Intellectsoft App Development - Video

| March 15, 2017

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Intellectsoft is a software development company that provides custom and robust mobile solutions for big name brands and well-funded startups. Our mobile application is essentially smart, with technologies that include: Smart push-up notifications, iBeacons, Track location, Marketing solutions, Data collection, And more.

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Freudenberg IT LP

Freudenberg IT (FIT) is an international provider of IT services, specializing in mission-critical hosting capabilities across the globe using its world-class FIT-owned data centers. As an SAP-certified global provider of hosting and cloud services with more than 40 years of experience running and hosting SAP, FIT is recognized as a strategic partner and top-tier hosting provider within the SAP ecosystem.

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How Enterprise Companies Are Changing Recruitment With AI

Article | March 16, 2020

The writing is on the wall, boldly: Artificial intelligence is here to stay, and it is transforming every aspect of life and business. Over the years, the adoption of AI in recruiting has increased, and that has subsequently improved the hiring processes of enterprises that take advantage of it. Initially, its introduction wasn’t so welcomed, not just because experts believed that it would take the jobs of HR personnel, but particularly because of the possibility that it would make the recruitment process overly robotic and remove the sacred "human" factor in HR.

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The 6 Biggest AI Marketing Challenges & Their Solutions

Article | July 8, 2021

More and more businesses are utilizing modern-day opportunities that Artificial Intelligence (AI) brings to the digital world. Perhaps, it is the most necessary step for the companies to stay competitive in 2021 and beyond. With the rise of technology, AI-powered marketing platforms are becoming more common and simpler to use. However, this does not mean that they do not have any challenges. A survey conducted by Teradata, a data analytics firm, reports that around 80% of enterprise-level organizations have already embraced some form of AI. Out of them, approximately 32% of businesses use AI algorithms for marketing purposes. However, more than 90% of these companies have already anticipated significant barriers to adopt and integrate AI. In this article, we shed light on the six biggest challenges in AI marketing. It will help you act and avoid common problems if you encounter such roadblocks when integrating AI into your marketing strategy. Here are some highlights of this article: Many popular media sources have created hype around AI. Therefore, people, in general, don’t trust it. There isn’t enough skilled workforce to fill AI-related positions in organizations. AI software needs high-quality data. Unfortunately, maintaining such data quality is not that easy. AI software needs significant investment. Many small businesses lack IT infrastructure resources. Cloud services help them overcome this problem. As you can now understand, most challenges in AI marketing revolve around business alignment, data, or people. While every organization varies and will face the AI adoption process differently, there are a few common challenges in AI marketing you should be aware of. So, without further ado, let’s take a look at the most common AI challenges that digital marketers face. Lack of Knowledge of AI Systems When it's about total AI implementation, your company’s management must have a deeper understanding of the role of AI in Digital Marketing, the latest AI trends, data challenges, and all other essential aspects. However, many marketers lack a proper understanding of the use of AI technologies in marketing. On top of this, unfortunately, AI comes with a variety of fears and myths. While some people think they need an in-house data science team for complete AI adoption, others believe in those sci-fi fantasies showing how smart robots can end humanity. Insufficient knowledge of AI is one of the biggest challenges in AI marketing. It hinders the AI implementation in several ways and ultimately delays the success. How to get rid of this? First things first — start by acquiring knowledge. It might sound a bit demotivating, but we do not mean you have to be a data scientist for this. You can look at other giants in the industry, carefully analyze how they are deploying AI into their business, and act accordingly. Next, know more about the current AI technologies for marketing — you can either DIY or get help from an expert. Once you have adequate knowledge about it, you know what to expect from AI and what not. Challenges in Integration Deployment and integration of new technology requires skills. Integrating Artificial Intelligence into your business is not an easy task. It is a complicated job and requires proper knowledge. You first have to set up interfaces and other elements to address all your business needs. Such steps may require complex coding. Developers must consider feeding the data into the system, labeling, data storage, data infrastructure needs, and much more while setting up the elements. Then comes the model training and testing part. It is necessary for the following reasons: To check the effectiveness of your AI Develop a feedback loop for constant improvement Data sampling for reducing the stored data and run models even faster The biggest challenge here is — how to confirm if it's working correctly? And, is it worth the money you are investing? Arguably, the only and the most effective way to overcome this hurdle is to work closely with your vendor to ensure that everyone is well aware of the process. Plus, there should not be any limitations in the vendor’s expertise. They should be capable of guiding you beyond building the AI models. When you implement Artificial Intelligence with the right strategy, you indirectly reduce the risk of failure. And, once you successfully implement AI into your system, you will still have to educate your marketers to use it efficiently. In this way, your people can understand how to interpret the results they receive by proper implementation and effective use of the AI model. Poor Data Quality or Lack of Data High-quality data is essential for Artificial Intelligence. Any AI system will come up with poor results if you provide it with insufficient or poor-quality data. As the Big Data world is evolving every day, businesses are gathering vast amounts of data. However, this data is not always up to the mark. It's either insufficient or not good enough to drive a profitable AI marketing strategy. Such data-related challenges in AI marketing prevent companies from capitalizing on Big Data. For this reason, as a business, you should always make sure the data you get is clean and rich in quality. Otherwise, you will experience unsatisfactory results from the AI, and it will negatively influence the overall success of your AI-powered marketing campaigns. Budget Constraints for AI Implementation Many companies lack the necessary budget for implementing AI into the system. Even though AI has the power to provide impressive Returns of Investment (ROI), hefty investments are still one of the biggest challenges in AI marketing, especially for smaller and mid-size companies where the budgets are already stretched. AI-powered platforms come with high-performance hardware and complex software. And, the deployment and maintenance of such components are costly. Such budgeting challenges in AI marketing can limit the opportunities for businesses to utilize AI technology to the fullest. Thankfully, this is now becoming a thing of the past as many affordable AI vendors are coming ahead for the rescue. With them, you do not have to invest in developing in-house solutions. Moreover, they allow you to implement AI tech in a relatively cheaper and faster way. Privacy and Regulations Artificial intelligence is still new to this world, and it's growing at an incredible pace. Chances are that the rules and regulations surrounding AI will change and tighten up over the coming days. The data collection and use of data policies already impact businesses that collect and use data from the customers based in the European Union and drive their Artificial Intelligence systems. The EU implemented GDPR in 2018, and it has made the data collection, and data usage rules even stricter for companies. Ultimately, companies now have to be extra careful while collecting and using customer data. Furthermore, several businesses are restricted from storing the data offsite for regulatory purposes. This means that they can no longer utilize cloud-based AI marketing services. Constantly Changing Marketing Landscape AI is a new marketing tool and can bring disruption to traditional marketing operations. For this reason, marketers evaluate how AI can create new jobs and, at the same time, replace older jobs. One survey suggests that AI marketing tools are more likely to replace the jobs of around 6 out of 10 marketing analysts and marketing specialists over the coming years. Overcoming The Challenges in AI Marketing Yes, such challenges in AI marketing can sometimes slow down your campaigns and affect the outcomes of your AI-driven software. But fortunately, there are a variety of alternative solutions. You need to consider the following steps to rule out the common challenges in AI marketing we discussed earlier. Develop a target oriented marketing strategy Get the money before you roll out AI in marketing Train your marketers Recruit the right talent Developing business cases, recruiting talented marketers, measuring the ROI, and getting the required investment — probably, none of these steps sound interesting. But, when it is about the reality check of your AI marketing strategies, they are absolute methods that can open the door to actual Artificial Intelligence payoffs. In the end, every company's responsibility is to make sure that they are using the AI system responsibly so that they can benefit their customers in the best way possible. Frequently Asked Questions How does AI affect marketing? AI helps marketers to spot the latest internet trends and predict them for the future. Such trends are necessary to learn the current marketing facts and eventually help with significant tasks such as budget allocation and setting up the target audience. Plus, AI effectively reduces the money and time usually spent by companies on digital advertising. Simultaneously, it leads businesses towards smarter and more targeted advertising campaigns. As a result, many companies have implemented AI into their digital marketing strategies as it can increase sales and save money at the same time. On a bigger scale, AI has an impact on global trends, sustainability, and scalability. Even government issues, major public concerns, and major cities around the globe have seen positive effects of AI. AI can make the world a better place if used in the right way! How is AI used in digital marketing? Companies are utilizing some stand-out developments for improving the customer experience with the proper use of AI. For example: Image recognition technology Predictive and targeted content Content creation Chatbots With these, AI enhances customer support, and provides more relevant and targeted content to the customers. Why is artificial intelligence critical in marketing? With the correct use of Artificial intelligence, businesses can collect, analyze and store a large amount of data. As a result, AI is the best way to learn the latest marketing trends and incorporate them into your marketing strategy. In general, Artificial Intelligence has the power to help your company reach potential customers and provide them with easy access to make purchases. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "How does AI affect marketing?", "acceptedAnswer": { "@type": "Answer", "text": "AI helps marketers to spot the latest internet trends and predict them for the future. Such trends are necessary to learn the current marketing facts and eventually help with significant tasks such as budget allocation and setting up the target audience. Plus, AI effectively reduces the money and time usually spent by companies on digital advertising. Simultaneously, it leads businesses towards smarter and more targeted advertising campaigns. As a result, many companies have implemented AI into their digital marketing strategies as it can increase sales and save money at the same time. On a bigger scale, AI has an impact on global trends, sustainability, and scalability. Even government issues, major public concerns, and major cities around the globe have seen positive effects of AI. AI can make the world a better place if used in the right way!" } },{ "@type": "Question", "name": "How is AI used in digital marketing?", "acceptedAnswer": { "@type": "Answer", "text": "Companies are utilizing some stand-out developments for improving the customer experience with the proper use of AI. For example: ● Image recognition technology ● Predictive and targeted content ● Content creation ● Chatbots With these, AI enhances customer support and provides more relevant and targeted content to the customers." } },{ "@type": "Question", "name": "Why is artificial intelligence critical in marketing?", "acceptedAnswer": { "@type": "Answer", "text": "With the correct use of Artificial intelligence, businesses can collect, analyze and store a large amount of data. As a result, AI is the best way to learn the latest marketing trends and incorporate them into your marketing strategy. In general, Artificial Intelligence has the power to help your company reach potential customers and provide them with easy access to make purchases." } }] }

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5 AI Trends Profoundly Benefiting Business Bottom Lines

Article | November 17, 2020

Expert cites machine learning advancements creating immediate, actionable value to drive data literacy, elevate cognitive insights and increase profitability in kind. In today’s tumultuous business-scape amid increasingly intricate, and often vexing, marketplace conditions, curating and mining data to drive analytics-based decision making is just no longer enough. For competing with maximum, sustained impact and mitigated opportunity loss, it’s rapidly monetizing data that’s now the name of the game—particularly when spurred by artificial intelligence (AI). Indeed, emerging AI methodologies are helping forward-thinking companies achieve and sustain true agility, fuel growth and compete far more aggressively than ever before. AI is critical as a means toward those ends and also certainly with respect to aptly predicting, preparing and responding to prospective crises as with the COVID-19 pandemic the globe is currently immersed in. In fact, Gartner recently cited the need for “smarter, faster, more responsible AI” as its No. 1 top trend that data and analytics leaders should focus on—particularly those looking to “make essential investments to prepare for a post-pandemic reset.” Novel coronavirus matters aside, Gartner underscored just how impactful AI will become, predicting that, “by the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.” “To innovate their way beyond the post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts,” said Rita Sallam, Distinguished VP Analyst, Gartner. However, employing AI techniques like machine learning (ML) and natural language processing (NLP) to glean insights and render projections is simply no longer “enough” to get the job done—especially for organizations seeking to compete efficiently on a national, multi-national or global scale. Today’s organizations must endeavor toward a culture of AI-driven data literacy that directly and positively influences their top and bottom lines. “To help data monetization-minded enterprises better future-proof their operations and asset-amplify their data value chain, there are a few key ways to implement and elevate machine intelligence so that it’s far smarter, faster and more accountable than protocols past,” said Microsoft alum Irfan Khan, founder and CEO of CLOUDSUFI—an AI solutions firm automating data supply chains to propel and actualize data monetization. Below, Khan details five benefits of leveraging AI data-driven insights and technology in a way that will create actual and actionable value right now—the kind of insights that enable new and evolved business models and empower companies to increase both revenue and profitability. Manifesting new market opportunities Today’s machine learning capabilities allow people to sift through data that previously could not be accessed, all at speeds faster than ever before. Present technology offers the opportunity to wholly analyze image, spoken or written inputs rather than just numerical, helping companies better find connections across these diverse data sets. This generates and maximizes value in a number of ways. Relative to the bottom and top lines, not only can it significantly reduce expenses, but it can also create new market opportunities. With COVID-19 as one recent example, algorithms speedily sifted through an extraordinary amount of data to identify diseases and potential cures that presented as similar, which allowed those methodologies to be readily tested against the coronavirus. Machine learning advancements also help companies better monetize their data and establish new revenue streams. In the above example, of course patient information would not be shared or sold in any way, but other highly valuable data points can be gleaned. This includes determining that a certain drug is only effective on woman between certain ages—critical insights for pharmaceutical developers and physicians. Emerging AI data processing protocols are far more rapid than prior iterations of machine learning technology, as are the resulting solutions, discoveries and profit-producing results thereof. Reconcile emotions with actualities Data generates value, which leads to the generation of money. It’s that simple. Previously, it was difficult, if not humanly impossible, to sift through mass amounts of data and pinpoint relationships. There existed very rudimentary tools like regression and correlation, but today’s analytics call for gaining a true understanding of what extracted data actually means. How do you convert data into a story you can actually tell? Often, decisions are made based on emotional foundations. Leaders are using data to either validate their gut or disagree with their instincts. Now, they are getting quicker insights that decisively validate or invalidate their thinking, while also prompting them to ask new questions. So, garnering meaning out of a company’s own data provides tremendous advantages. “Human nature is such that unless we can see it touch it feel it, it’s hard to understand it,” Khan says. “We as data scientists haven’t done a really great job of explaining AI-driven data technology in simple terms. Telling a story with data or demonstrating actual results is where real power and understanding lies.” Scale statistical models for actionable models We often separate our data as factuals, asserting “this is what happened.” Neural networks connect the “human decision-making process” to those factuals—a simulation practice that helps us make better decisions. Previously, we would look at data sets like demographics, customer behaviors and such in silos. But when these multiple data sets are connected, it becomes quite evident that no two humans—or customers—are exactly alike. Technology is now allowing us to understand trends on a factual level and then project outward. In the health realm, some companies are using this key learning to project whether or not a person is likely to suffer a certain affliction. It’s also allowing for far more efficacious “if this then what?” scenarios. If a diabetic person takes insulin controls, then their diet the treatment protocol will change. This is enabling highly personalized medicine. But the same processes, principles and benefits hold true in non-health categories as well—encompassing all industries, across the board. Future-proof, anti-fragile data supply chains From data connectors to pipelines; data lakes to statistical models; AI to Quantum; visual storyboards to data driven automation; ML to NLP to Neural Networks and more, there are highly effective methods for future-proofing your data value chain. The data supply chain is quite complex and, to make it future-proof and non-fragile, it requires thoughtful processing from the point of creation to the point of consumption of actionable insights. It starts with data acquisition—garnering a wide variety and volume of data from a number of internal and external sources where data is being generated by the millisecond. Once the data is identified and ingested, it needs to brought to a central point where it can be explored, cleansed, transformed, augmented and enriched and finally modelled for use toward a purpose. Then comes statistical and heuristic modeling. These models can be of different types using different algorithms yielding different levels of accuracy in different scenarios. Models then need to be tuned and provided and environment for continuous feedback, learning and monitoring. Finally, is the visualization of outcomes—an explanation demonstrated by drawing cause-effect relationships that highlight where the most impact happens. This leads to a conclusion on how a set of problems can be solved or opportunities uncovered. “Most organizations have some data and drive different levels of business process improvement and strategic decisions with it,” Khan notes. “However, few use data to the fullest. The right approach to data valuation and monetization can uncover limitless possibilities, including customer centricity, operational efficiency, competitive advantage, strategic partnerships, efficient operations, improved profitability and new revenue streams.” Multimedia monetization Up to now, we have been able to write algorithms, generate immense amounts of numerical or written data and make sense of it. However, there is a significant amount of data that comes as images or voice, which has not been easy to process and manage until recent developments. The applications for the processing of visual and auditory inputs are endless. In fact, retail and finance industries have been early adopters of this technology—and with good reason. They’ve seen costs go down, engagement go up, sales increase and benefitted from other highly substantial points of monetization. Now, a large department store can digitize their video data every night and determine that “X” amount of people saw “X” number of jeans, but they had to walk further to get to it. As a result, the department store can put those items closer to the door and walkways to determine if sales increase in kind. Even the education realm is tapping AI-driven data. The technology is tracking retina movement to discern if kids are engaged amid the remote learning paradigm ushered in by the pandemic. They’re exploring how to measure the retina to determine whether or not a child is actually engaged in the lesson. In radiology, they are starting to convert visual data and track it to gain a deeper understanding of digital images and video. MRIs are better able to track brain tumors—whether they are growing or shrinking and at what rate and if they are getting darker or lighter in terms of the regions. This kind of AI-driven learning is helping doctors better detect cancer and treat it more rapidly. Video data processing of the human eye can also be used to determine if a person is drunk, fatigued or even has a disease. Voice machine learning has also keenly evolved. Originally, voice recognition was being utilized to discern if a person was actually suicidal, which could be accurately predicted by inflection points in a person’s voice. Now, if that person can be captured on video, it is deemed to be about 20 times more accurate. “All of this possibly had previously demanded a hefty price tag using systems and solutions of yore,” Khan notes. “Today, integrating multiple processes across hybrid multi-cloud environments has made data processing and analytics much more accessible and outsourceable. This negates the need for companies to purchase cost-prohibitive servers and other machine hardware.” As one of the world's leading experts on building transparency into supply chains, Khan doesn’t just talk the talk, he’s walked the walk. As a revered marketplace change agent, he’s known for driving business transformation and customer-centric turnaround growth strategies in a multitude of environments. In addition to engineering partnerships with MIT, Khan has successfully led organizational changes and process improvement in markets across the Americas, Europe, Middle East and Asia. “New AI solutions and trends will eliminate patchwork processes that cause data, and interpretations thereof, to get lost in translation or, even worse, remain entirely undiscovered,” Khan says. “Next-Gen platforms are solving such problems by executing all functions required to create and govern AI products— single-source systems that pull data, transform, model, tunes and recommend actions with cause-effect transparency.” For niche players, today’s leading-edge AI technology also aptly provides for vertical industry specialization. “Emerging solutions enable common data models, compliance and interoperability requirements that, in turn, accelerate model validation, refinement and implementation that’s specific to a given sector or marketplace,” notes Khan. “All of this ultimately drives speed to insights on previously unsolved problems, which reveals untapped opportunities and automates workflow integrated cognitive solutions.” “Overall, AI is ushering in a new and more sophisticated era of data literacy,” he continues. “It’s a new paradigm founded on automated, comprehensive and holistic data discovery, which is fostering elevated cognitive insights and actionable strategies that positively impact the top and bottom line.” Perhaps the future mandate for AI should not only focus on becoming smarter, faster and more accountable than predecessors, but actually bridge the gap between human intuition and data-backed decisions. Doing so will assuredly advance an organization’s ability to transact with utmost trust.

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Common B2B SaaS Marketing Challenges: Best Tips to Overcome Them

Article | July 15, 2021

There’s a lot of difference between consumer marketing and SaaS marketing. Unlike the traditional consumer marketing techniques, SaaS marketing is complex, unconventional, and has nothing physical to endorse. In simple words, B2B SaaS marketing does not involve physical items that can be easily promoted and popularized in the market. Consumer marketing has products like phones, washing machines, books, bread, etc. Marketers use creative commercials to promote them and ultimately reach the crowd. Consumers relate to such ads and commercials and memorize the products. On the other hand, B2B SaaS marketing has its own set of hurdles, and only solid marketing strategies can get rid of them. The B2B SaaS marketing is all about promoting software that is not as fun and thrilling as the consumer items. Its existence is non-physical, and marketers often struggle to convince customers to buy it. Eventually, they have to adopt some out-of-the-box marketing strategies to overcome such B2B SaaS marketing challenges. What are the Basic B2B SaaS Marketing Challenges? The SaaS marketing industry is rapidly growing and most marketers face a set of challenges. They have to promote and sell virtual products such as software which requires deep technical knowledge and proper analysis, along with a few unique marketing strategies. With all such critical B2B SaaS marketing challenges to deal with, it's quite interesting to develop and implement the right SaaS marketing strategy for long-term growth. Here are some of the basic B2B SaaS marketing challenges: The attitude of the customers is one of the most critical B2B SaaS marketing challenges. Unfortunately, traditional marketing strategies for SaaS cannot effectively target the emotional quotient of the customers. The entire management team typically makes the purchase decisions of such products, and not a single person. SaaS offerings are dynamic and change with time. Releasing newer versions or updates for the same product can lead to the delivery of redundant marketing messaging. Marketers get little time for convincing the customers. Due to the short sales cycles, it's quite difficult to close the deals within the available window. Users often get confused with SaaS core terms such as cloud computing. You need to ensure your marketing strategy is informative enough. It’s essential to know the exact niche to market your SaaS products. Additional B2B SaaS Marketing Challenges Finding Ways to Stand Out from The Crowd The major concern of your SaaS business is satisfying the customer needs without following the same marketing strategies your competitor is using. The SaaS marketing world is evolving every day and company owners spend sleepless nights over even the slightest tweak in a competitor’s design or marketing strategy. It is an already crowded niche and maneuvering the way through it is the responsibility of every SaaS marketer. Failure can prevent you from racing ahead and your competitor will win the customers and growth in the end. Acquiring Low-Value Consumers in Big Volume SaaS marketers get relatively fewer client leads due to shorter sales cycles. There’s no room for a mistake in such cases as it may delay the process. For this reason, B2B SaaS companies should consider targeting low-value consumers in significant volumes just like the traditional B2C companies. A unique inbound marketing strategy is the best way to reap all the benefits of this tactic. Earning Customer Loyalty The SaaS pricing model makes sure the payments are received on time. For getting new customers, this is the primary agenda of every SaaS marketer. However, there is equal importance of customer retention programs to generate more revenue. The growth in customer retention can lead to huge profits in the long run. In layman’s terms, you need a robust marketing strategy for catering to customer needs and, at the same time, preventing them from switching to other companies. It will benefit you in: Developing unique marketing campaigns to convince the customers Better engagement with your customers through social channels Offering enhanced solutions to customer issues Delivering value-added products When you have an effective and carefully planned marketing strategy, you have more chances of captivating the attention of devoted SaaS fans. Now, let’s discuss some knockout marketing tactics that will help you conquer the common B2B SaaS marketing challenges discussed so far. Best Tips & Strategies to Overcome B2B SaaS Marketing Challenges Offer Free Trials to Get More Customers When selling physical products, you won’t be interested in giving away the stuff for free and incurring a loss. You may consider offering a few free products to the retailer for advertising purposes but you definitely won’t offer free stuff in huge volumes even to the retailer as it's not economically infeasible. The same strategy needs attention in the case of SaaS products. And, giving free trials of the product is a renowned and widely approved SaaS marketing strategy for growth. This SaaS marketing plan is a tactical way for acquiring new customers and onboarding them. Using the term FREE in your plan is the most effective way to overcome SaaS challenges. Many B2B SaaS companies use the free model in a variety of ways. For example: Free trial without payment information Free trial with payment information 30/60/90-day free trial Limited feature free trial Trial-to-paid Freemium Free Trial Develop a Great Content Marketing Strategy Any SaaS marketing strategy cannot rule without a carefully planned content marketing strategy. However, organizing and presenting the SaaS information in the form of content is one of the biggest challenges in marketing. Even though this is a fundamental thing, it is often overlooked. SaaS companies should consider posting regular blogs that serve as a source of reliable information for the users. If people know your product well, they are more likely to make the purchase. Your SaaS marketing plan should be able to explore the power of content and market it in different forms. Marketers can use techniques such as content syndication to effectively deal with SaaS challenges by delivering critical content to customers. Market your content through tweets, blogs, webinars, and all other ways. Let Your Product Sell Itself Whether it's a consumer marketing strategy or a SaaS marketing strategy, its results depend heavily on the product quality. And, when it's about the products, you have to focus on the following crucial things: Incredible products Excellent customer support All the great SaaS marketing examples always excel in these two factors. When your products and support are up to the mark, the rest of the things automatically fall in line. Develop a state-of-the-art software product and provide remarkable customer support with or without goodies and gifts, and then experiment how this strategy becomes a game-changer for you in overcoming all the SaaS challenges. Give Reassurance and a Demo to Deal With Rapid Sales Cycles The short sales cycles are one of the biggest challenges in marketing the SaaS products. The purchasing process spans over a few days or weeks. This is extremely lower than the purchasing process period of B2B sales marketing. SaaS products change continuously. If there are delays in the sales process, the software may undergo several iterations during this time. As a result, you will need to give a demo and reassurance to SaaS customers who may turn away to avoid longer sales cycles. Final Words In summary, SaaS marketers must understand this authentic and logical advice — great products and excellent customer services are the foundation of every successful marketing strategy. And, this has the power to overcome all the challenges in marketing. Frequently Asked Questions Which tool should we use to overcome the SaaS challenges? The following are the tools you need to overcome B2B SaaS marketing challenges: LinkedIn Sales Navigator UserVoice Intercom Hunter Databox VWO ActiveCampaign TrueNorth Wistia Slack What are the best SaaS marketing examples? Here are the best SaaS marketing examples of 2021: Shopify Mint Apple Music Movable Ink Spotify Netflix Adobe Invision Amazon Prime What are the top 3 challenges faced by SaaS marketers? The following are the three biggest SaaS marketing challenges: Dealing with traditional complainers Building customer loyalty and trust Standing out in the crowd { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "Which tool should we use to overcome the SaaS challenges?", "acceptedAnswer": { "@type": "Answer", "text": "The following are the tools you need to overcome B2B SaaS marketing challenges: LinkedIn Sales Navigator UserVoice Intercom Hunter Databox VWO ActiveCampaign TrueNorth Wistia Slack" } },{ "@type": "Question", "name": "What are the best SaaS marketing examples?", "acceptedAnswer": { "@type": "Answer", "text": "Here are the best SaaS marketing examples of 2021: Shopify Mint Apple Music Movable Ink Spotify Netflix Adobe Invision Amazon Prime" } },{ "@type": "Question", "name": "What are the top 3 challenges faced by SaaS marketers?", "acceptedAnswer": { "@type": "Answer", "text": "The following are the three biggest SaaS marketing challenges: Dealing with traditional complainers Building customer loyalty and trust Standing out in the crow" } }] }

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Freudenberg IT LP

Freudenberg IT (FIT) is an international provider of IT services, specializing in mission-critical hosting capabilities across the globe using its world-class FIT-owned data centers. As an SAP-certified global provider of hosting and cloud services with more than 40 years of experience running and hosting SAP, FIT is recognized as a strategic partner and top-tier hosting provider within the SAP ecosystem.

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