Tag: Int.

HCP centric Branding in Pharma Industry

Pioneering HCP-centric Branding: Revolutionising the Pharma Industry

July 19, 2023

Healthcare professionals (HCPs) are the target customer base for pharmaceutical companies. Now when it comes to offering them more innovative solutions for better patient outcomes and trust building, there has to be a better understanding at work for tailoring campaigns. In a highly regulated pharmaceutical marketing environment, is it possible for companies/brands to build better relationships with healthcare professionals (HCPs)? The answer is yes, provided the brand-building activity is done in a suitable manner. Here’s knowing a little more about the same. Understanding the Needs of HCPs Understanding the requirements of healthcare professionals (HCPs) is crucial for not just trust building but also to ensure more purposeful pharma brand-building. HCP engagement can be summed up as an approach that is diverse and targeted throughout multiple channels, with a view towards actively responding and seeking the requirements and feedback of HCPs. This may be done through various forms of communication, marketing, outreach, events, programs, educational and awareness initiatives, tailored content, and more. Here are some key points that should be understood clearly in this context: Building Trust with HCPs Trust building with healthcare professionals (HCPs) is a two-way street, based on the principles of authenticity, credibility, reliability, and transparency as outlined above. Here are some other points worth noting in this regard: Creating a Purpose-Driven Brand Here are a few ways in which a purpose-driven brand can be built by pharmaceutical companies in order to engage better with HCPs. FAQs 1. What are the key insights and data sources that pharma companies can utilise to better understand HCPs and their needs? Pharmaceutical companies can utilise various sources of data and insights to understand HCPs and their needs better. These include direct or indirect feedback and insights from HCPs, social media communication and interactions, posts and relevant content from HCPs and stakeholders regarding products, services, and their challenges, and so on.  2. In what ways can HCP insights be integrated into the branding process to create a brand that resonates with HCPs and builds trust? HCP insights can be integrated seamlessly into the process of branding in order to enable better brand-building. This can be done through collaborative initiatives for engagement, awareness, and education. Other methods include community-based information and learning sessions.  3. What ethical considerations should pharma companies keep in mind when leveraging HCP understanding for brand development? Some of the ethical considerations to be kept in mind by pharmaceutical companies in this case include respecting data privacy and adhering to consent regulations. Pharmaceutical companies should also abide by data security policies and maintain open and transparent communication with regard to information sharing.  4. What are the primary challenges and obstacles faced by pharma companies when it comes to leveraging HCP understanding for brand development? Some of the primary hurdles faced by pharmaceutical companies for tapping an understanding of HCPs to enable better brand development include the lack of proper opportunities for face-to-face interaction and higher competition for HCP attention and time spans in the market. Other challenges include aligning products and value propositions at the right time for the right HCPs.

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InsureTech Insights: Leveraging Alternate Data for Risk Assessment

InsureTech Insights: Leveraging Alternate Data for Risk Assessment

July 12, 2023

InsureTech is the latest buzzword that is making the headlines in the insurance sector and with good reason. From suitable risk assessment using alternate data to tapping big data analytics and artificial intelligence in insurance for better outcomes in diverse arenas, insurers are expected to step on the gas further across the next couple of years in this domain. Here is a brief glimpse into the same. 1.What are the key drivers of InsureTech? These are some of the major driving forces behind the InsureTech revolution that is sweeping the world today. Let us now learn a little more about the deployment of artificial intelligence in insurance. 2. How is AI used in insurtech?  Artificial intelligence in insurance and InsureTech are symbiotically linked due to the multifarious applications and use cases that have transformed the industry in recent years. Here are a few aspects worth noting in this regard: AI is beneficial for the entire InsureTech ecosystem in multiple ways, as is mentioned above. A closer look is also necessary at the various sources or types of alternate data that insurance companies can use for better risk assessment. 3.What kind of alternate data can help towards solving the credit risk? FAQs 1.What are the privacy and ethical considerations associated with using alternate data in risk assessment for InsureTech? InsureTech players must address privacy, ethics, and data validity when using alternate data. Key considerations include responsible data collection and usage, obtaining consent, ensuring analytical tool validity, fairness, and unbiased systems, data quality, regulatory compliance, and full disclosure principles. 2.Are there any successful case studies or real-world examples of InsureTech companies leveraging alternate data for risk assessment? There are many examples of InsureTech entities making use of alternate data for risk assessments. ZestFinance, for instance, deploys AI for evaluating both traditional and non-traditional information to gauge risks while automating its underwriting procedure for lower risks. Nauto has already been using AI for forecasting purposes. The aim here is to avoid collisions of commercial fleets (driverless) by lowering distracted driving. The AI system uses data from the vehicle, camera, and other sources to predict risky behavior. 3. What future trends do you foresee in the use of alternate data for risk assessment in the InsureTech industry? There will be greater emphasis on leveraging telematics and usage data garnered through connected vehicles and IoT devices along with smart home devices. At the same time, more machine learning models will be used for algorithm-based risk assessments. The Metaverse will be another channel for insurers to combine their AI-backed Chatbots with sales pitches, internal training, data gathering, and even NFTs for personal document verification. 4. Are there any challenges or limitations in leveraging alternate data for risk assessment in InsureTech? There are a few limitations/challenges in using alternate data for assessing risks in the InsureTech space. The quality of the data and whether it tells the whole story is one challenge along with the fact that there are ethical and privacy-related considerations, regulatory aspects, and the issues related to disclosures, user consent, and the methods of gathering data.

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The Power of Retail Analytics in Elevating Customer Experience

The power of retail analytics in elevating customer experience

July 11, 2023

Behavioral analysis, retail trends and customer experience could well seem quasi-scientific terms that are increasingly used throughout the industry. But what will pleasantly surprise you is the fact that they are commonplace for retailers today.  What is powering things like conversion rate optimisation and insights driven by customer feedback? AI-powered analytics of course! Retail analytics is no longer restricted to only a subset of use cases for these technologies, but has come into the mainstream. It has spawned an entire universe of its own, redefining the operations and strategies of both digital and brick-and-motor retailers in recent years. It is not just instincts alone that shape successful retail operations today, but also data-driven insights and decision-making. Let us know more about the power of retail analytics especially in terms of boosting customer experiences. What is the power of retail analytics? To understand the power of retail analytics and how it has become a catalyst for change in the industry, here are some core points that deserve to be noted: How does analytics improve customer experience?  What are the future trends in retail analytics? Retail analytics will thus power not only future innovations in terms of better experiences and personalisation for customers, but also optimisation at scale. This will include inventory, logistics, supply chain, deliveries, operations, marketing, and advertising. Brands will depend more on analytic and other technological tools to revamp their core propositions in the coming years. It can safely be said that exciting times are afoot in space. FAQs 1.What are some advanced analytics techniques used in retail analytics? There are many advanced techniques of analytics that can be used within the spectrum of retail analytics. These include descriptive analytics, predictive analytics, and prescriptive analytics. Diagnostic analytics may also be used in some cases. 2. Provide us some of the best practices for using retail analytics. Some of the best practices include ensuring the quality of data across points along with suitable data gathering in compliance with regulatory guidelines. At the same time, customer data and forecasting should be done on a real-time basis with complete visibility and tracking. 3. How does retail analytics enable retailers to deliver personalised experiences to customers? Retailers can use retail analytics to unearth valuable insights on what customers desire at specific times of the year, what they browse for, and their previous purchase history. They will also know about the products selling well in particular locations and at particular times of the year. Individual customer engagement can also be tracked for a specific duration. All this data can be analysed to help improve customer experiences with personalisation recommendations, offers, and promotions. 4.What are some of the challenges of using retail analytics? Technological expertise and integration of legacy systems aside, the need to have proper data gathering and analysis infrastructure is another challenge. At the same time, data quality and compliance with privacy regulations are other challenges in this space.

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Predictive Models for Chronic Diseases: Transforming Healthcare

Predictive Models for Chronic Diseases: Transforming Healthcare

July 10, 2023

A major healthcare transformation is in the works, considering the growing integration of the sector with cutting-edge technologies. Along with data-driven insights and personalised medicine, there are other steps being taken for early detection of chronic diseases, such as the usage of advanced predictive models. If implemented suitably, this could herald a mega healthcare revolution in the near future.  Predictive analytics may become a tool for preventing chronic ailments, while enabling providers to swiftly detect early signs of ailments and intervene accordingly. Here is a closer look at these aspects. 1.What are disease prediction models? Disease prediction models are essentially advanced predictive models that are deployed for early detection based on data-driven insights. Machine learning (ML) models help in the swift diagnosis of chronic ailments. Those suffering from the same usually require lifelong medical aid. Here are a few other points worth noting in this regard:  2.What predictive models are used in healthcare? 3. What types of data are used in predictive modelsfor chronic diseases? There are various kinds of data used by advanced predictive models for chronic ailments. Here are a few aspects worth keeping in mind:  FAQs 1.What are the potential benefits of using predictive models for chronic diseases in healthcare resource allocation? Predictive models can help healthcare providers detect early signs of chronic diseases in patients based on diverse data points. At the same time, they can speed up early interventions and reduce the chances of disease contraction and fatalities with these insights. It will also reduce a major chunk of healthcare costs and resources allocated towards the treatment of these diseases. 2.How can predictive models contribute to cost savings in healthcare? Predictive models can help save costs that are otherwise allocated for treating chronic ailments. Early detection of signs and vulnerabilities can help facilitate strategic interventions and medical advice that may prevent these diseases from occurring. Naturally, this helps reduce healthcare costs related to treatment and resource allocation. 3.How do predictive models improve their performance with time? Predictive models keep enhancing their overall performance with the passage of time due to the nature of their algorithms. The more a provider feeds data into algorithms, the more the accuracy levels of predictive models. This helps in the generation of more accurate and helpful insights. 4.What are some of the challenges associated with implementing predictive models for chronic diseases? Some of the common challenges associated with implementing predictive models for chronic ailments include poor data quality, insufficient data, issues with accuracy levels at times due to the complexity of medical data, and technological integration.

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Transforming Banking Experiences with Generative AI

Transforming Banking Experiences with Generative AI

July 5, 2023

Digital transformation is the cornerstone of almost every industry today and banking is no exception. Generative AI and machine learning are technological innovations that are fast revamping the entire banking landscape in the recent scenario, redefining automation for financial services, personalisation and customer engagement and overall operations including risk management. Generative AI is a specialised category of AI (artificial intelligence) which helps in the generation of fresh ideas and content along with pattern-based solutions that are gleaned from pre-existing information. It is thus suitable for diverse applications throughout the banking sector. It can enable intelligent decision-making along with better risk management, fraud detection, and real-time decisions. Here is a deeper look at the same.   How is generative AI used in banking? Generative AI and machine learning can enable the analysis of huge volumes of data sets and then generate responses accordingly. Trends and patterns can be easily identified and the information leveraged to take informed decisions accordingly. Here are some of the core aspects worth noting in this regard:  Customer service with generative AI Customer engagement and service can be radically transformed with the help of generative AI. Here are some points worth noting:  Risk management with generative AI Generative AI will be a huge game-changer and harbinger of digital transformation in the near future. Chatbots and virtual assistants will steadily take over the customer support space with human resources focusing on more crucial duties. Loan processing and other duties will be streamlined and customer experiences will be more personalized and fulfilling. FAQs 1.How can banks effectively adopt generative AI technologies? Banks can adopt generative AI technologies for identifying potential frauds, managing risks, predicting future risks, and also automating customer evaluation including credit and financial history checks. Banks can also use these technologies for improving customer service and enabling higher personalization. 2.Are there any challenges or risks associated with implementing generative AI in banking? There are challenges like data privacy and the need to use synthetic data in the right manner for avoiding breaches and security hassles. Generative AI models may sometimes have higher complexity and interpretation may be tough in some cases. Maintaining transparency and adhering to legal/regulatory mechanisms are other challenges in this regard. 3.How can generative AI help banks make better financial decisions? Generative AI can enable banks to take better decisions through analyzing customer data and offering insights in real-time. Naturally, banks can take more accurate and informed decisions about sanctioning loans and other customer-facing aspects. 4.Can generative AI replace human bankers in the future? While generative AI will automate and streamline repetitive tasks in the future and possibly take care of customer communication and support, it will not be a full replacement for human beings. It will help in policy-building, decision-making, fraud detection, risk management, and personalization. However, human bankers will always be required for taking care of more crucial and complex tasks

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Retail Trends: 2023 & Beyond

Retail Trends: 2023 & Beyond

June 30, 2023

Current retail industry trends indicate considerable transformation with the advent of new-age technologies, changes in consumer behavior and expectations, and most importantly, the growth of e-commerce.  Customer experience in retail looks set to undergo an exponential shift over the next few years. The future of retail will also take several aspects into account including sustainability in retail and personalisation. Here’s looking at them in brief. How is technology shaping the future of retail? The future of retail is a technology-driven one, going by the latest retail industry trends. Ecommerce trends point to the same reality. Forbes has even pointed out the importance of artificial intelligence in retail in the future. Here are some key points worth noting:  What are the key factors driving the evolution of retail in the coming years? What role does e-commerce play in the future of retail? These aspects point at the increasingly revolutionary future of retail where the customer point of view will ultimately shape the course of the industry. This is where technology and advanced strategies will play a crucial role in ensuring sustained growth and development.  FAQs 1.Are there any specific trends related to sustainable and ethical practices in retail? There are several trends with regard to ethical and sustainable practices in the retail industry. These include resource conservation, waste reduction, environment- friendly packaging, a circular ecosystem (recycle, reuse, reduce), sustainable materials for products, fair prices, transparent supply chains, and more.  2.What are the potential challenges and opportunities for retailers in the future? Some of the potential challenges include building core differentiators in a fast-changing and highly competitive market and also ensuring higher customer retention. Changing consumer needs and expectations and technological adoption are other challenges. The opportunities are numerous, including innovating to come up with more convenient and personalised shopping experiences, automating consumer communication and support, shifting towards dynamic pricing models, real-time supply chain tracking, and more.  3.How will artificial intelligence and automation impact the retail landscape? Artificial intelligence and automation will have a hugely positive impact on the retail landscape. Companies will gain invaluable insights on consumer preferences and needs, while managing inventory better. They can automate several labor and time-consuming tasks for lowering costs and improving overall accuracy and efficiency. They can build smooth multi-channel shopping experiences while taking care of customer support through AI-backed Chatbots and virtual assistants. They can also offer personalised recommendations to customers.  4.What role will personalisation and customisation play in the future of retail? Customisation and personalisation will be the defining aspect for the retail industry in the future. Customers will expect personalised recommendations and guidance, along with products/services tailored to their needs just when they require the same. It will help brands tailor and improve consumer journeys and experiences across shopping channels.

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INT. Pulse

INT. PULSE

June 29, 2023

Dear Colleague, these days, if a tech newsletter does not start with the acronyms AI or ML, it can be safely assumed that its writers are probably living under a rock. Thus, to mask our prehistoric addresses from over 40 thousand monthly readers, we are starting June’s Pulse with an AI story, but there’s a twist. Out With It, Please. Okay, so word on the street is, contrary to the doomsayers, AI and ML seem to be creating new jobs for humans, faster than they are killing employment. No Way! Surpriiise. A good 75% of companies think that they will adopt AI in their businesses soon enough; the great challenge now is to get the staff with the necessary expertise to fill these new jobs. Many human resources folk are also suddenly agreeing that the labour market is being shaken by the demand for new workers in AI-related areas. Where Are These New Jobs? Alright, we will start you off with two.  For instance, AI can create personalised medicinal treatments, precision farming and sophisticated industrial methods. These new products and services can lead to new responsibilities in research, development and marketing, along with new skills and experience requirements.   The emergence of AI-powered digital assistants and smart home appliances has opened up new career prospects for hardware engineers, data analysts and software developers, akin to how autonomous vehicle and drones have opened up new career prospects for engineers, technicians and logistics specialists. Always A Catch While more employment is always cool, AI is expected to make some jobs obsolete too, especially in the content generation area.  This means, pretty soon, as generative AI begins to write this newsletter, you may receive INT. Pulse twice a day instead of once a month, and we will have retired to the Himalayas.  Win-Win  STATS: When Every Analytical Tool Failed Sample this.  On 14th June, Sweden reported unexpectedly high inflation for May, causing economists and all their tech tools to wonder: What on earth kept prices that high? (Side Talk: Dealing with an analytical crisis? Solve it over a  with Dipak Singh, our analytics & AI head honcho). And then it dawned upon them like a Manali sunrise: Beyoncé.  The pop superstar put her Renaissance tour on the road in Stockholm last month, pulling 80,000+ fans to the city over two nights.  Danske Bank finally deciphered that for this influx of concertgoers, hotels and restaurants upped their prices to such a degree, it skyrocketed overall inflation.   Your TakeawayThe fact that one person, by the sheer force of her popularity, was responsible for higher inflation in an entire country is…beyond analytics.  Danske’s chief Swedish economist, Michael Grahn, quoted, “It’s quite astonishing for a single event. We haven’t seen this before.” Well, now we have.  GA4: Underreporting Traffic? No Papa Alright peeps, the Universal Analytics (UA) sun is finally setting and come July, Google Analytics 4 (GA4) is all we have, making migration to this new platform mandatory. But, but, but… with GA4, you may also be saying hello to underreported website traffic. Before you start comparing it to the train wreck some recent Windows upgrades were, we advise you to read on. What Happened Here? Indian proptech giant, Square Yards, usually gets ~70% of its web traffic from mobile devices and the rest from Desktops/tablets. But on switching to GA4, they found a hot-potato drop in traffic stats. On fishing in deeper waters, the folks at SY found that mobile traffic was under-reported by the GA4 tracker. This could also be-happening/happen to you. GA4, Give Me Everything Please So, there’s this important setting in the GA4 Console that allows Google to collect metadata about granular device details of your site and app visitors, so it can provide you with location and device-based info. This is turned OFF by default for any new property being created in the GA Dashboard. 🤷🏻‍♀️ Fix? 1️⃣ Go to your GA4 property settings 2️⃣ Select Data Collection, and 3️⃣ Enable Granular location and device data collection. Aaaand, you’re done. 📌 We are keeping our crawlers active on GA4 stories for a ‘best hacks and tips blurb’ in our July edition. Btw, if you need help with GA4 migration, or perhaps, take GA4 to its optimum limits to power growth for your business, Sanjeeb and his team are all set to help you out. Reach out to Sanjeeb here. AI/ML: How Nvidia Hit A Jackpot Selling Chips We’ve all heard of cashing in your chips post winning big but selling chips to hit a jackpot? That’s a new one and the trophy goes out to hardware giant, Nvidia. How Come? 1️⃣ For starters, you should know Nvidia (USD960B)* is now worth more than: *as on May 27, 2023. 2️⃣ This is the company that started 30 years ago and was for almost all its life, only a video game chip maker. 3️⃣ You should also know that over three decades, Nvidia was on the verge of bankruptcy 3 times. How On Earth Then……? It turns out Nvidia’s GPUs (originally created to improve gaming graphics) are also well suited for the data processing and model training demands of generative AI. Like this analyst said, “Training AI models demands chips that have large memory…Nvidia is the only company making those chips.” The rest is history (in the making). ­Stuff We Are Watching 📌Instant Productivity Boost: You know what’s hot? Bring your own device (BYOD) programs are, as they can potentially save organisations big money on equipment – and they might increase productivity.. but there are flip sides too. 📌 A Funding Tip That Works: CEOs and CFOs looking for funding? Remember, when framing your competition to investors without having to buy them antacids or anti-stress pills, follow this hierarchy of competition, from most worrisome to least. 📌 Are Cookies Dying? In one word? Yes. In fact, we may have only 50-odd weeks left. That’s the estimate for cookie-pocalypse. Educated guesses we gathered from all over indicate that Google will get rid of 3P cookies in Chrome around

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Reviving Retail: The Role of Supply Chain Reorganization in Overcoming Challenges

Reviving Retail: The Role of Supply Chain Reorganisation in Overcoming Challenges

June 29, 2023

The global retail industry is facing numerous challenges in the current scenario, especially with regard to the supply chain, customer experience and overall sustainability. The complexities of supply chains worldwide are throwing up multiple issues for retailers in terms of management of risks and ensuring higher profitability at the same time. This is where thorough reorganisation is necessary to overcome these hurdles with gusto. Here’s looking at these aspects in this article. The future of retail and the role of supply chain reorganisation There is a steadily evolving retail and consumer landscape today, with more companies fighting to play catch-up with e-tailers who have completely disrupted the market. From on-shelf availability, there will be a switch towards on-demand availability. Greener supply chains will also be a major trend in the future, particularly with the higher focus on sustainability. Here is a look at how reorganisation of the supply chain can help retail brands overcome several common obstacles. How supply chain reorganisation can help retailers overcome challenges As can be seen, an intensive reorganisation of the supply chain is the need of the day, considering the current complexities that retailers are grappling with. The challenges facing the retail industry The retail sector is already facing several core challenges that are affecting their supply chains. Some of them include the following:  FAQs 1.What technologies and tools can support supply chain reorganization in retail? There are several technologies and other tools that can enable a better reorganization of supply chains in retail. These include artificial intelligence (AI), machine learning (ML), data analytics, digital supply chain twins in the Metaverse, robotics, drones, 3D printing, and more. 2. How long does it typically take for a retail organization to complete supply chain reorganization? Supply chain reorganization does not have any fixed timeline that all retail organizations have to follow. It depends on the technological capabilities and adaptability of the company along with its ability to bring together all stakeholders for implementation. It may take a few months to a year or more. 3.What are some successful examples of retailers that have undergone supply chain reorganization? There are many successful examples of retailers and brands that have already undergone supply chain reorganization. Adidas, for example, is already switching close to 20% of production to automated factories by 2023 while Burlington Coat Factory has revamped its supply chain with newer processes in Burlington, New Jersey. It has integrated its approach throughout IT and operations, getting a new WMS and warehouse control software in place. 4.Are there any specific legal or regulatory considerations to keep in mind during the supply chain reorganization process? There are a few regulatory and legal considerations to note during the reorganization process for supply chains. These include economic sanctions, trade policies, export controls, fraud, compliance needs, and so on.

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Smart Insurance: Exploring Blockchain and AI Integration

Smart Insurance: Exploring Blockchain and AI Integration

June 27, 2023

Smart insurance has increasingly become representative of the evolutionary headwinds in the industry. Digital transformation has been a key feature of the sector for the last few years, especially with the integration of AI insurance and blockchain insurance technologies. Insurtech is no longer an exception as well. These digital advancements are boosting customer experiences while enabling better risk management, claims processing and fraud detection at the same time. Insurers are completely redefining consumer engagement, while moving towards greater personalisation simultaneously. A McKinsey report estimates that 25% of the insurance sector will be fully automated by the year 2025, backed by AI, ML and blockchain, among other technologies. Here’s taking a deeper look at the same. How Blockchain and AI are Changing the Insurance Industry AI insurance technologies are completely revolutionising the landscape. The same can be said for blockchain insurance solutions. Here are some core points worth noting in this regard. The Benefits of Blockchain and AI for Insurance As expected, there are multifarious advantages offered by AI and blockchain in the insurance industry. Some of them include the following:  The Future of Smart Insurance What does smart insurance look like in the future? Here are some ways in which digital transformation can completely reshape the insurance industry: FAQs 1.What role does artificial intelligence play in smart insurance? Artificial intelligence has a vital role to play in smart insurance, automating repetitive tasks including claims submissions, processing, and more. It also helps detect fraud, assess risks, and enhance customer experiences through Chatbot-based communication. 2.What are some real-world examples of smart insurance applications using blockchain and AI? Some real-world examples include micro-insurance, parametric insurance models, usage-based insurance, fraud detection, data structuring through Blockchain and IoT and also multiple risk participation or reinsurance. 3.Are there any challenges or limitations to consider when implementing blockchain and AI in smart insurance? Blockchain networks may require higher computational capabilities for transaction validation. Other challenges for AI and blockchain implementation include data quality, digital adoption, integration of legacy systems, and more. 4.How does the integration of blockchain and AI in insurance impact customer experience? Customer experiences are automatically enhanced through the integration of blockchain and AI. Their information remains tamper-proof and secure, while they can swiftly be onboarded and file/process claims without hassles. They can get quicker automated responses to queries along with personalised recommendations. Claims processing timelines are also greatly reduced due to these technologies.

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future of customer acquisition in banking and finance (bfs)

Expanding Horizons: Enhancing Customer Acquisition with External Data in BFS

June 26, 2023

Customer acquisition is a vital aspect for any BFS entity. There are instances where tapping external data for the same has proved to be a bigger value proposition for these companies. Here’s taking a closer look at the same. How to Use External Data for Customer Acquisition in BFS As is evident, external data is increasingly proving to be a game-changer for banking and financial services entities. It is helping them get a better profile and view of the customer. This is naturally enhancing customer acquisition efforts greatly, helping personalise products/services along with interactions. It is naturally leading to higher customer loyalty and retention. The Benefits of Using External Data for Customer Acquisition Customer acquisition will increasingly be driven by the need to gather sufficient data about customers and then personalise their journeys. This will be the guiding principle for banking and financial services companies in the future. FAQs 1.What types of external data are commonly used to enhance customer acquisition in the BFS sector? Some external data types include geopolitical and economic data, historical data, weather data, satellite imagery, demographic data and so on. 2.What are some specific examples of how external data has been successfully utilised to enhance customer acquisition in BFS? External data can help companies understand customers better in relation to external events and factors. It helps predict market and consumer behavioral patterns and other dynamics. 3.What privacy and data protection measures are in place when using external data for customer acquisition in the BFS industry? Companies should follow strict data privacy protocols including informed consumer consent while gathering data, encryption, multi-factor authentication, transparent privacy and usage policies, and so on. 4.What are the challenges or considerations when integrating external data into customer acquisition strategies in BFS? Some challenges include data quality and delivery issues along with privacy and security risks. The absence of actionability may be another challenge, in addition to resourcing-related constraints.

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