technology

TensorFlow is an open-source library created by the Google Brain team to build enterprise-grade machine learning algorithms. 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, bringing us into a new world of advanced algorithmic thinking, plus deep learning too. Deep learning makes use of algorithms that people usually call Neural Networks , and yes the title is pretty telling. They take inspiration from our biological nervous systems, sort of guided by the brain, in order to process information in a more natural style. In practice, deep learning algorithms help computing devices pick up on virtually every single bit of data, figure out what it means, and then learn repeatable patterns over time. And, honestly, these same advancements are also supporting organizations to boost customer engagement, automate routine workflows, and strengthen decision-making. All of this is happening through salesforce consulting services that lean on AI-driven insights and smart automation, which feels almost like a quiet upgrade happening in the background. 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. companies that use salesforce often integrate these AI-powered solutions for better customer engagement and analytics. Threat detection, used in social media and government applications, and fraud detection, used by insurance and 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. Today, these advancements are also being enhanced through generative ai prompts, which help AI systems create, interpret, and refine visual outputs more accurately across image recognition and computer vision applications. 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 in recommendations, you know , the kind where history kinda repeats itself. Time Series recommendations has been used a lot across major orgs like Netflix, Google, and Amazon, where they look at and compare the activity of millions of people to figure out what someone might want to watch or buy. With each interaction, and recorded action, these recs get sharper over time. Like, they might suggest a show or video that your family members, or even friends, tend to enjoy, or maybe recommend gifts you could actually like. In a similar way, a salesforce implementation partner can use time series data plus predictive analytics to help companies craft more personalized customer experiences, boost engagement, and deliver more effective recommendations across sales and marketing channels. 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 success of a business is directly proportional to the extent their customers are satisfied. With a robust CRM (Customer Relationship Management) system like Salesforce in place, organizations will not just be able to improve their customer relationship but will also be able to increase their sales and revenue. As a leader in the CRM space, Salesforce has displayed promising statistics, which includes an annual growth of 29% in the financial year 2020. Apart from this, its customer base, which currently stands at 150000 is also increasing year on year. To, implement Salesforce, organizations should seek Salesforce Support from a reliable service provider. Here are some of the latest trends that will rein the Salesforce CRM space in the year 2021 and beyond: Customer 360: Uncertain situations like the recent Covid-19 pandemic have forced businesses to change the way they function. While customers have become digital-first, organizations too are moving towards digitalization to retain their customers. However, to sustain the market competition, organizations should equally focus on creating innovative solutions rather than just digitalization. Statistics reveal that around 69% of customers look for digital ways to get their products and services while 54 percent of them are seeking other ways to connect with a company. For the year 2021, Salesforce is coming up with a customer 360 tool that will enable companies to get a complete view of their customers.  Data Analysis:  Organizations these days are looking to analyze the data of their customers from multiple sources such as social media profiles, phone support, newsletter, etc. However, a report shared by Salesforce reveals that only 50 percent of companies can access all the data. This happens because most of the data is dispersed, which makes it difficult for organizations to analyze and visualize it. Consequently, organizations are adopting Salesforce as it helps them to gather data in a single place so that the sales, service, and marketing teams can work together without any doubt. Salesforce empowers organizations to create a data-driven approach by bringing together sales, service, and marketing cloud to arrange for data using customer 360 solutions.  Artificial Intelligence and Machine Learning: The AI-powered platform by Salesforce i.e. Salesforce Einstein provides actionable insights and suggestions that help organizations to make informed decisions. The platform has built-in deep learning models that predict by reading CRM data. Few other features that show how Salesforce is utilizing AI and ML to develop intelligent platforms for an organization include the following: Tableau:Tableau is a business intelligence and analytics software that helps in creating a dashboard within a few clicks.  AppExchange:It is a marketplace for third-party apps. Salesforce is working to upgrade it by offering apps that are more installable to increase sales and client interactions of the businesses. Analytics that is Voice Enabled:Salesforce launched Einstein’s voice, which uses NLP (Natural Language Processing) for processing the user request. Users can update records using their voice. Einstein voice bots empower users to create their voice assistants for custom apps.  Salesforce Ecosystem: As one of the most popular and widely adopted CRM software, numerous applications can be integrated within the platform, which in turn helps in extending its capabilities and functionalities.  Lightning Web Components: These are HTML elements that empower users to create lightweight UI designs.  Mobile Applications: Salesforce is striving to get the entire CRM into a mobile app for which it has laid a solid foundation to achieve the goals. Some of the upgrades aimed at by Salesforce include the following: Salesforce has re-developed the native Salesforce app by partnering with Apple to make the most of Siri and other Apple features. It has developed low-code mobile builders that allow users to convert spreadsheets to apps without the need for any formal training. Industry-specific Platforms: Though Salesforce is more efficient in Sales, marketing, retail, and finance, it has now started developing solutions for other industries given the growing demand.   Final Words: Being one of the most popular and widely adopted CRM, Salesforce keeps on innovating to provide users with a more efficient and robust platform. By implementing the aforementioned trends, Salesforce is striving to transform the CRM industry. To stay ahead of the race, organizations will look for business-specific solutions, which Salesforce is striving to fulfill. It’s prudent to partner with a certified and experienced Salesforce Consultant to know more about Salesforce and its latest offerings.

To keep their sales pipeline fresh and full of prospects, organizations indulge in lead generation. However, knowledge of lead management can be the difference between losing them and turning them into paying customers. Proper lead management will help in lead scoring leads, guiding them through the sales funnel, and ultimately converting them into paying customers. It is therefore important to ensure the effectiveness of your lead management process as an improvement in lead management efforts can empower sales teams to frequently convert prospects while enhancing customer satisfaction.    According to statistics shared by Sales Lead Management Association, organizations fail to follow up on the majority (80%) of leads generated.  Though lead management is typically considered to be the purview of a sales team, marketing, and customer service teams too play a significant role in the proper management of leads. By creating processes that are integrated across units, training employees on best practices for lead management, and leveraging robust lead management software, organizations can ensure that their best leads remain in their pipeline. This will bound to have a positive impact on their bottom line. A powerful lead management system does away with weak leads and nurtures those that are most likely to convert. Such a system will help organizations streamline their customer experience and make their sales and marketing teams more efficient and productive. Why Should Organizations Use CRM for Managing their Leads? Today, most organizations are looking forward to leveraging the benefits of lead management CRM software such as sales pipeline visibility, real-time analytics, contact management, and so on. Listed below are some instances when leveraging CRM software can be highly beneficial: Managing Leads that can’t be Handled Manually: Managing leads without a CRM becomes more difficult as lead volume grows. With a robust CRM by their side, the entire lead management process can be automated. Apart from this, sales and lead activity can be monitored via a single centralized dashboard. When Using Different Tools: If an organization is using disparate tools and applications to manage their lead management, they might not get a consolidated view of the process, are keeping duplicate data or wasting time shifting between the tools. Since, CRM systems have integration capabilities; organizations can manage their data from a single place. Couldn’t Understand the Reason behind Losing Leads: To provide their prospects with appropriate content during the lead nurturing stage, it’s important to understand their buying behavior, their preferences, and where they are in their buying journey. In other words, organizations have to track how leads behave on their website, the number of times prospects have visited their site, what type of content they take in, and much more. By leveraging robust CRM software like Salesforce, organizations can get access to every single detail, which means they can take all the necessary steps to move their leads from one stage to the other in the Sales pipeline. To understand how Salesforce CRM can help organizations manage their leads, it’s important to get in touch with a reliable Salesforce partner. Quick Wrap-up: Despite having sufficient lead in their sales pipeline, often businesses fail to generate the desired revenue. This calls for the need of evaluation for your lead management strategy, which also includes the implementation of robust lead management software. Implementing powerful lead management software like Salesforce can be leveraged to not just increase the number of leads but also manage them effectively. Such a system provides an efficient process for accessing and organizing contact details including information required for qualifying prospects and closing sales. Apart from this, the system also helps in managing the flow of information to make sure that none of the leads fall through the crack. Some advanced tools can gather all data in a single place and provide meaningful insights for a superior relationship with your prospects. So, if you too are looking to implement robust lead management software like Salesforce then it makes sense to get in touch with one of the best Salesforce implementation partners.