Latest news with #DataScience
Forbes
3 days ago
- Business
- Forbes
Digital Marketing ROI: From Clicks To Causality
Rahul Wankhede, Director Data Science and Marketing Analytics, Humana. getty Digital marketing is an indispensable engine for growth. Year over year, big brands allocate a significant portion of their annual marketing budget toward digital channels and—with an ever-increasing digitally savvy and available audience—effective strategies can promise precision, personalization and reach. While it's easier to spend in digital channels than traditional ones, proving return on investment (ROI) at both the top and bottom of the funnel is complicated. That complexity is further amplified when you think of the impact on brand awareness or recall, which may not always yield immediate conversions. The real question is incrementality: Did the ad cause the action, or would you expect it to occur organically? To understand the trade-offs, think of measurement approaches as a funnel, ranging from simplest at the top to more complex—and possibly more accurate—methods at the bottom. With each layer, you can expect more rigor but also more stringent data requirements, sophisticated models, investment, deeper analytical expertise and patience. Here are a few different approaches for measuring the ROI of digital marketing efforts: At the top of that funnel is last-touch attribution, a model that assigns full credit for a conversion to the last interaction before the action. Since it's easy to implement and interpret, last-touch remains a popular method. However, the methodology ignores all other touchpoints that may have influenced the customer along their path to conversion. The method doesn't include the causal impact of interactions like first touch, linear or time decay. That's where multi-touch attribution (MTA) comes in. MTA attempts to use user-level data and models to assign fractional or partial credit to each touchpoint. The approach attempts to account for the full customer journey, offering a more granular view of performance. This method typically includes large volumes of granular data such as cookies, device IDs and timestamps across multiple channels. The model is also becoming more difficult to manage, with the world moving toward more stringent privacy laws and cookie deprecation. Ultimately, even with the data stack available, MTA is prone to bias. Some consumers who view your ads are likely to inherently be more engaged or loyal, while some may just be online more, potentially leading to overstated impact. Without proper controls, MTA could mistake correlation for causation. To supplement attribution, marketing mix models (MMMs) are a popular technique that leverages aggregated data—typically at the geographic and weekly level—to estimate the impact of marketing on sales. MMM can capture both online and offline media impact and is valuable when you add saturation curves to assist with long-term planning and budget optimization. While MMM does not require user-level data, which makes it resilient to privacy changes, results can skew heavily based on model specifications, assumptions and data quality. It also tends to smooth over short-term fluctuations and isn't necessarily useful for more tactical-level optimizations. Randomized control trials (RCTs) remain the gold standard in measuring the incremental impact of marketing. They work by comparing outcomes between a treatment group (those exposed to the marketing) and a control group (those not exposed), isolating the causal effect of the campaign. There are several ways to do this: • Randomization can occur at the market, geographic level or individual user level, where users are assigned to either a treatment or control group. • You can use synthetic controls after a campaign, which constructs a comparison group based on modeled data when a true control group wasn't established in advance. Running and implementing RCTs at scale, where reach remains such a crucial factor, is a challenge. You also run into issues with exposure bias; for example, being in the treatment group doesn't necessarily mean they saw the ad. Other factors, like viewability and fraud, can skew results. Conversely, control users might inadvertently be exposed to the ad due to retargeting, low match rates or media leakage. These issues can compromise the validity of the test. One solution is the implementation of placebo or ghost ads, which appear as real ads to the user but promote unrelated content, serving as a true control. With ghost ads, the control group is selected at the time of the bidding process, solving the question of ad exposure. This method requires a robust internal data infrastructure—including impression logs, user IDs, clean conversion data and analytical expertise. Many organizations don't have the engineering resources to deploy these tests at scale. Still, despite these challenges, well-executed experiments can validate and supplement predictive models, inform bidding strategies and answer key questions about channel effectiveness. When paired with MMM or MTA, RCTs act as a calibration layer, bringing rigor to existing methods. While much of marketing measurement focuses on conversions, upper funnel metrics like brand awareness, consideration and recall are equally critical. Though harder to measure, these indicators play a significant role in driving long-term growth. Surveys remain a primary tool to measure brand awareness, comparing consumer perceptions before and after ad exposure. Modern brand lift studies use randomized control designs to capture these shifts more accurately, while longitudinal brand equity trackers monitor changes over time to link campaigns with brand health. These methods offer insights that performance metrics alone can't answer, essentially the emotional connection with an audience the brand is trying to win. They also come with challenges: response bias, sampling variability and difficulty with linking surveys to actual exposure. Each approach—attribution, experimentation, modeling, surveys—has trade-offs. The key is integrating them into a unified strategy aligned with business goals, campaign needs and data maturity. Success lies in using the right mix at the right time, validating results without unnecessary complexity and building on insights over time. While machine learning will help automate and surface patterns, human expertise remains essential. The complexity is real—but so is the opportunity. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Hospitality Net
22-05-2025
- Business
- Hospitality Net
Precision Over Hype: Why hospitality finance demands a different kind of AI
Firechat with Sundar Sitaula, Data Scientist at Fairmas Host: Kinza Raheel Introduction The hospitality industry has traditionally been cautious about adopting new technologies, but change is no longer optional. As artificial intelligence (AI) gains momentum, it is not just a buzzword. It is a practical next step with the potential to transform how hotels plan, analyze, and optimize their finances. At Fairmas, we are not focused on flashy features. We believe AI must be secure, purposeful, and designed to solve real business challenges, especially in a data-sensitive and highly complex field like hospitality finance. To explore this further, our International Marketing and Communications Manager, Kinza Raheel sat down with Sundar Sitaula, our Data Scientist at Fairmas, for an open conversation about the real role of AI in financial planning. From revenue optimization to access security, Sundar shares an honest and insightful view of how AI is evolving within our industry and at Fairmas. Read the interview below for more insights on the subject: Kinza: Sundar, welcome! Let's get right into it. The hospitality industry often seems hesitant to embrace new technologies. Why do you think AI still feels 'futuristic' to so many in this space? Sundar: Thanks, Kinza! Great to be here. Yes, AI still feels futuristic because most people associate it with consumer experiences, like voice assistants or travel apps. But when we talk about AI in hospitality financial planning and analysis, we are dealing with a whole other level of complexity. It is not about novelty. It is about precision, security, and actual business outcomes. Kinza: So let's talk application. Where exactly is AI making an impact in hospitality finance? Sundar: There is a lot of value across key areas: Dynamic pricing and revenue management . AI fine-tunes rates based on real-time demand. . AI fine-tunes rates based on real-time demand. Forecasting and budgeting . It identifies patterns that humans might miss. . It identifies patterns that humans might miss. Operational efficiency and cost optimization . Smarter allocation, lower waste. . Smarter allocation, lower waste. Energy management . AI helps reduce utility costs. . AI helps reduce utility costs. Automated reporting . Time-saving and consistent. . Time-saving and consistent. Anomaly and fraud detection. Especially important for maintaining financial integrity. Kinza: That's powerful. But I imagine with great data comes great responsibility. Let's talk about access security. Sundar: Exactly. And this is where things get serious. Most general AI models are not built with deep access control in mind. Anyone can ask anything, and that just does not work for us. In hospitality finance: Not everyone should be allowed to access everything. Nobody from outside the organization should have entry. Internally, access must be very granular and highly specific, based on user roles. And 100 percent accuracy is essential. Financial data leaves no room for approximation or 99% accuracy. This makes our use case far more complex than consumer tools. We are not asking AI for a holiday recommendation. We are asking it to support critical business decisions that can impact millions in profit. Kinza: So AI in this context is not just another fancy tool. It has to be secure and purposeful. Sundar: Absolutely. Even though, from a sales & marketing point of view, it is sometimes tempting to offer a quick and simple 'wow' effect. However, it is not in our nature to resort to sudden or superficial gimmicks. Our goal is to provide relevant solutions that deliver genuine value to users, while upholding our established standards of excellence in data protection and confidentiality. Kinza: That's a great philosophy. On a more personal note, based on your experience, how does this compare to other B2B AI use cases? Sundar: It is significantly more demanding. Compared to other B2B AI use cases, hospitality finance involves a unique level of complexity and unpredictability. Unlike industries such as manufacturing, where conditions are relatively stable, hotels operate in an environment that can change hourly. Factors like weather, local events, traffic conditions, and even last-minute group bookings can dramatically impact performance and planning. What makes it even more challenging is that no two hotels are exactly the same. A hotel near a major attraction will have very different business dynamics than one in a quiet business district. These differences mean that financial planning must be highly customized, with constant adjustments based on real-time inputs. That's why at Fairmas, we focus on building AI that is not just powerful but also deeply tailored, responsible, and secure, because in hospitality finance, there is no one-size-fits-all approach. Kinza: Well said. Thank you, Sundar, for sharing these real-world insights. It is clear that AI in hospitality finance is not about hype. It is about building meaningful, secure, and practical solutions that truly make a difference. Conclusion AI is not a magic solution, and it should not be treated as one. As Sundar highlighted, it is a tool that supports human decision-making. The strength of AI in hospitality finance lies in its ability to process vast amounts of data, identify trends, and enhance accuracy and efficiency, all while keeping data protection front and centre. At Fairmas, we do not chase hype. We are focused on delivering solutions that provide real value to our customers. Every AI feature we are currently developing must meet the highest standards of data security, accuracy, and business relevance. The financial planning needs of the hospitality industry are complex. But with the right application of AI, they can be handled more intelligently, more securely, and more effectively. Fairmas software solutions, further integrated with AI, will help drive smarter decision-making and ultimately boost profitability. Kinza: So AI in this context is not just another fancy tool. It has to be secure and purposeful. Sundar: Absolutely. Even though, from a sales & marketing point of view, it is sometimes tempting to offer a quick and simple 'wow' effect. However, it is not in our nature to resort to sudden or superficial gimmicks. Our goal is to provide relevant solutions that deliver genuine value to users, while upholding our established standards of excellence in data protection and confidentiality. Kinza: That's a great philosophy. On a more personal note, based on your experience, how does this compare to other B2B AI use cases? Sundar: It is significantly more demanding. Compared to other B2B AI use cases, hospitality finance involves a unique level of complexity and unpredictability. Unlike industries such as manufacturing, where conditions are relatively stable, hotels operate in an environment that can change hourly. Factors like weather, local events, traffic conditions, and even last-minute group bookings can dramatically impact performance and planning. What makes it even more challenging is that no two hotels are exactly the same. A hotel near a major attraction will have very different business dynamics than one in a quiet business district. These differences mean that financial planning must be highly customized, with constant adjustments based on real-time inputs. That's why at Fairmas, we focus on building AI that is not just powerful but also deeply tailored, responsible, and secure, because in hospitality finance, there is no one-size-fits-all approach. Kinza: Well said. Thank you, Sundar, for sharing these real-world insights. It is clear that AI in hospitality finance is not about hype. It is about building meaningful, secure, and practical solutions that truly make a difference. Conclusion AI is not a magic solution, and it should not be treated as one. As Sundar highlighted, it is a tool that supports human decision-making. The strength of AI in hospitality finance lies in its ability to process vast amounts of data, identify trends, and enhance accuracy and efficiency, all while keeping data protection front and centre. About Fairmas Fairmas is a software development company, with a global presence, offering financial planning, management reporting and controlling solutions including data management for hotel assets to hotels. Since our foundation in 2003, our focus has been on the development of innovative, tailor-made hotel software. With our continuous growth and steady expansion of our product range, more than 5,500+ hotels worldwide are satisfied with Fairmas solutions. ( Kinza Raheel International Marketing & Communications Manager Fairmas GmbH View source
Times of Oman
15-05-2025
- Business
- Times of Oman
SP Jain Global' s BBA and BDS Graduates Secure Global Opportunities with Class of 2024 Placements
SP Jain School of Global Management (SP Jain Global), an Australian business school with campuses in Dubai, Singapore, Sydney, London, and Mumbai, has once again demonstrated its commitment to excellence with remarkable placement results for its Bachelor of Business Administration (BBA) and Bachelor of Data Science (BDS) programs. The Class of 2024 achieved new milestones in career outcomes, reinforcing SP Jain Global' s reputation as a leading institution for global, future-ready education. Graduates from SP Jain Global' s flagship Bachelor of Business Administration (BBA) program have achieved strong placements, with the highest recorded salary for an undergraduate at the institution—AUD 115,000. The class also reported an average salary of AUD 74,944, with offers coming from leading firms across diverse sectors such as marketing, finance, retail, consumer goods, technology, and professional services. The BBA program, which follows a multi-city format (Singapore, Dubai, and Sydney), immerses students in international business environments throughout their undergraduate journey. This model not only enriches their academic experience but also enhances employability by fostering global perspectives, cross-cultural agility, and adaptability—traits highly valued in today's interconnected workforce. Some of the top recruiters in 2024 include Teltonika Telematics, Calvin Klein, TMGM, TechAnts Solutions, Wedded Wonderland, Signal Brands Australia, Ability Street Community Service and Ferrero, among others. Notably, the majority of placements were secured in Sydney, reflecting the city's status as a global business and innovation hub. 'Sydney has become a springboard for our students to launch their international careers,' said Dr. John Lodewijks, Vice President – Academic at SP Jain Global. 'Our students' ability to adapt and thrive in dynamic global environments reflects the strength of our curriculum and the immersive nature of our pedagogy.' Rucha Navsalkar (BBA, Class of 2022), Associate Brand Manager - Pediatrics at Nestlé, shared: "Studying at SP Jain Global Management truly broadened my horizons, preparing me to thrive in a globalized workforce. The diverse experiences across multiple destinations honed my cultural agility, making me comfortable in any international setting. From Dubai to Singapore, each city offered unique lessons and memories, but it was in Singapore where I truly found independence and embraced 'adulting' alongside lifelong friends. SP Jain didn't just educate me; it transformed me, nurturing my confidence and professional growth while fostering unforgettable connections and experiences." In addition to the placements for BBA, SP Jain Global' s Bachelor of Data Science (BDS) program has achieved 100% placements for the third consecutive year. The Class of 2024 reported an average salary of AUD 84,750 and a highest salary package of AUD 105,000, a testament to the growing demand for data-savvy professionals in today's economy. Employers recruiting BDS graduates this year include Greenstone Financials, Telstra Corporation Limited, Morgan Stanley Capital International, Bupa Australia, Eucalyptus Australia, and Ingrity Pty Ltd. Roles span across IT Services & Consulting (46%), Financial Services (23%), Telecommunications (8%), and Healthcare (8%), reflecting the versatility and relevance of the BDS program. According to Abhijit Dasgupta, Director of the BDS Program, the achievements reflect the strength of SP Jain Global' s approach to technical education: 'The fact that our students are earning salaries 40% higher than the average graduate compensation in Australia reflects not only their exceptional technical expertise and problem-solving capabilities but also the rigorous training and industry-relevant exposure they receive during the program. It is a proud moment for us to see their talents being recognized and rewarded on a global stage.'
Time of India
14-05-2025
- Health
- Time of India
Study Biotech & Health Tech Abroad: Top Courses at Harvard, Stanford
Want to launch a global career in biotech or health tech? Ms. Niharika Sondhi, Founder and CEO, Ednet Consultants in this video, features top programs for international students—from Harvard Medical School's HMX Physiology to Stanford's Biomedical Data Science Certificate and Northeastern's Bioinformatics program. Looking to learn high-demand skills like human biology, data analysis, predictive modeling, and system simulations? With flexible durations and rolling admissions, these courses are ideal for students with a background in biology, chemistry, or computing.
New Indian Express
11-05-2025
- New Indian Express
Coimbatore college holding UG admissions for unapproved courses, alleges AUT
COIMBATORE: The Association of University Teachers (AUT) has alleged that an aided college near the city has started the admission process for unaided undergraduate courses without getting approval from Bharathiar University. The AUT also charged that officers from Bharathiar University are showing laxity in this regard. AUT Vice-President P Thirunavukkarasu told TNIE that an aided college near Kuniyamuthur has released an admission pamphlet for the next academic year. "On this, it stated that admission is held for undergraduate unaided courses of (CA), Computer Science and Artificial Intelligence and Data Science," he claimed. He alleged that they came to know the college had not obtained permission from Bharathiar University for the Computer Science and Artificial Intelligence and Data Science courses. He questioned how the college could start admissions for these unapproved courses without approval by mentioning the pamphlet. He also claimed many other colleges follow this wrong practice. Thirunavukkarasu said students' future will be at stake if colleges admit students to unapproved courses. He alleged that top officers from Bharathiar University are allowing this violation instead of taking action against colleges. A teaching staff from the college, who did not wish to be named, told TNIE that the college administration did not conduct admissions for the Statistics course in the aided section, citing a lack of teaching staff.
