the overall efficiency and profitability of their business.  One such technology that has the potential to transform the retail sector is artificial intelligence, which has helped retail businesses boost their speed, efficiency, and precision. Let’s dive in a bit deeper to find out how: Cognitive Inventory Management: The thinking capability of AI can be leveraged to determine the inventory components basis of the current sales trend. Depending upon the data available, AI draws inferences and forecasts of probable scenarios, provides recommendations, and even takes necessary actions with human approval. Well trained algorithms can be used to make decisions, which improve efficiency. An AI-powered system can process huge amounts of data and help create an inventory that is tailored as per the current market trends and customer demand.  Customer Purchase History: With an AI-powered CRM, organizations can target the specific interest of buyers. By breaking down huge volumes of data, AI tools can be used to identify the purchasing pattern of customers and understand their preferences. The AI-powered systems can further recommend similar kinds of items to the customers for purchase. To integrate an AI-powered CRM, retailers should consider partnering with a Salesforce implementation partner. Interactive Interactions: Customers always look for a prompt response and with an AI-driven chatbot customer service can be improved significantly. These chatbots are trained to communicate with customers and provide answers to frequently asked questions and provide customers with the necessary support. These bots gather useful customer data and use it to determine their preferences and purchasing pattern. Streamlining Supply Chain: The retail industry can streamline their supply chain by leveraging AI-powered tools. This might involve categorizing in-demand products, transporting them, tracking the shipment, and inventory management. With an AI-powered CRM in place, the retail industry is poised to attain new levels of efficiency while maintaining a good customer relationship. Why Should Retail Businesses Consider Leveraging Salesforce Einstein?  Salesforce Einstein is an AI-powered CRM that integrates predictive analytics, and machine learning. Apart from this, there are other features such as Einstein discovery that allow users to figure out hidden data patterns in customer data. Other important features such as Einstein prediction builder provides for business predictions through custom AI models. Bots connected to CRM along with the integration of computer vision make use of deep learning models for identifying the make, model, or brand via product images. Another innovative feature is the Einstein voice assistant that helps in interacting with users in real-time. Quick Wrap-up:  Given the growing preference for online retailing by customers, organizations working in the retail sector are gradually adopting AI-powered solutions to enhance operational efficiency and business productivity. With a robust AI-powered platform like Salesforce Einstein in place, retailers can provide exclusive customer experience while taking business efficiency to the next level. If you are looking for implementing Salesforce Einstein within your business ecosystem, it’s in your best interest to get in touch with a reliable Salesforce consultant.