Big Data

Big Data for Consumer Services

Big data is crucial for strengthening the customer experience. It enables marketers to better understand and speak to their customers, making it easier to optimize your marketing strategy for maximum impact and efficiency. Information about your customers’ behavior, including location, order preferences and weekly routines, enables marketers to group customers within customized audience segments for improved targeting. By utilizing an in-depth understanding of the varied habits and preferences of your customer base, restaurant marketers will be able to deliver more personalized interactions and more effective marketing and remarketing efforts.

Big data assists hotels with revenue management strategies. In particular, it allows you to carry out predictive analysis, allowing to more accurately anticipate levels of demand for hotel rooms. Big data provides marketers with the ability to identify the best opportunities for their particular business to target key demographics much more specifically, especially through online advertising. This can also include both location and time-specific marketing, which can allow hotels to reach customers at the time advertising will be most relevant to them, or in the place where they are most likely to want to see it. Big data can help your customer service teams to spot significant trends in terms of customer opinions, learning about their strengths and weaknesses in the process. This may require the gathering and analyzing of service usage data, feedback from customers on social media, reviews posted on websites, and other related information.

Schools, universities, colleges and other educational bodies hold very large amounts of data related to students and faculty. This educational institutions can analyze this data to get insights to improve their operational effectiveness. Educators can reap maximum benefits of big data analytics due to the processing of data-driven systems. These can help institutions create many learning experiences according to a student’s learning capability and preference. Educators can improve their teaching skills after receiving feedback from students. Moreover, digging deep into a student’s performance report will help the responsible authority to understand a student’s progress, strengths and, weaknesses. The reports will suggest the areas in which a student is interested, helping them further pursue a career in that same field.

Big Data for Retail

Big data analytics significantly improve distributors' performance. Identifying new cross-and up-selling opportunities with existing customers is a way to quickly and easily increase sales. With data analytics, you have instant access to real-time and fact-based analysis with just the click of a button. Furthermore, your report can be drilled down for more detailed information. Data analytics tools allow inventory and operations managers to quickly analyze stock-turns, inventory-to-purchase ratio, or slow-moving stock to identify over- or under-stock and enable you to avoid the cost of a product becoming dead stock. Big data analytics can improve efficiency, help managers to save time and accomplish more in an average work day. The ability to quickly slice and dice data for fast insights eliminates the time spent waiting for static reports. This enables managers to closely monitor activities, and detect and prevent any potential challenges before they become problems.

Big data and behavioral analytics enhance customer targeting and assist in creating personalized, micro-marketing campaigns designed to increase the probability of completing a sale. Big data analytics tools help dealers precisely predict automobile-buying behavior and assist in the creation of micro-targeted consumer communications, leading to proven numbers of higher sales and consistent customer retention. You can easily analyze information and use it to anticipate when consumers will be ready for their next car purchase. You can decipher the consumer’s preferred features, make, model, and more. This knowledge empowers and prepares you to make a sales experience tailored to the consumer.

Big data provides extreme value for consumer-focused retailers. It helps to analyze sales, marketing and information about customer interaction. Big data provides insight into how customers discover, buy and engage with a product or brand, making it possible for you to craft campaigns around customer journeys. Improved insight into data, sheds light on comprehensive data sets (such as the steps taken throughout a consumer’s purchasing experience) just as well as it can for small, isolated interactions. Analyzing and reacting to the small interactions, such as a phone call, can be key to delivering the best direct mail strategies that boost conversion rates.

Big Data for Finance

With the help of big data analytics, banks can analyze the market trends and decide on lowering or increasing interest rates for different individuals across various regions. Big data points out anomalies in customer data which helps business analysts and product managers be more accurate. With fraud detection algorithms, customers who have poor credit scores can be identified so that banks don’t loan money to them. With performance analytics, employee performance can be assessed whether or not they have achieved the monthly/quarterly/yearly targets. Based on the figures derived from current sales, big data analytics can determine ways to help a company scale more efficiently. Even banking as a whole can be checked to know what works and what doesn’t.

Big Data allows businesses to scale up as requirements change. Access to a complete picture of all transactions, every day, enables you to automate manual processes, save IT resources, and offer insights into customers’ daily transactions. Advanced data analytics allows you to exploit data in an easier, faster, and more reliable manner. Using big data, you are able to provide better more systemized financial services.

Big Data analytics can help enterprises add more sophistication to their internal operations and management. Most enterprise systems today are built around financial reporting and regulatory processes—most of the data remains in raw, unstructured formats. Big data analytics allows you to dissect, relate and cross-compare information in new ways. For example, using visualization techniques to better understand your portfolio against other market players gives you competitive advantages.

Big Data analytics allows you to see investment opportunities from an entirely new perspective. Analyzing published financial statements with a mountain of publicly available information, Big Data can help target investment opportunities based on risk, industry-type and holding period. You can increasingly bolster your analysis by analyzing market variables such as trade volumes, price ratios and momentum against other unstructured variables, like social media presence and political volatility. Big Data combined with AI can help to refine the robustness of models, identify better investment targets and better manage data in real time.

Big data analytics can be used in predictive models to estimate rates of return and probably outcomes. Increasing access to big data results in more precise predictions, and thus the ability to more effectively mitigate the inherent risks associated with financial trading. Big data can organize information into a system that thrives on always knowing what influences what. Big data analytics makes it possible to trade more accurately and better informed: dramatically impacting how financial transactions are executed.

Big data analytics help companies evaluate accident statistics, policyholder’s personal information, as well as third-party sources to group people into different risk categories, prevent fraud losses and optimize expenses. Introducing and applying information from new sources, it helps to create a complex behavioral pattern for each particular customer and precisely determine their risk level. Analyzing unstructured data gathered from social networks, emails, and feedback, you can increase your efficiency by creating targeted marketing campaigns that help acquire new leads and customers.

Big Data for Healthcare

Most data that’s gathered and stored is used for routine documentation, planning and forecasting. Companies with a lot of data are growing in their ability to use data to support strategic initiatives and create predictive models under different scenarios. A more data-driven supply chain also allows them to more easily benchmark within the company compared to competitors who don’t efficiently use data analytics. For example, companies can use predictive analytics to accurately forecast how their supply chain might evolve, while prescriptive analytics can be used to develop risk-mitigation strategies to fix identified weaknesses.

Big data helps analyze large amounts of info gathered from wearables, like blood pressure, sleep activity and more. This allows healthcare providers to get a more complete picture of patients’ health and devise preventive care plans that result in much better patient outcomes. Big data is also used to identify high-risk areas where patients can be provided with more efficient healthcare to reduce spend and increase patient satisfaction. By helping healthcare providers identify high-risk patients, big data and analytic tools are able to provide information about solutions for adequate intervention and reduced expenses, such as preventive care well ahead of time.

Big data makes new product development more cost- and time-effective. For example, during the research phase of product development, it’s not always clear how to use all the data discovered. With big data techniques, you’ll able to find useful data much faster and more efficiently, therefore reducing the time needed to develop the product and get it to market. Product development involves lengthy trial and error experimenting, which requires a lot of time. Big data removes the guesswork allowing research and development companies to get results quicker.

Big data creates the opportunity for more efficient trials and higher-quality results. This leads to better modeling for predictive tools and predicting drugs and biological processes through clinical and molecular data. Real-time trial monitoring improves safety and operational procedures.

Big data helps improve research efficiency, optimize innovation and clinical trials making new drugs safer. Big Data analytics allows companies to choose patients more accurately, based on those patients’ health characteristics. Large pockets of data can be analyzed quickly, making it easier to target specific populations or persons with genetic-, diseases- and/or health-related issues. Thus, trials can include smaller sample sizes with higher success rates, lower expenses and faster results.

Big Data makes Biotechnology research commercially sustainable, relating to both cost and time. For example, DNA sequencing generates a huge amount of data that needs to be analyzed with care, as the information and conclusions drawn are applicable in a wide range of spheres. Powerful analytics tools allow a company to explore, systemize, analyze and visualize mass amounts of information quickly and organize it in various layouts, showing correlations and helping identify problems and patterns.