Business intelligence is all the rage in the highly digitized world we live in. Every second of every day, some business or the other makes very important decisions driven by data analytics application.
In order to understand what data analytics application looks like in businesses, we first need to understand what data and data analytics are in this context.
Let’s dive in.
What is data?
If we were to define data literally, it is meaningful information often represented in the form of statistics.
Think of data as pieces of a jigsaw puzzle. Each piece, on its own, doesn’t make much sense. However, all the pieces put together create a complete picture that you can make logical sense of.
What is data analytics?
The concept of data analytics is derived from data analysis, a term that dates back to the early 1960s. However, data analytics is a way more modern concept that emerged with the popularity of big data in 2005.
.Without getting too technical, let’s define data analytics. It’s basically the systematic analysis of statistical data with the objective of drawing meaningful inferences.
Before self service data analytics tools were invented, data analytics used to be computed using Microsoft Excel and, later, SPSS along with other more modern statistical tools.
Why is data analytics application important for businesses?
Let’s admit it. Making a decision on the basis of opinions as opposed to facts is like shooting arrows in the dark – you’re bound to move in a directionless manner and miss your target 99% of the time. That’s something that no business can afford if it’s striving for success.
Because business decisions need to be made logically, they need numbers. The good thing about numbers, apart from them being logical, is that they don’t lie (at least when they’re not window dressed). In addition to that, data analytics is one of the core growth levers of businesses today. With this in mind, more and more business managers around the world now turn to their data to inform key decisions.
Data analytics application is found in every industry and profession barring Art, Drama and Music. It’s that widely used. Whether you’re an investment banker assessing the profitability of a client’s asset portfolio or a sales manager who needs month-on-month revenue growth figures to gauge business performance, you’ll find yourself turning to data analytics time and time again.
If you were to list down the possibilities of data analytics application, there would probably be no end to it. Let’s simplify things and focus on the most common uses of data analytics in the business context:
5 common data analytics application methods in businesses
When we talk about digital advertisements, we refer to online ads.
Data analytics are used in digital advertising to measure ad performance. Digital marketers make key business decisions based on the numbers that online ads render. Let’s explore the data analytics application of some of the most commonly promoted ads in today’s world.
If you surf the internet at all, you’ve probably seen advertisement banners displayed on different websites. Those are display ads. The digital marketers behind them observe their data analytics or performance metrics using Google Ads as frequently as every day to see if they’ve driven the desired results.
Social media ads
The sponsored advertisements that pop up in your Facebook and Instagram feeds are all games of numbers. On a daily basis, digital marketers around the world observe trends in the metrics of social media advertisements such as click-through rate (CTR) and return on ad spend (ROAS) in order to gauge how well an advertisement performs.
More importantly, ad management tools such as Facebook Ads Manager are built with data analytics to help digital marketers make key decisions such as increasing (or decreasing) advertising budget allocations, multiplying the promotion efforts of the most-clicked items and so on.
The data analytics application is evident in present-day digital advertising.
Other than that, pay-per-click advertising, popularly known as PPC. Have you ever seen an ad appear at the top of Google search results? That’s a PPC advertisement.
When a logistics company expands its delivery zones, there is data to back such a decision.
Data analytics is an essential decision-making tool for both third-party logistics companies and businesses with in-house delivery fleets.
In e-commerce businesses, metrics such as the percentage of orders delivered in a day or the return rate in a particular month inform logistics managers and other concerned stakeholders on what steps to take.
For example, if the return rate for the month of May 2022 is above 50%, decision-makers will most likely use self reporting data analytics tools to investigate why customers returned more than half of their dispatched orders. Simply put, data analytics informs a key decision in this scenario.
Financial analysis and reporting
Day in and day out, finance professionals everywhere in the world use self reporting data analytics tools to make decisions.
Let’s say that the finance manager of a business uses a business intelligence software to observe the month-on-month profitability trend for the last six months. He learns that profits have actually increased in the current month compared to last month, which means that the company can afford to dole out a going away gift to a resigning employee or spend on a team building activity.
In the scenario we just presented, data analytics drives important financial decisions.
Businesses in the transportation industry such as logistics companies, mass transit service providers and ride hailing businesses all depend on data analytics to operate successfully.
Let’s take a simple example. The Live Operations team of a private pick-and-drop service looks at its average journey time of a particular route, Route X, and discovers that it exceeds the industry average by 40% – that’s a significant number.
In an ideal scenario, managers will decide to uncover why the vehicles assigned to Route X take more time than is required to drop passengers off to their destinations.
In the example above, do you see how an important decision was made using data analytics?
Have you ever wondered how the customer service team a of business draws data analytics from recorded calls on their helplines? It’s no longer the work of Microsoft Excel. Businesses need self reporting data analytics tools to find out if their customers are satisfied with their products or services.
Customer service professionals in different industries use response rate, customer satisfaction rate (CSAT) and other metrics as performance benchmarks to serve their customers better. These metrics can easily be built into business intelligence software for them to access ad hoc reports when needed.
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Whether a business is in the e-commerce sector or the transportation industry, data analytics application is a rulebook for them to follow.
It’s safe to say that every business needs an embedded business intelligence software in order to work towards achieving its smaller and broader objectives.Need embedded business intelligence software? Schedule a demo today.