[00:08] of business data and turn them into clear answers about what happened, why it happened, what may happen the next, and what action a company should take. That is exactly what business analytics using Excel is all about. Hello everyone
[00:22] and welcome back to Simply Learn. In this session, we will learn how Excel can be used to organize data, analyze performances, identify patterns, create reports, and support smarter business decisions. This topic is important
[00:36] because businesses generate data through sales, customers, marketing, finance, and operations. Collecting data is not enough. Companies must use it to improve performances and plan ahead. So, in this session, we will first understand
[00:50] business analytics, learn how the analytics process works, and explore how Excel supports smarter business decisions. We will also explore the four main types of analytics. Descriptive analytics that explains what happened.
[01:04] Diagnostic analytics explains why it happened. Predictive analytics estimates what may happen the next. And prescriptive analytics suggests what action should be taken. Then, we will understand why Excel remains a used tool
[01:18] for business analytics. We will compare Excel with Power BI and learn how Excel supports data exploration, statistical analysis, reporting, dashboards, forecasting, and data modeling. After that, we will take a walk through of the
[01:32] Excel interface. We will understand rows, columns, cells, worksheets, the formulas bars, tabs, file formats, and options for importing data from files, folders, databases, and other sources. Then, we will move into data
[01:47] organization and preparation. We will learn shortcuts, paste special, sorting, filtering, grouping, subtotals, removing duplicates, text to columns, and data validation. And not only this, we will also see how drop down list and error
[02:02] alerts can prevent incorrect entities. Wait, next we will work with important Excel functions including sum, average, count, subtotal, sum if, sum ifs, count
[02:14] if, if, and and or. We will also understand relative, absolute, and mixed cell references. We will see why dollar signs are used in formula and how the F4 key can lock rows, columns, or cell references. Finally, we will move into
[02:30] reporting using pivot tables. We will learn how to summarize large data sets, calculate totals and averages, compare products and regions, create calculated fields and items, and use slicers and timelines for interactive filtering. By
[02:45] understand how to organize, analyze, summarize, visualize, and report business data using Microsoft Excel. So, before we continue, here is a quick opportunity for every one of you. If you want to lead AI-driven business
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[04:07] Here is a quick quiz question for you just to test your understanding. And the question is, which type of business analytics uses historical data to explain what has already happened? And your options are option A, diagnostic
[04:19] analytics, option B, predictive analytics, option C, descriptive analytics, or option D, perspective analytics. Let us know your answers in the comment section below and let's see who will give the right answer first.
[04:31] So, now without any further delay, let's get started. So, let us begin with the most basic question. That is, what is business analytics? So, business analytics is the process of collecting, organizing, analyzing, and
[04:44] interpretating business data to support better decision-making. So, in simple words, it helps a company to understand its performance, identify problems, discover opportunities, and decide what action should be taken. See, every
[04:58] business generate data. When a customer purchases a product, visits a website, clicks on an advertisement, contacts customer support, or submits a review, data is created. Business also generate information through financial
[05:12] transaction, marketing campaigns, inventory systems, mobile applications, employee activities, and social media platforms. However, simply collecting large amount of data is not enough. The data must be studied and converted into
[05:26] meaningful insights. For example, consider a clothing company that sells product through physical stores and an online website. Every day, the company collects information about customer purchases, product returns, website
[05:39] visits, payment methods, delivery times, locations, and product ratings. Looking at thousands of individual records may not provide a clear answer. However, when the company analyze this, it can identify which products are selling,
[05:54] which locations generate the most revenue, when customer demand is highest, and why certain products are frequently returned. And based on these insights, the company may increase the stock of popular products, reduce the
[06:08] price of slow-selling items, improve product quality, or create personalized offers for customers. This is how the raw data is converted into useful business information. See, business analytics does not
[06:23] decision-making. Instead, it provides reliable information that helps managers and business leaders make better choices rather than depending only on assumptions. They can make decisions
[06:36] using evidence. Now that we have understand the meaning of business analytics, let us look at how the complete process works. Business analytics generally follows a series of connected steps. First step, identify
[06:50] the business problem. The first step is to clearly identify the business problem or question. A company may want to know why sales are falling, which customers are not valuable, which marketing campaign is performing well, and how
[07:04] operational costs can be reduced. The business question should be clear and measurable. For example, instead of asking how can we improve the business, the company may ask why did online sales decrease during the last 3 months? A
[07:19] clear question helps analytics identify the right data and choose the appropriate method of analysis. Now step two, collect relevant data. The next step is collecting data related to business problem. Businesses collect
[07:32] information from websites, mobile applications, sales system, customer surveys, social media platforms, financial records, customer support systems, and many other sources. They may also use external information
[07:46] such as market research reports, competitor pricing, economic data, and industry trends. However, collecting more data does not always produce better insights. The information must be relevant to the problem. For example, if
[08:01] a company wants to understand why online sales have dropped, it may study website traffic, product prices, checkout performance, customer feedback, and marketing campaign data. So, now the step three, clean and prepare the data.
[08:16] After collecting the data, it must be cleaned and prepared. Business data may contain duplicate records, missing information, incorrect values, spelling differences, or inconsistent formats. For example, the same city may be
[08:30] entered as Bengaluru, Bangalore, and BLR. The customer's age may be missing, or the same transaction may appear more than once. If these issues are not corrected, the final analysis may produce inaccurate
[08:45] results. Data cleaning involves correcting errors, removing duplicate records, handling missing values, and converting information into consistent format. Now, let's look at the step four. Analyze the data. The next step is
[08:59] analyzing the prepared data. Analysts study the information and find patterns, trends, relationships, differences, and unusual changes. For example, an analyst may discover that most product returns are coming from one particular region.
[09:15] The analyst may also discover that customers who receive personalized emails are more likely to purchase more. Depending on the business problem, analysts may calculate averages, compare performances, study customer groups,
[09:29] create forecast, and identify relationships between different factors. So, the purpose is not only to work with numbers, but the purpose is to find information that can help the business take action. Now, the step fifth.
[09:43] Present the insights. Once the analysis is complete, the findings must be communicated clearly. Business leaders may not have the time to study thousands of rows of the data. They need a simple explanation of what the data is showing.
[09:58] Analyst commonly use charts, dashboards, reports, and presentations to communicate their findings. A good analyst does not simply display numbers. The analyst explains what changed, why it mattered, what actions
[10:12] the businesses can consider. For example, instead of only saying that example, instead of only saying that website sales dropped by 15%, the analyst may explain that most customers are leaving the website during the
[10:25] payment stage. This makes the insight more meaningful and actionable. Now, let's look at the step six, that is taking action and measuring results, which is the final step. Business leaders may change a marketing strategy,
[10:40] improve a product, increase inventory, reduce prices, redesign a website, or introduce a new customer service process. After taking action, the company must measure the results. It should check whether the decision
[10:54] improved performance or solved the original problem. Business analytics is therefore not one-time activity. It is a continuous process of collecting data, analyzing information, taking action, measuring
[11:07] results, and improving strategies. Now, let's look at the tools and technologies required. So, the choice of the tools depends upon the size of the data, the complexity of the problem, and the requirements of the organization. So,
[11:22] let us understand some of the most commonly used tools. First, Microsoft Excel. Microsoft Excel is one of the most widely used tools in business analytics. It allow analysts to organize data, apply formulas, create pivot
[11:37] tables, perform calculations, and build charts. For example, a sales manager can use Excel to compare monthly sales, identify the best performing products, and calculate revenue growth. Excel is specially useful for beginners because
[11:51] it is simple, accessible, and commonly used across industries. However, when the amount of data becomes very large, businesses may need more advanced tools. And that's bring us to SQL. SQL stands for Structured Query Language. It is
[12:07] used to access, manage, and analyze data stored in databases. Many organizations store customer records, transactions, product details, and employee information inside databases. An analyst can use SQL to
[12:21] retrieve specific information. For example, an analyst may use SQL to find all customers who purchased a product during the last 6 months, or identify the top-selling products in a particular region. SQL is an important skill
[12:35] because it allows analysts to work directly with large business data sets. Now, let's look at the other tool. That is Power BI. So, Power BI is a business intelligence and data visualization tool developed by
[12:50] Microsoft. It helps analysts connect data from different sources and convert it into interactive dashboards and reports. For example, a company can create a Power BI dashboard that shows total sales, revenue, a customer growth,
[13:05] performance. Managers can use filters to explore the data and compare results. Power BI makes complex information easier to understand through charts, graphs, maps, and performance indicators. Now, Tableau.
[13:22] Tableau is another powerful data visualization and business intelligence tool. It helps analysts create interactive charts and dashboards without writing large amount of code. Tableau is commonly used to present data
[13:35] visibility visually and communicate business findings. For example, a company may use Tableau to display customer locations and map, compare sales across regions, or analyze changes in business performance over time. Both
[13:49] Tableau and Power BI help decision makers understand data quickly. Python. Python is a programming language widely used for data analysis, automation, statistics, and machine learning. It is specially useful when analysts need to
[14:04] work with large data sets and perform advanced analysis. Python provides libraries that can help clean data, perform calculations, create visualization, and build predictive models. For example, a retail company
[14:17] may use Python to forecast future product demand or identify customers who may stop purchasing from the company. Python may require more technical knowledge than Excel, but it provides greater flexibility. Wait, we do have
[14:32] another technology, that is cloud-based analytics platform. Many businesses now use cloud-based analysis that allows company to store and analyze large amount of data without managing all the physical technology themselves. Teams
[14:47] can access information from different locations, collaborate on reports, and scale their systems as amount of data increases. Cloud analyst is especially useful for large organizations that collect information from websites,
[15:02] mobile applications, customer platforms, and global operations. The important point to remember is that the tool alone do not create business value. An analyst must understand the business problems, select the right information, perform
[15:17] the correct analysis, and communicate the results clearly. So, these are all the tools and technologies required for business analysis. Now, let's look at the applications of business analytics. Business analytics is used in almost
[15:31] every department of an organization. So, now let us explore some of the most important applications. First, marketing analytics. In marketing, analytics helps companies understand customers, identify target, measure campaign performance,
[15:46] and calculate the result on marketing investment. For example, a company may compare social media, emails, search advertising, and video campaigns to identify which channel generates the
[15:59] highest number of customers. It also analyze customer age, location, interest, and purchasing behavior to create more relevant advertisements. Marketing analytics helps companies spend their budgets more effectively.
[16:14] Now, the second application is in sales analytics. In sales, analytics helps teams identify high-quality leads, forecast future revenue, compare sales performance, and understand customer behavior. Sales
[16:28] managers can use dashboards to see which representatives are achieving their target and which products are generating the most revenue. Analytics can also help identify why customers abandon the purchase or why certain leads do not
[16:45] convert into customers. Now, the another application, financial analytics. In finance, business analytics supports budgeting, cost control, revenue forecasting, fraud detection, and risk management.
[16:59] Companies can analyze expenses to identify unnecessary cost and improve profitability. Banks and financial institutions use analytics to detect unusual transactions that may indicate fraud. This also helps
[17:14] organization understand cash flow and prepare for the future financial requirements. Now, operations and supply chain analytics. The another application of business analytics. In operations, analytics helps companies manage
[17:29] inventory, supply chains, production schedules, delivery routes, and business processes. For example, a retail company can forecast demand and ensure that the right products are available in the right locations. This reduce excess
[17:44] inventory and prevents product shortages. Delivery companies can analyze traffic, weather conditions, and delivery history to identify faster routes. Now, the human resource analytics. Human resource teams use
[17:58] analytics to study employee performance, hiring trends, training requirements, satisfaction levels, and employee turnover. For example, a company may analyze why employees are leaving and identify common factors such as
[18:11] workload, salaries, management, or limited growth opportunities. These insights can help the company improve employee retention and create better workplace policies. Last but not the least, customer service analytics.
[18:27] Customer service teams analyze complaints, customer feedback, response times, resolution rates, and satisfaction scores. If many customers complain about the same product feature, the company can
[18:41] identify the issue and improve the product. Analytics can help customer service teams understand the most common customer questions. This allows businesses to provide faster and more personalized support. So, these were
[18:56] business analysis. Now, let's see how data drives smarter business decisions. In the past, many business decisions were based mainly on experience, assumptions, and personal opinions.
[19:13] Experience is useful, but it may not always show the complete picture. Business analytics combines human experience with reliable data to help companies make better decisions. For example, a company may believe that
[19:28] young students are its main customers. However, after studying its sales data, it may discover that working professional between the ages of 30s and 40s bring the highest revenue. The company can then change its
[19:42] advertisement, product features, and marketing budget based on the real customer behavior. Data also helps businesses find problems early. A sudden increase in product returns may mean there is a quality issue. A drop in
[19:57] website sales may show that customers are having trouble during checkout. More customers complain may indicate poor service. When companies identify these warning signs early, they can fix the problem before it becomes more serious.
[20:11] Analytics also help companies provide a more personal experience. Streaming platforms suggest movies and shows based upon what people love to watch. Analytics also help companies provide a more personal experience.
[20:26] Streaming platforms suggest movies and shows based upon what people watch. Online shopping websites recommend products based upon their browsing and purchase history. Banks provide financial offers based on customer
[20:40] needs. This improves a customer experience because people receive products, services, and information that are more useful to them. Data can also help businesses reduce risks. Companies can predict customer demand, identify
[20:55] problem possible financial losses, detect suspicious activities, and prepare for changes in customer behavior. Instead of waiting for a problem to happen, they can plan and take actions in advance. However,
[21:08] analytics is only useful when the data is correct and reliable. Incomplete, outdated, incorrect, or biased data can lead to poor decisions. Companies must also protect customer information and use it responsibly. Data privacy,
[21:23] security, accuracy, transparency are all important parts of business analytics. And that brings us to the end of this session on business analytics using Excel. In this video, we understood what business analytics, importance of
[21:38] business analytics, and explored descriptive, diagnostic, predictive, and prospective analytics. So, the key takeaway is simple. Business analytics is not only about collecting data. It is about organizing data, asking the right
[21:52] questions, finding useful patterns, and converting results into actions that improve performance. So, if you found this video helpful, make sure to like, share, and subscribe to Simply Learn for more videos on business analytics,
[22:05] Microsoft Excel, Power BI, data analytics, and the latest business and technology skills. Thanks for watching, and I'll see you in the next session.