Data Engineering

16 Amazing Benefits of Data Analytics for Healthcare Industry

Digital innovation and data analysis will and have been shaping the direction of healthcare. Analytics technologies will be a top priority for health CIOs in 2023 , especially as health information systems try to use big data to provide better care, prevent diseases, and automate all aspects of the continuum of care. Moving to a new decade, let’s go over the fundamentals of healthcare data analytics and why opting for data analytics services are beneficial for the healthcare sector: what it entails, what it can do, and how healthcare systems should continue. In the field of healthcare, we better understand what big data is and how the 3 Vs work within our environment than most businesses do. EMRs also improved by exponential factors the amount and quality of the data available to us. At the light speed-literally-the rate at which data is collected and transmitted into the networks, we are accountable for communicating from occurs. It is obvious that healthcare data analytics operates in a world of big data. The question for BI teams is how we leverage the data to transform it into something useful for our clients and actionable. Big data is capable of giving clinical professionals and physicians the opportunity to gain actionable insights into the enormous amount of data at their fingertips, with the right tools in place. It can allow them to: What Is Healthcare Data Analytics? Data analytics for healthcare is the processing and analysis of data in the healthcare industry to gain insight and improve decision-making. Through key areas such as medical costs, clinical data, consumer behavior, and pharmaceuticals, macro-and micro-level healthcare data analytics can be used to effectively streamline processes, optimize patient care, and reduce overall costs. Healthcare data is the most dynamic of all fields. Including electronic health records (EHR) and real-time recording of vital signs, data comes not only from multiple sources but must conform with government regulations. It is a complicated and complex operation, which requires a level of protection and accessibility that can only be supported by an embedded analytics system. Importance of Data Analysis in Healthcare Analytics is considered the way forward in the healthcare industry. The Covid-19 pandemic has increased the dependence on data analytics, artificial intelligence, and computer vision to provide healthcare centers and doctors with the necessary information to speed up the treatment process and increase the patient’s chances of survival. Early adoption of data analytics in healthcare helped hospitals provide quality treatment and care to patients while also reducing the pressure on doctors, nurses, and administrative staff. Data analytics in healthcare can also be termed healthcare analytics. It helps streamline and automate recurring tasks, assists the medical personnel in making a correct diagnosis of the patient’s condition, and provides care even remotely. Doctors can rely on the data-driven model to make medical decisions based on the patient’s health history. Data analysis in healthcare plays a prominent role in the following: Benefits of Data Analytics for Healthcare Industry A Business Intelligence (BI) and monitoring system, like any business, will significantly improve operational efficiency, reduce costs and streamline operations by evaluating and exploiting KPIs to recognize gaps and guide decision-making. Unlocking the usefulness of the data helps everyone from patients and caregivers to payers and vendors. Let’s look at all the aspects in which a data analytics system will affect the healthcare sector. 1. Analytics for Health Providers While healthcare organizations switch from fee-for-service to value-based payment models, the desire to maximize productivity and treatment renders data processing a key component of routine operations. Organizations can use an embedded analytics and reporting solution to: 2. High-Risk Inpatient Care Treatment for those needing emergency services can be expensive and complicated. While the costs increase, the patients do not always enjoy better care, there is a need for significant change in-hospital procedures. Patient behaviors and experiences can be detected more effectively using digitized healthcare data. Predictive analytics will identify patients at risk from chronic health problems for crisis situations, allowing doctors the ability to provide intervention measures that will reduce access to hospitals. It is impossible to monitor these patients and deliver personalized treatment plans without sufficient data, hence the use of a Business Intelligence (BI) system in healthcare is of paramount importance to safeguard high-risk patients. 3. Patient Satisfaction Most healthcare facilities are worried about patient satisfaction and participation. Through wearables and other health tracking tools, doctors may play a more active role in patient preventive care and consumers can become more mindful about their role in their own health. Not only does this information strengthen the interaction between doctors and their patients but it also reduces hospitalization levels and identifies serious health concerns that could be avoided. 4. Human Error Most preventable health concerns or appeals of insurers stem from human error, such as a doctor prescribing the wrong medication or the wrong dose. This not only increases the risk of patients but also increases the cost of premiums and the cost of paying hospital facility lawsuits. A BI tool can be used to monitor patient data and medicine taken and corroborate evidence to alert consumers of irregular medications or dosages to reduce human error to avoid patient health problems or death. This is particularly useful in fast-paced situations where doctors handle multiple patients on the same day, which is a scenario that is ideal for mistakes. 5. Health Insurance Health insurance companies undergo constantly changing regulations. And as one of the biggest family expenditures, health insurance relies on success efficiency. By collecting and interpreting data through a solution for analytics, the payers can: 6. Personal Injury Claims for personal injury are a particular concern of insurance companies, particularly in the case of fraud. But the best tool for healthcare BI will evaluate these incidents and fix the redundancies that contribute to these issues. Cases of personal injury are more effective and productive, with claim course descriptions that can be aggregated and analyzed according to typical patterns of behavior. Then, personal injury lawyers and

Data Analytics in Travel Industry: Stand Out in the Crowd

Data Analytics brings endless opportunities for the travel industry. A large amount of valuable data is generated at every stage of a trip and with a lot of people traveling around the globe, this data can offer significant insights. Travelers buy stuff online, create an itinerary, save dates on calendars, use GPS to reach their destination and so on. At every stage of their trip, they leave a data trail. The experts now analyze this data and infer various insights to enhance the customer traveling experience. However, collecting this data and connecting it is a bit of a challenging task for data analysts, but the discernments obtained can revolutionize the travel industry and make this venture a more profitable business than it was ever before. There are several ways in which data analytics services are currently assisting the travel industry to do better and meet its goals. Business Data Analytics in Travel Industry Machine Learning and Artificial Intelligence are picking up traction in the travel industry to help airlines and hotels make data-driven decisions based on accurate and actionable insights. Business analytics provide real-time insights about customers using data from multiple sources. Data like flight bookings, hotel stays, schedule patterns, repeat bookings, flight preferences, and so on is collected from websites, apps, social media, customer accounts, etc., and stored in a centralized database. This data is cleaned and processed to avoid redundancy. Analytical tools are used to analyze this data in real-time and share insights with the business to help make strategic decisions. Business analytics in the travel industry help airlines and hotels understand customer behavior and market trends. When the airline knows what a customer expects or wants, it can customize the services to enhance customer experience and thus inspire brand loyalty. Business analytics helps in the data-driven transformation of the travel industry. The pandemic has pushed the travel industry into losses and havoc. Business analytics is a way to bring the necessary change and empower the airlines to recover from the dire situation and come out stronger. Experts claim that the use of predictive and prescriptive analytics will be a game-changer in the industry. Data science and predictive modeling can help airlines uncover critical intelligence to provide real-time actionable insights that help in recovering and gearing up to make the most of new market opportunities. An important aspect to consider is automation. Even though many businesses are already using data analytics, they spend too much time, energy, and money on cleaning the data rather than running analytics and using the insights. Artificial intelligence and machine learning make automation possible by streamlining data collection, cleaning, and storage processes. This gives airlines more time to run queries and use the insights without delay. Designing an Effective Business Strategy To build an effective business strategy it is important for an organization to be aware of its customer base and its preference. With the help of data analytics and predictive analytics tools, the data collected in the form of feedback, customer reviews, social media posts, etc. is utilized to infer the behavior pattern of the customers. This, thus, causes the organizations to comprehend their customer’s needs and offer services that will bring them more benefits. The bits of knowledge are utilized by the firm to tailor customized plans for its clients. Better services offered by the firm not only help to retain loyal customers but also boost sales and improve reputation. Let’s You Stand Out in the Crowd When a traveling firm uses data analytics services to distinguish itself from its competitors, it is able to create lucrative offers that cater to the needs of the clients and at the same time help you to gain an edge over others. For instance, Amadeus (a Global Distribution System) allows its users to ask simple travel-related questions without entering any personal details. The questions can be as basic as – When will I get the cheapest flight to Italy? Or is it possible to travel somewhere for just 700 $? These types of services make it very convenient for users to clear their travel-related doubts and get reliable advice. Once a customer starts to trust the product, he/she will definitely return to use it and in the process become a loyal customer. However, retaining customers is another challenge that must be carefully dealt. Improve the Pricing Strategy While planning travel, money is one of the major concerns. People spend hours and days finding that one tour package that can offer them the best deals without cutting a hole in their pocket. But how would the firms know beforehand what the customer wants from them? The simple answer could be data analytics. All searches made by the visitors’ on a firm’s website can be used to infer the budget that a major chunk of people can afford to travel. Keeping this in mind a well-tailored travel plan can be devised and placed on the website. This is the strength of applying data analytics, you can predict what will happen next. For instance, firms like KAYAK have been using data analytics to forecast the changes in flight prices for the coming seven days. Taking Better Decisions Taking upright decisions is one of the key aspects of improving a business. With the help of data analytics, travel planners are able to draw better choices based on real predictions and not just intuitions. Data analytics allows the firms to develop customers’ profiles and thereby helping them to accurately target the market campaigns. As per Forrester, data analytics tends to increase customer responsiveness up to 36%. Being able to comprehend the demand of the customer and recent ongoing trends in the market lets the companies peep into the future and perform accordingly. The famous Nippon Airways uses data analytics to optimize its cargo management system thus making it one of the largest airlines in Japan. Making the Tough Easy Having data analytics at your disposal prevents painful losses and enhances revenue for the travel industry. To understand this,

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Easily Fixable Data Analytics Challenges Faced by Your Business Enterprise

Data analytics has become an indispensable part of the business world. Look all around and you will realize that everything is already data-driven. A bigger pool of organizations is moving towards executing this practice on their premises also. However, as per a 2016 report from Gartner, it was discovered that lone 15 percent of the aggregate businesses who attempt to execute data analytics, win the battle, and the rest stall out in the pilot phase of the venture. After running a background check on this problem, it was understood that there is a set of common issues that every one of the firms is confronting. In this article, you will discover the 10 most regular concerns upsetting the execution of data analytics ventures and the approaches to effectively resolve them. 10 Data Analytics Challenges 1. Large Volume of Data to Store The first and foremost challenge faced by the companies implementing data analytics is associated with data storage and analysis. Higher traffic websites such as the New York Times and Amazon may generate petabyte data or more in a single month. IDC in its Digital Universe reported estimated that the information stored in the IT systems of the world is doubling every two years. Another issue with all this immense data is that a major chunk of it is in the unstructured form. Documents, videos, audios, and photos are comparatively difficult to search, analyze and occupy a lot of space. To deal with these data problems, organizations are turning to various types of technologies. Technologies like tiering, compression and deduplication are being utilized to reduce the amount of space required to store the data. To manage the analysis part, firms use tools like Hadoop, NoSQL databases, Spark, BI applications, big data analytics software, ML, and AI to dig out the insights that they want. Data literacy is the solution to this challenge. Instead of collecting any data available from various sources, enterprises need to work on collecting meaningful data. Hiring data analysts and training employees to understand data literacy will help businesses collect data that is useful for decision-making. Another method to overcome the challenge is to scale the data warehouses/ data lakes in stages rather than going for a complete upgrade. This allows enterprises to manage the incoming data without spending billions of dollars at once. 2. Timely Generation of Insights The data doesn’t have to be just stored, it has to be used to achieve the business goals. As per the NewVantage Partners Survey, there are some common goals that are shared by almost every organization that deals with data analytics. Some of which include All these goals when achieved help businesses gain an edge over others in the market. However, the success of which usually depends on how quickly the generated insights are being acted upon. In case, the action time is less the data and insights tend to lose their value in the market. In order to achieve faster speed, some companies are looking forward to using new generation analytics tools and at the same time investing in real-time analytics that will dramatically reduce the time taken to generate reports. Real-time analytics are ruling the industry, thanks to powerful tools like Tableau, Power BI, Qlik, etc. The best way to generate timely insights is to choose the right tools for data storage and analytics. Where should a business store the data? In-house servers or cloud solutions like Microsoft Azure? Which analytical tools can easily handle big data and deliver real-time results? Talking to an expert will help businesses choose the right tools and customize them for their requirements. 3. Less Understanding of Analytics Data analytics has the ability to bring in precise and accurate decisions for the organizations that tend to use it. It helps them in managing their finances, launching new products, understanding their customers and much more. However, there is still a lot that needs to be done so that people have a clear picture of data analytics and its importance in today’s world. NewVantage found that only 27% of organizations in 2020 called their businesses data-driven. Moreover, 73% of businesses felt that big data management is an ongoing challenge. Seminars, small workshops on the office premises, discussions, and real-life examples are some of the ways that organizations are using to improve the understanding of data analytics among their staff. Training and empowering employees is vital to getting desired results from the data-driven model. It’s not sufficient if the top management and C-level executives understand the need for analytics. Every employee in the organization who needs to work with the new tools and systems has to realize the importance of quality data and accurate insights. 4. Recruiting Skilled Talent Organizations find it a challenging task to both retain and recruit talent that can handle their data and utilize it to derive useful insights. The 2017’s Robert Half Technology Salary Guide has suggested huge pay raise for the positions of data scientists and business analysts all over the globe. Companies are also trying to train their staff to learn some of the tools and techniques that can help them handle their data needs. But, there is still a large gap in the understanding of this field. The trend is continuing even in 2022, with Revenue Cycle Analyst and Database Administrator being the top two positions with the highest pay increase. Also, there are many firms that solely deal with data analytics and all the related operations. In case, the organizations are unable to find a suitable recruit for their firm, they can consult the professionals and get their data needs satisfied. These data analytics firms have all the expertise that is required to accomplish the given task. As an added advantage, outsourcing the work to another firm proves to be more economical than setting up a whole new section in an already established company. Hiring offshore solution providers and dedicated teams to manage data analytics for the business is a cost-effective solution. 5. Integrating

Why is Building a Data Strategy Important For Your Business Growth?

We all know that an immense amount of data is generated with every passing second. From our Uber ride to ordering a burger at McDonald’s or every transaction that we make at the ATM, everything is recorded and stockpiled for further analysis. In the past, data was perceived as nothing but a by-product of business activity, but today it has a value and is considered more of an economic asset. All the big enterprises generate numerous data, which they want to utilize for the benefit of their company but still struggle in managing, sharing, and turning it into useful information. If you are among one those business owners who are looking forward to utilizing the data that has been just stored in your systems, you have come to the right place. In this article, you will know all about why is data strategy important, how to strategically manage your data, and whom to consult by uncovering data strategy. What Are the Advantages of Implementing a Data Strategy? Listed below are some of the benefits of implementing a data strategy in your organization’s operations:- What is Data Strategy? In this modern world, we are bombarded with a continuous flow of data in our lives. The same can be said for business houses as well. But, having said so, the raw data will render useless unless we cleanse, sort, process, and churn out the insights out of it. Though we all understand the importance of it, unfortunately, most organizations are unable to leverage the benefit of the most powerful weapon in their arsenal – their data set. As per research, less than half of the organizations can leverage their data for decision making and only 1% of the unstructured data is being utilized as of now. Organizations need a proper data strategy to smoothen the operational flow. Data Strategy can be defined in simple terms as a complete and comprehensive strategy for collating, governing, analyzing, and identifying the relevant intel out of the raw data and putting it to use for making business decisions in a data-driven manner. Data strategy is inherently driven by the organization’s goal and overall business strategy. Whether it is better decision making, understanding the pain areas of the customers, or designing a product–data strategy can make a paradigm shift in the organizations’ business approach. A well-defined data strategy will comprise of – In this fiercely competitive world, having a well-defined data strategy puts a business in a better position than its competitors. A well-round strategy defines all the aspects and considers all the factors so the management can make effective data-driven decisions to drive the organization. What is a Data Strategy Framework? Any strategy can be only defined when put in a proper and systematic framework. A framework goes by as the supporting structure underlying the concept or strategy. The entirety of data strategy success is solely dependent on how properly defined the framework is. With sophisticated platforms and methodology for data retention, the organizations do get the half job done but the other half is completely reliant on the tactical and strategic understanding of the 360-degree data strategy. A framework comes here for the rescue. A properly defined data strategy framework covers multitudes of disciplines from data management. It comprises five core components that collaboratively work together as the building block for the comprehensive data management strategy – Identify, Store, Provision, Process, and Govern. Identify No matter how many terabytes of data we possess, none of this would matter much if we don’t know the proper identification and representation of the relevant content. Whether it is structured or unstructured, modifying and processing wouldn’t be possible if the data doesn’t cater to a properly defined format and value representation. Identification comprises the establishment of pertinent data element naming and proper value conventions. Having precise and accurate metadata (data about data) for identifying and referencing purposes is the sole essence of this first stage. Store Once the data is identified, the data needs to be stored somewhere safe and in a secure manner. In simple terms, putting data in a proper structure and safe storage so it can be retrieved, accessed, and analyzed whenever in need in the future, is the main agenda of data storing. Many organizations do effectively define the storage mechanism, but in practicality, there is a lot of scope for improvement that organizations need to focus on. Provision Previously organizations used to store data in silos and whenever needed, they used to retrieve the data for an individual business need. But now there is a complete shift in the business management process. Having data always ready for retrieval and usage is not an add-on capability, rather it is the need of the hour. Provision is defined as the packaging of data systematically so it can be shared and reused. Also, it provides the appropriate rules and access as the guideline for data usage. Process All other steps will fall apart if the processing of raw data into meaningful information is not done properly. Processing is the most complex part. From data cleansing to data formulating – it takes care of all the steps required to provide a unified data view. It hides all the complexities in the back end and gives a complete viewpoint for the users. Govern The last part lies with governance to ensure the efficacy and usability of the data remain high. It constitutes multiple steps such as managing data security, establishing data correction logic, setting up new data management rules, and many more. Data governance ensures that the data is consistently usable and adheres to standard data policies. Data Strategy Roadmap Once we understand what data strategy is, putting all the points together and making an actionable plan is what a data strategy roadmap does. It is the culmination of operation and strategy. Roadmap collates all the activities and puts a proper structure around them. In the initial phase, all activities look equally important, but it is crucial from

Marketing Analytics: The Secret To Your Business Success

Monitoring websites to track the success of a business is a culture and has since become a practice. It is usually interpreted that if the graph is good, the business is on the road to success and this somehow leads to significant aspects being neglected. Now, you must be wondering why so? This is a fact though, that simply by analyzing your website, you might just never be able to reach potential leads or evaluate the conversion rate of your potential customers or maybe even understand what is actually desired of your product to fulfill the demands of the market. Marketing analytics fills in the blank here and is the answer to all the forecasting you have been doing to date based on the evidence only. Implementing marketing analytics is what is to be learned now. When it comes to analytics there are people who will behave as if it doesn’t matter. But it sure does. Smart people across the globe are investing in this science so as to improve campaigns and gain insights. Data from HBR (2015) shows various ways in which marketing analytics is being used by different companies. Given the importance of marketing analytics for business growth, let me take you through it so we can see if it actually does matter to make it a part of your everyday business operations. Customer Behaviour Prediction: The key to all questions Prediction of customer behavior has been a significant part of major e-commerce firms like e-bay and Amazon. It gives them an edge over other businesses and allows them to predict the demands and needs that may arise in the future. According to a Silicon Valley-based predictive marketing company, AgilOne, these three principal classes of predictive models are recognized: Segmentation models Algorithms for grouping customers on basis of factors like the type of products they buy or the amount of money they spend etc. Prediction models Insights like customer conversion ratio (the number of people who visited the website to the ones who got influenced by the marketing campaign and bought products from the company), the likelihood of engagement, propensity to churn, the probability to buy etc. are derived from these models. Prediction models built on customer analytics can make a significant impact on any business. Recommendation models Offering recommendations about the product that the customers may like depending upon their previous purchases. All these models collectively utilize all the data from the present as well as the past and transform it into useful insights allowing businesses to improve in lucrative terms like sales and the RoI (return on investment). Prioritize and Qualify Leads Ranking your leads based on the likelihood of the actions they might end up taking is another front marketing analytics could help you peep into. This further assists in identifying prospects with similar attributes. The leads thus generated become prospects that can be turned into permanent customers. In this scenario, it is important to consider that, to generate optimum results a large number of data sets are needed. This gives bigger firms an edge over the ones that might not own such huge data quantities, by improving their return on investment. But with the right approach and help, smaller industries may also be able to gain this insight as per their needs. Driving Better Products in Market To launch a product that will suit the present demands and patterns of the market, a deep and well-predicted insight is a must. Again, marketing analytics is the answer here, the predictive algorithms allow the companies to improve the new product based on customer data piled up while taking sales and feedbacks into consideration. All these factors integrate to form a part of the bigger marketing strategy and allow the business to enhance its earnings. Creating Better Marketing Campaigns We all are proof that marketing campaigns via emails or social media allow the firms to improve their customer base. Marketing analytics gives companies the power to monitor and optimize these campaigns so that sales and ROI can see growth. The integrated tools and techniques help rule out the lacuna that might hamper the campaigns and their implementation as decided. Also, it becomes possible to monitor the current outcome of any campaign, whether it works properly or needs to be altered for the future. Taking Wiser Decisions A company may find out that digital, print, TV and radio marketing takes up around 85% of its market spending. Now amongst these many of the activities can be directly measured but spending on digital marketing can be refined using marketing analytics tools. The company can then use the results to optimize its strategy. Using these types of approaches allows the marketers to track marketing performance on real-time marketing analytics metrics and make wiser decisions for its better implementation. How Is Data Analytics Used in Marketing? Data analytics helps the marketing teams to combine structured and unstructured data and derive real-time insights about customer behavior, purchase patterns, sales graph, market trends, and more. It helps enterprises with the following: Customer Acquisition A study said that 42% of marketers use data analytics to make decisions about customer acquisition. Customer acquisition is a technique of winning new customers and convincing them to buy the products by the brand. The process includes several stages and is represented in a funnel-shaped design. It consists of targeted promotions to reach prospective customers and then provide them with relevant information to spark interest. Attracting the audience, engaging them through promotional material, convincing them to buy, and keeping them happy with customer service can be called the customer acquisition flywheel. Data analytics helps marketers by providing actionable insights to attract customers. Customer Insight Customer insight is the process of understanding and interpreting the purchase behavior of customers. This helps in knowing which products are more popular and why. Businesses can suggest products to customers based on customer insight and increase sales. Data analytics processes the unstructured data from the World Wide Web to share reports and data visuals about customers’ preferences.

Customer Analytics – Win Your Customers and Increase Revenue

Corporations across the globe are trying their best to look at the business from a customer-centric view. This exercise opens for them a window to peek into the interests of their clientele and create policies accordingly. But in today’s volatile business environment judgments built simply from past experience or intuition is increasingly unreliable. Customers today are more connected and empowered. Access to the internet all the time has allowed them to become more specific about their needs. They are aware of everything that is trending in the market. In such a scenario it becomes important for a business owner to predict a customer’s response with respect to his organization. The deeper businesses understand their customers’ preferences and lifestyle habits, the more they are able to attract potential buyers. However, it is not as simple as it seems. It is a big challenge for organizations to understand customer feedback, behavior and needs, well enough so as to make data-driven decisions about what customers are likely to respond to or what they are likely to purchase. Customer analytics or customer data analytics is that significant insight gained with the help of data science, that allows businesses to use customer data in order to make key business decisions. The information obtained from the process is used for designing effective marketing campaigns, site selection, customer relationship management, and secure decisions for the future. Insights pertaining to the customer’s feedbacks and responses drive the organizations to directions that help them outperform their competitors. Strategizing everything beginning from their production to their supply far before the demand arises, helps them improve their key performing metrics. Why Do We Need Customer Analysis? We already know how important customers are to any business. But knowing how to attract more customers and retaining existing ones is easier said than done. Using customer analysis helps get a better insight into what a customer wants and how we can keep them happy. Increase Customer Retention Rate Around 82% of businesses said that retaining customers is cheaper than acquiring new ones. Moreover, 65% of sales come from existing customers. Having repeat customers is good for the business. But, for this to happen, providing the customers with what they want is important. You have to understand their product preferences, their expectations from your business and find ways to prevent them from going to your competitors. Customer analysis helps answer these questions and increase the retention rate. The more you know your customers, the better you can meet their expectations. Better Customer Segmentation Segmenting customers and grouping them into different categories helps in targeted campaigning. There are different ways to segment customers- Segmenting customers into neat categories is possible only when you know enough about them and their preferences. Consumer data points are vital metrics that provide insights into customer preferences and behavior. The data points give enterprises a picture of the products preferred by customers, the frequency of purchase/ usage, and most used features/ functionalities, and so on. Customer analysis gives you the insights needed to know your customers. Develop Personalized Marketing Strategies Once you segment customers and prospective leads, you can plan a comprehensive marketing campaign for each segment. For example, sending emails to a customer who is old-fashioned and doesn’t check emails every day is not an effective marketing strategy. From choosing the marketing channel to determining the type of approach, customer analysis can help your sales and marketing teams fine-tune promotional tactics to increase market reach, sales, and returns. It also helps in understanding the market trends in relation to customer preferences. Accurately Predict Customer Behavior Customer behavior is hard to predict without using historical and real-time data. Customers decide whether or not to buy a product based on several factors. Customer behavior is broadly classified into the following- Reduce Customer Acquisition Costs As already discussed, acquiring a new customer is an expensive affair. However, knowing what the target audiences want can help reduce the acquisition costs. This is very useful for SMEs as they tend to have limited budgets for marketing and promotions. Acquiring customers means you need to spend on building a brand image that aligns with the preferences of your target audiences. Customer analysis provides you with the necessary information. For example, sustainability and eco-friendliness are being given more importance by some customers. If the target customer base is eco-conscious, emphasizing transparency in the supply chain and highlighting the use of sustainable resources will help build the brand image as an eco-friendly business. Enhance Customer Services Almost 90% of companies say that customer experience is the key to customer retention. Contented customers are more likely to stay with the business or come back even if they exhibit variety-seeking behavior. Even if your products are of good quality and match customers’ tastes, but your customer service doesn’t meet their expectations, you are at a high risk of losing your consumer base. Customer analysis allows you to streamline your customer service department and empower the agents to be more efficient at work. The insights derived from customer feedback will tell you exactly what is annoying them or what they expect for your customer service agents. You can use this information to hire more agents or train them to effectively deal with customers’ queries and complaints. Accurate Sales Forecasting When you acquaint yourself with your customers thoroughly, you can accurately predict the percentage of sales a product will generate. This helps in manufacturing, maintaining inventory levels, and calculating profits. Sales forecasting usually uses data from CRM systems, sales teams, and finance departments to get the complete picture. Moreover, enterprises can save money when making large investments in a product or service. Revamped Product Portfolio What if your customers want more features from your products? Could that be the reason they aren’t buying your products? Maybe your competitor offers more choices in terms of color, sizes, designs, etc. Customer analysis also helps in making changes to your products and revamping your product portfolio. The customer analysis reports can aid your R&D teams

Tips to Boost Your OTA Business Using Data Analytics

The marketing method known as “Spray & Pray” is used by many organizations. They try a whole bunch of all tactics, on a whole bunch of customers, all the time. When something works, they stick to it but when nothing works, they take up the loss and try something new until they find the magical solution. The problem is that they never pay attention to the reason why certain campaign works well and other bring losses. They do not realize that something working now, may not work tomorrow or what did not work today may work tomorrow. The essence is they don’t think analytically. OTA analytics is a game-changer for this industry. Online travel agencies (OTAs) are no exception to the “spray & pray” methodology. In fact, OTA businesses have a very complex conversion funnel as compared to any other e-commerce website, making their problem even worse. The main reason for the buying complexity is that travel booking is not part of the impulsive buy product cohort. Almost all customers do their research before booking travel tickets as travel transactions involve significant money. Every step of the sales funnel ranging from ad click to ticket booking has a significant churn rate. To understand attrition at every step of the sales funnel, online travel agencies need to have a stronghold of analytics. Understanding key performance indicators (KPI) and their impact on business are very important in any business and online travel agencies are no exception. Digital Marketing OTA Analytics Travel agencies can better utilize their marketing resources & they can strategize accordingly if they know the answers to such questions. Agencies should know customer churn rate at every sale funnel step. There are numerous marketing channels e.g TV, radio, newspaper, Facebook, Google, Bing, third-party search engines, etc. Various options in selecting marketing channels reinforce the requirement of digital marketing analytics by understanding the multi-channel marketing attribution model. Key Performance Indicators To understand digital marketing, one has to get a hold of the KPIs. Each KPI has its own business objective attached to it, KPIs monitoring makes it urgent to optimize business objectives in the first place. Starting from acquisition strategy to retention, each has its strings attached to KPIs. Here is a list of a few important KPIs which need to be monitored regularly Above mentioned KPIs are self-explanatory except the Adstock rate. Let’s understand what is adstock rate. Digital marketing does not give you immediate results. Here comes the adstock rate in the picture. You got to understand the latency effect of each channel‘s marketing campaign. Some channels have a larger delayed effect in converting the sales lead as compared to other channels. Agencies need to know the adstock rate for each marketing channel for better marketing attribution modeling. We will cover adstock rate in detail in a separate blog. The mentioned KPIs vary for each marketing channel e.g Facebook may have higher retention but the CAC of Facebook may be higher. OTA needs to understand & create its marketing strategy accordingly. In certain seasons e.g. in Nov-Dec they may see a large inflow of recurring customers as compared to other months so this type of analytics insight can help in molding marketing strategy accordingly in those months. Lead Scoring Algorithm Imagine booking agents can see the lead conversion score for every inbound lead on their screens. It is possible by making use of predictive analytics abilities. Based on historic trends of involved variables, we can predict the probability of lead to sales conversion. By predicting we can actually detach agent lead conversion skills. Pitching the right product to the right customer at right time can help in increasing the conversion rate resulting in an increase in revenue. Below mentioned data would be fetched for data warehousing to create a central database. Using Predictive propensity to buy lead score modeling, we can target the right customer at the right point in time. Right Discount selection based on lead conversion probability can also help in increasing overall profitability. Chatbots Yes, chatbots are not just fads. It can add value to your business in many ways. Considering travel leads coming to you from all across the world from different time zones, you have to employ people for 24 hours to manage demand fluctuations. Chatbots can fill that empty time gap. Chatbots can be used to filter junk leads to optimize human resources. Informative assistants can be another utility of chatbots for all travel-related inquiries and chatbots can also be used as virtual travel booking assistants. By making use of deep learning techniques in NLP, chatbots can be made really smart. Demand Forecasting Demand forecasting is predicting the future demand for travel booking. If online travel agencies knew the number of inbound leads for travel booking for the coming days, they can manage their resources efficiently. By figuring out trends, seasonality & cyclical movements in historic data, one can better predict future demand. Demand forecasting can also help to optimize manpower costs. Customer Segmentation By creating a customer persona and segmenting users based on that, can really help in conversion uplift. It is a very well-known fact that if we target a selected user set for any campaign, it gives better ROI e.g. sending direct mail detailing offers to users who have a higher probability to respond to those offers, which is better than sending direct mail to every user. Statistical clustering can be a good point to start if you need to segment your users. The mentioned techniques can help you to maximize business profit by boosting lead conversions for your online travel agency business. Do not forget to A/B test any change you are thinking to adopt. Supplier analytics Choosing the best supplier and tracking the trends and commissions is called supplier analytics. Online travel agencies survive because of the exclusive partnerships they have with their suppliers. There are a lot of factors and data that the revenue managers rely on for selecting the best suppliers and negotiating a competitive deal

E-commerce analytics: Product Recommendation Engines

Have you ever come across a business offering you more when you have already purchased one product or service? I get offers even from my hairdresser. Saloons offer head massages or facials when you go for a haircut. Many times, offers to get converted to revenue for saloons. This is a perfect daily life example of product/service recommendations. We could see such relevant offers more when we purchase products online from Amazon, Flipkart, etc. One of the premier examples of a product recommender is a contest organized by Netflix with a prize money of $1,00,000. One can easily get an idea about the business benefit Netflix might have earned by paying a huge amount as prize money for improving their movie recommendation engine. Introduction In layman’s terms, the outcome of this technique is a simple set of product/service rules based on customer product purchasing behavior. e.g. if a customer bought milk, then will he go to buy eggs too? In this data analytics technique, what is being purchased with what is been analyzed? Is buying one specific item increases the chances of buying other items? We will explore the business grocery dataset to get such answers. Product recommendation engines are also known by a few other names such as Apriori Algorithm, Affinity Analysis, Association rules, and Market basket analysis. We will not go into technical details of how it will work in this blog. The objective is to make aware smaller & medium organizations about the topic & how it adds value to the business. Why is this technique useful? Acquiring a new customer is always more costly for any business than keeping an existing customer. By this technique, businesses can increase revenue from existing customers on the basis of customer product buying rules. Product & services up-selling and cross-selling can be one of the very intuitive use cases of basket analysis. In addition to these product combos, shop floor/website layout can also be suggested accordingly. Last but not least, products can be suggested based on real-time purchasing behavior. Technical Definitions Here are the basic technical terms useful in this analysis are as below Support: The fraction of which our itemset occurs in our dataset. Confidence: Probability that a rule is correct for a new transaction with items on the left. Lift: The ratio by which the confidence of a rule exceeds the expected confidence.Note: if the lift is 1 it indicates that the items on the left and right are independent. Do not worry if these terms go off your head. You will get over them soon! R shiny playground R shiny toy product has been used for demonstration purposes. R — an open source tool can easily be downloadable from the cran website if you want to learn more about it, but it is not required for this demo purpose. We used an R package called ‘rules’ from Michael Hahsler who has implemented this algorithm in R. There’s public data of buying records in a grocery store which will be used for this exercise using the Shiny Demo App. How to use R shiny Demo product Step 1: Open R Shiny App Step 2: Upload grocery dataset public data (If you have your own dataset, make sure to change the format as per the sample dataset) Step 3: Select input data features a) Unselect header as provided dataset does not contain a header ( if your dataset has a header, please select accordingly) b) Select space separator as sample dataset having space separation. c) Keep all default values as it is for now if you find them too technical. Step 4: Explore shiny app tabs such as top 25 item frequency, basket analysis rules, sorting rules option e.g lift, support, etc. Step 5: Find out specific product rules e.g select beer from the select product dropdown. All the product rules for the selected products will be displayed under the product combo check. This feature can be used for creating specific product combos. Step 6 (Optional): if you understand the technical terms mentioned above, try to play with them to see the effect on rules. Why are E-commerce recommendations important? For an e-commerce business, recommendation solutions are a boon. It helps them sell more to their customers as the system identifies the items the customers usually like and recommend the products to them at the right time and place. Customers end up buying items that they never had thought of buying initially. This is why recommendation systems are important. Want to implement such a system in your business? You should be connecting with renowned data analytics consulting services for the same. Conclusion & business scope Isn’t it amazing! How ecommerce analytics solutions can provide what customers might need to add to the cart in real-time. This is a very basic toy example of product recommendations based on a rules algorithm. Advanced recommender engines make use of other data points from customer behavior in addition to advanced algorithms such as factorization machines, collaborative filtering, etc. Now you can fairly co-relate how Amazon recommends different products. Any small business can make use of this technique to add value to the business in some other ways: Product combo suggestions for a marketing campaign. Website or store layout re-alignment e.g if eggs are bought with milk, re-organize accordingly Product cross-selling, real-time web/App product recommendations. We here at DataToBiz with a team of data analytics and machine learning experts can support your business to solve problems by providing an affordable machine-learning platform for your business data. Contact Us for more info.