When was the last time you assessed your company’s sales information? More specifically, do you know how much money you make per every sales cycle? Or do you know all the different sources that bring traffic to your website? And have you analyzed every aspect of your competitor’s marketing campaigns to find out what’s working for them and what strategic missteps they’re making? If you can’t answer these and other business-related questions, then it’s time to adopt big data analytics into your business operations.
The concept of big data analytics for business is far-reaching and entails everything from deep analysis of organizational performance to simple analytics details. It’s nearly impossible to make big moves without keeping track of details that affect operations at different levels. That said, big data analytics could be what your business is waiting for to grow exponentially.
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However, data analytics for business is equally useless when you can’t understand how to use it to address issues and achieve short and long-term success. So how do you sort through the huge amount of big data analytics to find what is pertinent and valuable to your business? Let’s look at what data analytics is and how you can use it to drive business success.
What is big data analytics for business?
Data analytics is the process of analyzing big data in order to find trends and draw conclusions about the information they contain. It helps businesses optimize processes such as marketing and customer service, thus increasing the overall efficiency of operations.
There are different techniques and methods that are used to perform analysis. Many of these methods have been automated into algorithms and mechanical processes that work on raw data, making it easier for humans to consume.
Data analysis is broken down into four main categories as discussed below:
It’s focused on understanding changes that have occurred in a business by interpreting historical data. This often involves the measurement of metrics such as return on investment(ROI). Descriptive analytics doesn’t directly inform decisions. It summarizes data in a descriptive and meaningful way. For example, month-over-month sales growth, total revenue per subscriber, and year–over–year pricing changes. All these describe what has happened in a business within a certain period.
It uses statistical modeling, data mining, and machine learning to predict future outcomes. Predictive analytics scrutinizes current and historical data patterns to determine if those patterns are likely to occur again. This allows organizations and investors to use their resources according to possible future events.
It examines data to help businesses answer questions as to “why” something happened. Diagnostic analytics is characterized by techniques such as data discovery, data mining, drill-down, and correlations. These techniques dig deeper into the findings from descriptive analytics to find out why they got better or worse.
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Predictive analytics examines big data to determine the course of action. It does away with the guesswork, saving data scientists and marketers time to connect the dots to understand what their data means. It’s characterized by simulation, complex event processing, graph analysis, machine learning, and neural networks. Organizations use predictive analytics to limit risk, detect fraud, increase efficiency, and meet business goals.
The process of business data analysis involves several steps to make sense of the available data. Each step is equally vital in ensuring that data is correctly analyzed. Let’s take a look at the four most essential steps that make up a data analysis process.
1. Determining data requirements: Before diving into the nitty-gritty of data analysis, businesses must first define their goals regarding data use. The need for data analysis usually stems from business problems.
2. Collecting Data: It’s now time to collect data needed for analysis. This is an essential step because the type and quality of data collected determine how thorough the analysis will be. Data can be collected from sources such as cameras, environmental sources, computers, and online sources.
3. Organizing the Data: Data must be organized so that it becomes easier to analyze it. Organization can be done on spreadsheets or any other software that accommodates statistical data.
4. Cleaning the Data: This is the final step before analysis. Data is cleaned to ensure that it’s complete and that it has no errors or duplication. `
Applications of data analytics for business
IMPROVE THE DECISION-MAKING PROCESS
Data analytics is like a cornerstone for all strategic business decisions. It has diverse applications and far-reaching use-cases. From addressing operational inefficiencies to choosing what content to create to planning marketing campaigns, it gives you a 360-degree view of your business. By utilizing data-based facts, businesses can make fast and educated decisions, thus maintaining their competitive edge.
Big data is typically concrete and logical in ways that intuition and gut instincts aren’t. This instills confidence in decisions, allowing organizations to fully commit to a strategy without worrying about what could go wrong. And with modern data analytics technology, it’s easier to continuously collect and analyze new information to understand situations as they change.
The impact of data analysis is not only felt by big companies. Small businesses are beginning to embrace it as well in their decision-making processes. If your business isn’t yet following through with these trends, then you’re going to lag in every aspect of business operations.
A great example of a company that has successfully used data analytics to make significant business decisions is Starbucks. It uses data analytics such as traffic patterns and demographics to pinpoint ideal locations to set up its stores. This was after hundreds of its locations were shut down in 2008 due to less traffic.
When shopping online, it’s common to see advertisements for products you may have recently searched for. But sometimes, the pop-up ads may not necessarily align with your interests and may come across as annoying. If a business wants to deliver an optimal customer service experience, they need to rely on customer analytics.
Customer analytics refers to the process of collecting and analyzing customer feedback to gain valuable insights. It helps businesses understand customers’ needs and expectations, making it easier to develop targeted marketing strategies.
Below are two critical ways in which customer analytics can boost an organization’s customer service efforts:
• Identify trends: As companies scale up, it becomes challenging to capture feedback from every customer. But with the help of data analytics, businesses can now recognize trends and patterns in customer behavior and use that information to improve customer service standards. Data analytics solutions should include visualizations and dashboards that allow you to identify areas that need more profound analysis.
• Build better UX: Data analytics can help businesses assess how their customers perceive their brand. The data should enable you to dig deeper into fulfillment rates and delivery time and implement strategies to increase customer satisfaction.
Burberry has successfully used data analytics to boost sales, performance, and customer satisfaction. Through its loyalty and rewards programs, the company collects information about its customers. It then uses the data to offer personalized product recommendations.
Chief Marketing Officers (CMOs) are beginning to embrace data analytics for business in their decision-making processes. It provides the knowledge necessary to figure out the right marketing strategies for specific audiences.
Nowadays, customers no longer make a direct journey from learning about your product to purchasing it. Instead, they conduct online research to find out if it’s worth buying. This is why you need to tweak your marketing strategy to target and nurture potential customers across different digital platforms.
Data analytics, for example, can be used to identify deals and sales campaigns that generate high closing sales. It can also show how long it takes to close deals with specific customer groups. With such information, businesses can channel their marketing efforts in the right direction.
Taking this point further, data analytics has also made it possible to compare advertising channels such as search engine advertisements, banner ads, and email newsletters and determine which ones generate more sales to your company.
Netflix is an example of a company that uses big data analytics for effective marketing. They collect data from over 209 million subscribers and use it to send suggestions of movies they should watch, based on their past searches (they use machine learning to achieve this goal, but that’s a story for another article).
Read more about Real-time Big Data Analytics
Data analytics can help companies improve efficiency in their daily operations. Gathering and analyzing supply chain data, for example, gives insights into the flaws in the production process and predicts future problems that may arise. It also helps retail businesses optimize their inventory by determining demand patterns based on seasonality, secular trends, and holidays.
Data analytics is also helpful in human resources. It’s used to find hard-working and devoted workers by scrutinizing their professional portfolios from platforms such as Glassdoor, LinkedIn, and Indeed. It then examines their work experience based on the hiring manager’s requirements.
Data analytics for business: Summary
The vast amount of data generated by enterprises holds valuable insights that can be used to streamline operations, improve customer experience, perform effective marketing campaigns and make strategic business decisions. However, you can’t access all these benefits without deploying the right data analytics tools and processes. While raw data is valuable, you need data analytics to unlock the insights that your business needs to grow. And that’s why we are here for you. Addepto is an AI consulting company that’s experienced in data analytics for various business sectors. We work with big data every day and help our clients make the most of it. We can help you as well! Drop us a line for details.