AI

Business leaders, lawmakers, academicians, scientists, and many others are looking for ways to harness the power of generative AI, which can potentially transform the way we learn and work. In the corporate world, generative AI has the power to transform the way businesses interact with customers and drive growth. The latest research from Salesforce indicates that 2 out of 3 (67%) of IT leaders are looking to deploy generative AI in their business over the next 18 months, and 1 out of 3 are calling it their topmost priority. Organizations are exploring how this disruptive technology of generative AI could impact every aspect of their business, from sales, marketing, service, commerce, engineering, HR, and others. While there is no doubt about the promise of generative AI, business leaders want a trusted and secure way for their workforce to use this technology. Almost 4 out of 5 (79%) of business leaders voiced concerns that this technology brings along the baggage of security risks and biased outcomes. At a larger level, businesses must recognize the importance of ethical, transparent, and responsible use of this technology. A company using generative AI technology to interact with customers is in an entirely different setting from individuals using it for private consumption. There is an imminent need for businesses to adhere to regulations relevant to their industry.  Irresponsible, inaccurate, or offensive outcomes of generative AI could open a pandora’s box of legal, financial, and ethical consequences. For instance, the harm caused when a generative AI tool gives incorrect steps for baking a strawberry cake is much lower than when it gives incorrect instructions to a field technician for repairing a piece of machinery. If your generative AI tool is not founded on ethical guidelines with adequate guardrails in place, generative AI can have unintended harmful consequences that could back come to haunt you. Companies need a clearly defined framework for using generative AI and to align it with their business goals including how it will help their existing employees in sales, marketing, service, commerce, and other departments that generative AI touches. In 2019, Salesforce published a set of trusted AI practices that covered transparency, accountability, and reliability, to help guide the development of ethical AI systems. These can be applied to any business looking to invest in AI. But having a rule book on best practices for AI isn’t enough; companies must commit to operationalizing them during the development and adoption of AI. A mature and ethical AI initiative puts into practice its principles via responsible AI development and deployment by combining multiple disciplines associated with new product development such as product design, data management, engineering, and copyrights, to mitigate any potential risks and maximize the benefits of AI. There are existing models for how companies can initiate, nurture, and grow these practices, which provide roadmaps for how to create a holistic infrastructure for ethical, responsible, and trusted AI development. With the emergence and accessibility of mainstream generative AI, organizations have recognized that they need specific guidelines to address the potential risks of this technology. These guidelines don’t replace core values but act as a guiding light for how they can be put into practice as companies build tools and systems that leverage this new technology. Guidelines for the development of ethical generative AI The following set of guidelines can help companies evaluate the risks associated with generative AI as these tools enter the mainstream. They cover five key areas. Accuracy Businesses should be able to train their AI models on their own data to produce results that can be verified with the right balance of accuracy, relevance, and recall (the large language model’s ability to accurately identify positive cases from a given dataset). It’s important to recognize and communicate generative AI responses in cases of uncertainty so that people can validate them. The simplest way to do this is by mentioning the sources of data which the AI model is retrieving information from to create a response, elucidating why the AI gave those responses. By highlighting uncertainty and having adequate guardrails in place ensures certain tasks cannot be fully automated. Safety Businesses need to make every possible effort to reduce output bias and toxicity by prioritizing regular and consistent bias and explainability assessments. Companies need to protect and safeguard personally identifying information (PII) present in the training dataset to prevent any potential harm. Additionally, security assessments (such as reviewing guardrails) can help companies identify potential vulnerabilities that may be exploited by AI. Honesty When aggregating training data for your AI models, data provenance must be prioritized to make sure there is clear consent to use that data. This can be done by using open-source and user-provided data, and when AI generates outputs autonomously, it’s imperative to be transparent that this is AI-generated content. For this declaration (or disclaimer), watermarks can be used in the content or by in-app messaging. Empowerment While AI can be deployed autonomously for certain basic processes which can be fully automated, in most cases AI should play the role of a supporting actor. Generative AI today is proving to be a powerful assistant. In industries, such as financial services or healthcare, where building trust is of utmost importance, it’s critical to have human involvement in decision-making. For example, AI can provide data-driven insights and humans can take action based on that to build trust and transparency. Furthermore, make sure that your AI model’s outputs are accessible to everyone (e.g., provide ALT text with images). And lastly, businesses must respect content contributors and data labelers. Sustainability Language models are classified as “large” depending on the number of values or parameters they use. Some popular large language models (LLMs) have hundreds of billions of parameters and use a lot of machine time (translating to high consumption of energy and water) to train them. To put things in perspective, GPT3 consumed 1.3 gigawatt hours of energy, which is enough energy to power 120 U.S. homes for a year and 700k liters of clean water. When investigating AI models for your business, large does not necessarily mean better. As model development becomes a mainstream activity, businesses will endeavor to minimize the size of their models while maximizing their accuracy by training them on large volumes of high-quality data. In such a scenario, less energy will be consumed at data centers because of the lesser computation required, translating to a reduced carbon footprint. Integrating generative AI Most businesses will embed third-party generative AI tools into their operations instead of building one internally from the ground up. Here are some strategic tips for safely embedding generative AI in business apps to drive results: Use zero or first-party data Businesses should train their generative AI models on zero-party data (data that customers consent to), and first-party data, which they collect directly. Reliable data provenance is critical to ensure that your AI models are accurate, reliable, and trusted. When you depend on third-party data or data acquired from external sources, it becomes difficult to train AI models to provide accurate outputs. Let’s look at an example. Data brokers may be having legacy data or data combined incorrectly from accounts that don’t belong to the same individual or they could draw inaccurate inferences from that data. In the business context, this applies to customers when the AI models are being grounded in that data. Consequently, in Marketing Cloud, if all the customer’s data in the CRM came from data brokers, the personalization may be inaccurate. Keep data fresh and labeled Data is the backbone of AI. Language models that generate replies to customer service queries will likely provide inaccurate or outdated outputs if the training is grounded in data that is old, incomplete, or inaccurate. This can lead to something referred to as “hallucinations”, where an AI tool asserts that a misrepresentation is the truth. Likewise, if training data contains bias, the AI tool will only propagate that bias. Organizations must thoroughly review all their training data that will be used to train models and eliminate any bias, toxicity, and inaccuracy. This is the key to ensuring safety and accuracy. Ensure human intervention Just because a process can be automated doesn’t mean that’s the best way to go about it. Generative AI isn’t yet capable of empathy, understanding context or emotion, or knowing when they’re wrong or hurtful. Human involvement is necessary to review outputs for accuracy, remove bias, to ensure that their AI is working as intended. At a broader level, generative AI should be seen as a means to supplement human capabilities, not replace them. Businesses have a crucial role to play in the responsible adoption of generative AI, and integrating these tools into their everyday operations in ways that enhance the experience of their employees and customers. And this goes all the way back to ensuring the responsible use of AI – maintaining accuracy, safety, transparency, sustainability, and mitigating bias, toxicity, and harmful outcomes. And the commitment to responsible and trusted AI should extend beyond business objectives and include social responsibilities and ethical AI practices. Test thoroughly Generative AI tools need constant supervision. Businesses can begin by automating the review process (partially) by collecting AI metadata and defining standard mitigation methods for specific risks. Eventually, humans must be at the helm of affairs to validate generative AI output for accuracy, bias, toxicity, and hallucinations. Organizations can look at ethical AI training for engineers and managers to assess AI tools. Get feedback Listening to all stakeholders in AI – employees, advisors, customers, and impacted communities is vital to identify risks and refine your models. Organizations must create new communication channels for employees to report concerns. In fact, incentivizing issue reporting can be effective as well. Some companies have created ethics advisory councils comprising of employees and external experts to assess AI development. Having open channels of communication with the larger community is key to preventing unintended consequences. As generative AI becomes part of the mainstream, businesses have the responsibility to ensure that this emerging technology is being used ethically. By committing themselves to ethical practices and having adequate safeguards in place, they can ensure that the AI systems they deploy are accurate, safe, and reliable and that they help everyone connected flourish. As a Salesforce Consulting Partner, we are part of an ecosystem that is leading this transformation for businesses. Generative AI is evolving at breakneck speed, so the steps you take today need to evolve over time. But adopting and committing to a strong ethical framework can help you navigate this period of rapid change.

Businesses today are generating mountains of data and forward-looking business leaders recognize that there are critical insights hidden inside their data. With AI, businesses can unlock these insights to identify trends, opportunities, and challenges. Building a strong enterprise-wide data culture along with robust and trusted AI holds the key to unlocking these hidden insights. While business leaders recognize the value of data for decision-making, a recent global survey conducted by Salesforce amongst 10,000 of them reveals some interesting facts. 67% of them are not using data for making critical decisions like product or service pricing Less than 33% use data to drive strategies for new markets 79% don’t leverage data for diversity and inclusion While the above numbers revealed something unexpected, here is what the survey summarized. Companies that make data-driven decisions are more likely to beat sales targets than those that don't Companies that combine AI with their data showed an average increase of 30% in revenue Companies that embrace this approach are able to reassign human and financial capital quickly and can create personalized customer experiences much faster What can you do now? Here are some suggestions for creating a strong data culture. We will take these items up later in this article. Put together the right team Provide them with the right tools and training Test your theories on a pilot scale and iterate Prioritize the human aspect of your data culture Identify areas where AI can derive more value from your data Data-driven V/s data-informed In a data-driven company, most of the organization’s employees can access and analyze data, draw inferences about what it means, create a dashboard, visualize data, and use all of these to determine the next steps. Employees in a data-driven organization don't depend on data analysts to do this. Being data-informed enables organizations to make decisions based on a mix of data, research, experience, and insights. Data-informed organizations may or may not have the skills that data-driven organizations have. Why is it critical to build a strong data culture Business leaders have to deal with countless challenges before embarking on building a data culture. Avoid over-analysis by starting with a single use case that validates the value of your new data culture approach. McKinsey research has shown that data-driven organizations achieve their goals faster and their data culture initiatives contribute at least 20% to earnings. Here’s why this works: Data analysis identifies actionable trends Data analysis identifies patterns that unlock value and enable organizations to utilize opportunities faster. Adding AI to the mix can accelerate the process by doing a deeper dive into data analysis at scale and serving up recommendations. Combining data and AI drives growth, promotes innovation, fosters collaboration, and creates uniqueness. AI and machine learning increase success by 30% Organizations that still rely on legacy knowledge and instinct to guide decision-making are missing out on opportunities. With AI and machine learning, organizations can make quick and accurate decisions. According to Salesforce research, adding AI to organizational data and business functions eliminates the guesswork from the decision-making process and increases success by an average of 30% across important metrics like operational efficiency, employee productivity, and topline growth. Strategic work keeps employees engaged When decision-making is guided by data analysis, employees spend less time on mundane tasks that add little or no value and can focus on strategic or creative tasks. This keeps them engaged and improves their productivity. Salesforce research shows that 84% of organizations that have adopted a data culture observed higher employee retention. Empower the right team The best way to create a team of data champions is by showing not telling. Illustrate with real numbers how data-driven decision-making increases revenue and customer satisfaction and streamlines operations. Instead of choosing any random use case to illustrate your point, capture their attention by selecting a project that scores a financial win and one that you can scale for greater impact. Here’s how you can start: Step 1: Put together the right team Create a working group of employees from across the organization with diverse backgrounds and functions. These team members should have a collaborative mindset, unique skills and abilities, and individual organizational perspectives. Ensure that you include employees across the corporate strata such as senior executives, managers, engineers, consultants, and machine learning scientists. Step 2: Provide them with the right tools and training Salesforce research stats on data literacy don’t paint a pretty picture.  Only 35% of the surveyed workforce has received training on data visualization tools and 29% on statistical tools. 27% percent workers say they can interpret data outputs relevant to their job function, and only 26% say they can use that data to make decisions. With proper access to training on technology-driven data analytics, organizations can empower their entire workforce to unlock the power of data to drive decision-making. Step 3: Test your theories on a small scale and iterate Start small, analyze results, refine your theories and iterate. Eventually, a winner will reveal itself when your employees can measure the impact of your project on their bottom line. Step 4: Prioritize the human aspect of your data culture Encourage involvement of all team members in the entire process from setup, testing, fine tuning, to data analytics and its application for decision making. This will ensure that you avoid bias and guesswork which can have a negative long-term impact. Take data at face value to avoid bias by proxy. Let’s consider ZIP codes as an example. At face value, they are just a location indicator. But sometimes ZIP codes can be a proxy for an area’s racial makeup and financial services companies consider ZIP codes in loan applications. Decisions based on this data point must be free of bias. Step 5: Identify areas where AI can derive more value from your data You can start your AI journey at many places, in any department, for any function, or extend it further if you’ve already started. Start small, demonstrate results, and bring everyone on board. Establish guidelines and standards for consistency, security, accountability, and ethics from day one. Ensure completeness and accuracy of your data to make the best use of AI. Incorporating an AI-driven data culture can be a daunting task.  It takes time and effort to bring people on board, retain their interest, and demonstrate results. For most business leaders, this transformation may be a whole new experience. This is where working with a Salesforce Consulting Partner could prove to be very useful. At Girikon, our certified consultants can guide you on this transformational journey of embracing AI with a strong data culture. Contact us today.  Take the next step to build your AI-powered data culture.

In the rapidly evolving business environment, it is essential for companies to utilize state-of-the-art technology to stay competitive. Nowadays, forward-thinking businesses are incorporating artificial intelligence (AI) into their operations, particularly through the adoption of customer relationship management (CRM) software, to automate and enhance their CRM processes. Salesforce, a leading CRM platform, has consistently been a pioneer in innovation, especially in the realm of artificial intelligence (AI). Notably, Salesforce AI has transformed the way organizations handle their customer service processes. The integration of Salesforce and AI is more than just an augmentation. It has indeed opened new avenues in Customer Relationship Management (CRM). Rather, it offers a smarter, efficient, and a highly custom-made customer interaction. To know more about Salesforce AI integration, businesses should consider partnering with a reliable Salesforce consulting partner. Salesforce and Generative AI: A Dynamic Relationship As a cloud-based platform, Salesforce is highly customizable and configurable and can be leveraged by organizations to meet their unique business needs by tailoring their services. By leveraging tools like Salesforce Flow, users can automate intricate business processes, create agile service experiences, while streamlining data management. The next phase of transformation will involve incorporating the capabilities of generative AI into a versatile platform using Einstein GPT. This integration holds the potential to transform the way businesses function and engage with their customers How to Leverage AI to Improve Customer Service? Listed below are ways how AI can help businesses provide better service to their customers:  Improved Customization: Utilizing AI will empower businesses to deliver personalized experiences by harnessing customer data and their preferences. This will pave way for tailored recommendations, quick support, and a deeper comprehension of customer requirements. Unified Omnichannel Support: AI-driven chatbots can integrate easily with several communication channels such social media, web chat and more. This guarantees uniform interactions across several platforms, offering customers a unified experience. Intelligent Automation: AI can be leveraged to automate repetitive and mundane tasks thereby saving a lot of time that can be used up by human agents to focus on more complex and strategic activities. This will boost productivity, quicken response times, and optimize cost for businesses. Sustained Learning and Development: AI systems will keep gathering insights from customer interactions, feedback, and real-time data, which in turn will foster continuous improvement. This continuing improvement will yield more precise responses, intelligent recommendations, and enhanced overall performance. What are the benefits of AI in customer service? AI in customer service offers several benefits that can improve the overall customer experience and streamline business operations. Some of the crucial advantages include: Increased Productivity: Leading IT players believe that AI can be adopted by organizations to serve their customers in a better way. Research conducted reveal that access to AI assistants and tools can increase productivity for support agents significantly.  Increased Efficiency: Carrying out tasks manually can be burdensome for service agents. This includes tasks such as navigating between different systems to access customer history, searching for relevant informative articles, sending field staffs to service locations, and manually inputting responses. These manual processes are usually prone to errors as they are executed by humans. The integration of AI in customer service can provide intelligent suggestions to service workers drawn from knowledge bases, and customer data. A more Personalized Interaction: When a customer interacts with a chatbot, artificial intelligence (AI) has the capability to retrieve vital details, such as the name of customer, location, account type, and language preferred. If the inquiry demands the involvement of a field service technician, AI can promptly convey all relevant information to the technician, allowing them to deliver tailored service as soon as they arrive on-site. Less Exhaustion and Enhanced Morale: AI empowers agents to do away with monotonous, time-intensive tasks, enabling them to focus on tasks that demand creative thinking, problem-solving, and intricate critical thinking. These activities significantly impact the overall customer experience. Consequently, it shouldn’t come as surprise that majority of IT leaders anticipate that generative AI will alleviate workload of teams, while reducing burnout. Scalability: AI systems can simultaneously handle a huge rush of customer queries making it simpler for businesses to scale their customer service operations without consistently increasing staffing levels. A Practical Service Experience: AI has the capability to draw information from contracts of customers, warranties, buying history, and marketing data. This ensures the identification of optimal actions for agents to pursue with customers, even post the conclusion of the service engagement. The future of AI in Customer Service: The future AI seems to be quite promising in the customer service industry. In the years to come, artificial intelligence is poised to gain prominence in workplaces given the ongoing advancements in technologies such as machine learning and natural language processing (NLP). Besides handling routine tasks, these AI programs will offer significant insights into consumer behaviors and habits through big data analysis. Organizations can utilize this valuable data to optimize their return on investment in marketing strategies and branding initiatives. As technology evolves, AI is set to play a key role in uplifting customer experiences and boosting operational efficiency. Final Words: The fusion of AI and Salesforce is reshaping the CRM terrain, presenting matchless possibilities for organizations to elevate both their customer relationships, as well as their operational efficiencies. This integration when leveraged by businesses enables them to position themselves at the frontline of technological advancement, ensuring they stay competitive and in agreement to the ever-changing needs of their customers. In doing so, organizations can provide value to customers and stakeholders while future-proofing their operations in this quickly evolving digital era. Organizations should consider availing Salesforce implementation services if they wish to make the most of the integration of Salesforce and AI. 

Generative Artificial Intelligence (Generative AI) is the latest next generation technology. Generative AI tools have made it very easy for employees and professionals to compose and refine emails, fine tune presentations and reports, write code, put together social media campaigns, and fast track customer service interactions. But not everyone is able to maximize its full potential. More often than not it comes down to the prompt, the statements or questions you feed into a Generative AI tool. The better your prompts, the better will be the Generative AI response. The key takeaway If you want to get the most out of Generative AI and the generative pre-trained transformer (GPT) models that generate conversational language, you might want to get your hands dirty in prompt engineering. This gives the Generative AI model clearer details for what you want instead of being ambiguous. Generative AI is getting smarter as you read this, but it cannot read your mind. It can only give you responses based on its understanding of the prompt you give it. So be specific. GPT works better when the prompt is longer. The prompt, which is the question you are asking the tool, needs to be precise and contextual for it to generate the right response. And that’s the key to unlocking the full potential of Generative AI. What you need to know When writing a prompt, approach the tool like you’re having a normal day to day conversation with a colleague. Use clear language and descriptions.  The devil is in the detail. The Generative AI tools will work better for you if your prompts are precise and detailed. You can have an interactive conversation with your Generative AI tool and dive deeper into what you’re looking for. These following tips would be helpful when writing prompts for Generative AI: Write clearly and concisely so your Generative AI tool understands your specific request. Write linguistically correct, complete sentences with descriptive words, that clearly describes what you’re looking for. For precise responses, ask specific questions, and avoid questions that offer a yes/no response. Add context to your prompt. Explain what is it that you wish to achieve and define your target audience. Engage in a back-and-forth conversation. Follow up the initial response with further questions to go deeper and get even more specific and relevant responses.  What is prompt engineering? Prompt engineering is the art of asking clear, descriptive questions or providing detailed information to Generative AI tools, such as a GPT tool or chatbot, to fetch the best results. With the meteoric rise in adoption of Generative AI tools for personal as well as business use, effective prompt engineering skills can help you improve the efficacy of these generative AI tools. The more specific and descriptive your prompt, the better the AI generated results. And you can get creative like you would ask an expert of the subject of your enquiry. For instance, you can even ask the Generative AI tool to reply as someone well known, like Isaac Newton, to get a response from that individual’s point of view. Generative AI uncovers information from piles of data available on the internet. However, narrowing down your query by providing specific questions or instructions in your prompt and adding context will deliver better results. So get creative. Whether you are an expert prompt engineer or a novice in generative AI, it would be prudent to follow these 6 tips mentioned below to get the most from this disruptive technology. Be specific: For example, instead of writing, “Create a social media campaign,” which is a very generic instruction, you can write, “Create a social media campaign for an ecommerce website that sells sports apparel for tennis fans of Roger Federer and Rafael Nadal.” Engage conversationally: Generative AI may not understand localized dialect or colloquial language. Imagine you are speaking to a co-worker, not a computer. Use open-ended questions: Avoid question with binary responses like a yes/no response.  These prompts limit the Generative AI’s ability to surface detailed, contextual information. Set a persona: Get creative. Ask the Generative AI tool to give answers from the point of view of a public figure (past or present) like Isaac Newton or Christine Amanpour depending on the subject you want to ask about. In fact, you can even define a specific role for specific answers like an operations manager or lawyer.   Define your audience and channel: Specify in your prompt whether you are writing for millennials or GenX. Specify where the audience is going to read it – such as on a social media platform, a blog post, or on website. Ask follow-up questions: The beauty of Generative AI is that you can engage in a back-and-forth conversation with it while maintaining context. It’s akin to speaking with a human. Except that it’s not. If you are not happy with the initial response, you can ask follow-up questions to get more specific responses. This technique is sometimes referred to as “prompt chaining,” where you split your prompts sequentially to get more specific and tailored answers and use answers from one prompt to draw out the next. Use prompt engineering effectively for Generative AI products to work with you and not against you Generative AI tools are new and evolving as you read this. They are not perfect and they’re definitely not human. They are designed to make you feel like you’re having a conversation with a human on the other side, but in reality, it’s a back-and-forth with a computer that has access to heaps of data. Keep these points in mind during your Generative AI prompt writing and subsequent usage of the responses: Generative AI is not always factual. Sometimes it makes up answers, so ensure that you verify what you get. Avoid any copyright infringements. Ensure that what your Generative AI tool gives you isn’t plagiarized from somewhere. Generative AI tools do not understand nuance, local dialects and subtlety. They are not from your neighbourhood. Ensure that your prompts are as specific and clear as possible. Generative AI is not always completely accurate. It’s a fundamental reality of this technology and as a user you need to ensure that you verify any factual data or information before publishing it. Generative AI is already creating a revolution in CRM applications. Girikon is a Certified Salesforce Implementation Partner with a global delivery model. To know more about how Generative AI can work for you, connect with an expert today.

COVID has changed the way we live. Today, we spend most of our time online, looking for products and services. For marketing professionals, this translates into a great opportunity to stay connected with their audience in today’s pandemic context. But there is a catch. Customers are overwhelmed with digital information, and they are interacting with brands across multiple channels with so many options available. Which means not every interaction is attracting engagement. Today, engaging with customers is a whole new ball game. It requires faster and precise decision making about what needs to be communicated, how it needs to be communicated, when and where. The need of the hour is to deliver relevant messaging that conveys a deep understanding of the customer context. AI-driven solutions, with their ability to make intelligent recommendations, can help marketing teams make the right decisions, in quick time. They can also help marketing teams to launch contextual campaigns with personalised communication with their audience, achieving better engagement levels and enhanced customer experiences. So what exactly is AI Marketing? AI marketing is based on technology that uses machine learning algorithms to make automated decisions based on data aggregation and its analysis, along with analysing market trends and data that may impact marketing initiatives. Typically, AI technology is used in digital marketing activities where speed is critical. AI-powered marketing solutions use data to understand the best fit ways to communicate with your audience, and deliver personalised messaging at the right time, on the right channel without intervention from marketers, thereby ensuring a high level of efficiency. Digital marketers across the world today use AI to augment the efficacy of their efforts or to perform more complex tasks that would otherwise consume huge amount of time or resources. Here’s a look at how AI-powered solutions are transforming the marketing game: 1.  Smarter segmentation for enhanced audience discovery Understanding the customer is the foundation of today’s digital marketing strategy. AI-powered solutions can intelligently segment your audience. By scouring hundreds of data points across multiple marketing campaigns, AI-powered solutions can help marketers discover new sub-segments. Let’s look at an example. An outdoor hiking gear brand has an audience segment of “trekkers”. AI reveals to marketers that there are two categories in this segment – leisure trekkers and tech-savvy trekkers. With these insights, they customize their product recommendations based on the segment they are targeting. For example, leisure trekkers are shown apparel and accessories, and tech-savvy trekkers are shown the latest navigation devices or solar chargers. Identifying the right segment helps marketing teams deliver hyper-personalised marketing messages. Marketing solutions powered by AI technology can reveal unique customer traits within a segment. This can then be used by marketers to customize the marketing content and drive more engaging interactions. 2.  Accurate predictions to improve lead conversion Before the digital revolution, marketers relied on their experience and “gut-feel” to arrive at marketing decisions. AI-powered marketing solutions eliminate the guesswork by surfacing accurate, data-driven predictions and recommendations. These predictions allow marketing teams to personalise every customer journey and improving lead conversion through the marketing funnel. Let’s look at Einstein Engagement Scoring. This AI-powered feature of Marketing Cloud applies machine learning algorithms on customer data to arrive at a score for a company's email subscribers. The score tell you how likely each subscriber is to engage with your email campaigns, and eventually, to convert. The feature can also tell you the likelihood of each subscriber opening an email, click the links within the email body, or to un-subscribe. Marketers can use these AI powered predictions to build more tailored customer journeys. For instance, customers with a low likelihood of opening emails can be targeted through social media and mobile messaging. 3.  Personalised messaging across channels to drive engagement AI algorithms can use data such as browsing history, age, and recent interaction history with a brand to serve up a campaign landing page that is most likely to resonate with the subscriber. The same applies in advertising. The algorithm can use that data and serve up the right content for an ad in real time based on the user’s profile. This allows marketing teams run tailored ad campaigns that are relevant and better targeted at users, thus boosting ROI. With the evolution of technology, today’s AI-powered marketing solutions can be further targeted to help marketing teams deliver just the right amount of content. For example, Marketing Cloud Einstein has a feature called Engagement Frequency. It tells you the just the right number of emails to send out to customers and prospects for brand recall without being perceived as spam. Likewise, it also tells you which subscribers are being left out or being contacted too often. Based on this intelligence, marketing teams can customize their messaging strategy for improved customer engagement. In fact, AI-powered solutions can also tell you if a social media strategy would be a better bet than an email marketing campaign. Customers value the experience as much as the product and service, and brands will need to deliver personalised messaging across channels to stay ahead of the curve. AI can take your marketing team's understanding of your customers to the next level. Conclusion Marketing teams across industries are rapidly adopting intelligent technology solutions to improve overall operational efficiency and the customer experience and drive growth. This need for customer intelligence has ushered in a new era of Artificial Intelligence (AI) marketing solutions. With these AI-powered marketing solutions, marketing teams can get a deeper and nuanced understanding of their audience. The AI-powered insights can help marketing teams to drive conversions at scale. Regardless of the size of your marketing team, AI-powered marketing technology can help improve productivity, boost ROI, improve organizational efficiency, all while processing heaps of data your team may not have the bandwidth to deal with. If you are new to AI, even your first small step into AI-marketing like using a machine learning program to draft an email subject line and a greeting for your upcoming marketing campaign, can keep your brand ahead of the curve. It’s a small but significant step towards an AI-powered future. As a Gold Salesforce Consulting Partner, Girikon is in a great position to help you leverage the powerful technology of the World’s No1 CRM platform. To know more about how your marketing teams can use Einstein for Marketing Cloud to deliver personalised, contextual marketing campaigns, contact us today.

Imagine a scenario where a customer calls customer support only to navigate through multiple options and then being put on hold for several minutes before getting through to an agent. Only to be put on hold again as they look for answers to your problem. We’ve all had to deal with this at some point. And this is only half the story. Imagine being asked the same question over a hundred times a day by different customers. And not knowing answers to most of those questions. That’s what customer service teams have to deal with on a daily basis. In a world of fickle customer loyalty, how do businesses deliver excellent customer service? Disruptive technology like Artificial Intelligence (AI) may have the answer. Let us look at 10 ways in which AI can enhance the customer experience. 1. Chatbots Customer service reps today have to deal with a large number of calls on any given day. And on top of that there’s performance pressure to reduce the average resolution time. Enter Chatbots. Not only can chatbots provide quick answers in real-time, they can also reduce the case load on human agents by resolving common customer queries quickly. 2. Cost reduction Chatbots can help businesses trim customer service costs significantly by accelerating response times, freeing up agents to work on more complex cases, and resolving a very high % of routine customer queries automatically. A great example of this is call automation, which combines machine learning and voice recognition to augment existing IVR systems while delivering a significant cost reduction as compared to human agent assisted set ups. 3. Round-the-clock support Customers want service delivered at the time and on the channel of their choice. Businesses must be available at all times to customers to support them. Automated customer service makes that possible. It allows enterprises to deliver 24/7 customer service and resolve cases as soon as they come to light. This means customers don’t have to wait for long periods for a response. Prompt case resolution improves customer satisfaction and builds trust, loyalty and brand reputation. 4. Improved human interactions with customers AI can play a key role in supporting human interactions with customers. Two of the most common ways in which AI is supporting customer service is through AI-driven messaging and email tagging. AI-driven messaging allows service reps to handle a big chunk of cases with chatbot assistants. With AI-driven email tagging, service reps don’t have to read every customer email. AI-powered tools can scan and tag emails, and direct them to the right inbox. This frees up time for service reps so they can work on more complex tasks that necessitate human intervention. 5. Personalized experiences According to Salesforce, 72% of customers want to be able to solve service issues by themselves. AI technology can play a significant role in enabling customers to find what they are looking for more efficiently. AI analyzes customer data and key metrics, and makes intelligent recommendations on products or services to customers.  AI is always working in the background, analyzing every incoming piece of data, and suggests best fit content to customers. AI enables service reps to have a better understanding of customers, so they can send relevant content to them at the right time on their preferred channel. As a result, customers are able to find what they are looking for without having to call customer service. 6. Gathering data AI-powered technology simplifies data aggregation and serves a unified customer snapshot. Earlier, AI relied on existing customer data that was fed manually. Today however, things are far more advanced. Today’s AI-powered solutions proactively request data automatically. They can easily analyze patterns in behaviour, understand customer sentiment and quickly respond to their needs. 7. Predictive insights It is critical for businesses to deliver engaging and personalised experiences to customers.  AI powered personalization makes it easy for businesses to serve up tailored products or services to customers. Many businesses around the world that have integrated AI technology into their systems to deliver relevant information to customer, have seen significant improvement in their customer satisfaction scores. This improves brand reputation and builds loyalty. 8. Deeper insights from customer data In the early days, data mining was tedious and time-consuming. Today with AI-powered tools and solutions, huge amounts of data can be captured and analysed faster than ever, to get deeper customer insight, opening up new market segments and opportunities for brands. With AI, businesses can capture every customer action, uncover their interests, and apply these insights to drive targeted campaigns. AI can help businesses get faster results, get deeper insight, and eliminate human error and bias. And freed up human resources can be utilized for more complex tasks. 9. Assisting customers to drive decision making In today’s COVID 19 context, customers spend a lot more time online. They engage with brands across devices, and personalization across every touchpoint becomes all the more critical to assist customers in making the right decision. AI-powered assistants respond to customer queries in real time, and with a deeper insight on customers, are able to serve up intelligent recommendations to accelerate decision making. This frees up agent time and they can focus on more pressing tasks. In case of service requests when the conversation between a chatbot and a customer becomes complex, the interactions is automatically handed over to a human agent with a snapshot of the entire interaction history. AI powered solutions can sense behaviour patterns based on which they can make smart predictions. 10. Simplified task management One huge advantage of customer service chatbots is that you need only one chatbot to handle literally thousands of concurrent customers. Imagine the amount of agent time that can be freed up to resolve routine issues like serving up expected delivery date of a product they ordered or when is their insurance renewal due. This has transformed the relationship between brands and customers. How many times have you hung up on the customer support line because you lost your patience wating for an answer? And how many times have your support calls been left unresolved? You are not alone. Brands around the world are proactively investigating the use of AI into their business to interact directly with customers. While human agents can get overwhelmed performing tasks when they have to deal with mountains of data, AI can deliver answers without breaking a sweat. AI can easily sift through piles of data, analyzing, searching and serving up relevant information to customers in real time. AI can analyze unstructured data at lightning speed, something a human cannot do. AI analyses data and identifies patterns, which can be easily overlooked by a human. AI has in-built Natural Language Processing (NLP) capability. It can read a support ticket and instantly direct it to the right team. Customers are growing increasingly digital today.  It is becoming imperative for businesses to integrate AI into their existing systems to acquire new customers and retain them, at scale. AI has the potential to take the customer experience to whole new level. By making the customer journey more engaging, it can help you stay a step ahead of your competition. And it also eases the lives of service reps. In many ways they are the flag bearers of your brand. Automated responses, personalization, case routing, data analysis and intelligent recommendations, predictive insights, case prioritization are some of the things AI can do without breaking a sweat.   As a Gold Salesforce Consulting Partner, Girikon has been helping organizations around the world leverage the world’s most powerful CRM platform to drive productivity and growth. We recognise that improved efficiency and quality of your customer support will lead to happier customers. To know more about how AI can help your customer service teams improve your CSAT scores, contact us today.

Customers are very demanding today, primarily because of the options available to them. When they reach out to a brand for support, they expect minimal wait times and fast resolution, regardless of the channel they use. Agents, however, have limited bandwidth and can only handle a limited number of cases at a time. So how does one scale customer support? Enter chatbots. But what exactly is a chatbot? That’s a significant question considering the fact that this technology is increasingly becoming a bigger part of our daily lives.  In fact, Gartner research predicts that soon the average person will spend more time interacting with chatbots than with their spouse. With round-the-clock digital support becoming a critical value proposition for brands, more and more businesses are using chatbots to engage with customers to deliver the SLA expected of them. At a technical level, a chatbot (derived from “chat robot”) is a piece of code (program) that simulates human interaction through text or voice communication. Today, chatbots can be customised and used in multiple ways such as: Chatbots that interact through smart speakers Chatbots deployed on smart home devices Chatbots that can be deployed on popular messaging platforms and web In addition to having a conversation with a service agent, customers can now interact with an intelligent software that helps them to find answers fast. Whether through text or voice, chatbots can communicate with customers and respond to requests faster. To put it simply, Chatbots are Artificial Intelligence (AI) powered digital assistants that answer common customer questions. They help customers quickly resolve simple and routine issues freeing up agent time to work on more complex issues that require human interactions. How does one create customer service chatbots Customer service chatbots resolve simple, repetitive tasks that don’t require interacting with a human customer service agent. For instance, if a customer wants to know how to reset a password or the estimated delivery time for a product they ordered, a customer service chatbot quickly accesses the relevant information and answers the question without keeping the customer waiting at the other end. And while this is happening, your service agents can focus on resolving more complex customer issues and build stronger customer relationships. If you are looking to invest in chatbot technology, your first goal is to establish the most common customer requests to identify what to automate. We suggest the following six tips that you should keep in mind when designing your first AI-powered chatbot: 1. Personalise every greeting Customer service agents are trained to be warm, greet customers by name, and recognise their service privilege status. A chatbot can do the same thing in the background, powered by AI. Chatbots can be programmed to retrieve their name to ensure chatbots greet them like a human agent would. 2. Move from static to conversational Customers hate the idea of filling out an online form and then having to wait for 12 -24 hours for a response. An AI powered service chatbot can dynamically ask a series of relevant questions based on customer inputs and make the interaction more engaging. It also helps resolve the customer issues faster. And in the case where agent intervention is necessary, they will already have all the relevant information logged by the chatbot available in their panel. 3. Create interactive FAQs Traditionally, customers are prompted to visit the FAQ section of a website or app to resolve issues in a self-service mode. Chatbots turn this process around its head. They bring the FAQ answers to customers. You can stack your common FAQs and their answers into your AI interface, including all related questions and their answers.  And with natural language processing (NLP) capability built into the AI engine, chatbots recognise everyday use language and respond to customer queries. Now your customers can find what they are looking for faster than before. 4. Deploy chatbots to additional channels Businesses today deploy customer service across multiple digital channels such as web, messaging, and social allowing customers to connect with your brand in the way they want.  Salesforce research indicates that an average customer today uses nine different channels to interact with brands. This variety of options creates multiple opportunities to deliver 360-degree customer service to meet their ever-changing behaviour. You can dive deeper into your analytics to identify the channel that gets the maximum traffic for your brand, and then identify the top customer service requests on that channel. Automate your chatbot to respond to these requests and save time for your agents. 5. Engage customers with formatted text and content Basic text is all right for answering simple questions, but professionally formatted text using a range of font styles, sizes and colors enhances the customer experience. You can even insert images and interactive menus into the chat. And because it is powered by AI, your chatbot can surface a product menu, a list of articles, or customer support options, based on wat the customer asked, all within the chat. 6. Embed process automation in chatbots With AI, you can empower customers to self-serve themselves by assisting them with guided, step-by-step instruction right within the chat console. Work with your teams to identify tasks that are easy for customers to complete on their own. Therese are typically tasks that can be easily automated without needing any human intervention like renewing an insurance policy. Once your team has identified these simple and common use tasks, you can program your chatbot to guide customers throughout the service journey. And for more complex issues, when the chatbot has to hand over the conversation to an agent, the agent is already empowered with all relevant information about the case so they can resolve it quickly. Scale customer service with chatbots Your customers will recognize the value your customer service chatbots bring to them with quick, efficient resolutions to their requests and concerns. And you agents will have more tie to focus on complex customer service requests instead of answering FAQs. With AI powered chatbots, you can easily scale support to handle any case overload as and when they come your way. AI-powered chatbot technology holds the promise to reinvent the customer experience. And high-performing service teams are leading the AI powered chatbot revolution to augment their existing human customer support teams. In today’s digital first context, where speed of service is king, chatbots are helping companies stay ahead of the curve. As a Gold Salesforce Consultant, Girikon can help you deploy AI powered chatbot based customer service at scale. Contact one of our experts to learn more.