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Continuation Funds: Fueling the Rise in Secondaries
Continuation funds are redefining the GP-led secondary market amid the recent resurgence, positioning themselves as a vital alternative exit route for private equity (PE) firms. Despite a few challenges, these funds are gaining traction among stakeholders. According to Jefferies, GP-led transaction volume in the global secondary market hit $28 billion in 1H24, exhibiting 56% YoY growth and the highest volume since 2021 in the first half of any year. Continuation funds accounted for 90% of these transactions, an improvement from 88% in 2023. The resurgence in enthusiasm for these funds can be attributed to LP’s demand for liquidity in a market where the exit activity is muted. Continuation funds gained popularity in 2021 and 2024, two years with contrasting market conditions. In 2021, deal-making was robust in contrast to the present scenario. The motivation for LPs then was to gain more from the assets that were performing well, thereby allowing their continuation. Figure 1: Continuation Fund Volume ($B) Source: Jefferies Global Secondary Market Review July 2024 Read more: AI’s Power Conundrum: Will Renewables Save the Day? Continuation Funds provide fresh capital to invest in high-quality assets without needing to exit prematurely, enabling continued value creation. They realign incentives and provide liquidity options to cash-constrained investors. The demand for continuation vehicles (CVs) remains high, with single-asset continuation vehicles (SACVs) priced at 94% of NAV and multi-asset continuation vehicles (MACVs) at 88% of NAV, as per the Blackrock Secondary Market Update 2H24. The report also highlights growing participation as PE firms and family offices expand in GP-led secondaries. On September 18, 2024, Thompson Street Capital Partners, a St. Louis-based PE firm, completed a SACV to continue its partnership with Gurobi, a provider of mathematical optimization software. Similarly, on September 9, 2024, Gryphon, a San Francisco-based PE firm, closed a SACV for Vessco Water, a distributor of wastewater technology products. SACV dominated the market with 55% of total GP-led fund volume, followed by MACV at 31%, according to the Evercore 1H24 Secondary Market Survey. The high volume of SAVC transactions reflects a trend among mid-market sponsors increasingly adopting highly focused CVs as a strategic tool. In contrast, MACVs remain popular among large-cap sponsors for portfolio management to generate flexible liquidity and extend the holding periods of best-performing assets. However, the process in MACV is costly and complex, involving multiple funds and uncertainty around the final asset composition, making it harder for final buyers to pay attractive prices. These deals carry the risk of having underperforming assets bundled in them, requiring additional capital and negatively impacting the entire fund’s return. In comparison, SAVC focuses on a single high-quality asset, allowing investors to do deeper due diligence. Figure 2: SACV surpassed MACV in transaction volume Breakdown of Transaction Types (% of Transaction Volume) Source: Evercore, H1 2024 Secondary Market Survey, July 2024. “Other” is comprised of preferred equity, which represented 9% of transactions, and tender offers, which represented 5% Read more: Unlocking M&A Potential: The Impact of Fed’s Rate Strategy Challenges in Continuation Funds Bankruptcy within continuation funds has raised concerns about the sustainability of these vehicles, highlighting potential risks. Wheel Pros, rebranded as Hoonigan, filed for Chapter 11 bankruptcy in September 2024, accruing $1.74 billion in debt, which moved into the continuation fund by Clearlake in 2021. Another rare CV bankruptcy occurred in March 2024 with Riverstone-backed Enviva Holdings, which had been moved into a continuation fund in 2020. Continuation funds are more complex than traditional sales, often requiring extra time and resources for structuring and managing LP elections. They can incur higher costs, though usually shared between the parties. Unlike third-party sales, management doesn’t usually receive immediate liquidity in continuation funds, requiring extra incentives to stay motivated. This can lead to uncertainty for stakeholders expecting instant financial returns. Secondary investors often face challenges selling an ideal combination of a significantly discounted asset and finding a GP with a strong familiarity with the portfolio. Small secondary investors are disadvantaged in GP-led deals when advisors prioritize larger buyers who can swiftly commit substantial amounts of capital and provide favorable terms for LPs. Further, original LPs are most likely to be affected by misalignment of interest when they bear greater responsibility for liquidity choices while paying similar fees in continuation funds. Assets in continuation funds are often undervalued when transferred from the original fund. This lowered valuation benefits GPs by inflating future profits and increasing carried interest when assets are sold at higher prices. A Whitehorse Liquidity Partners’ analysis of over 1,000 continuation fund transactions revealed that the assets were sold at an average of 28% higher valuations than GP’s assessment six months earlier, posing a risk to LPs and secondary investors who may face distorted valuations. Thus, conflict of interest can arise as GPs oversee both sides of the deal, making fair pricing a key concern. LPAC (Limited Partnership Advisory Committee) approval and transparency are critical to mitigating these risks and avoiding broken-deal fees. Read more: Dividend Recapitalization Pick up in 2024 Conclusion Continuation funds have provided the much-needed liquidity, enabling the GPs to provide liquidity to the investors while allowing underperforming portfolio companies extended timelines to realize their potential. However, they require careful structuring to address challenges such as conflict of interest and transparency in valuation. Apart from these, the CVs are poised to enhance the scope of secondaries. Partner of choice for lower middle market-focused investment banks and private equity firms, SG Analytics provides offshore analysts with support across the deal life cycle. Our complimentary access to a full back-office research ecosystem (database access, graphics team, sector & and domain experts, and technology-driven automation of tactical processes) positions our clients to win more deal mandates and execute these deals in the most efficient manner. About SG Analytics SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India. Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista.
How to Use AI for Data Analysis? - Ultimate Guide
In today's data-driven landscape, the ability to extract valuable insights from large data sets is not a luxury but a necessity. However, many businesses are still facing the challenges of leveraging data effectively. The obstacles include a lack of specialized skills, limited resources, or not enough hours in the day. With the rise of artificial intelligence lies new possibilities and user-friendly tools for non-technical professionals. These innovative solutions are transforming the data analysis landscape, enabling organizations to derive valuable insights from their data. Getting Started with AI to Analyze Data The advent of AI-powered data analysis tools portrays a significant leap forward for businesses of all sizes. These solutions level the playing field and equip them to derive the same quality of insights that were once preserved for larger corporations with dedicated data science teams. Choosing the Right Tool Several AI-powered platforms cater to diverse needs and skill levels. These analysis and automation tools allow businesses to manipulate data using simple, natural language commands. They also integrate seamlessly with other common formats and are perfect for creating interactive dashboards. Preparing the Data Organizations need to ensure that the data is organized and clean before diving in. When working with PDFs, they should focus on documents with structured data like reports. Uploading and Analyzing Once organizations have selected the right tool, they can upload their data and start exploring. Many AI tools allow them to ask questions about data in natural language. They can also leverage the AI agents to analyze the key points from this customer feedback report. Visualize and Interpret AI tools can guide the creation of visual representations of the accumulated data. These include charts, graphs, or interactive dashboards. Teams should explore the variety of visualizations that would work best for them while also effectively monitoring the patterns, trends, or anomalies that could inform their business decisions. Share and Act Many AI tools present easy sharing options, allowing organizations to collaborate with their team or present findings to stakeholders. These insights can be integrated to drive informed decision-making across organizations. Read more: Synthetic Data Generation - Meaning, Benefits, Methods & Use Cases Using AI for Data Analysis From raw data to decisions, let’s examine the role of artificial intelligence at every data analytics stage. Data Collection Data collection is the fundamental first step for organizations to get valuable insights from their data using AI. They need to extract data from different sources to feed their AI algorithm. Otherwise, it will not have input from which to learn. They can train AI systems with any data, whether it be product analytics, sales transactions, or automated data collection through web scraping. Data Cleaning The cleaner the data, the more valuable insights there will be. However, data cleaning is a tiresome process and prone to human error if done manually. Organizations can use artificial intelligence to do the heavy lifting and normalize their data. Data Analysis After training AI models with clean, relevant data, they can start analyzing the data and yielding actionable insights. AI models can identify patterns, anomalies, and trends in the data. As with any technology, it is important to be careful about accuracy and system bias. Data Visualization After finding interesting patterns in the data, organizations need to present them in an easy, understandable format. With the help of AI-powered business intelligence tools, they can build visual dashboards to support decision-making. Interactive charts and graphs will further assist in exploring the data deeply and drill down into specific information to enhance workflows. Predictive Analytics Compared to traditional business analytics, artificial intelligence excels in forecasting outcomes. Based on patterns in historical data, the tools can run predictive models and make accurate predictions. Read more: Reimagining the Role of AI in Customer Engagement Benefits of Leveraging AI-based Tools for Data Analysis The use of AI in data analytics involves integrating advanced tools to enhance the speed and accuracy of processing large volumes of structured, unstructured, and semi-structured data. By leveraging high-end AI systems, organizations can efficiently extract valuable insights from complex data for informed, data-driven decision-making. AI tools are essential as they expedite the collection and preparation process, as well as recognize patterns in data more efficiently. They can also detect anomalies in data better than any traditional approach. Accessibility: These tools are developed with non-technical users in mind and integrate intuitive interfaces and natural language processing capabilities. Versatility: The tools can handle different data formats, ranging from Excel spreadsheets to PDF reports, making them adaptable to the existing operational workflows. Speed: AI algorithms are able to process and analyze large datasets at a faster pace than traditional methods, thereby offering quick insights. Cost-effectiveness: Many AI tools offer affordable pricing plans, making advanced data analysis accessible to all. Integration of AI in Analytics: A Threat for Data Analysts? As your data grows, data exploration becomes harder and time-consuming. AI in data analysis integrates different artificial intelligence techniques to extract critical insights from large data sets. Machine learning algorithms help extract patterns or make predictions on large datasets. Deep learning uses neural networks for image recognition and time-series analysis. Natural language processing (NLP) derives insights from unstructured data. AI algorithms search for patterns in large datasets and help find answers to these business questions. Businesses can make better, data-driven decisions quickly by automating this difficult task. The new wave of generative AI tools is empowering organizations to analyze data effectively. However, with the growing popularity of generative AI tools like ChatGPT, many are raising immediate concerns about job security. With the data growth expected to increase by 150% in 2025, there is no cause for concern. Data will become more important in the coming years, and the world needs data analysts and scientists to make sense of it. While AI tools can transform job roles and workflows, data analytics experts will be even more integral in data-driven companies. Read more: The New Data Economy: Navigating the Future of Data-Driven Value Creation Future of AI Data Analysis Trends Artificial intelligence advancements, including generative AI, are assisting organizations in creating new content like images, music, audio, videos, and text that can increase their productivity. Generative AI tools can be integrated as a writing assistant for creating images when responding to a prompt or generating text when prompted. Additional Future Trends of AI in Analytics Automated Storytelling: AI can be integrated to narrate a data-driven story with the use of data-documented programmatically. Conversational Analytics: Chatbots can be used to answer analysis queries through NLP. Advanced Simulations: AI can be used to test thousands of complex configurations concurrently. Real-time Problem Detection: The Internet of Things (IoT), edge computing, and live streaming can help detect problems in real time before humans realize they exist. Embedded Analytics: Seamlessly embedded models can help constantly monitor services and products. Prescriptive Intelligence: AI-recommended solutions based on specific scenarios can possibly help in producing the desired outcome. Key Takeaways In organizations, voluminous data stored as structured or unstructured data needs AI in a data analysis process to identify useful information. Multiple AI techniques are used in data analysis to extract the exact data an organization can use for decision-making. Using AI and data analytics for decision-making does not imply that a business will always make the right decision. Read more: Generative AI is Increasing Employee Productivity and Expanding Capabilities. How? Final Thoughts – AI for Data Analysis In today's fast-paced environment, the ability to harness data effectively has become a necessity. And with AI, it is within reach for everyone. By embracing these tools, organizations can not just analyze data but unlock the full potential of their business information. They will be equipped with the ability to make faster and more informed decisions, as well as identify new opportunities, and respond nimbly to changing market trends. The operational performance of an application is a critical metric for selecting a data analysis application. Organizations need to establish what metrics are required to estimate the performance of an organization. Industries like manufacturing, financial, healthcare, or retail can start by examining business-industry-specific data analysis applications that cater to their needs. More importantly, enterprises must also have a list of the metrics that need to be measured. They can also measure financial performance, operational efficiency, customer behavior, market trends, and revenue growth, including a combination of more than one metric. While these tools offer exciting possibilities, it is important to approach them with due diligence. Before uploading any data, organizations need to review the provider's privacy policies carefully. Ensuring that data protection measures and clear data usage policies are followed will further help them deal with sensitive information effectively. A leading enterprise in Data Analytics, SG Analytics focuses on leveraging data management solutions, analytics, and data science to help businesses across industries discover new insights and craft tailored growth strategies. Contact us today to make critical data-driven decisions, prompting accelerated business expansion and breakthrough performance. About SG Analytics SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India. Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista.
Embarking on A Successful Digital Transformation Journey: Key Considerations
Embarking on a digital transformation journey is critical for organizations to enhance competitiveness, drive innovation, and adapt to evolving market needs. However, the success of digital transformation relies on the effectiveness of organizations’ implementation. A robust approach requires careful planning, alignment, and a clear understanding of organizational objectives. Digital Transformation Journey The digital transformation journey is necessary for many industries. A BCG report highlighted that only 30% of such projects succeed. Success hinges on putting people at the center of the transformation process. Key Considerations for a Successful Digital Transformation Journey Setting Clear Objectives and Goals The foundation of a successful digital transformation relies on setting clear objectives and goals that align with the organization’s strategic vision and priorities. Before embarking on the implementation journey, organizations need to articulate what they aim to achieve through digital transformation, such as enhancing operational efficiency, improving customer experience, or driving revenue growth. By setting measurable, relevant, and time-bound goals, organizations can deliver clarity and direction to stakeholders. They can also align resources effectively and measure the success of the digital transformation initiative against set benchmarks and KPIs. Develop a Robust Governance Structure Establishing a robust governance structure is critical for effectively managing digital transformation initiatives, assuring alignment with organizational objectives, and mitigating risks. Organizations need to define roles and decision-making processes for critical stakeholders involved in the implementation approach. A well-defined governance structure also helps streamline communication and facilitate trust that may arise during the implementation process. Moreover, regular milestone checkpoints and performance metrics can enable organizations to track progress and take corrective actions as needed to keep the initiative on track. Read more: Digital Transformation is Navigating Sustainable Business Growth. How? Conducting a Comprehensive Stakeholder Analysis Stakeholder engagement is an important factor in the success of digital transformation initiatives. Organizations need to conduct a comprehensive stakeholder analysis to determine key stakeholders, understand their perspectives, and address their concerns. Stakeholders can include executives, employees, department heads, customers, and external vendors or consultants. By involving stakeholders early in the process and fostering open communication channels, organizations can build, mitigate, and secure commitment to the digital transformation initiative throughout the organization. Assessing Organizational Capabilities Assessing organizational capabilities is essential for determining the organization’s ability to successfully undertake and sustain digital transformation initiatives. Organizations need to evaluate factors like leadership commitment, organizational culture, workforce skills, and technological infrastructure to determine strengths, weaknesses, and gaps that can impact the implementation approach. Conducting a thorough assessment allows organizations to develop tailored strategies for managing change, addressing gaps, and building internal capabilities to support digital transformation effectively. Moreover, organizations also need to invest in training programs to upskill their employees as well as foster a culture of constant learning. Read more: The Intersection of Digital Transformation and ESG for Sustainable Innovation Celebrating Accomplishments With teams making progress in their digital transformation journey, it is important to celebrate successes along the way. Longer-term projects mean that when one milestone is passed, another looms on the horizon. Celebrating achievements as they happen keeps morale high and helps momentum. Showcasing ROI is one of the best ways to highlight your team’s accomplishments. Demonstrating tangible results helps prove the value of digital transformation and secure additional resources for future projects. Fostering a Culture of Innovation Digital transformation is about fostering a culture of innovation, experimentation, and continuous improvement across the organization. Organizations must foster an environment that encourages employees to challenge the status quo as well as embrace change as an opportunity for growth and learning. By nurturing a culture of innovation, organizations need to empower employees and experiment with new technologies, processes, and business models, fostering creativity, resilience, and adaptability. Moreover, organizations can establish innovation labs and incubation programs to nurture promising ideas and bring innovative products to market faster. Focus on Security Security is vital when embarking on digital transformation, especially as systems are moving to the cloud. The cloud enables instant access to critical systems but also introduces new vulnerabilities. Cyber threats, like ransomware attacks, have increased dramatically, and organizations can rely on digital tools without updating their security measures, leaving themselves exposed. With digital footprints growing, digital attacks are also surfacing. Integrating security measures at every stage of digital transformation makes it important to ensure no weak points are left open for exploitation. Every digital advancement should have a corresponding security upgrade, minimizing the chances of new systems becoming cyberattack targets. Read more: Generative AI is Increasing Employee Productivity and Expanding Capabilities. How? In Conclusion: Digital Transformation Journey Digital transformation is a complex journey that needs more than just technological advancements—it requires strategic planning, collaboration, and a people-centric approach. Success lies not in the deployment of new systems but in overcoming digital resistance and fostering a culture of continuous improvement. Building an implementation approach for successful digital transformation demands careful planning, stakeholder engagement, and a commitment to driving change throughout the organization. By setting clear objectives, conducting comprehensive stakeholder analysis, and prioritizing initiatives strategically, organizations can set the groundwork for a successful transformation. Today, businesses are embarking on this path, and security must be prioritized alongside gradual progress to ensure sustainable growth and safety. By tracking ROI and celebrating wins, teams can build momentum, positioning themselves among brands that can successfully transform their operations and achieve long-term benefits. Moreover, designing a robust governance structure, fostering a culture of innovation, and investing in change management is essential for driving adoption, engagement, and commitment to digital transformation initiatives. A leader in Business Insights, SG Analytics enables organizations to achieve actionable insights into products, technology, customers, competition, and the marketplace to make insight-driven decisions. Contact us today if you are an enterprise looking to make critical data-driven decisions to prompt accelerated growth and breakthrough performance. About SG Analytics SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and data analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India. Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista.
Holiday Retail Strategies: Retailers Can Win the Challenging Holiday Season
Holiday spending in 2024 is expected to show modest growth, with forecasts predicting growth ranging between 2% and 5% over last year. A recent Deloitte report highlighted that holiday retail sales in 2024 are set to increase by 2.3–3.3%, which is slower than the 4.3% growth in 2023. This is lower than the growth witnessed in previous years, partially due to economic pressures like inflation and election-year uncertainties. Consumers are anticipated to be more price-conscious, with a notable portion planning to delay their shopping until after Halloween and focusing on discounted objects. The retail landscape has been witnessing a steady downtrend. To solve this conundrum and find new ways to innovate, brands are putting data to use. Retail brands need to acknowledge that many of them operate with an outdated view of who their shoppers are and what affects them most at a particular time. Market conditions that shape consumer attitudes are in a state of constant flux, and the existing data often fail to reflect the deeper issues facing most consumers as well as the attitudinal shifts that come with an ever-changing economic landscape. All these factors create blind spots for retailers. Due to this, they need to adopt a new model that goes beyond understanding what consumers buy, why they buy, what they buy, and what matters most to them. Today, consumers have become more pragmatic in shopping, prioritizing cost savings and focusing on necessities over other forms of spending. Read more: Google Vs. Instagram and TikTok: Where do Customers Prefer to Shop and Search? Consumers guard their privacy more than ever, and attention is increasingly fragmented across a range of data-hoarding platforms. However, data collection has become stricter, with increased consumer protection laws leaving retailers with few places to mine meaningful shopper data. On a brighter note, high-quality customer data is out there. Drive Sales with Great Holiday Retail Strategies Effective planning is crucial in ensuring a successful holiday season with the increasing shift towards online shopping. It is essential not to overlook seasonal promotions and holiday strategies, especially as competitors may have already begun their preparations. The following holiday retail strategies and ideas will help refine marketing efforts as well as ramp up sales during the most profitable time of year. Prioritize Customer Retention Efforts While it is important to focus on attracting new customers, it is equally essential not to overlook the existing loyal clientele. Reports have highlighted that repeat customers contribute to almost two-thirds of a brand's total sales, highlighting the importance of maintaining their connection. Brands can seize the chance to express appreciation to the most valued customers by offering them special incentives like substantial discounts or exclusive previews of upcoming products. Additionally, brands should also consider sending their loyal customers heartfelt holiday cards through traditional mail while also showcasing genuine care for their continued support. Read more: 12 Highest-Paying Tech Jobs In The US Provide Free Shipping Options As an e-commerce business, brands can offer free shipping to their customers. However, free shipping is crucial if they want to boost their product sales during the holiday season. During all holiday seasons, offering free shipping will ensure that the customers know about it. Optimize the Brand's Mobile Presence In recent years, mobile marketing has gained increasing significance in e-commerce. To capitalize on this trend, it is important for brands to craft mobile-friendly email marketing campaigns and ensure their online store is optimized for mobile devices. They can opt for messaging in their holiday marketing strategy, making it convenient for customers to click on links and access webpages directly from their mobile phones. Share Thoughtful Gift Recommendations As the holiday season draws nearer, customers' schedules become busier. Simplify their holiday shopping experience by creating email marketing campaigns with thoughtful recommendations. Incorporate compelling calls to action and shoppable links that swiftly direct the audience to your website's product pages. Consider Discount Strategy Carefully Consumers are well-informed shoppers and recognize that businesses compete for customers. Thus grabbing their attention by providing discounts and sales opportunities. Provide discount when payments are made through Debit or Credit cards Provide different discount coupons Boost sales through exchange offers on selective products Businesses must always be ready for the imminent holiday season, as it accounts for a significant portion of their annual sales. Customize gift suggestions by leveraging past purchases, highlighting distinctive gifting solutions, and illustrating innovative ways to create ideal gift bundles. To sweeten the deal, provide discounts on gift bundles as a bonus. Strategic preparation can be a game-changer. The holiday season intensifies competition and presents a chance to attract new customers. Read more: Pinkwashing: What is Pinkwashing and How to Avoid it? Avoid Holiday Marketing Mistakes to Stay Ahead With customers demanding an increasingly personalized experience, businesses are empowering their service agents through a revamped training program and better data management. They are also introducing new performance feedback channels and support tools. The customer care team is integrating artificial intelligence (AI) technologies to recognize customer emotions and diffuse difficult situations. Especially during a busy holiday season like Black Friday, customers expect quick responses through the communication channels of their choice. With conversational apps like Instagram and WhatsApp, businesses can connect with their buyers in the most convenient way. However, while they are running holiday marketing campaigns, there are specific pointers that should be kept in mind: Sending too many marketing emails Last-minute planning Focusing on one marketing tactic Not understanding the customer personas The holiday season can have an impact on the revenue goals of nearly every retailer. Combining a growing number of online shoppers and fierce competition presents significant challenges for brands, but proactive steps can help set the brand apart and foster customer loyalty. Read more: Best Buy Black Friday Deals 2024 - Everything You Need to Know Predicting the Future In past holiday seasons, consumers were willing to wait in long lines and go the extra mile to find the perfect presents. However, today, the theme to focus on is effortless. Consumers now want personalized experiences and products that can be found, purchased, delivered, and returned with zero or minimal effort. Retailers that make the end-to-end experience easy for their customers can retain them in the next year. The integration of data and analytics, as well as AI tools to discover audience preferences and motivations, offers retailers a powerful tool when preparing for the future. Constantly refreshed behavioral data that reflect consumer attitudes can better predict and influence future trends. When executed against timely research and scaled through advertising, data can be gathered as late as Thanksgiving and still give retailers a powerful way to attract new shoppers. By beginning the preparations early, focusing on content optimization, and prioritizing customers in the decision-making process, brands can build lasting relationships with your brand. And by doing so, the holidays can become the busiest time of the year and the most successful. A leader in Business Insights, SG Analytics enables organizations to achieve actionable insights into products, technology, customers, competition, and the marketplace to make insight-driven decisions. Contact us today if you are an enterprise looking to make critical data-driven decisions to prompt accelerated growth and breakthrough performance. About SG Analytics SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and data analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India. Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista.
Reimagining the Role of AI in Customer Engagement
Customers have more choices than ever before in today's fast-paced digital world. This abundance of choices means small and medium-sized businesses (SMBs) need to work smarter to attract and retain their customers. However, many SMBs often struggle with providing consistent, high-quality customer service due to limited resources and the challenge of managing unpredictable call volumes. They face difficulties scaling their operations, maintaining 24/7 availability, and handling multi-channel communications efficiently. AI is assisting organizations in revolutionizing customer service by offering unprecedented opportunities to address these pain points and enhance the customer experience. Read more: The New Data Economy: Navigating the Future of Data-Driven Value Creation Digital Shift is Reshaping Customer Engagement The digital shift is bringing a significant change in the way brands interact with customers, driven by the growing use of mobile apps and data collection. Leading organizations are utilizing technology to create superior customer experiences as well as to stay ahead of the competition. Today, customers are willing to pay a premium for better service, and companies are prioritizing data and technology to capitalize on this opportunity. To stay ahead of the curve, they need to navigate the challenges as well as the opportunities that come with data-driven customer experiences. Predictive insights are further equipping organizations to forecast customer behavior and identify real-time opportunities. How AI is Revolutionizing Customer Engagement? As customer expectations rise, businesses must offer personalized experiences catering to individual preferences. Artificial Intelligence today is playing a critical role in customer engagement, with chatbots, predictive analytics, and virtual assistants shaping the future of customer interactions. By delivering personalized and efficient customer experiences, organizations can warrant a price premium for their services. Furthermore, an AI-supported new model for customer service authorizes businesses to communicate with customers before they reach out and evaluate performance post-interaction. Leveraging AI-led capabilities can help transform customer service, enhance customer experience, and boost sales. Enhancing Customer Experience with AI The true power of AI in customer service goes beyond cost-cutting. It is about building experiences that please customers and provide them with a positive perception of the business. Let's explore some of the ways AI is elevating customer experience for businesses of any size: 24/7 Availability: AI-powered chatbots deliver around-the-clock service, eliminating wait times during off hours. Faster Query Resolution: AI can help consistently resolve customer queries, significantly reducing Average Handling Time (AHT). Personalized Interactions: AI uses customer insights to create relevant and engaging conversations. Omnichannel Support: AI facilitates seamless interactions across different channels, ensuring a cohesive customer journey. AI can also be integrated with existing customer resource management platforms to save customer conversations and provide automated summaries. As AI systems mature, the list of benefits is only set to grow, enabling organizations to continuously democratize accessibility for SMBs to adopt AI solutions. Read more: Beyond ChatGPT: How Generative AI is Shaping the Future of Enterprises How AI Chatbots Help Businesses? Businesses are turning to AI to deliver proactive, personalized services that customers want, when, and how they want it. For transformed organizations, AI-enabled customer service can help improve customer engagement, increase cross-sell and upsell opportunities, and reduce cost-to-serve. Here is how AI chatbots are assisting businesses in offering swift customer service: Find Missing Elements in Customer Chats: AI chatbots enable businesses to find gaps in customer chats and conversations, making problem-solving faster and helping businesses keep customers happy. Chats with Smart Helpers: AI chatbots act as quick helpers, answering basic customer queries with smart tricks and making them sound like humans. Easily Navigating Websites: AI chatbots assist users in navigating websites, offering guidance on finding specific items. 24x7 Availability: AI chatbots are available 24/7 to provide assistance to customers anytime, anywhere, thus ensuring quick query resolution. Zero Waiting Time: AI chatbots help reduce waiting times, thus ensuring customers are happy. Sharing Secrets With AI: AI chatbots remember details shared during conversations, helping enterprises understand customer preferences. AI chatbots further help enhance customer experience across different industries worldwide by offering customized and convenient interactions. Future Of AI Chatbots: What to Expect? In the upcoming years, the chatbot industry is expected to grow at an impressive rate. Current projections show that the market is set to exceed $994 million soon, representing a phenomenal year-over-year gain of around $200 million. Businesses of all sizes are incorporating AI chatbots to enhance customer service. They are recognizing the potential of chatbots to enable efficient customer connections. Some of the key trends that will further shape the future of AI chatbots are as follows: More Human-Like Conversations Advancements across machine learning and natural language processing are making conversations with chatbots more natural and human-like, leading to the creation of better user experiences. Access to Critical Customer Insights Sophisticated AI will empower chatbots to analyze user data and gain individualized insights. With this critical understanding, chatbots will be able to deliver highly personalized and relevant responses. Read more: Navigating the Impact of AI in Social Media Management Transforming Contact Centers With the progress of technology, chatbots are set to play a central role in reimagining contact centers. Chatbots are capable of efficiently and consistently handling routine inquiries. While human interaction is vital for sensitive matters, integrating chatbots and humans will enable fully autonomous contact centers in the future. Mainstream Adoption of Voice Bots With the growing popularity of voice assistants, voice-enabled chatbots are becoming commonplace. Users can engage with chatbots using voice commands, enhancing convenience. Social Media Adoption Platforms like Instagram and Facebook can increasingly harness chatbots to engage with users. Businesses can integrate social media chatbots to address inquiries, recommend products, as well as offer real-time support. Business Integration Organizations can use chatbots for internal purposes such as HR, IT support, etc. By automating these processes, chatbots will further help enhance workplace efficiency and productivity. Key Takeaways Today, AI is revolutionizing consumer engagement with the integration of chatbots, virtual assistants, and predictive analytics. Organizations need to embrace digital transformation to design personalized customer experiences. Empowering employees with the right tech, as well as fostering a customer-centric culture, are emerging as the keys to a brand's success. Read more: Generative AI is Increasing Employee Productivity and Expanding Capabilities. How? Traversing the Future of Customer Journey Today, it is evident that innovations in AI and omnichannel strategies are at the forefront of the consumer-centric revolution. AI-enabled customer service is offering organizations the quickest and most effective way to deliver personalized experiences that drive customer engagement. The personalized touch of AI-driven interactions contributes to a symphony of customer engagement. The integration, coupled with a focus on employee empowerment as well as a commitment to transparency, form the cornerstone of successful strategies. Organizations are venturing into the uncharted territories of augmented reality and sculpting the landscape of customer interactions. A leading enterprise in Generative AI solutions, SG Analytics focuses on unlocking unparalleled efficiency, customer satisfaction, and innovation for the client with end-to-end AI solutions. Contact us today to harness the immense power of artificial intelligence and set new benchmarks in operational efficiency, customer satisfaction, and revenue generation. About SG Analytics SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India. Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista.
AI’s Power Conundrum: Will Renewables Save the Day?
As AI reshapes industries and accelerates innovation, it brings new energy challenges. To meet AI’s immense processing demands, tech companies are leveraging nuclear power, renewable energy, and AI-driven strategies while prioritizing sustainability goals. The energy consumption required to support AI systems is rapidly increasing, with data centers projected to consume up to 9% of US electricity by 2030, more than double the current amount, per EPRI. AI computing power is doubling roughly every 100 days, according to research published by Science Partner Journal in January 2023. Unlike traditional software, AI programs continuously process vast datasets, often using 33 times more energy to perform similar tasks. The growing demand has even led to the extended use of fossil fuel plants, delaying their planned retirements to meet the electricity needs of AI-driven systems. By the end of the decade, around 54 GW of US coal power, representing 4% of the country’s electricity, is expected to retire, a 40% decrease from last year’s forecast, per S&P Global Commodity Insights. With AI programs running 24/7, tech companies are grappling with both rising electricity costs and the challenge of reducing greenhouse emissions. Tech Giants Transitions to Clean Energy In 2024, tech giants Google and Microsoft acknowledged that the increasing energy demands of AI are making it difficult to meet their pre-established climate targets. In 2020, Google committed to running all global operations on 24/7 carbon-free energy by 2030, ensuring continuous use of clean energy across all locations and power grids. Since 2020, there has been an increase of 30% in carbon emissions due to the expansion of data centers, as per Microsoft's Sustainability Report. Microsoft signed a 20-year power purchase agreement (PPA) with Constellation Energy in September 2024 to provide clean energy to power these data centers. This agreement involves restarting a unit of a decommissioned Three Mile Island nuclear plant in Pennsylvania. The plant will generate 835 megawatts of carbon-free electricity, helping to power Microsoft’s data centers as part of its broader goal of becoming carbon-negative by 2030. Read more: Unlocking M&A Potential: The Impact of Fed’s Rate Strategy Navigating the Financial and Environment Trade-offs Figure: Big Tech’s Soaring Capital Expenditure Bill Note: 2024 and 2025 are analysts’ consensus estimates Source: Visible Alpha|G.Hay|Breakingviews|Sept. 30, 2024 The combined capex from Amazon, Alphabet, Microsoft, Meta platforms, and Apple is projected to increase from $80 billion in 2019 to $247 billion by 2025, per combined research by Visible Alpha, G.Hay, and Breakingviews. Tech giants are increasingly turning to PPA to meet the soaring energy demands of AI as high capex squeezes the operating margins. This helps them avoid the added financial burden of building their own solar or wind plants, which carries significant risks. In May 2024, Microsoft and Brookfield signed a PPA to develop 10.5 GW of renewables at an estimated cost of $10 billion. Although not cheap, PPAs, typically lasting around 15 years, provide predictable pricing and shield companies from energy market volatility. However, there is a risk of overspending on unused energy if demand for AI services doesn’t grow as anticipated, leaving firms with surplus power and potentially leading to losses if energy prices fall below the PPA rates. The potential revenue boosts from AI advancements could offset these challenges, justifying the high upfront investments in energy and infrastructure. Read more: Dividend Recapitalization Pick up in 2024 The Role of Nuclear and Fusion Energy As tech companies ramp up their AI operations, they face significant challenges in meeting the energy demands of data centers around the clock. PPAs have emerged as an essential strategy due to the inconsistent supply of renewable sources such as solar and wind energy. Nuclear, with its ability to provide what Microsoft refers to as “firm electricity,” is emerging as a key solution, filling the gaps of less reliable clean sources. Microsoft’s nuclear power investment will likely be the only viable clean energy option to foster AI growth until infrastructure expands. Despite being a clean energy source that helps tech companies achieve their climate goal, the environmental dangers linked with radioactive waste in nuclear energy present significant problems. Fusion startups raised over $7.1 billion by July 2024, with most investments concentrated in a few key companies, per the Fusion Industry Association 2024 Report. These start-ups aim to develop nuclear fusion, a cleaner and more powerful energy source than traditional fission, offering carbon-free energy without long-lived radioactive waste. If fusion becomes commercially viable, it is expected to revolutionize trillion-dollar energy markets. Although still experimental and expensive, nuclear fusion shows great potential for meeting AI’s growing energy demands. Optimizing Renewables with AI Renewable energy sources face challenges like inconsistent production and grid instability as AI’s energy demands surge. However, AI can provide synergy and address these issues by optimizing renewable energy use. Researchers at the National Renewable Energy Laboratory in the US have reported that using invertible neutral networks has accelerated the wind turbine design process 100 times compared to conventional methods. AI algorithms analyze weather patterns and energy trends, allowing precise solar and wind availability forecasting, improving grid stability, and reducing waste. Google’s project with DeepMind implemented machine learning to optimize energy efficiency in its data centers, leading to a 40% reduction in the electricity used for cooling. Moreover, AI-powered smart grids can manage energy storage, ensuring excess power is saved and redistributed efficiently. AI’s efficiency gains could help mitigate its energy demands, making it a key player in the future of sustainable power management. Read more: US Merger and Acquisition (M&A) 2H24 Outlook Conclusion Meeting AI’s immense energy needs requires a multifaceted approach. While tech companies are investing in nuclear power and exploring experimental fusion, AI’s potential to optimize renewable energy usage could be a key to achieving long-term sustainability. AI-powered systems can learn from bioinspired methods, providing intelligent control for large-scale energy integration, which remains underdeveloped. As the energy landscape evolves, AI will help mitigate the strain on energy infrastructure, emerging as a key player in energy consumption and an indispensable resource for boosting global efficiency. Partner of choice for lower middle market-focused investment banks and private equity firms, SG Analytics provides offshore analysts with support across the deal life cycle. Our complimentary access to a full back-office research ecosystem (database access, graphics team, sector & and domain experts, and technology-driven automation of tactical processes) positions our clients to win more deal mandates and execute these deals in the most efficient manner. About SG Analytics SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India. Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista.
What Is Synthetic Data Generation: Meaning, Benefits & Use Cases
In today's data-driven world, enterprises of all sizes rely on data for informed decision-making, driving growth, and staying competitive. However, sometimes, they cannot access the exact real-world insights required to generate data-driven decisions. This creates a demand for artificially generated data that simulates real-world events and patterns, enabling them to have insights and perform predictive modeling. Synthetic data is an increasingly popular approach to leveraging data. But what is synthetic data, and how can organizations benefit from it? Synthetic Data Developing successful AI and ML models requires access to high-quality datasets. However, collecting such data is a challenging task because: Many business problems that AI/ML models solve require access to sensitive customer data. Collecting and using sensitive data presents privacy concerns and leaves businesses vulnerable to data breaches. Privacy regulations like GDPR and CCPA restrict the collection and use of personal data and impose fines on enterprises that violate them. Some types of data are expensive to collect or are rare. For instance - Collecting data representing real-world road events for an autonomous automobile can be prohibitively expensive. Collecting sufficient data to design ML models to predict fraudulent transactions is challenging, as fraudulent transactions are rare. These growing concerns are compelling businesses to turn to data-centric approaches to AI/ML development, including synthetic data. Generating synthetic data is inexpensive compared to collecting large data sets. It can also help support AI/deep learning model development or software testing without compromising customer privacy. This growing popularity has led to an estimation that by the year 2024, 60% of the data utilized to develop AI and analytics projects will be synthetically generated. What is Synthetic Data? Advances in data generation techniques have promoted the creation of synthetic data that is indistinguishable from real-world data. This has opened up new opportunities for enterprises to test and validate their systems and strategies using synthetic test data. Synthetic data is computer-generated data that imitates the characteristics of real-world data. Instead of using authentic data collected from different sources, synthetic data is developed using computer simulations. This approach is used when real data isn't unavailable or kept private due to data protection laws. Synthetic Data Meaning Real-world data is gathered from authentic sources like customer interactions, sensor readings, or financial transactions. While this data is valuable for analysis, it can be challenging to acquire and manage due to privacy concerns and other constraints. In contrast, synthetic test data can be developed on demand, enabling enterprises to bypass these challenges and gain valuable insights for decision-making processes. In summary, synthetic data has far-reaching importance for businesses across industries. By leveraging synthetic data, organizations can overcome limitations associated with real-world data, equipping them to access high-quality training data for machine-learning models. As a result, enterprises can develop more accurate systems, leading to better decision-making and enhanced outcomes. How to Create Synthetic Data? To create synthetic data, data scientists are integrating different synthetic data generation tools. Synthetic data is computer-generated and resembles real-world data in structure and statistical approach without using actual data points from the real world. Let's understand why it is important and how it can benefit businesses. Why is Synthetic Data Important? Synthetic data has become increasingly necessary for multiple reasons, such as its potential to overcome limitations associated with real-world data, such as privacy concerns, bias, and cost. With consumer information privacy becoming more stringent, the need for synthetic data is becoming significant. Today’s businesses are operating in different contingencies and have numerous outcomes for different user scenarios. Synthetic data allows them to respond to potential user situations that may arise. Synthetic training data is critical in developing machine learning models. The quantity and quality of training data can affect the performance of these models. With synthetic data generation, businesses can generate large volumes of diverse, high-quality training data that represents everyday scenarios. This further equips data scientists to fine-tune their models effectively, leading to better predictions and outcomes. Data models can assess previously used training data to optimize it for future applications. Data models can use synthetic data as a refinement for existing training data sets to root out negative iterations. Synthetic Data Generation When selecting the best method for generating synthetic data, it is important to first consider what type of synthetic data an organization aims to have. There are three general categories to choose from, each offering different benefits and drawbacks: Fully synthetic: This data does not include any original data. This indicates that re-identifying any single unit is almost impossible, and all variables in the data are still fully available. Partially synthetic: In this data, only sensitive data is substituted with synthetic data. This demands a heavy dependency on the imputation model. It can lead to decreased model dependence. However, this does imply that disclosure is possible due to the true values that remain within the dataset. Hybrid Synthetic: Hybrid synthetic data is derived from real and synthetic data. In a hybrid synthetic dataset, the underlying distribution of original data is investigated, and the nearest neighbor of every data point is formed. A near-record in the synthetic data is selected for each record of real data, and the two are converged to generate hybrid data. There are two broad strategies to build synthetic data. They are as follows: Drawing numbers from a distribution: This approach involves observing real statistical distributions and reproducing fake data. This can include the creation of generative models. Agent-based modeling: To achieve synthetic data in this method, a model is developed that explains an observed behavior, and then random data is produced using the same model. Synthetic Data Use Cases Diverse industries and sectors are benefiting from the integration of synthetic data. From healthcare to fraud detection, synthetic data is equipping applications almost everywhere. Machine Learning: Synthetic data is employed to train machine learning systems when real data is expensive and poses privacy risks. Healthcare: Within a highly regulated industry like healthcare, synthetic data can help practitioners and researchers access valuable insights without violating their patient privacy. Finance: Synthetic data can be utilized to predict financial trends, test algorithms, and ensure compliance with regulations. Retail and Marketing: Businesses are using synthetic data to optimize pricing strategies, understand consumer behavior, and enhance marketing automation. Automotive: Synthetic data is important in developing self-driving vehicles, as it authorizes extensive testing and validation without the need for real-world testing. Challenges of Synthetic Data Despite the numerous advantages of synthetic data, it has some limitations. Creating accurate and representative synthetic data can be a challenging task. There are concerns about the validity of the generated data when compared to real-world data. Synthetic data generation tools are still evolving, which indicates there is room for improvement in accuracy and efficiency. Synthetic data presents a range of advantages and use cases for businesses of all sizes. By leveraging synthetic data, they can overcome limitations associated with sensitive data, enhance data privacy, and discover new opportunities. There are different tools and services that can help businesses take advantage of synthetic data. Integrating synthetic data into business strategy can further help unlock new insights, optimize operations, and make informed decisions. Benefits of Synthetic Data The ability to generate diverse data is a key benefit of synthetic data. By creating synthetic data that imitates the characteristics of real-world data, enterprises can test their systems for different scenarios, thereby ensuring that they are robust and reliable. This can be useful across industries where access to real-world data is limited or poses privacy risks. Let's understand some of the key advantages of integrating synthetic datasets into business operations: Cost-effective Generating synthetic data is more cost-efficient than collecting real data. It does not require the same resources or effort. Data privacy Synthetic data equips businesses to comply with data regulations and protect sensitive customer data. Not having to deal with the privacy concerns or legal complications that often arise with real-world data indicates fewer hurdles for the company to use data. Scalability Synthetic data can be developed in large volumes. This provides more opportunities for testing and training machine learning models. With the right algorithms, organizations can work on training models, and an output generator can further help create infinite synthetic data for ongoing use. Diversity of data Businesses can test their systems across different scenarios by generating various synthetic data. Synthetic data generation can help produce diverse datasets that represent realistic situations that probably would not have been able to be sourced from authentic data. Reduction of bias Data bias poses a big concern for any organization as it does not accurately represent insights. However, data bias can be removed by generating synthetic data carefully designed to be representative and unbiased. The Future of Synthetic Data Generation Synthetic data generation processes are evolving rapidly. The following areas pledge to introduce innovation that delivers better business outcomes. Synthetic data operations: Artificial data generation is just one of the significant steps in the synthetic data lifecycle. Data teams are seeking new solutions to manage and automate the entire synthetic data lifecycle. Improved data quality and reliability: Data professionals rely on high-quality data for their workloads. Due to this, synthetic data companies are being driven to optimize their synthetic data generation algorithms, and new solutions are emerging that will help generate vertical-specific synthetic data. Ethical and legal perspectives: With the growth in synthetic data, regulators are paying more attention to its ethical implications. Businesses need to be aware of these growing concerns and take them into account as they develop. Integration with production data: By integrating artificial data with real-life data, data teams can generate more comprehensive datasets. Fake or artificial data can be used to close gaps in actual datasets and augment real-life details to cover a broader scope of edge cases. They can also create test data to cover the new application functionality that is being developed. Summary - Synthetic Data Generation With increasingly stringent data privacy laws and the ever-increasing complexity of accessing multi-source production data, the need for synthetic data generation and management is rising. Organizations should not settle for a pointed tool to generate tabular synthetic data for a specific use case. They should rather seek a future-ready solution that can assist in addressing many use cases with the needed accuracy and agility while also managing their entire synthetic data lifecycle. A leading enterprise in Data Analytics, SG Analytics focuses on leveraging data management solutions, analytics, and data science to help businesses across industries discover new insights and craft tailored growth strategies. Contact us today to make critical data-driven decisions, prompting accelerated business expansion and breakthrough performance. About SG Analytics SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India. Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista.
Dividend Recapitalization Pick up in 2024
Dividend recapitalization (recap) has seen a sharp rise in 2024, which is unusual in a high-interest rate environment. Private equity (PE) firms turn to dividend recap to generate returns, though it carries potential risk for portfolio companies due to the challenging exit environment. What Is Dividend Recapitalization This comes after a two-year lull in such activity, after the peak of 2021 when interest rates were low. The trend occurred at a time when central banks kept their interest rates at record highs, making borrowing more expensive. The YTD dividend recap volume through September reached $69.3 billion, surpassing the $66.4 billion recorded during the same period in 2021 and nearing the annual record of $76 billion set that year, according to Pitchbook. Figure 1: Dividend Recap Institutional Loan Volume ($B) Jan. 1-Sept.24 Source: Pitchbook, Data through Sept.24, 2024 Why Is Dividend Recap Surging Despite High Interest Rates? Dividend recaps by PE generally increase during periods of low interest rates, as seen in 2021, when companies can borrow at lower costs to fund dividend payments. However, despite high borrowing rates currently, PE sponsors are indulging in this. According to Pitchbook, PE-backed companies accounted for 86% of the leveraged loans issued to fund dividends in early 2024. Read more: Unlocking M&A Potential: The Impact of Fed’s Rate Strategy The primary reason for this is the limited opportunities for PE firms to exit their investments through other alternatives such as mergers and Acquisitions (M&A) and IPOs. In 2023, US corporate M&A activity for deals exceeding $100 million dropped by 17% from 2022 and was roughly 45% below its peak in 2021, per EY. US IPO exits in 2023 amounted to $21.5 billion, significantly lower than the $295.9 billion recorded in 2021, reflecting a subdued year, per Pitchbook. High interest rates have stifled deal-making activity, reducing the number of exits for PE investors from 1,893 in 2021 to 1,243 in 2023, according to Pitchbook. The trend in the US PE exit continued with a slow start, with 316 exits totaling $66.7 billion in 1Q24, a 19% drop in value from 4Q23, highlighting persistent volatility. In 3Q24, the exit value increased 50.5% YoY, but the number of exits remained flat compared to the previous year on an annualized basis, per Pitchbook. The exit-to-investment ratio dropped to a record low of 0.36x in 2Q24 and is yet to show improvement in 3Q24. Ongoing valuation gaps between buyers and sellers have been a key factor behind the subdued exit activity. Figure 2: PE Exit Activity by Quarter Source: Pitchbook *As of September 30, 2024 With fewer exits, PE sponsors have turned to dividend recap to return capital to their limited partners (LPs). This allows PE firms to deliver returns while waiting for more favorable conditions for exits. Moreover, many LPs have been patient throughout these prolonged holding periods and are eager to see a return on their capital. For the year ending 2023, distribution rates hit their lowest level since 2009. Using dividend recaps, PE firms can meet LP's expectations without being forced to exit at suboptimal valuations. Short-Term Outlook: Will This Trend Continue? The recent 50 bps rate cut by the Fed accelerated the trend of dividend recap. Fitch Ratings project the fed rate to fall to 3.5% by the end of 2025. The decision to cut rates by 50 bps instead of the expected 25 bps signals the Fed’s commitment to easing monetary policy, especially now that inflation appears to be under control. In the short term, this rate reduction lowers the cost of borrowing, making dividend recap more financially viable. With the burden of debt servicing lessened, PE firms are likely to continue using this strategy to generate returns for LPs, especially as we head into the final quarter of the fiscal year. The increased cash returns to LPs would allow general partners (GPs) to attract new funds by showcasing steady interim performance. Read more: Google Search and Generative AI: Navigating the Paradigm Shift Long-Term Perspective: M&A Recovery and Lender Preferences While dividend recap is poised to continue in the near term, the distant outlook is expected to differ. According to a Grant Thornton survey of M&A professionals, 67% anticipate an increase in US deal volume in the upcoming months. As the economic environment improves and M&A activity picks up, PE sponsors will have more opportunities to exit alternatives. With this, the need for dividend recaps as a tool for giving returns to investors might decline. Moreover, lenders are likely to shift their preference toward providing leverage for M&A deals or leveraged buyouts rather than for dividend recaps. M&A deals generally involve healthier uses of leverage, as they facilitate growth or consolidation; in comparison, dividend recaps are viewed as riskier, benefiting only investors but adding debt to the company without a corresponding increase in value. Potential Risks While dividend recaps offer short-term benefits, they also bring potential risks, especially for portfolio companies that take on excessive debt. Even in a low-rate environment, companies with weaker financial profiles may struggle to pay interest expenses and principal repayment, potentially leading to bankruptcies. If interest rates were to rise again, although the chances seem low, companies with high leverage from dividend recaps could face severe financial distress. In addition, excessive use of dividend recap is likely to attract regulatory scrutiny. Companies that consistently distribute dividends by leveraging capital borrowed at the cost of their financial strength usually face legal challenges, mostly in scenarios of bankruptcy or default. Read more: US Merger and Acquisition (M&A) 2H24 Outlook Conclusion - Dividend Recapitalization Dividend recap has emerged as an efficient tool for delivering returns to LPs, particularly in a competitive environment where GPs compete for capital. Nevertheless, financial sponsors must assess their portfolio companies’ capacity to generate sustainable cash flows and service the added debt over time. Although imminent rate cuts by the Fed may offer some relief, GPs must still start exploring alternative exit routes. Maintaining a balance between liquidity and leverage is crucial, as taking excessive debt by portfolio companies to enhance returns can endanger their financial stability. Partner of choice for lower middle market-focused investment banks and private equity firms, SG Analytics, provides offshore analysts with support across the deal life cycle. Our complimentary access to a full back-office research ecosystem (database access, graphics team, sector & and domain experts, and technology-driven automation of tactical processes) positions our clients to win more deal mandates and execute these deals in the most efficient manner. About SG Analytics SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India. Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista.
Predictive Analytics in Healthcare Industry: Examples & Benefits of Predictive Analytics
The healthcare industry is changing. The abundance of data available at the organization’s fingertips is transforming the entire industry. How diseases are discovered, how effectively patients are being treated, and how healthcare institutions are utilizing their resources to coordinate and perform. This is where healthcare analytics tools come in handy. Predictive analytics helps healthcare organizations access, analyze, and process patient data to improve data-based quality care and personalize treatments. Predictive Analytics in Healthcare Industry Predictive analytics in the healthcare industry highlights the analysis of current as well as historical healthcare data, enabling healthcare professionals to discover opportunities and make more effective and efficient operational decisions, predict future trends, and manage the spread of diseases. What Is Predictive Analytics in the Healthcare Industry? Predictive analytics is a discipline in data analytics that relies on modeling, data mining, AI, and machine learning techniques. It is utilized to evaluate historical and real-time data insights and make future predictions. Healthcare data is any data related to the health conditions of a patient or a group of patients and is collected from administrative as well as medical records, health surveys, claims-based datasets, and EHRs. Healthcare analytics is a tool the healthcare industry can use and benefit from providing better-quality care to healthcare organizations, hospitals, doctors, physicians, pharmaceutical companies, and healthcare stakeholders. Read more: Read more: Top 8 Applications of Data Analytics in Real Life Use of Predictive Analytics in Healthcare Industry The healthcare industry is known to generate a tremendous amount of data. However, they often struggle to transform that data into valuable insights to improve patient outcomes. Data analytics in healthcare is applied to every aspect of patient care and operations management. It is also used to investigate methods to enhance patient care, predict disease outbreaks, and reduce the cost of treatment. At a business level, integrating analytics in healthcare can help simplify internal operations, refine the utilization of resources, and enhance care teams’ coordination as well as efficiency. The ability of data analytics tools to transform healthcare data into actionable insights has a significant influence on the following healthcare: Clinical research Development of new treatments Discovery of new drugs Prediction as well as prevention of diseases Clinical decision support Accurate diagnosis of medical conditions High success rates of surgeries Automation of administrative processes and operations Read more: Omni Channel Analytics in Pharmaceutical Industry: Challenges and Considerations in omnichannel Marketing Benefits of Predictive Analytics in Healthcare With technological advancement, predictive analytics is tremendously impacting the healthcare industry. AI and machine learning techniques utilize data to diagnose diseases, decide the best treatment for a patient’s use case, and much more. Let's explore some of the most important ways in which healthcare institutions can benefit from predictive analytics: Enhanced Patient Care The most important benefit of predictive analytics to the healthcare industry is the accessibility to different data – medical history, demographics, economics, and comorbidities. All this data presents doctors and healthcare professionals with valuable insights to guide their decisions. Predictive analytics is used to enhance patient outcomes. By examining data and outcomes of old patients, machine learning algorithms can provide insights into treatment methods that will work best for every patient. Personalized Patient Treatments Traditionally, treatments and drugs were prescribed based on limited data based on statistics of a broad population. With predictive analytics, medical professionals can more accurately diagnose patients and identify an effective course of treatment tailored to the patient’s unique health situation. Digitalization & Tracking The digitalization of the health industry can transform the way patients and health professionals interact. Digital healthcare devices attached to our bodies assist in tracking our health and body performance at any given time. Reduce Healthcare Costs Predictive analytics can be integrated to reduce healthcare costs. It can help cut patient costs by reducing unnecessary hospitalization care when it is unnecessary. It can be beneficial in controlling hospital supplies costs and predicting hospital staffing needs. Chronic Disease Management Chronic diseases are considered a leading cause of death and disability across the US. They are also the main drivers of the country’s $3.5 trillion in annual health costs. Chronic disease management relies on the ability of healthcare professionals to contain the development of these diseases and to control them. Preventing chronic conditions is a challenging task. Predictive analytics can help healthcare providers make timely and fact-based decisions to provide effective treatments and reduce the costs of this care. Fraud Detection Fraud in healthcare is a common concern. Fraudulent healthcare schemes take place in many forms. Individuals obtain subsidized or fully covered prescription pills that are unnecessary and then sell them on the black market for a profit, incorrect reporting of diagnoses to maximize payment, prescribing additional treatment, etc. Recognizing At-Risk Patients Predictive analytics in healthcare predict patients are at a higher risk. It can help identify patients with cardiovascular disease with the highest probability of hospitalization basis age-coexisting chronic illnesses and medication adherence. Predictions on the probability of disease and chronic illness can assist doctors and healthcare organizations in proactively providing patient care. Read more: What is Descriptive Analytics: Definition & Examples Forecast Equipment Maintenance Predictive analytics has long been used across manufacturing and telecommunication to identify maintenance needs. In a similar way, the healthcare industry can benefit from predictive analytics. By analyzing the collected data from sensors in MRI machines, predictive analytics can help predict any failures. Hospitals can also schedule maintenance activity when the devices are not in use, thereby minimizing workflow disruption. Prevent Human Errors The impact that human errors can have on healthcare could prove fatal. With real-time and accurate insights, healthcare professionals can guide their actions, and data can further help flag potential errors as well as prevent fatal mistakes. Predictive Analytics in Healthcare Examples Today, predictive analytics is transforming the field of healthcare in multiple ways. Let's explore some exciting examples of predictive analytics in the healthcare industry: Preventing Readmission The costs of hospital readmission are high. Reports have highlighted that the US healthcare system spends almost $52.4 billion on readmissions annually. Additionally, hospitals are likely to pay heavy penalties under Medicare’s Hospital Readmission Reduction Program. This adds financial incentives to combat readmission. Healthcare predictive analytics can help identify patients with traits indicating a high possibility of readmission. This can assist doctors in allocating more resources for follow-ups and personalizing discharge protocols. Managing Population Health Another prominent example of predictive analytics in healthcare covers two aspects. Recognizing public health trends with predictive analytics: Predictive analytics in healthcare helps in identifying potential population health trends. The healthcare industry is integrating predictive analytics to uncover health trends. Detecting disease outbreaks with predictive analytics: Predictive analytics can help predict the emergence of the outbreak, just like predictive analytics had foreseen the COVID-19 pandemic. Predicting Onset of Diseases Predictive analytics with machine learning can help healthcare professionals forecast diseases when patients do not show any visible signs or tendencies for onsets. A research team designed an ML-powered tool that could help predict multiple myeloma. This solution takes into understanding tumor genomics and the prescribed treatment. During this project, researchers were able to identify 90 genes that could be present in a tumor and have a high potential for mutation. Speeding up Insurance Claim Submissions Predictive analytics in healthcare can help spot insurance claims that could get rejected based on historical data and rejection patterns. The technology can also help detect insufficient reimbursement codes or suggest matching fields. These tools can help hospitals in preparing insurance claims faster. Apixio, headquartered in California, built an analytics-powered tool for healthcare that helps hospital coders identify the correct codes for insurance claims. Enhancing Cybersecurity As per the HIPAA Healthcare Data Breach Report, cyberattacks are frequent in the healthcare industry. The report further revealed that between January and October 2023, almost 82.6 million healthcare records were exposed. Cybersecurity predictive analytics in healthcare can help contribute to this situation. Healthcare organizations can integrate predictive analytics solutions and artificial intelligence for the healthcare sector to calculate risk for different online transactions in real time. Predictive analytics in cybersecurity has two high-level types. Vulnerability-based solutions help search for weaknesses in the healthcare system that can be exploited. Threat-focused platforms help search for potential threats. Predictive modeling in healthcare further assists in monitoring data and identifying any changes in patterns that could indicate an intrusion. Read more: Top Data Analytics Companies in India [Updated 2024] Predict Suicide Attempts Suicide is considered the tenth leading cause of death across the US, taking the lives of 14 Americans out of 100,000 annually. Predictive analytics tools can help factor in a patient’s previous attempts and social and economic conditions to understand the likelihood of future attempts. How to Use Predictive Analytics in Healthcare Industry Predictive analytics in healthcare helps enhance decision-making, streamline operations, and improve patient outcomes. By leveraging data-driven insights generated using predictive analytics frameworks, healthcare providers can predict disease outbreaks, optimize resource allocation, and personalize patient treatment plans. It further assists in transforming patient care by facilitating early intervention, reducing treatment costs, and increasing overall efficiency. Key Highlights AI systems are equipping healthcare to analyze patient records and identify subtle patterns, facilitating early diagnosis and treatment, thereby significantly enhancing patient outcomes. AI models help predict adverse drug reactions by analyzing molecular interactions and treatment efficacy. AI leverages genetic, environmental, and lifestyle data to design personalized treatment plans, optimize medication effectiveness, and reduce unnecessary interventions. Wearable devices and predictive algorithms help in monitoring vital signs in real-time, thereby alerting healthcare service providers and enabling timely interventions, thus enhancing patient health management. AI helps accelerate drug discovery by scanning chemical libraries as well as predicting drug interactions, shortening the time to market. AI identifies at-risk patients and recommends personalized follow-up care plans, reducing readmissions and improving patient outcomes. Read more: Top 10 Data Science Companies in India - 2024 In Conclusion - Predictive Analytics in Healthcare Global predictive analytics in the healthcare market is expected to grow at a CAGR of 24.4% from the year 2023 to 2030. It presents plenty of opportunities for healthcare providers as well as health tech organizations. By deploying predictive analytics, healthcare organizations can develop fair and suitable frameworks for the targeted population segments. Undoubtedly, AI-powered predictive analytics consulting is set to transform the healthcare sector significantly by setting up instruments for early disease detection, personalized care plans, and optimized resource usage. This technology further demonstrates the potential to future-proof healthcare systems, putting patients at the forefront of everything. A leader in the healthcare research domain, SG Analytics assists healthcare companies in leveraging the power of information. Contact us today if you are in search of efficient Healthcare solutions to make sound business decisions. About SG Analytics SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies, across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India. Apart from being recognized by reputed firms such as Gartner, Everest Group, and ISG, SGA has been featured in the elite Deloitte Technology Fast 50 India 2023 and APAC 2024 High Growth Companies by the Financial Times & Statista. FAQs - Predictive Analytics in Healthcare What is predictive analytics in healthcare? Predictive analytics in healthcare refers to analyzing historical data to identify patterns as well as predict future events. Healthcare institutions can identify and treat patients through predictive analytics before they develop chronic conditions. It is an analytical approach integrated to score patients across some characteristics like demographics, disabilities, age, and past care patterns. How is predictive analytics used in healthcare? Predictive analytics in healthcare plays a critical role in enhancing care delivery and patient outcomes. By leveraging historical data, predictive analytics enables health systems to measure what is likely to happen in the future from an operational as well as clinical perspective. What data is used for predictive analytics in healthcare? Predictive analytics in healthcare uses advanced data analysis on past health data. The goal is to discover useful patterns and insights within this data that can help healthcare professionals predict future events and outcomes accurately. What are the benefits of predictive analytics in healthcare? Let's explore some examples of how predictive analytics can be beneficial for healthcare leaders are as follows: Reducing costs on appointment and readmission penalties. Speeding up administrative tasks like discharge procedures and insurance claims submission. Preventing ransom and cyberattacks by analyzing ongoing transactions along with assigning risk scores. Proactively preparing for upcoming health trends.
