Business Analytics analyses data to build mathematical models that aid organizations’ decision-making process and assist them in achieving their objectives. There is a wide range of information available for organizations to use, but the data they select and the reasons they use it differ by industry. In the rapidly evolving marketplace of today, data is a significant resource. Understanding how to interpret and convey data is a crucial ability for business professionals that may help them make wise decisions.
Big Data analytics is carried out with the use of sophisticated software. Businesses can do this to shorten the analytics process and make decisions more quickly. In essence, Big Data Analytics technologies enable quick and effective analytical processes. Businesses benefit from this ability to operate more quickly and be more agile. That’s why Business Analytics for marketing strategies has attracted investment from businesses. Consider a company you are familiar with that relies on rapid and nimble judgments to be competitive. This article provides five examples of large brands leveraging big data analytics in the real world. To learn more, continue reading.
The Global Business Analytics industry will increase at a compound annual growth rate of about 30% in the years ahead, with revenue exceeding 68 billion US dollars by 2025, up from roughly 15 billion US dollars in 2019.
The way businesses conduct their operations is changing because of the developing business tool known as real-time analysis. In other words, increasingly, organizations are now using Business Analytics to enable proactive decision-making instead of responding to events as they happen.
Business Analytics is a flourishing technology as it can be used in every sector where data is accessible and collected. This information can be utilized for a variety of purposes, such as enhancing customer service, enhancing the organization’s ability to detect fraud, and providing insightful information on online and digital sources.
Regardless of how to leverage Business Analytics, the main result is always the same: business problems are solved by utilizing the pertinent data and converting it into insights, giving the firm the information it needs to make proactive decisions. The company will have a competitive advantage in the market in this way.
Understanding what the firm would like to do better or the issue it wants to fix is the first step in the Business Analytics process. The goal may occasionally be divided into smaller goals. The business stakeholders, business users with the necessary domain knowledge, and the Business Analyst decide on the pertinent data that is required to achieve these business objectives. Important concerns like “what data is available,” “how can we use it,” and “do we have enough data” must be addressed at this point.
This stage includes cleaning the data, computing for missing data, eliminating outliers, and combining variables to create new variables. Plotting time series graphs is done so that any patterns or outliers can be seen. Excluding outliers from the dataset is crucial since they frequently influence the model’s accuracy if left in the data set. Garbage in, garbage out (GIGO), as the phrase goes!
At this point, the analyst will identify every element that is connected to the target variable utilizing statistical analysis techniques, including correlation analysis and hypothesis testing. The analyst will conduct a simple regression analysis to examine if straightforward predictions can be made. In addition, several assumptions are used to compare various groups, and these assumptions are evaluated via hypothesis testing. When attempting to glean useful insights from the data, it is common for the data to be chopped, diced, and compared at this stage.
Being proactive in your decision-making is key to Business Analytics. At this point, the analyst will use decision trees, neural networks, and logistic regression to model the data using predictive methods. These methods will reveal trends and insights that emphasize connections and “hidden evidence” of the most important variables. The analyst will compute the prediction errors by comparing the predictive values with the actual ones. Predictive models are typically run multiple times, with the best model being chosen based on model accuracy and results.
To find the optimum solution within the available constraints and limits, the analyst will now use the predictive model coefficients and results to run “what-if” scenarios utilizing targets defined by managers. Based on the lowest error, management goals, and instinctive awareness of the amounts most aligned with the organization’s strategic aim, the analyst will choose the best sales strategy analytics solution and model.
The analyst will next decide and act per the model’s deduced insights and the business ambitions. The result of the action is measured when the required amount of time has passed.
Finally, the decision and action outcomes and any fresh model-derived insights are documented and upgraded in the database. The database is updated with details like “was the choice and action effective?” “What differences did the treatment group show from the control group?” and “what was the return on investment?” As a result, a dynamic database is constantly updated once fresh information and understanding are discovered.
Let’s explore some real-world examples to understand different strategies of marketing.
Microsoft: Enhanced teamwork and productivity with the business behemoth MS moving the offices of its engineering departments in 2015, one of the top Business Analytics application instances was observed. The business realized that to improve performance by fostering greater collaboration, they needed to interact with their personnel more face-to-face. The MS Workplace Analytics team proposed the idea that if a group of 1200 individuals downsized from 5 offices to 4, it might improve employee collaboration because fewer staff members would need to travel as far for meetings. This action resulted in a net annual employee time savings of 520,000 USD, or 100 hours of labor each week.
Increase in online shop sales after a company installed a sales dashboard in response to uneven sales. The company changed its sales approach by adjusting the target setting system to adapt to data after the firm’s dashboard made it clear that data was not driving sales. With this deployment, their sales were able to increase by 24%.
Uber improved its customer service with BA – COTA (customer obsession ticket assistant), which was based on Machine Learning and Natural Language Processing and was developed by Uber in 2018. It helps agents reply to support tickets more quickly and accurately. After the first iteration, they saw a 10% reduction in ticket resolution time. After that, Uber developed COTA v2, which put an emphasis on deep learning architecture and underwent A/B testing before going live.
The following real-world Business Analytics examples are given to show how these tools may be used to address various problems and assist organizations in achieving their goals:
Enhancing financial effectiveness: A bioscience company used Business Analytics to figure out why, despite the recent expansion, it had a low collection percent with many claims being denied, and a high amount of money owing to it. The organization employed account-based metrics, a tactic that boosts interaction with specific accounts, to find the reason behind the disproportionate claim’s denials with the use of software that enabled simple online reporting. The corporation was able to settle millions of dollars worth of disputed claims because of Business Analytics.
Increasing sales: An online store established a sales dashboard to stabilize and increase its sales. It was evident from the sales dashboard that data wasn’t influencing sales. This prompted the retailer to adjust its target-setting methodology and sales strategy. Sales increased by 24 percent.
Designing marketing plans: After experiencing initial success, a clothes retailer saw a plateau in consumer purchases and sales. The company decided to set up a retail dashboard customized to the demographics of its current and potential customers. The retailer found areas for improvement and determined where sales were highest thanks to improved access to this information. Thus, the shop could divide the customer base into relevant groups and use Business Analytics for marketing strategies for each. The store might increase its client base and market to customers more effectively by leveraging internal data and analyzing potential consequences.
Data Analytics is the key to unlocking the useful insights businesses generate in their wide variety of data. Data Analytics may assist a company with everything from tailoring a marketing message to a specific client to recognizing and reducing business hazards. Let’s examine the five advantages of adopting data analytics.
1. Personalize the Shopping Experience for Customers
Businesses collect customer data from a range of sources, including social media, conventional retail, and online shopping. Businesses can use Data Analytics to create detailed customer profiles from this data and learn about consumer behavior to provide a more individualized experience.
2. Promote Thoughtful Business Decisions
Businesses can use Data Analytics to improve decision-making and lower financial losses. While predictive analytics can forecast potential outcomes, prescriptive analytics can suggest how the company should react to these developments.
3. Streamline Business Processes
Organizations can improve operational effectiveness with the aid of Business Analytics. The supply chain’s data can be collected and analyzed to help identify manufacturing bottlenecks or delays as well as to predict potential future problems. If a demand prediction suggests that this vendor won’t be able to handle the volume required for the festival season, an organization may choose to augment or replace them. Production snags would be avoided in this way.
4. Minimize Risk and Handle Setbacks
Business involves several dangers. They include customer or staff theft, legal responsibilities, team member safety, and uncollected receivables. A company can use Data Analytics to assess risks more accurately and implement precautions. For example, a retail chain could utilize a propensity model, a statistical tool that forecasts future behavior or occurrences, to determine which locations are most susceptible to theft. The business may use this data to determine the level of protection needed at the stores or even if it should leave any locations.
5. Increase Security
There are risks to data security for all businesses. Organizations can use data analytics to identify the underlying reasons for earlier data breaches by analyzing and visualizing relevant data. For instance, the IT division can use data analytics programs to examine and visualize audit records to determine an attack’s direction and place of origin. This data can be used by IT to identify vulnerabilities and fix them.
For a developing company, Big Data analytics is a crucial investment. Businesses can gain a competitive advantage, lower operating costs, and increase client retention by utilizing big data marketing analytics. Businesses can use a variety of ways to obtain customer data. Data is becoming more readily available to all enterprises as technology advances. It is acceptable to state that organizations already have access to data. It is up to each organization to ensure it implements the proper data analysis systems to handle massive amounts of data. Unext Jigsaw provides one of the best platforms to study a Business Analytics course. You get an IIM Indore certified Business Analytics certification with a hands-on learning experience. We hope you have understood how to use Business Analytics for marketing strategies.