Business

Business leaders, lawmakers, academicians, scientists, and many others are looking for ways to harness the power of generative AI, and reduce the risks of Generative AI. This can potentially transform the way they 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. Business Adoption Trends and Risk Perceptions 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. Why Managing Generative AI Risk Matters to Enterprises A company using generative AI services & 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. Ethical and Responsible AI as a Business Imperative A while back, 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 development 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 and Reliability 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, Bias, and Toxicity Mitigation 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, Transparency, and Data Provenance 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. Human Empowerment and Responsible Automation 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 and Environmental Impact of AI Models 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. How to Safely Integrate Generative AI into Business Operations 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: Using Zero-Party and 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. Keeping Training Data Fresh, Labeled, and Bias-Free 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. Ensuring Human-in-the-Loop Oversight 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. Testing, Validation, and Continuous Monitoring 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. Feedback Loops and Ethics Review Councils 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. The Future of Trusted and Responsible Generative AI 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.

In today’s competitive business landscape, businesses are moving online to promote their business primarily because of the wide array of tools online marketers have access to. Marketing automation has become a critical element of every online marketer’s arsenal. Consequently, implementing marketing automation software like HubSpot has become the need of the hour. It is crucial to get in touch with a reliable HubSpot Consultant to make the most of this platform. According to Emailmonday, 49% of businesses use marketing automation, and the benefits of which are being enjoyed by 55% of companies.  Marketing automation has proved to be a blessing in disguise for B2B marketers as they significantly benefit from the increased number and quality of leads flow into their pipeline. B2B companies using marketing automation software have realized a jump of 10% in their sales pipeline. Today, small, medium and large companies are banking on tools, concepts, and marketing automation processes to achieve their business goals.    Let’s take a hard look at the reasons that have compelled B2B marketers to swear by marketing automation: Generate Quality Leads: Marketers can gather data about the visitors visiting their website through the forms and landing pages. The information gathered can be leveraged by the sales team for following up with the visitors. Moreover, the quality of leads obtained can be determined using lead scoring. This allows the sales team to emphasize leads that have a high probability of converting.  Engage Your Leads: It is essential to understand that organizations that nurture their leads could provide their sales team with around 50% more sales-ready leads. Marketing automation paves the way for lead nurturing – the way by which prospects are gently pushed down the sales funnel by providing something that will influence their decision.   Optimize Email Marketing: Email marketing is vital for B2B marketers and thus should be effective enough to ensure maximum open and click rates. Automation tools like A/B testing can be leveraged to enable changing individual elements such as email, customer list segmentation, and more to test their success rate. Check the Trail of Campaigns: One of the most important features of marketing automation is closed-loop reporting. This feature helps in checking the trail of successful campaigns that generated high sales, to be replicated. On the other hand, this feature can be used to end the plans that don’t yield results.    Visibility into the funnel: By integrating CRM with marketing automation, one can get a detailed view of the funnel. In other words, insights regarding customers’ journeys can be achieved, which can be leveraged to create more effective campaigns in the future. Quick Wrap-up: It can rightly be inferred that a robust marketing automation platform like HubSpot can provide wide range of functionalities such as lead generation, email marketing, social media management, lead scoring, analytics and reporting, which ultimately drives increased revenue for B2B marketers. To make the most of this amazing platform, it’s essential to get in touch with a certified HubSpot partner. 

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.      

Businesses across all industries have realized the significance of integrating a robust CRM (Customer Relationship Management) platform within their business ecosystem. With a powerful CRM in place, organizations can efficiently manage their customer relationships while enjoying operational efficiency by streamlining processes. Though, cloud-based CRM like Salesforce enjoys a great adoption rate by businesses of all sizes, most of them aren’t aware of the different beneficial integrations they could make use of. One such integration that can boost organizational performance significantly includes the integration of messaging apps with CRM systems and the Smart SMS app is the perfect example of Salesforce SMS integration. What is meant by SMS CRM integration? CRM SMS integration happens when your business messaging platform and CRM system are unified via their APIs. With this integration, the two platforms can interact with one another, allowing them to share and sync data. This integration can enable organizations to: Text Customers directly from the CRM System: For doing this, users need to click on the phone number listed by the customer’s name following which a widget or a chat window will pop up. The customer’s name and the number will get populated automatically in the window allowing users to send messages immediately. Sync Contacts: With SMS CRM integration, CRM systems no longer have to function in a silo. For instance, if any sort of modification is done in the contacts and custom fields of a CRM system, the changes will automatically get updated in the messaging platform and vice-versa.  Some major messaging platforms can enter SMS into a CRM for maintaining additional records and can be useful if dashboards, reports, or machine learning capabilities are built-in within a CRM. Add SMS to your CRM workflows: Premium CRM systems like Salesforce have robust workflow features like SFA (Sales force automation), email drip campaigns, and more. With such integration, text messaging can be included in the workflows as a trigger. What Makes Smart SMS App Preferred Choice of Customers? The Smart SMS app is a robust Salesforce messaging app that is 100% built natively on the Salesforce platform. The app empowers organizations to personalize their communication, streamline workflows, and eventually improve customer service by reaching out to them quickly and with advanced features. Here are some of the unique features that this robust and easily implementable app offers. Let’s take a quick look: SMS Automation through Workflow: The sending of SMS can be automated by using a simple workflow configuration. By sending automated messages, the workflow can be simplified, which reduces the burden on the users/ sales reps resulting in an improved bottom line. Send SMS/MMS in Bulk: Users can leverage this feature to send individual or bulk SMS/MMS in a batch of 200 from ListView on button click or using SMS/MMS interface.  SMS scheduler: Sales reps can schedule the sending of messages (promotional, welcome, etc.) to customers based across different geographies and time zone. To schedule the sending of messages at a desired time and date, Sales reps would have to enter the name and the number of the intended recipient along with the date and time. At the scheduled date and time, the SMS automatically gets disbursed without the need for any manual intervention. SMS Template: This feature can be used to create, modify, and use SMS templates easily by using any of the objects and associated fields.  Final Words: An SMS CRM integrated platform can be a powerful addition to any business. By enabling teams to leverage the contacts and reach out to them in an automated fashion but with a personalized touch will help organizations to improve their customer service, as well as their bottom line significantly.

Are you dealing with duplicate data?  Does your data not fall under exact match?  Are the duplicates in your data not consistent for an exact match?  Are you struggling with cleansing of different types of data duplicates?  If you have answered yes to most or all of the aforementioned questions then the solution to your problem is Fuzzy Matching. Fuzzy matching allows you to deal with the above mentioned problems easily and efficiently.  What is Data Matching? Data Matching is the process of discovering records that refer to the same data set. When records come from multiple data sets and do not have any common key identifier, we can use data matching techniques to detect duplicate records within a single dataset.  We perform the following steps: Standardize the dataset Pick unique and standard attributes Break dataset into similar sized blocks Match and Assigning weights to the matches Add it all up — get a TOTAL weight What is Fuzzy matching? Fuzzy matching allows you to identify non-exact matches of your dataset. It is the foundation of many search engine frameworks and it helps you get relevant search results even if you have a typo in your query or a different verbal tense. There are many algorithms that can be used for fuzzy searching on text, but virtually all search engine frameworks (including bleve) use primarily the Levenshtein Distance for fuzzy string matching: Levenshtein Distance: Also known as Edit Distance, it is the number of transformations (deletions, insertions, or substitutions) required to transform a source string into the target one. For example, if the target term is “book” and the source is “back”, you will need to change the first “o” to “a” and the second “o” to “c”, which will give us a Levenshtein Distance of 2. Additionally, some frameworks also support the Damerau-Levenshtein distance: Damerau-Levenshtein distance: It is an extension to Levenshtein Distance, allowing one extra operation: Transposition of two adjacent characters: Ex: TSAR to STAR Damerau-Levenshtein distance = 1 (Switching S and T positions cost only one operation) Levenshtein distance = 2 (Replace S by T and T by S) How to Use Fuzzy Matching in TALEND? Step 1: Create an Excel “Sample Data” with 2 columns “Demo Event 1” and “Demo Event 2”. Demo Event 1: This column contains the records on which we need to apply Fuzzy Logic. Demo Event 2: This column contains the records that need to be compared with the Column 1 for Fuzzy match. Step 2: In TALEND use the above Excel as input in the tfileInputExcel component and provide the same file again as input to the same component as shown in the diagram. Step 3: In the tFuzzyMAtch component choose the following configurations as shown in the below diagram.  Step 4: In the tMap we need to choose the following column to take an output. Demo_Events_1 MATCHING VALUE Step 5: Finally, you need to select an tFileOutputExcel component for the desired output.  In the final Extracted file, the Column “VALUE” shows the difference between the records and matches the records to their duplicate.  Conclusion: In a nutshell, we can say that the use of TALEND’s Fuzzy Matching helps in ensuring the data quality of any source data against a reference data source by identifying and removing any kind of duplicity created from inconsistent data. This technique is also useful for complex data matching and data duplicate analysis.  About Girikon Girikon is a reputed provider of high-quality IT services including but not limited to Salesforce consulting, Salesforce implementation and Salesforce support.

Sales Analysis Provides Logical Answers to your Question For every organization, the sales function continues to be the most important function as it plays a significant role in building trust between a customer and a business entity. Organizations usually spend enormously in sales-related activities to gain edge over their competitors. However,  sales related business decisions taken on others advice or by own “gut feeling” could be quite risky that too when a large amount of money is at stake. Sales Analysis is the process which is used to understand, identify and predict sales trend. It leverages the historic data to determine the failure/success of the previous sales drive and helps in identifying the area of improvement. With sales analysis organizations no longer need to take decisions based on their gut feelings as it provides logical answers to their sales-related queries. Importance of Sales Analytics: There are several benefits of incorporating Sales Analytics into the Sales function. Let’s take a quick look at some of them. Fill the gaps: By Analyzing their available data, organizations can figure out where they may have missed out on the opportunity of converting a prospect to a customer. This will eventually help organizations fill the gaps in the sales process, as well as the gaps in their products. Organizations should analyze their product and understand what their competitors are doing to remain competitive, what’s not working for you and how can you fill in the gaps. Increase Retention Rate: If organizations can figure out why any deal got closed, then they can keep their customers happy and build a strong relationship with them. It is important to understand the need of the customers so that organizations can upsell and cross-sell other products to the existing customers, which significantly adds to the revenue. In addition, by looking at the purchasing patterns of your existing customers you can recommend what customers can buy next, based on what these customers have bought in the past. Accurate Sales Forecasting One of the most obvious and important feature of Sales Analysis is the ability to predict the future sales based on the available data. Unlike setting up goals based on the “gut feeling”, historic data gives you an accurate, realistic picture of how much your organization should be able to earn within a specified time period. Competitor Sales Analysis In industries like automobiles, sales made by all the organizations are made available on the public platforms. Organizations can use their competitor’s data to see which product of their competitor is selling the most. They can even get the answer to the question like where and why a particular competitor’s product is selling the most. Based on their analysis they can revamp their product lineup, product pricing, introduce new product etc. Future Decision Making Organizations can even make future decisions like whether an organization need to introduce new products or discontinue any existing product. It even help organizations effectively manage inventory, marketing activities, sales offer roll-out time, manufacturing process etc. More efficient Marketing Sales Analysis helps organizations identify market opportunities and trends to support product marketing. If an organization is planning to launch a new product then it is very important to know the answer of below questions When should the product be launched? Q2. Where should the organization launch the product? Q3. How should it be marketed? Q4. Who is the target audience? All the above questions can be answered by using sales analysis. By having answers to all the above question, organizations can launch their product at the right time and can target the right audience, which will help them have a good sales number. Type of Analysis Sales Analysis could be done in many different ways based on the available data with the organization. Common types of Sales Analysis performed are mentioned below: Product-wise Analysis Organizations can use product-wise analysis to identify which product is selling most and during which time of the year. For example, winter clothes are sold most between November – January whereas summer clothes are sold most between May – September. Based on this analysis, organization can manage the new product launch date, inventory etc. Forecast vs Achievement analysis With the help of Forecast Analysis, organizations can see whether they are able to meet their monthly/weekly/quarterly targets or not. They can even identify how much they should be looking for in the month/week/quarter. Based on their analysis they can make required changes in order to meet the desired organizational targets. Periodic Analysis Periodic sales analysis are the ones which help organizations compare two or more different time period sales. Organizations can analyze the data and see which decisions of the organization has helped them in boosting up the sales and which decisions have led to dip in sales. Conclusion To sum up, Sales Analysis is something which will help you identify the gaps not just in the organization’s sales process but even in other areas as well, like gaps in product, release of the product etc. It even provides a logical solution to fill the gaps in order to increase the sales and reduce the losses. It enables organizations to take decision based on the facts rather than on their “gut feeling”. On being leveraged appropriately, Sales Analytics can help organizations deal with all the roadblocks, which they might encounter during their Sales journey. About Girikon As a global provider of end-to-end IT services, Girikon boasts of a team of certified, and experienced Salesforce consultants, administrators and developers who have the knack for catering to the customer requirement in the most efficient way.