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Forbes
6 days ago
- Entertainment
- Forbes
The New Playbook For Personalized Fan Experiences
Ab Gaur is the Founder & CEO of Verticurl and also serves as Ogilvy's Chief Data and Technology Officer. Fandom is a powerful force. It's a tremendous outlet for energy, enthusiasm and community, creating connections that otherwise wouldn't exist and unlocking unparalleled economic potential. It also has many expressions. Fandom encompasses support for sports teams and players, as well as for musicians, entertainers, books, movies and other forms of popular culture. While sports are not the only fandom that matters, the industry's lucrative television contracts, loyal customers, strong investment returns and fan engagement models make it a compelling case study in the power of fandom for sports entities and industries with their own fans to emulate and learn from in building sustainable and highly engaged communities. In many ways, the sports industry serves as a case study in fandom's potential to drive significant economic growth and cultural engagement, offering illustrative lessons for the industry itself and other fandom entities seeking to cultivate loyal communities, generate substantial revenue and create lasting cultural impact. Understanding Modern Fandom Today's fans, much like consumers across all industries, are no longer content with passive spectatorship. They crave deeper connections with their favorite teams and players, seek personalized experiences and desire opportunities to weave their fandom into the fabric of their personal identities. It's both a challenge and an opportunity that is best accomplished by unlocking fandom's full potential and creating "immersive fandom.' This is an environment where being a fan is an active, engaging and integral part of life, fostered by rich, dynamic relationships with the team, the broader fan community and the brand itself. As chief data and technology officer at Ogilvy One—where I work to find data-driven relationships at the intersection of creativity and technology—I've had the privilege of leading efforts to innovate and deliver personalized fan engagement solutions for major sports leagues globally. Based on those experiences, here are three strategies that brands, business leaders and entertainment entities can take to achieve immersive fandom: 1. Build and monetize. Data is an abundant resource, and entertainment entities collect valuable insights at every customer touchpoint. In other words, the problem isn't volume; it's fragmentation. Information about ticket purchases, merchandise sales, app interactions and marketing engagement often resides in separate, disconnected silos. As a result, brands have a disjointed view of their customers, which produces an incomplete understanding of individual fans. To this end, experiences and interactions feel generic and mundane rather than novel and individualistic. For one leading North American sports league that I worked with, the challenge was to break down data silos and create a unified view of their fans across the league. They aimed to move from siloed communications to 1:1 real-time fan journeys and from fragmented physical and digital experiences to connected experiences. With an integrated data platform, the league was able to create 360-degree fan profiles that provide strategic insights and measured impact, informing the next best message and content. This approach can reduce time to market and enterprise costs through orchestrated marketing experiences that are governed centrally and executed locally. To try it yourself, start by unifying and activating your data. Then, consolidate all available fan data—including demographic details, behavioral patterns, transaction histories and stated preferences—into a comprehensive profile. This actionable data will help you understand who the fans are, what they value, how they interact across various channels and what motivates their loyalty. This process allows you to move beyond guesswork to intimately understand fan segments and individual preferences, which enables the first wave of targeted personalization efforts and helps identify previously unseen monetization avenues. 2. Deliver personalized, timely experiences. With a unified data foundation in place, the next step is to move beyond one-size-fits-all messaging and cultivate truly individualized engagement. Today's sports fans expect interactions that recognize their unique history with the team, acknowledge their specific preferences and are relevant to their current context. To achieve this, leverage the latest technologies—yes, AI—to deliver these personalized experiences at the scale required by large fan bases. These technologies can analyze the unified fan data to predict individual needs, anticipate behaviors and automate the delivery of tailored fan journeys. For instance, Wimbledon integrated AI into its fan experience to create personalized video highlights, spoken-word commentary and interactive match summaries across its owned platforms. This produced 'double-digit growth in engagement and content consumption across its owned platforms.' These personalized and timely interactions significantly enhance engagement (and revenue potential) across the entire fan lifecycle. 3. Embed measurements, governance and optimization. What gets measured matters, and what matters is improvement over time, ethical data governance and continued improvement through optimization. Building a sophisticated fan engagement model is not a one-time project; it's an ongoing commitment to refinement and improvement. This starts with strong data governance frameworks. They ensure the accuracy, consistency, security and regulatory compliance of fan data. This mitigates risk and builds and maintains the trust needed to ensure that fans willingly share their information and continue engaging with the personalized experiences that information enables. Couple data governance efforts with analytics and reporting that measure the effectiveness of their personalized engagement strategies and understand the return on investment. Metrics worth measuring include: • Acquisition costs • Engagement rates across various channels • Conversion rates on personalized offers • Customer lifetime value • Overall fan sentiment. This work is never finished. Fan engagement strategies that work well today can become stale tomorrow as expectations and desires inevitably change over time. Rewards Worth The Work Tapping into fandom's full potential is hard work. It's also worth it. The rewards are transformative: Deeply entrenched fan loyalty, the conversion of casual supporters into passionate advocates, the creation of new and diversified revenue streams and the establishment of a sustainable competitive advantage. Fandom is a powerful force. Immersive fandom is the best way to make the most of this valuable asset. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Harvard Business Review
28-05-2025
- Business
- Harvard Business Review
To Create Value with AI, Improve the Quality of Your Unstructured Data
A company's content lies largely in 'unstructured data'—those emails, contracts, forms, Sharepoint files, recordings of meetings and so forth created via work processes. That proprietary content makes gen AI more distinctive, more knowledgeable about your products and services, less likely to hallucinate, and more likely to bring economic value. As a chief data officer we interviewed pointed out, 'You're unlikely to get much return on your investment by simply installing CoPilot.' Many companies have concluded that the most value from gen AI lies in combining the astounding language, reasoning, and general knowledge of large language models (LLMs) with their own proprietary content. That combination is necessary, for example, in enterprise-level gen AI applications in customer service, marketing, legal, and software development, and product/service offerings for customer use. The most common approach by far to adding a company's own content is 'retrieval augmented generation' or RAG, which combines traditional information-gathering tools like databases with information retrieved by LLMs. It is used because submitting vast quantities of content in a prompt is often technically infeasible or expensive. While technically complex, the RAG approach is quite feasible and yields accurate responses to user prompts if the unstructured data used in RAG is of high quality. Therein lies the problem. Unstructured data is frequently of poor quality—obsolete, duplicative, inaccurate, and poorly-structured, among other problems. Most companies have not done well with the quality of structured data, even as this data is used every day to complete business transactions and understand performance. Unstructured data is tougher. The last serious attempts to address unstructured data date to the 1990s and 2000s when knowledge management was popular. Most efforts proved unsuccessful. Surveys confirm that most leaders are aware that poor quality hampers their generative AI efforts, and that they did not have a strong focus on unstructured data until the advent of gen AI. Of course, the best way to deal with data quality problems is to prevent them. Over the long-term, companies serious about AI must develop programs to do just that. Those who create documents, for example, need to learn to evaluate them for quality and tag key elements. But this will take much concerted effort and is no help in the short term. To get value from gen AI, companies need to build RAG applications using high-quality unstructured data. Our objective in this article is to help them do so by summarizing the most important data problems and the best approaches for dealing with them, both human and technical. What Is Data Quality for Unstructured Data? High-quality data, whether structured or unstructured, only results from focused effort, led by active, engaged leadership, some well-placed professionals, clear management responsibilities for all who touch data, and a relentless commitment to continuous improvement. Absent these things, chances are high your data is not up-to-snuff. As coach and advisor, Alex Borek of the Data Masterclass told us, 'When AI doesn't work, it often reveals flaws in the human system.' Indeed, the best estimate is that 80% of time spent on an AI project will be devoted to data. For example, a Philippines-based Morgan Stanley team spent several years curating research reports in advance of their AI @ Morgan Stanley assistant project. The curation started before gen AI became widespread, which allowed Morgan Stanley to more quickly get their application into production. To work effectively, RAG requires documents directly relevant to the problem at hand, a minimum of duplicated content, and the information contained in those documents to be complete, accurate, and up-to-date. Further, as Seth Earley of Earley Information Science noted, 'You must supply context, as much as possible, if a LLM is to properly interpret these documents.' Unstructured data does not come pre-loaded with the needed context, and gen AI is largely incapable of determining what is the best information to solve a particular business question or issue. It is also not good at 'entity resolution,' i.e., 'Is this 'John Smith' in document A, about customers, the same person as 'J. A. Smith' in that document B, about vendors, and/or the same person as 'Mr. J Smith' in the other document C, about a donation to our foundation?' Most structured data is defined in a data model or dictionary. This provides some context and helps reduce the John Smith/J. A. Smith problem described above. For structured data it is easier to find the data desired, learn who is responsible for it, and understand what the data means. As John Duncan, the head of data governance for the large car retailer CarMax told us, unstructured data also requires the same need for clarity on data ownership, producers, consumers, and stewards. It also benefits from standards for data quality thresholds, data lineage, access controls, and retention durations. This metadata is typically included in a data dictionary. However, with unstructured data, there is seldom a dictionary. Often there is no centralized management of such content; documents are stored haphazardly using different naming conventions and on different computers or cloud providers across the company. There is often no common definition of a content type; an ad agency data leader confessed that there is no common definition of a 'pitch' across the agency. Finally, unstructured documents were often developed with a different purpose than feeding gen AI. A contract with a supplier, for example, was not designed to provide insight about the level of risk in a supplier relationship. We believe it was the late management thinker Charles Handy who observed, 'Information gathered for one purpose is seldom useful for another.' An Unstructured Data Quality Process Fortunately, there are several approaches and tools that can help to improve unstructured data. We recommend that all AI projects follow a disciplined process, building quality in wherever they can. Such a process must embrace the following steps: Address unstructured data quality issues problem by problem, not all at once. Identify and assess the data to be used. Assemble the team to address the problem. Prepare the data, employing both humans (D1) and AI (D2), when possible. Develop your application and validate that it works. Support the application and try to inculcate quality in content creation processes. 1. Address unstructured data quality issues problem by problem, not all at once. There is too much unstructured data to improve all at once. Project leaders should ensure that all involved agree on the problem/opportunity to be addressed. Priorities should be based first on the value to the business of solving the problem, and second on the feasibility and cost of developing a solution—including data quality improvement. Areas of the business with data that is already of reasonably good quality should receive higher priority. That's the approach Nelson Frederick Bamundagea, IT director at the truck refrigeration servicing company W&B Services, has taken. His knowledge retrieval application for service technicians uses the schematics of some 20 (refrigerator) models provided by two manufacturers. These have been used over and over and the vocabulary employed is relatively small, providing for a high level of trust. More generally, Alex Borek advises companies to 'first look to highly curated data products whenever possible.' 2. Identify and assess the data to be used. Since the data is critical to the success of an LLM-based knowledge project, it's important to assess the data at an early stage. There is a human tendency to include any possibly relevant document in a RAG, but companies should adopt a healthy skepticism and a 'less is more' philosophy: absent a good reason to trust a document or content source, don't include it. It's not likely that experts can evaluate every document, but they can dig deeply into a small sample. Are the sample documents loaded with errors, internal inconsistencies, or confusing language—or are they relatively clean? Use your judgment: Keep clean data and proceed with caution; toss bad data. If the data are in horrible shape or you can't find enough good data, reconsider the project. 3. Assemble the team to address the problem. Given the need for some human curation of unstructured data, it's unlikely that a small team of experts can accomplish the necessary work. In addition, those who work with the data from day-to-day typically have a better idea of what constitutes high quality and how to achieve it. In many cases, then, it may be helpful to make data quality improvement a broadly participative project. For example, at Scotiabank, the contact center organization needed to curate documents for a customer chatbot. Center staff took responsibility for the quality of its customer support knowledge base and ensured that each document fed into the RAG-based chatbot was clear, unique, and up to date. 4a. Prepare the data. If you've concluded—and you should—that there must be a human contribution to improving unstructured data quality, this is the time to engage it. That contribution could include having a stakeholder group agree on the key terms—e,g., 'contract,' 'proposal,' 'technical note,' and 'customer' might be examples—and how they are defined. Document this work in a business glossary. This can be hard: Consistent with 'Davenport's Law'—first stated more than 30 years ago —the more an organization knows or cares about a particular information element, the less likely it is to have a common term and meaning for it. This issue can be overcome through 'data arguing' (not data architecture) until the group arrives at a consensus. And, of course, if there is a human curation role, this is the time to begin it. That entails deciding which documents or content sources are the best for a particular issue, 'tagging' it with metadata, and scoring content on such attributes as recency, clarity, and relevance to the topic. Morgan Stanley has a team of 20 or so analysts based in the Philippines that scores each document along 20 different criteria. 4b. Prepare the data with AI. Gen AI itself is quite good at some tasks needed to prepare unstructured data for other gen AI applications. It can, for example, summarize content, classify documents by category of content, and tag key data element. For example, CarMax uses generative AI to translate different car manufacturers' specific language for describing automotive components and capabilities into a standard set of descriptions that is meant to enable a consumer to compare cars across manufacturers. Gen AI can also create good first drafts of 'knowledge graphs,' or displays of what information is related to other information in a network. Knowledge graphs improve the ability of RAG to find the best content quickly. Gen AI is also good at de-duplication, or the process of finding exact or very similar copies of documents and eliminating all but one. Since RAG approaches pick documents based on specified criteria, these criteria (recency, authorship, etc.) can be changed ('re-ranked') to give higher weight to certain ones in content search. We have found, however, that AI is not particularly good at identifying the best document in a set of similar ones, even when given a grading rubric. For that and reviewing tasks humans are still necessary. As a starting point, we recommend using humans to figure what needs to be done, and machines to increase scale and decrease unit cost in execution. 5. Develop your application and validate that it works. The process of developing a RAG models from curated data involves several rather technical steps, best performed by qualified tech staff. Even after having done everything possible to prepare the data, it is essential that organizations rigorously test their RAG applications before putting them into production. This is particularly important for applications that are highly regulated or involve human well-being. One way to validate the model involves identifying '50 Golden Questions,' in which a team identifies questions that the RAG must get right, determines whether it does so, and acts accordingly. The validation should be done over time given that foundational LLMs change often. When a European insurer tried to validate its system for knowledge on how to address claims, it found that customers' contracts, call center personnel, the company's knowledge base, and the claims department often disagreed. This led the company to clarify that the Claims Department 'owned' the answer, i.e., served as the 'gold standard.' Changes to the chatbot, customer contracts, and call center training followed. 6. Support the application and try to inculcate ongoing quality. As a practical matter, no RAG application will enjoy universal acclaim the minute it is deployed. The application can still hallucinate, there will be bugs to be worked out and some level of customer dissatisfaction. We find that some discount a well-performing RAG application if it makes any errors whatsoever. Finally, changes will be needed as the application is used in new ways. So, plan for ongoing quality management and improvement. The plan should include: Some amount of 'qualified-human-in-the-loop' especially in more critical situations A means to trap errors, conduct root cause analysis, and prevent them going forward Efforts to understand who the customers of the RAG are, how they use it, and how they define 'good' Feedback to managers responsible for business processes that create unstructured data to improve future inputs. Content creators can be trained, for example, to create higher-quality documents, tag them as they create, and add them to a central repository. It appears that RAG, featuring proprietary content, combined with LLMs, is going to be with us for the foreseeable future. It is one of the best ways to gain value from gen AI if one can feed models high-quality unstructured data. We know there is a lot here, but it is certainly within reach of those who buckle down and do the work.
