NVIDIA was today named an Autonomous Grand Challenge winner at the Computer Vision and Pattern Recognition (CVPR) conference, held this week in Nashville, Tennessee. The announcement was made at the Embodied Intelligence for Autonomous Systems on the Horizon Workshop. This marks the second consecutive year that NVIDIA’s topped the leaderboard in the End-to-End Driving at Scale category and the third year in a row winning an Autonomous Grand Challenge award at CVPR. The theme of this year’s challenge was “Towards Generalizable Embodied Systems” — based on NAVSIM v2, a data-driven, nonreactive autonomous vehicle (AV) simulation framework. The challenge offered researchers the opportunity to explore ways to handle unexpected situations, beyond using only real-world human driving data, to accelerate the development of smarter, safer AVs. Generating Safe and Adaptive Driving Trajectories Participants of the challenge were tasked with generating driving trajectories from multi-sensor data in a semi-reactive simulation, where the ego vehicle’s plan is fixed at the start, but background traffic changes dynamically. Submissions were evaluated using the Extended Predictive Driver Model Score, which measures safety, comfort, compliance and generalization across real-world and synthetic scenarios — pushing the boundaries of robust and generalizable autonomous driving research. The NVIDIA AV Applied Research Team’s key innovation was the Generalized Trajectory Scoring (GTRS) method, which generates a variety of trajectories and progressively filters out the best one. GTRS model architecture showing a unified system for generating and scoring diverse driving trajectories using diffusion- and vocabulary-based trajectories. GTRS introduces a combination of coarse sets of trajectories covering a wide range of situations and fine-grained trajectories for safety-critical situations, created using a diffusion policy conditioned on the environment. GTRS then uses a transformer decoder distilled from perception-dependent metrics, focusing on safety, comfort and traffic rule compliance. This decoder progressively filters out the most promising trajectory candidates by capturing subtle but critical differences between similar trajectories. This system has proved to generalize well to a wide range of scenarios, achieving state-of-the-art results on challenging benchmarks and enabling robust, adaptive trajectory selection in diverse and challenging driving conditions. NVIDIA Automotive Research at CVPR  More than 60 NVIDIA papers were accepted for CVPR 2025, spanning automotive, healthcare, robotics and more. In automotive, NVIDIA researchers are advancing physical AI with innovation in perception, planning and data generation. This year, three NVIDIA papers were nominated for the Best Paper Award: FoundationStereo, Zero-Shot Monocular Scene Flow and Difix3D+. The NVIDIA papers listed below showcase breakthroughs in stereo depth estimation, monocular motion understanding, 3D reconstruction, closed-loop planning, vision-language modeling and generative simulation — all critical to building safer, more generalizable AVs: Diffusion Renderer: Neural Inverse and Forward Rendering With Video Diffusion Models (Read more in this blog.) FoundationStereo: Zero-Shot Stereo Matching (Best Paper nominee) Zero-Shot Monocular Scene Flow Estimation in the Wild (Best Paper nominee) Difix3D+: Improving 3D Reconstructions With Single-Step Diffusion Models (Best Paper nominee) 3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models Zero-Shot 4D Lidar Panoptic Segmentation NVILA: Efficient Frontier Visual Language Models RADIO Amplified: Improved Baselines for Agglomerative Vision Foundation Models OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving With Counterfactual Reasoning Explore automotive workshops and tutorials at CVPR, including: Workshop on Data-Driven Autonomous Driving Simulation, featuring Marco Pavone, senior director of AV research at NVIDIA, and Sanja Fidler, vice president of AI research at NVIDIA Workshop on Autonomous Driving, featuring Laura Leal-Taixe, senior research manager at NVIDIA Workshop on Open-World 3D Scene Understanding with Foundation Models, featuring Leal-Taixe Safe Artificial Intelligence for All Domains, featuring Jose Alvarez, director of AV applied research at NVIDIA Workshop on Foundation Models for V2X-Based Cooperative Autonomous Driving, featuring Pavone and Leal-Taixe Workshop on Multi-Agent Embodied Intelligent Systems Meet Generative AI Era, featuring Pavone LatinX in CV Workshop, featuring Leal-Taixe Workshop on Exploring the Next Generation of Data, featuring Alvarez Full-Stack, GPU-Based Acceleration of Deep Learning and Foundation Models, led by NVIDIA Continuous Data Cycle via Foundation Models, led by NVIDIA Distillation of Foundation Models for Autonomous Driving, led by NVIDIA Explore the NVIDIA research papers to be presented at CVPR and watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang. Learn more about NVIDIA Research, a global team of hundreds of scientists and engineers focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics. The featured image above shows how an autonomous vehicle adapts its trajectory to navigate an urban environment with dynamic traffic using the GTRS model.

Reading Time: 9 minutes In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns. But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question (and many others), we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic. This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results. Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.   Ankur Kothari Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns. Second would be demographic information: age, location, income, and other relevant personal characteristics. Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews. Fourth would be the customer journey data. We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data. 2. How do you distinguish between data that is actionable versus data that is just noise? First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance. Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in. You also want to make sure that there is consistency across sources. Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory. Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy. By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions. 3. How can customer engagement data be used to identify and prioritize new business opportunities? First, it helps us to uncover unmet needs. By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points. Second would be identifying emerging needs. Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly. Third would be segmentation analysis. Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies. Last is to build competitive differentiation. Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions. 4. Can you share an example of where data insights directly influenced a critical decision? I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings. We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms. That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs. That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial. 5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time? When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences. We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments. Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content. With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns. 6. How are you doing the 1:1 personalization? We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer. So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer. That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience. We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers. 7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service? Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved. The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments. Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention. So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization. 8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights? I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights. Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement. Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant. As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively. So there’s a lack of understanding of marketing and sales as domains. It’s a huge effort and can take a lot of investment. Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing. 9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data? If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge. Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side. Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important. 10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before? First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do. And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations. The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it. Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one. 11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI. We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals. We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization. 12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data? I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points. Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us. We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels. Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms. Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps. 13. How do you ensure data quality and consistency across multiple channels to make these informed decisions? We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies. While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing. We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats. On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically. 14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices. Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities. We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases. As the world is collecting more data, privacy concerns and regulations come into play. I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies. And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture. So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.   This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage.

Reading Time: 9 minutes In the ever-evolving landscape of customer engagement, staying ahead of the curve is not just advantageous, it’s essential. That’s why, for Chapter 7 of “The Customer Engagement Book: Adapt or Die,” we sat down with Mirela Cialai, a seasoned expert in CRM and Martech strategies at brands like Equinox. Mirela brings a wealth of knowledge in aligning technology roadmaps with business goals, shifting organizational focuses from acquisition to retention, and leveraging hyper-personalization to drive success. In this interview, Mirela dives deep into building robust customer engagement technology roadmaps. She unveils the “PAPER” framework—Plan, Audit, Prioritize, Execute, Refine—a simple yet effective strategy for marketers. You’ll gain insights into identifying gaps in your Martech stack, ensuring data accuracy, and prioritizing initiatives that deliver the greatest impact and ROI. Whether you’re navigating data silos, striving for cross-functional alignment, or aiming for seamless tech integration, Mirela’s expertise provides practical solutions and actionable takeaways.   Mirela Cialai Q&A Interview 1. How do you define the vision for a customer engagement platform roadmap in alignment with the broader business goals? Can you share any examples of successful visions from your experience? Defining the vision for the roadmap in alignment with the broader business goals involves creating a strategic framework that connects the team’s objectives with the organization’s overarching mission or primary objectives. This could be revenue growth, customer retention, market expansion, or operational efficiency. We then break down these goals into actionable areas where the team can contribute, such as improving engagement, increasing lifetime value, or driving acquisition. We articulate how the team will support business goals by defining the KPIs that link CRM outcomes — the team’s outcomes — to business goals. In a previous role, the CRM team I was leading faced significant challenges due to the lack of attribution capabilities and a reliance on surface-level metrics such as open rates and click-through rates to measure performance. This approach made it difficult to quantify the impact of our efforts on broader business objectives such as revenue growth. Recognizing this gap, I worked on defining a vision for the CRM team to address these shortcomings. Our vision was to drive measurable growth through enhanced data accuracy and improved attribution capabilities, which allowed us to deliver targeted, data-driven, and personalized customer experiences. To bring this vision to life, I developed a roadmap that focused on first improving data accuracy, building our attribution capabilities, and delivering personalization at scale. By aligning the vision with these strategic priorities, we were able to demonstrate the tangible impact of our efforts on the key business goals. 2. What steps did you take to ensure data accuracy? The data team was very diligent in ensuring that our data warehouse had accurate data. So taking that as the source of truth, we started cleaning the data in all the other platforms that were integrated with our data warehouse — our CRM platform, our attribution analytics platform, etc. That’s where we started, looking at all the different integrations and ensuring that the data flows were correct and that we had all the right flows in place. And also validating and cleaning our email database — that helped, having more accurate data. 3. How do you recommend shifting organizational focus from acquisition to retention within a customer engagement strategy? Shifting an organization’s focus from acquisition to retention requires a cultural and strategic shift, emphasizing the immense value that existing customers bring to long-term growth and profitability. I would start by quantifying the value of retention, showcasing how retaining customers is significantly more cost-effective than acquiring new ones. Research consistently shows that increasing retention rates by just 5% can boost profits by at least 25 to 95%. This data helps make a compelling case to stakeholders about the importance of prioritizing retention. Next, I would link retention to core business goals by demonstrating how enhancing customer lifetime value and loyalty can directly drive revenue growth. This involves shifting the organization’s focus to retention-specific metrics such as churn rate, repeat purchase rate, and customer LTV. These metrics provide actionable insights into customer behaviors and highlight the financial impact of retention initiatives, ensuring alignment with the broader company objectives. By framing retention as a driver of sustainable growth, the organization can see it not as a competing priority, but as a complementary strategy to acquisition, ultimately leading to a more balanced and effective customer engagement strategy. 4. What are the key steps in analyzing a brand’s current Martech stack capabilities to identify gaps and opportunities for improvement? Developing a clear understanding of the Martech stack’s current state and ensuring it aligns with a brand’s strategic needs and future goals requires a structured and strategic approach. The process begins with defining what success looks like in terms of technology capabilities such as scalability, integration, automation, and data accessibility, and linking these capabilities directly to the brand’s broader business objectives. I start by doing an inventory of all tools currently in use, including their purpose, owner, and key functionalities, assessing if these tools are being used to their full potential or if there are features that remain unused, and reviewing how well tools integrate with one another and with our core systems, the data warehouse. Also, comparing the capabilities of each tool and results against industry standards and competitor practices and looking for missing functionalities such as personalization, omnichannel orchestration, or advanced analytics, and identifying overlapping tools that could be consolidated to save costs and streamline workflows. Finally, review the costs of the current tools against their impact on business outcomes and identify technologies that could reduce costs, increase efficiency, or deliver higher ROI through enhanced capabilities. Establish a regular review cycle for the Martech stack to ensure it evolves alongside the business and the technological landscape. 5. How do you evaluate whether a company’s tech stack can support innovative customer-focused campaigns, and what red flags should marketers look out for? I recommend taking a structured approach and first ensure there is seamless integration across all tools to support a unified customer view and data sharing across the different channels. Determine if the stack can handle increasing data volumes, larger audiences, and additional channels as the campaigns grow, and check if it supports dynamic content, behavior-based triggers, and advanced segmentation and can process and act on data in real time through emerging technologies like AI/ML predictive analytics to enable marketers to launch responsive and timely campaigns. Most importantly, we need to ensure that the stack offers robust reporting tools that provide actionable insights, allowing teams to track performance and optimize campaigns. Some of the red flags are: data silos where customer data is fragmented across platforms and not easily accessible or integrated, inability to process or respond to customer behavior in real time, a reliance on manual intervention for tasks like segmentation, data extraction, campaign deployment, and poor scalability. If the stack struggles with growing data volumes or expanding to new channels, it won’t support the company’s evolving needs. 6. What role do hyper-personalization and timely communication play in a successful customer engagement strategy? How do you ensure they’re built into the technology roadmap? Hyper-personalization and timely communication are essential components of a successful customer engagement strategy because they create meaningful, relevant, and impactful experiences that deepen the relationship with customers, enhance loyalty, and drive business outcomes. Hyper-personalization leverages data to deliver tailored content that resonates with each individual based on their preferences, behavior, or past interactions, and timely communication ensures these personalized interactions occur at the most relevant moments, which ultimately increases their impact. Customers are more likely to engage with messages that feel relevant and align with their needs, and real-time triggers such as cart abandonment or post-purchase upsells capitalize on moments when customers are most likely to convert. By embedding these capabilities into the roadmap through data integration, AI-driven insights, automation, and continuous optimization, we can deliver impactful, relevant, and timely experiences that foster deeper customer relationships and drive long-term success. 7. What’s your approach to breaking down the customer engagement technology roadmap into manageable phases? How do you prioritize the initiatives? To create a manageable roadmap, we need to divide it into distinct phases, starting with building the foundation by addressing data cleanup, system integrations, and establishing metrics, which lays the groundwork for success. Next, we can focus on early wins and quick impact by launching behavior-based campaigns, automating workflows, and improving personalization to drive immediate value. Then we can move to optimization and expansion, incorporating predictive analytics, cross-channel orchestration, and refined attribution models to enhance our capabilities. Finally, prioritize innovation and scalability, leveraging AI/ML for hyper-personalization, scaling campaigns to new markets, and ensuring the system is equipped for future growth. By starting with foundational projects, delivering quick wins, and building towards scalable innovation, we can drive measurable outcomes while maintaining our agility to adapt to evolving needs. In terms of prioritizing initiatives effectively, I would focus on projects that deliver the greatest impact on business goals, on customer experience and ROI, while we consider feasibility, urgency, and resource availability. In the past, I’ve used frameworks like Impact Effort Matrix to identify the high-impact, low-effort initiatives and ensure that the most critical projects are addressed first. 8. How do you ensure cross-functional alignment around this roadmap? What processes have worked best for you? Ensuring cross-functional alignment requires clear communication, collaborative planning, and shared accountability. We need to establish a shared understanding of the roadmap’s purpose and how it ties to the company’s overall goals by clearly articulating the “why” behind the roadmap and how each team can contribute to its success. To foster buy-in and ensure the roadmap reflects diverse perspectives and needs, we need to involve all stakeholders early on during the roadmap development and clearly outline each team’s role in executing the roadmap to ensure accountability across the different teams. To keep teams informed and aligned, we use meetings such as roadmap kickoff sessions and regular check-ins to share updates, address challenges collaboratively, and celebrate milestones together. 9. If you were to outline a simple framework for marketers to follow when building a customer engagement technology roadmap, what would it look like? A simple framework for marketers to follow when building the roadmap can be summarized in five clear steps: Plan, Audit, Prioritize, Execute, and Refine. In one word: PAPER. Here’s how it breaks down. Plan: We lay the groundwork for the roadmap by defining the CRM strategy and aligning it with the business goals. Audit: We evaluate the current state of our CRM capabilities. We conduct a comprehensive assessment of our tools, our data, the processes, and team workflows to identify any potential gaps. Prioritize: initiatives based on impact, feasibility, and ROI potential. Execute: by implementing the roadmap in manageable phases. Refine: by continuously improving CRM performance and refining the roadmap. So the PAPER framework — Plan, Audit, Prioritize, Execute, and Refine — provides a structured, iterative approach allowing marketers to create a scalable and impactful customer engagement strategy. 10. What are the most common challenges marketers face in creating or executing a customer engagement strategy, and how can they address these effectively? The most critical is when the customer data is siloed across different tools and platforms, making it very difficult to get a unified view of the customer. This limits the ability to deliver personalized and consistent experiences. The solution is to invest in tools that can centralize data from all touchpoints and ensure seamless integration between different platforms to create a single source of truth. Another challenge is the lack of clear metrics and ROI measurement and the inability to connect engagement efforts to tangible business outcomes, making it very hard to justify investment or optimize strategies. The solution for that is to define clear KPIs at the outset and use attribution models to link customer interactions to revenue and other key outcomes. Overcoming internal silos is another challenge where there is misalignment between teams, which can lead to inconsistent messaging and delayed execution. A solution to this is to foster cross-functional collaboration through shared goals, regular communication, and joint planning sessions. Besides these, other challenges marketers can face are delivering personalization at scale, keeping up with changing customer expectations, resource and budget constraints, resistance to change, and others. While creating and executing a customer engagement strategy can be challenging, these obstacles can be addressed through strategic planning, leveraging the right tools, fostering collaboration, and staying adaptable to customer needs and industry trends. By tackling these challenges proactively, marketers can deliver impactful customer-centric strategies that drive long-term success. 11. What are the top takeaways or lessons that you’ve learned from building customer engagement technology roadmaps that others should keep in mind? I would say one of the most important takeaways is to ensure that the roadmap directly supports the company’s broader objectives. Whether the focus is on retention, customer lifetime value, or revenue growth, the roadmap must bridge the gap between high-level business goals and actionable initiatives. Another important lesson: The roadmap is only as effective as the data and systems it’s built upon. I’ve learned the importance of prioritizing foundational elements like data cleanup, integrations, and governance before tackling advanced initiatives like personalization or predictive analytics. Skipping this step can lead to inefficiencies or missed opportunities later on. A Customer Engagement Roadmap is a strategic tool that evolves alongside the business and its customers. So by aligning with business goals, building a solid foundation, focusing on impact, fostering collaboration, and remaining adaptable, you can create a roadmap that delivers measurable results and meaningful customer experiences.     This interview Q&A was hosted with Mirela Cialai, Director of CRM & MarTech at Equinox, for Chapter 7 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Mirela Cialai Q&A: Customer Engagement Book Interview appeared first on MoEngage.

Reading Time: 10 minutes Picking an omnichannel marketing platform can feel like walking into a tech maze. Every vendor promises personalized customer experiences and 360-degree views. But behind the technical terminologies, most platforms just add noise. Right now, your customers expect seamless, intuitive experiences, whether they’re on your website, checking email, scrolling Instagram, or walking into your store. And they don’t care what platform you’re using. They care that it works. That it feels cohesive. And if your current tools can’t keep up, you’ll feel it in your churn rate, in your campaign ROI, and in the silence when nobody clicks. This article explains how to actually choose an omnichannel marketing automation platform that delivers a measurable, meaningful impact.   What is an Omnichannel Marketing Platform? An omnichannel marketing platform brings all your customer touchpoints—email, SMS, social, web, and more—into a single, unified connected system. It tracks customer behavior across different devices, personalizes communication in real time, and automates actions based on where each customer is in their journey. In short, such platforms allow you to completely harness omnichannel marketing — no more juggling five tools to launch a campaign or manually syncing customer profiles. It’s about delivering the right message, on the right channel, at exactly the right moment, without missing a beat. How Do Omnichannel Marketing Automation Platforms Work? At the core of omnichannel marketing platforms is automation, and it’s what makes them truly powerful. These tools don’t just streamline your marketing, they do the heavy lifting for you by intelligently responding to customer behavior in real time, on the right channel. Here’s how it works: Data Integration: The platform gathers data from every interaction your customers have with your brand, whether it’s a social media click, an email open, or a web visit. It stores and syncs that data, building a comprehensive customer profile. Behavioral Triggers: Based on that profile, the platform automatically triggers personalized actions. If a customer browses a product on your website and abandons it, the system can send them a targeted follow-up email with a discount. Or, if they engage with a post on Instagram, they might receive an SMS with relevant content. Multi-Channel Coordination: Instead of running isolated campaigns across different platforms, omnichannel automation makes sure your messaging is synchronized, no matter where your customer interacts with your brand. The experience is seamless regardless of whether it is conducted on a mobile device, desktop, or in-store. Ultimately, these platforms use automation to enable smarter, more personal marketing, allowing brands to swiftly reach their customers with the right message at the right time. Why is an Omnichannel Marketing Platform So Important for Marketing Teams? Marketing today is about being seen everywhere your customers are and delivering the right message in the right way. An omnichannel marketing platform makes that possible, and here’s why it’s crucial: Drives Better Engagement: Customers expect a smooth experience no matter where they interact with your brand. When your marketing is consistent across email, social media, and your website, it creates a sense of connection. More than just seeing your ads, they’re actually engaging with your brand in a way that feels personalized, familiar, and authentic. Makes Campaigns More Effective: With an omnichannel platform, you can monitor your campaigns in real-time and quickly adjust them if things aren’t going as planned. No more waiting for reports to trickle in. You can make changes on the fly, which means your marketing efforts are always aligned with what’s working at the moment. Allows for True Personalization: Forget generic messaging. An omnichannel marketing platform helps you gather data from every customer interaction, allowing you to tailor your approach based on what each customer is interested in. Builds Stronger Customer Relationships: When you engage with customers across multiple channels in a relevant, timely way, you make them feel seen and valued. This connection goes beyond a single sale. It turns casual buyers into loyal customers who trust your brand to deliver on their needs, no matter where they are. Delivers More Bang for Your Buck: The beauty of automation is that it frees up your team to focus on your omnichannel strategy, while the omnichannel marketing automation platform handles the routine tasks. This efficiency leads to better targeting, reduced costs, and a stronger ROI. You’re reaching the right customers at the right time without wasting effort. Helps You Make Smarter Decisions: All your customer data is centralized, so you’re not guessing anymore. Whether you need to optimize a campaign or uncover new opportunities, the insights provided by the platform allow you to make data-driven decisions that move your business forward. Responds to Customers in Real Time: When someone shows interest in your product, the last thing you want is to lose that momentum. Omnichannel marketing platforms trigger automatic follow-ups based on actions customers take, so they get the right message when they’re most interested, whether it’s through an email, text, or on social media. Keeps Your Brand Consistent: With everything tied together in one platform, your message, tone, and offers stay consistent across channels. No matter where a customer encounters your brand, they’ll have the same experience and brand recognition each time. Elevates the Customer Experience: When all of your channels work together, customers feel like they’re interacting with a brand that really gets them. The result? A better overall customer experience, which translates to stronger loyalty, higher engagement, and more referrals. In short, an omnichannel marketing platform is a game-changer for B2C marketing teams. It brings your efforts together in a way that’s efficient, intelligent, and most importantly, human, making it easier to engage your audience and deliver results.   How to Choose the Right Omnichannel Marketing Platform for Your Team Choosing the right omnichannel marketing platform is a big decision for any team. It’s an investment that can elevate your marketing strategies, enhance omnichannel customer engagement, and improve overall performance. But with so many options available, how do you ensure you’re making the best choice for your team? Here are some key factors to think about. Integration Capabilities: Your platform needs to seamlessly integrate with the tools you already use, so you can focus on what really matters. Whether it’s your CRM, email marketing automation software, or social media platforms, you don’t want to waste time and energy managing multiple systems that don’t talk to each other. Ask yourself, how easily will this platform integrate with and support the tools and platforms you already use? Ease of Use and Customization: A platform that’s difficult to use is more of a burden than a help. Look for one that’s intuitive and easy for your team to get comfortable with. The last thing you want is a tool that causes frustration and slows things down. Think about how user-friendly the platform is for your team. Can you easily customize features to fit your unique needs? Data and Analytics: An omnichannel marketing platform should give you clear, actionable insights so you can make smart, informed decisions. Does the platform provide detailed omnichannel analytics that you can use to refine your campaigns? Can it help you track customer journeys and behaviors across channels? Scalability and Support: Your business is growing, and your platform needs to grow with it. The last thing you want is to outgrow your tool in six months and have to go through the hassle of switching to something new. Check whether the platform is scalable enough to accommodate your future growth. What level of customer support is offered, and how responsive is the team? Now that you have a sense of what to consider when choosing an omnichannel marketing automation platform, let’s dive into the key features to look for.   5 Key Features to Look for in an Omnichannel Marketing Automation Platform You can’t build a high-performing marketing engine founded on disconnected tools and vague customer data. If your team is working across five different platforms to run a single campaign, something’s broken. The tool you pick will either streamline your marketing or slow it down. So, forget the bells and whistles for a moment. What you really need are features that bring clarity to your customer experience. Here are the five features that serve as the foundation of a platform that can scale with your brand. 1. Unified Customer Profiles across Channels Modern marketing starts with understanding. A powerful omnichannel marketing platform should combine customer data from every touchpoint (website visits, email clicks, mobile app activity, purchase history, and other interactions) into a single, continuously updated profile. This unified view helps your team move from being ‘reactive’ to being ‘proactive’. Instead of blasting generic messages, you can deliver relevant content tailored to where that customer is in their journey. It’s the difference between feeling like a brand knows you… and feeling like it’s spam. 2. Behavior-Driven Automation Workflows Static email blasts are outdated. Customers expect relevant, timely communication based on how they engage with your brand. The platform you choose should let you create workflows that automatically trigger actions like sending a reminder, adjusting content, or changing the message channel, based on real-time behavior. For example, a user who browses a product but doesn’t buy it, should receive a well-timed nudge — maybe an email, or a push notification. The point is: your platform needs to adapt to customer intent and behavior, not force every lead down the same path. 3. Real-Time Analytics and Reporting You shouldn’t have to wait 48 hours to know if something flopped. You need to be able to tweak your omnichannel marketing automation strategy in real-time based on what’s happening right now. Does the platform offer real-time insights into campaign performance? Can you adjust tactics immediately based on live data? Also, no one wants 20 meaningless charts. Look for a platform that shows you the vital metrics, like clicks, conversions, and drop-offs, so your team can make quick, sure choices. 4. A Centralized Campaign Builder for Every Channel If you’re logging in and out of separate tools to handle email, SMS, web and mobile push notifications, and in-app messaging, something’s broken. Your omnichannel marketing platform should let you map and launch all of those in one place, from one logic flow. That way, if you’re running a product launch, you can build the entire experience—email, follow-up text, in-app message—all from the same workspace. This keeps your messaging consistent, and it saves your team loads of time. 5. Scalable Personalization Powered by AI Everyone talks about omnichannel personalization. But if it takes your team hours to set up basic name merges or segments, it’s not scalable. A strong platform should help you personalize campaigns at scale using AI, not by making you do more, but by automating based on real customer data. Whether that’s product recommendations, timing messages based on when someone usually engages, or switching up channels depending on preferences, the goal is simple: make it feel like a one-on-one conversation, not a broadcast.   5 Best Omnichannel Marketing Platforms to Increase Customer Engagement 1. MoEngage MoEngage is a powerful customer engagement platform built for B2C marketing teams that want to create seamless, personalized journeys across channels like email, mobile and web push notifications, SMS, WhatsApp, in-app messaging, web, and more. It’s designed to help brands understand their customers deeply and act on that understanding in real time. What makes MoEngage stand out is how it brings all your customer data into one unified platform, allowing you to automate complex journeys without jumping between tools or teams. Whether you’re re-engaging an app user, launching an omnichannel campaign, or sending a personalized offer, this platform makes it easy and efficient. No wonder so many brands switch to MoEngage! How Pricing Works: MoEngage offers a tiered pricing model based on Monthly Tracked Users (MTUs). The Growth plan starts around $750/month, but it depends on what features you want to use. Larger brands can opt for the Enterprise plan, which includes more advanced features, higher limits, and dedicated support. Best For: Growth-focused brands that want to deeply personalize customer experiences at scale, particularly in industries like Ecommerce, fintech, media, quick-service restaurants (QSRs), travel, and mobile-first apps. In fact, it’s the perfect Ecommerce marketing automation platform for omnichannel customer engagement. If your team values real-time insights, automation, and customer-centric engagement, MoEngage is likely a great fit. 2. Shopify Plus Shopify Plus is an enterprise-level Ecommerce platform designed to help brands deliver a seamless shopping experience across all customer touchpoints. It enables brands to sell on various platforms, including online stores, mobile apps, social media, and physical retail locations, all managed from a single dashboard. While Shopify Plus does a great job managing omnichannel commerce, it’s not as feature-rich on the marketing automation side. If your team is looking for deep personalization, behavioral segmentation, or advanced journey building, you’ll likely need to connect Shopify with a marketing automation software platform. How Pricing Works: Shopify Plus pricing varies based on specific business requirements and sales volumes. Best For: High-volume omnichannel retail marketers and brands looking for a scalable Ecommerce solution that offers basic omnichannel capabilities and centralized management of multiple sales channels. 3. ActiveCampaign ActiveCampaign does a great job of bridging marketing automation with a personal touch. It’s built for businesses that don’t just want to send mass emails, but send smart ones. The platform shines when it comes to customer segmentation, letting you trigger emails, SMS, or even site messages based on real behavior, not just assumptions. That said, it’s not the kind of tool you just open and instantly “get.” The automation builder is powerful, but there’s a learning curve. Still, once you set it up, it runs like a machine that’s personalized, timely, and way less manual effort than you’d expect. How Pricing Works: Pricing starts at $29 per month and scales based on contact volume and feature set. Plans can go up to $149/month or more, depending on business needs. Best For: Small to mid-sized businesses looking for a cost-effective, flexible marketing automation platform with strong segmentation and multichannel engagement tools. (No, we’re not getting into the omnichannel vs. multichannel marketing debate now). 4. HubSpot HubSpot is an all-in-one platform that helps brands grow by focusing on inbound marketing, sales, and customer service. It’s well-suited for those looking to integrate their marketing efforts across various channels, without the need for complex technical skills. Honestly, HubSpot’s strength is in inbound marketing and CRM. However, its capabilities in advanced omnichannel campaign automation aren’t as extensive as those in some other platforms. If you need highly sophisticated marketing features, you may want to look elsewhere. How Pricing Works: Pricing starts at $800 per month for the Marketing Hub. For full access to all features, the Enterprise plan starts at $3,600 per month. Best For: Brands that focus on inbound marketing and CRM, looking for a user-friendly platform to manage omnichannel efforts easily.   Migrate to a Better Omnichannel Marketing Platform Today One thing’s clear: not all omnichannel marketing platforms are created equal. The right platform should feel less like another tool and more like a strategic partner, helping you understand your audience, stay consistent across touchpoints, and make marketing smarter, not harder. MoEngage stands out for this very reason. In fact, our omnichannel marketing platform is built for teams who want more than just dashboards. It’s for marketers who want results. And the best part is, migrating to a customer engagement platform has never been easier. Want to see MoEngage in action? Book a demo today and explore what better marketing looks like. The post How to Choose an Omnichannel Marketing Platform appeared first on MoEngage.

Reading Time: 3 minutes In a recent webinar hosted by MoEngage, QSR marketing experts from Radar and Bottle Rocket came together to unpack the findings of the 2025 State of Cross-Channel Marketing for QSRs report.  With more than 800 total survey responses, including 70 from QSR marketers, this report revealed where quick-service restaurants are focusing their energy, what is holding them back, and the emerging strategies reshaping the guest experience. Here is a recap of the key takeaways, expert insights, and actionable advice shared by our panelists.   Where QSRs Are Focused in 2025: Loyalty, Personalization & Speed The webinar kicked off with a deep dive into shifting priorities. Customer engagement and loyalty emerged as the top focus for QSR marketers in 2025, with 80% of respondents increasing investment in customer experience technology. Mobile-first experiences and real-time personalization are no longer optional; they are essential for effective QSR marketing. Nick Patrick, CEO of Radar, put it simply: “Mobile has become the primary interface between QSRs and their customers. Real-time context is what makes that interface intelligent.” Challenges: Disconnected Data, Tech Silos, and Execution Speed While QSRs have the vision, many struggle with execution.  The report found that 60% of QSR leaders still struggle with personalization, and more than a quarter cited siloed data as a top challenge. The panelists echoed these findings, pointing to fragmented systems and misaligned teams as major hurdles. Brendan shared: “The POS system was designed for speed and accuracy, not personalization. But when you can use even a basic signal, like a loyalty status, to prompt a more human, high-touch experience, it makes a real difference.” Strategies That Work: Start Small, Focus Deep, Earn Trust Panelists emphasized the value of starting small with high-impact initiatives like curbside pickup or loyalty nudges. Cross-functional alignment and choosing scalable tech partners were key themes. “Don’t boil the ocean,” Brendan advised. “Start with one moment in the journey that can be improved and work cross-functionally to get it right.” Earning Location Opt-ins the Right Way Location data is a powerful lever for marketing and operations, but only if opt-ins are earned with care. Nick shared a practical framework: “It comes down to three things: transparency, value, and timing. You can’t just ask up front with no context and expect users to say yes.” He pointed to Outback Steakhouse as a standout example: “They clearly explain the value, guide users through branded screens, and then request OS-level permissions. It’s thoughtful, and it works.” AI in Action: Real Business Impact Artificial intelligence was another hot topic. Brendan shared two areas where AI is delivering real value: smarter cross-sell/upsell and feedback intelligence. “Even simple segmentation by daypart or region can lift basket size. Some tools report a 10% increase just by turning it on,” he said. “It doesn’t need to be complex to be effective.” QSR Webinar Recap: Closing Thoughts Whether optimizing app experiences, trying to unify your tech stack, automating manual processes, or building stronger loyalty loops, the advice was clear: start small, stay focused, and partner with tools and teams that can scale with you. Watch the full webinar on demand to explore these examples further and learn how MoEngage, Radar, and Bottle Rocket can help your team accelerate QSR engagement.   The post Digital Meets Dine-In: 5 Expert QSR Engagement Strategies appeared first on MoEngage.

In this Q&A, get a glimpse into the future of artificial intelligence (AI) and computer vision through the lens of longtime Unity user Gerard Espona, whose robot digital twin project was featured in the Made with Unity: AI series. Working as simulation lead at Luxonis, whose core technology makes it possible to embed human-level perception into robotics, Espona uses his years of experience in the industry to weigh in on the current state and anticipated progression of computer vision.During recent years, computer vision (CV) and AI have become the fastest-growing fields both in market size and industry adoption rate. Spatial CV and edge AI have been used to improve and automate repetitive tasks as well as complex processes.This new reality is thanks to the democratization of CV/AI. Increasingly affordable hardware access, including depth perception capability as well as improvements in machine learning (ML), has enabled the deployment of real solutions on edge CV/AI systems.Spatial CV using edge AI enables depth-based applications to be deployed without the need of a data center service, and also allows the user to preserve privacy by processing images on the device itself.Along with more accessible hardware, software and machine learning workflows are undergoing important improvements. Although they are still very specialized and full of technical challenges, they have become much more accessible, offering tools that allow users to train their own models.Within the standard ML pipeline/workflow, large-scale edge computing and deployment can still pose issues. One of the biggest general challenges is to reduce the costs and timelines currently required to create and/or improve machine learning models on real-world applications. In other words, the challenge is how to manage all these devices to enable a smooth pipeline for continuous improvement.Also, the implicit limitations in terms of compute processing need extra effort on the final model deployed on the device (that is, apps need to be lightweight, performant, etc.). That said, embedded technology evolves really fast, and each iteration is a big leap in processing capabilities.Spatial CV/AI is a field that still requires a lot of specialization and systems. Workflows are often complicated and tedious due to numerous technical challenges, so a lot of time is devoted to smoothing out the workflow instead of focusing on value-added tasks.Creating datasets (collecting and filtering images and videos), annotating the images, preprocessing/augmentation process, training, deploying and closing the feedback loop for continuous improvement is a complex process. Each step of the workflow is technically difficult and usually involves time and financial cost, and more so for systems working in remote areas with limited connectivity.At Luxonis, we help our customers build and deploy solutions to solve and automate complex tasks at scale, so we’re facing all these issues directly. Our mission, “Robotic vision made simple,” provides not only great and affordable depth-capable hardware, but also a solid and smooth ML pipeline with synthetic datasets and simulation.Another important challenge is the work that needs to be done on the interpretability of models and the creation of datasets from an ethical, privacy and bias point of view.Last but not least, global chip supply issues are making it difficult to get the hardware into everybody’s hands.Data-centric AI is potentially useful when a working model is underperforming. Investing a large amount of time to optimize the model often leads to almost zero real improvement. Instead, with data-centric AI the investment is in analysis, cleaning and improving of the dataset.Usually when a model is underperforming, the issue is within the dataset itself, as there is not enough data for the model to outperform. This could be the result of two possible reasons: 1) the model needs a much larger amount of data, which is difficult to collect in the real world, or 2) the model doesn’t have enough examples of rare cases, which take a lot of time to happen in the real world.In both situations, synthetic datasets could help.Thanks to Unity’s computer vision tools, it is very easy to create photorealistic scenes and randomize elements like materials, light conditions and object placement. The tools come with common labels like 2D bounding boxes, 3D bounding boxes, semantic and instance segmentation, and even human body key points. Additionally, these can be easily extended with custom randomizers, labelers and annotations.Almost any task you want to automate or improve using edge CV/AI very likely involves detecting people for obvious safety and security reasons. It’s critical to guarantee user safety around autonomous systems or robots when they’re working, requiring models to be trained on data about humans.That means we need to capture a large amount of images, including information like poses and physical appearance, that are representative of the entire human population. This task raises some concerns about privacy, ethics and bias when starting to capture real human data to train the model.Fortunately, we can use synthetic datasets to mitigate some of these concerns using human 3D models and poses. A very good example is the work done by the Unity team with PeopleSansPeople.PeopleSansPeople is a human-centric synthetic dataset creator using 3D models and standard animations to randomize human body poses. Also, we can use a Unity project template, to which we add our own 3D models and poses to create our own human synthetic dataset.At Luxonis, we’re using this project as the basis for creating our own human synthetic dataset and training models. In general, we use Unity’s computer vision tools to create large and complex datasets with a high level of customization on labelers, annotations and randomizations. This allows our ML team to iterate faster with our customers, without needing to wait for real-world data collection and manual annotation.Since the introduction of transformer architecture, CV tasks are more accessible. Generative models like DALL-E 2 could also be used to create synthetic datasets, and NeRF as a neural approach to generate novel point of views of known objects and scenes. It’s clear all these innovations are catching the attention of audiences.On the other hand, having access to better annotation tools and model zoos and libraries with pre-trained, ready-to-use models are helping drive wide adoption.One key element contributing to the uptick in computer vision use is the fast evolution of vision processing units (VPUs) that currently allow users to perform model inferences on device (without the need for any host) at 4 TOPS of processing power (current Intel Movidius Myriad X). The new generation of VPUs promises a big leap in capabilities, allowing even more complex CV/AI applications to be deployed on edge.Any application related to agriculture and farming always captures my attention. For example, there is now a cow tracking and monitoring CV/AI application using drones.Our thanks to Gerard for sharing his perspective with us – keep up with his latest thoughts on LinkedIn and Twitter. And, learn more about how Unity can help your team generate synthetic data to improve computer vision model training with Unity Computer Vision.

Posted on : June 3, 2025 By Tech World Times Technology  Rate this post This article walks you through five practical steps to implement network segmentation effectively, backed by real-world use cases that showcase its value in different industries. Networks are constantly expanding across offices, cloud services, remote users, and connected devices. With so many moving parts, security gaps can easily form. Once attackers breach a weak point, they often move freely across the network, targeting critical systems and sensitive data. That’s where network segmentation comes in. It’s a practical approach to divide your network into smaller, manageable zones to control access, limit exposure, and isolate threats before they spread. But simply deploying VLANs or access rules isn’t enough. True segmentation needs planning, alignment with your business, and the right mix of technology. Step 1: Assess and Map Your Current Network Start by figuring out what’s on your network and how it communicates. Inventory Devices and Applications: List all system servers, user machines, IoT devices, cloud assets. Map Data Flows: Understand how applications and services interact. Which systems talk to each other? What ports and protocols are used? Identify Critical Assets: Highlight the systems that handle sensitive data, such as payment processing, health records, or intellectual property. Tip: Network discovery tools or NAC solutions can automate asset inventory and reveal communication paths you might miss. Step 2: Define Segmentation Goals and Policies Once you understand your environment, it’s time to set your objectives. Security Objectives: Do you want to reduce lateral movement, isolate sensitive systems, or meet a compliance mandate? Business Alignment: Segment by business unit, sensitivity of data, or risk profile-whatever makes the most operational sense. Compliance Requirements: PCI DSS, HIPAA, and other standards often require network segmentation. Example: A healthcare provider might create separate zones for patient records, lab equipment, guest Wi-Fi, and billing systems. Step 3: Choose the Right Segmentation Method Segmentation can be done in several ways. The right approach depends on your infrastructure goals and types: a. Physical Segmentation Use separate routers, switches, and cables. This offers strong isolation but can be costly and harder to scale. b. Logical Segmentation (VLANs/Subnets) Group devices into virtual segments based on function or department. It’s efficient and easier to manage in most environments. c. Micro segmentation Control access at the workload or application level using software-defined policies. Ideal for cloud or virtualized environments where you need granular control. d. Cloud Segmentation In the cloud, segmentation happens using security groups, VPCs, and IAM roles to isolate workloads and define access rules. Use a combination- VLANs for broader segmentation and micro segmentation for finer control where it matters. Step 4: Implement Controls and Monitor Traffic Time to put those policies into action. Firewalls and ACLs: Use access controls to manage what can move between zones. Block anything that isn’t explicitly allowed. Zero Trust Principles: Never assume trust between segments. Always validate identity and permissions. Monitoring and Alerts: Use your SIEM, flow monitoring tools, or NDR platform to watch for unusual traffic or policy violations. Common Pitfall: Avoid “allow all” rules between segments, it defeats the purpose. Step 5: Test, Validate, and Fine-Tune Even a well-designed segmentation plan can have gaps. Regular validation helps ensure it works as expected. Penetration Testing: Simulate attacks to check if boundaries hold. Review Policies: Business needs to change your segmentation strategy too. Performance Monitoring: Make sure segmentation doesn’t impact legitimate operations or application performance. Automation tools can help simplify this process and ensure consistency. Real-World Use Cases of Network Segmentation 1. Healthcare – Protecting Patient Data and Devices Hospitals use segmentation to keep medical devices, patient records, and visitor Wi-Fi on separate zones. This prevents an infected guest device from interfering with critical systems. Result: Reduced attack surface and HIPAA compliance. 2. Manufacturing – Isolating Industrial Systems Production environments often have fragile legacy systems. Segmenting OT (Operational Technology) from IT ensures ransomware or malware doesn’t disrupt manufacturing lines. Result: More uptime and fewer operational risks. 3. Finance – Securing Payment Systems Banks and payment providers use segmentation to isolate cardholder data environments (CDE) from the rest of the corporate network. This helps meet PCI DSS and keeps sensitive data protected. Result: Easier audits and stronger data security. 4. Education – Managing High-Volume BYOD Traffic Universities segment student Wi-Fi, research labs, and administrative systems. This keeps a vulnerable student device from spreading malware to faculty or internal systems. Result: Safer environment for open access campuses. 5. Cloud – Segmenting Apps and Microservices In the cloud, developers use security groups, VPCs, and IAM roles to isolate applications and limit who can access what. This reduces risk if one workload is compromised. Result: Controlled access and better cloud hygiene. Common Challenges Legacy Tech: Older devices may not support modern segmentation. Lack of Visibility: Hard to secure what you don’t know exists. Operational Hiccups: Poorly planned segmentation can block business workflows. Policy Complexity: Keeping access rules up to date across dynamic environments takes effort. Best Practices Start with High-Risk Areas: Prioritize zones handling sensitive data or vulnerable systems. Keep Documentation Updated: Maintain clear diagrams and policy records. Align Teams: Get buy-in from IT, security, and business units. Automate Where You Can: Especially for monitoring and policy enforcement. Review Regularly: Networks evolve- so should your segmentation. Final Thoughts Segmentation isn’t about creating walls it’s about building smart pathways. Done right, it helps you take control of your network, reduce risk, and respond faster when something goes wrong. It’s a foundational layer of cybersecurity that pays off in resilience, compliance, and peace of mind. About the Author: Prajwal Gowda is a cybersecurity expert with 10+ years of experience. He has built businesses and was a Business Unit Head for Compliance and Testing services. Currently, he is the Chief Technology Officer at Ampcus Cyber, leading the company’s technology strategy and innovation efforts. He has also been involved in the Payment Card Industry, Software Security Framework, ISO 27001 Controls Gap Analysis, ISMS, Risk Analysis, OCTAVE, ISO 27005, Information Security Audit and Network Security. Prajwal is a Master Trainer who has conducted 100+ cybersecurity training sessions worldwide. Tech World TimesTech World Times (TWT), a global collective focusing on the latest tech news and trends in blockchain, Fintech, Development & Testing, AI and Startups. If you are looking for the guest post then contact at techworldtimes@gmail.com

Scientific research across fields like chemistry, biology, and artificial intelligence has long relied on human experts to explore knowledge, generate ideas, design experiments, and refine results. Yet, as problems grow more complex and data-intensive, discovery slows. While AI tools, such as language models and robotics, can handle specific tasks, like literature searches or code analysis, they rarely encompass the entire research cycle. Bridging the gap between idea generation and experimental validation remains a key challenge. For AI to autonomously advance science, it must propose hypotheses, design and execute experiments, analyze outcomes, and refine approaches in an iterative loop. Without this integration, AI risks producing disconnected ideas that depend on human supervision for validation. Before the introduction of a unified system, researchers relied on separate tools for each stage of the process. Large language models could help find relevant scientific papers, but they didn’t directly feed into experiment design or result analysis. Robotics can assist in automating physical experiments, and coding libraries like PyTorch can help build models; however, these tools operate independently of each other. There was no single system capable of handling the entire process, from forming ideas to verifying them through experiments. This led to bottlenecks, where researchers had to connect the dots manually, slowing progress and leaving room for errors or missed opportunities. The need for an integrated system that could handle the entire research cycle became clear. Researchers from the NovelSeek Team at the Shanghai Artificial Intelligence Laboratory developed NovelSeek, an AI system designed to run the entire scientific discovery process autonomously. NovelSeek comprises four main modules that work in tandem: a system that generates and refines research ideas, a feedback loop where human experts can interact with and refine these ideas, a method for translating ideas into code and experiment plans, and a process for conducting multiple rounds of experiments. What makes NovelSeek stand out is its versatility; it works across 12 scientific research tasks, including predicting chemical reaction yields, understanding molecular dynamics, forecasting time-series data, and handling functions like 2D semantic segmentation and 3D object classification. The team designed NovelSeek to minimize human involvement, expedite discoveries, and deliver consistent, high-quality results. The system behind NovelSeek involves multiple specialized agents, each focused on a specific part of the research workflow. The “Survey Agent” helps the system understand the problem by searching scientific papers and identifying relevant information based on keywords and task definitions. It adapts its search strategy by first doing a broad survey of papers, then going deeper by analyzing full-text documents for detailed insights. This ensures that the system captures both general trends and specific technical knowledge. The “Code Review Agent” examines existing codebases, whether user-uploaded or sourced from public repositories like GitHub, to understand how current methods work and identify areas for improvement. It checks how code is structured, looks for errors, and creates summaries that help the system build on past work. The “Idea Innovation Agent” generates creative research ideas, pushing the system to explore different approaches and refine them by comparing them to related studies and previous results. The system even includes a “Planning and Execution Agent” that turns ideas into detailed experiments, handles errors during the testing process, and ensures smooth execution of multi-step research plans. NovelSeek delivered impressive results across various tasks. In chemical reaction yield prediction, NovelSeek improved performance from a baseline of 24.2% (with a variation of ±4.2) to 34.8% (with a much smaller variation of ±1.1) in just 12 hours, progress that human researchers typically need months to achieve. In enhancer activity prediction, a key task in biology, NovelSeek raised the Pearson correlation coefficient from 0.65 to 0.79 within 4 hours. For 2D semantic segmentation, a task used in computer vision, precision improved from 78.8% to 81.0% in just 30 hours. These performance boosts, achieved in a fraction of the time typically needed, highlight the system’s efficiency. NovelSeek also successfully managed large, complex codebases with multiple files, demonstrating its ability to handle research tasks at a project level, not just in small, isolated tests. The team has made the code open-source, allowing others to use, test, and contribute to its improvement. Several Key Takeaways from the Research on NovelSeek include: NovelSeek supports 12 research tasks, including chemical reaction prediction, molecular dynamics, and 3D object classification. Reaction yield prediction accuracy improved from 24.2% to 34.8% in 12 hours. Enhancer activity prediction performance increased from 0.65 to 0.79 in 4 hours. 2D semantic segmentation precision improved from 78.8% to 81.0% in 30 hours. NovelSeek includes agents for literature search, code analysis, idea generation, and experiment execution. The system is open-source, enabling reproducibility and collaboration across scientific fields. In conclusion, NovelSeek demonstrates how combining AI tools into a single system can accelerate scientific discovery and reduce its dependence on human effort. It ties together the key steps, generating ideas, turning them into methods, and testing them through experiments, into one streamlined process. What once took researchers months or years can now be done in days or even hours. By linking every stage of research into a continuous loop, NovelSeek helps teams move from rough ideas to real-world results more quickly. This system highlights the power of AI not just to assist, but to drive scientific research in a way that could reshape how discoveries are made across many fields. Check out the Paper and GitHub Page . All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. NikhilNikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute.Nikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces ARM and Ada-GRPO: Adaptive Reasoning Models for Efficient and Scalable Problem-SolvingNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces WEB-SHEPHERD: A Process Reward Model for Web Agents with 40K Dataset and 10× Cost EfficiencyNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces MMaDA: A Unified Multimodal Diffusion Model for Textual Reasoning, Visual Understanding, and Image GenerationNikhilhttps://www.marktechpost.com/author/nikhil0980/This AI Paper Introduces Differentiable MCMC Layers: A New AI Framework for Learning with Inexact Combinatorial Solvers in Neural Networks