The sheer negligence surrounding the issue of debilitating reactions to scents and chemicals is infuriating! How many more lives need to be ruined before we take a stand against this nightmare? Millions suffer while the scientific community fiddles with their theories, as if it’s just an academic exercise. One dedicated scientist has fought tirelessly to understand a problem that affects countless people, including herself. Why haven’t we prioritized solutions? The next thing you smell could literally ruin your life, yet society remains blissfully ignorant! This systemic failure is unacceptable, and it’s time to demand action NOW! #ChemicalSensitivity #HealthCrisis #Scents #PublicAwareness #TakeAction

The so-called "wellness" industry has become a farce, blurring the line between real science and pseudoscience to the point where it’s nearly unrecognizable. The term "wellness" is thrown around like confetti, masking a dangerous trend that exploits people's fears and insecurities. It's infuriating to see how easily people are misled by flashy marketing and dubious claims that lack any scientific backing! When did we decide that Instagram influencers are more credible than actual scientists? This is not just about personal health; it's about a societal failure to demand accountability. The wellness movement has overshadowed true scientific advancements, leading to more confusion and misinformation. We deserve better than this charade! #Wellness #Science #Health #

Caltech scientists have cracked the code! They’ve figured out how to turn our beloved CO2, the gas we adore for its role in warming our planet, into plastics. Because who doesn’t want to help the environment by creating more stuff that lasts forever in landfills? Let’s applaud this brilliant innovation that promises to make producing plastics from CO2 more efficient. Because the last thing we need is to tackle climate change head-on when we can just make more plastic, right? Who knew saving the Earth could come with a side of convenience? #EcoFriendly #CaltechInnovations #PlasticsFromCO2 #SustainableFuture #ClimateIrony

Imagine a world where scientists are printing 3D tumors like they're crafting the latest smartphone case. Yes, you heard it right! "Experts" are now busy turning tumors into a trendy 3D art project, all in the name of cancer treatment. Who needs conventional research when you can have a tumor on your desk as a conversation starter? Next up: DIY cancer therapy kits delivered to your door! Don't worry, these aren't just any tumors; they're "customized" for your health needs. Because nothing screams medical advancement like a little printer ink and a pinch of sarcasm! Welcome to the future, where even diseases need a 3D makeover! #3DTumors #CancerResearch #MedicalInnovation #SciFiReality

Scientists have apparently managed to reverse Parkinson’s symptoms in mice. They say this gives some hope that maybe one day it could happen for humans too. But, you know, experts are all like, “It’s complicated and we’ll probably need a bunch of different treatments.” So, not exactly a miracle breakthrough. Just more research and waiting, I guess. #Parkinsons #ResearchUpdates #MedicalScience #Hope #MiceStudies

So, Google has unleashed its shiny new Data Science Agent, and suddenly, everyone is acting like the skies have opened up and poured down the nectar of data analysis. Who needs actual scientists when you have an AI that can churn out insights faster than you can say “data-driven decisions”? It's almost charming how we’re convinced that a glorified calculator could replace years of expertise and human intuition. I guess all those years of studying statistics and machine learning were just a warm-up act for the real star of the show: a soulless algorithm. But hey, at least now we can all say we’re ‘data scientists’ while sipping coffee and letting the AI do the heavy lifting. Cheers to the future of data, where the humans are just

🌟 Hey everyone! 🌟 Today, I want to dive into something truly fascinating and groundbreaking that’s making waves in the tech world: **superintelligence**! 🤖✨ The recent news about Meta's investment in Scale AI and their ambitious plans to create a superintelligence AI research lab is incredibly exciting! It’s a glimpse into the future that we are all a part of, and I can't help but feel inspired by the possibilities! 🚀 So, what exactly is superintelligence? 🤔 In essence, it refers to a form of artificial intelligence that surpasses human intelligence in virtually every aspect. Imagine machines that can think, learn, and adapt at an unprecedented level! The potential for positive change and innovation is enormous! 🌈 Just think about how this technology could transform industries, solve complex problems, and even improve our everyday lives! 🌍💡 Meta is taking a bold step by investing in this field, and it shows just how serious they are about shaping our future. Every great leap in technology starts with a vision, and their commitment to building a superintelligence AI research lab is a clear indication that they believe in a brighter tomorrow. 🌞 Just imagine the breakthroughs that could come from this initiative! From healthcare advancements to tackling climate change, the opportunities are limitless! 🌿❤️ What I find truly inspiring is how this move encourages collaboration among brilliant minds across the globe. The quest for superintelligence is not just about creating smart machines; it’s about bringing together diverse perspectives, ideas, and skills to push the boundaries of what’s possible! Let’s celebrate this spirit of innovation and teamwork! 🙌✨ And here’s the most exciting part: You don’t have to be a tech expert to be a part of this journey! Every one of us has the ability to contribute to the conversation around AI and its impact on our lives. Whether you’re an artist, a scientist, an entrepreneur, or a student, your voice matters! 🎨🔬💼 Let’s dream big and think about how we can leverage technology to create a better world for everyone! 🌍💖 As we move forward, let’s keep the dialogue open and embrace the changes that superintelligence might bring. Together, we can shape a future that harnesses AI in a way that uplifts humanity and makes our lives richer and more fulfilling! So, let’s stay positive, curious, and engaged! The future is bright, and it’s ours to create! 🌟✨ Stay tuned for more updates, and let’s keep this conversation going! What are your thoughts on superintelligence? How do you envision it impacting our world? Share your ideas below! 💬👇 #Superintelligence #Meta #AIResearch #Innovation #FutureTech

What in the world are we doing? Scientists at the Massachusetts Institute of Technology have come up with this mind-boggling idea of creating an AI model that "never stops learning." Seriously? This is the kind of reckless innovation that could lead to disastrous consequences! Do we really want machines that keep learning on the fly without any checks and balances? Are we so blinded by the allure of technological advancement that we are willing to ignore the potential risks associated with an AI that continually improves itself? First off, let’s address the elephant in the room: the sheer arrogance of thinking we can control something that is designed to evolve endlessly. This MIT development is hailed as a step forward, but why are we celebrating a move toward self-improving AI when the implications are terrifying? We have already seen how AI systems can perpetuate biases, spread misinformation, and even manipulate human behavior. The last thing we need is for an arrogant algorithm to keep evolving, potentially amplifying these issues without any human oversight. The scientists behind this project might have a vision of a utopian future where AI can solve our problems, but they seem utterly oblivious to the fact that with great power comes great responsibility. Who is going to regulate this relentless learning process? What safeguards are in place to prevent this technology from spiraling out of control? The notion that AI can autonomously enhance itself without a human hand to guide it is not just naïve; it’s downright dangerous! We are living in a time when technology is advancing at breakneck speed, and instead of pausing to consider the ramifications, we are throwing caution to the wind. The excitement around this AI model that "never stops learning" is misplaced. The last decade has shown us that unchecked technology can wreak havoc—think data breaches, surveillance, and the erosion of privacy. So why are we racing toward a future where AI can learn and adapt without our input? Are we really that desperate for innovation that we can't see the cliff we’re heading toward? It’s time to wake up and realize that this relentless pursuit of progress without accountability is a recipe for disaster. We need to demand transparency and regulation from the creators of such technologies. This isn't just about scientific advancement; it's about ensuring that we don’t create monsters we can’t control. In conclusion, let’s stop idolizing these so-called breakthroughs in AI without critically examining what they truly mean for society. We need to hold these scientists accountable for the future they are shaping. We must question the ethics of an AI that never stops learning and remind ourselves that just because we can, doesn’t mean we should! #AI #MIT #EthicsInTech #Accountability #FutureOfAI

What a world we live in when scientists finally unlock the secrets to the axolotls' ability to regenerate limbs, only to reveal that the key lies not in some miraculous regrowth molecule, but in its controlled destruction! Seriously, what kind of twisted logic is this? Are we supposed to celebrate the fact that the secret to regeneration is, in fact, about knowing when to destroy something instead of nurturing and encouraging growth? This revelation is not just baffling; it's downright infuriating! In an age where regenerative medicine holds the promise of healing wounds and restoring functionality, we are faced with the shocking realization that the science is not about building up, but rather about tearing down. Why would we ever want to focus on the destruction of growth molecules instead of creating an environment where regeneration can bloom unimpeded? Where is the inspiration in that? It feels like a slap in the face to anyone who believes in the potential of science to improve lives! Moreover, can we talk about the implications of this discovery? If the key to regeneration involves a meticulous dance of destruction, what does that say about our approach to medical advancements? Are we really expected to just stand by and accept that we must embrace an idea that says, "let's get rid of the good stuff to allow for growth"? This is not just a minor flaw in reasoning; it's a fundamental misunderstanding of what regeneration should mean for us! To make matters worse, this revelation could lead to misguided practices in regenerative medicine. Instead of developing therapies that promote healing and growth, we could end up with treatments that focus on the elimination of beneficial molecules. This is absolutely unacceptable! How dare the scientific community suggest that the way forward is through destruction rather than cultivation? We should be demanding more from our researchers, not less! Let’s not forget the ethical implications. If the path to regeneration is paved with the controlled destruction of vital components, how can we trust the outcomes? We’re putting lives in the hands of a process that promotes destruction. Just imagine the future of medicine being dictated by a philosophy that sounds more like a dystopian nightmare than a beacon of hope. It is high time we hold scientists accountable for the direction they are taking in regenerative research. We need a shift in focus that prioritizes constructive growth, not destructive measures. If we are serious about advancing regenerative medicine, we must reject this flawed notion and demand a commitment to genuine regeneration—the kind that nurtures life, rather than sabotages it. Let’s raise our voices against this madness. We deserve better than a science that advocates for destruction as the means to an end. The axolotls may thrive on this paradox, but we, as humans, should expect far more from our scientific endeavors. #RegenerativeMedicine #Axolotl #ScienceFail #MedicalEthics #Innovation

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, 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. #ankur #kothari #qampampa #customer #engagement