Research shows that increasing data accessibility by a mere 10% can significantly result in an increase of net income for some businesses. Analyzing data enables businesses to increase revenue, improve profitability, and reduce costs by making informed, data-driven decisions. Retailers who use data extensively during their business operations can improve their profit margins by as much as 60%.
Highly-optimistic numbers like this are extremely exciting. To achieve these numbers, organizations need to make use of data to better understand the market they operate in, the customers they serve and the competitors they compete with.
All these objectives can be achieved if organizations leverage the power of business analytics. Business analysis is a strategic process that uses data-driven methods and analytics to improve overall business performance, gain a competitive edge, and drive financial gains.
This makes business analytics important for organizations seeking to optimize operations, personalize customer experiences, and stay ahead in a competitive marketplace.
What is business analytics?
Business analytics is a branch of management that uses raw customer and industry data to understand how well the organisation is performing in the market. The data collected is processed and analysed using statistical models, operational analysis and optimisation procedures. Core business analytics involves fundamental techniques such as statistical analysis, predictive modeling, and data mining to derive meaningful insights from data and support strategic decision-making. The objective is to derive actionable insights that can be used to make positive changes to the business in as needed or in real-time. Effective business analytics requires technical skills and familiarity with data analysis tools to extract and interpret insights efficiently.
Let’s take the example of banking giant American Express. The company uses business analytics insights to understand the likelihood of customer churn. By collecting important real-time customer transaction data (such as frequency of transactions, nature of transactions, etc.) and using predictive statistical models, including statistical modeling as a key technique, the company analyses what their customer database might look like in the future. Using this information, the company puts in place better customer service strategies that are custom-designed to retain customers and reduce churn.
It was through business analytics insights that the company was able to predict in mid-2016 that 24% of its Australian customer base would close accounts and switch banks in less than four months. This insight helped them take measures to prevent account closures. Utilising the information the data analytics had given them, American Express introduced multiple changes in their processes, product/service offerings and customer support. This move helped them retain a percentage of their attrition-prone customers and helped them increase their ROI by almost 10% over the course of the year.
Business analytics, predictive analytics, and big data
The fundamental element of any business analytics program is data. Effective data management is essential for collecting, storing, and analyzing business data to support decision-making and develop actionable insights. Organizations can mine the data they need through various online and offline platforms. When this data becomes very large in volume, growing exponentially in a very limited period of time, it becomes what is known as ‘Big Data’.
Essentially, big data is just data that is found in very large quantities. Multiple data streams are combined and used in conjunction with each other, to derive real-time inputs and insights about the customer and the market. Integrating various data sources, including both historical and real-time data, is crucial for comprehensive data management and accurate analytics.
To qualify as big data, data sets need to:
- Exceed 1 Terabyte (TB) in volume (ex: petabytes, exabytes etc.)
- Must grow in size and scope extremely quickly (in days)
- Must include all types of data – numbers, alphabets, videos, audios and more
Big data is used in business analytics to derive sensible and actionable information that can help organizations improve their top and bottom lines. Focusing on relevant data is essential to generate meaningful insights and support effective decision-making. This big data also helps organizations understand if any new trends or fads are being formed in the market. Timely analysis of big data will help brands actively engage customers, evade operational and market challenges and comply with dynamic industry requirements with ease.
The role of business intelligence in business analytics
Business Intelligence is a process where organizations analyse their own data to understand their performance.
In business intelligence, organizations are more concerned with understanding how something happened and what they can do to improve their performance and avoid costly mistakes. The focus here is internal analysis. Business analytics, on the other hand, focuses on why something happened. Here, organizations focus on the external factors that impact the organisation’s internal performance.
It could be said that business intelligence is one of the many facets of business analytics, in addition to external environmental data, technological tools, predictive models and statistical analytical processes. Combining internal organisational analysis with external organisational analysis, brands can derive valuable and actionable insights that can help them. Extracting meaningful insights from data requires both analytical thinking and technical expertise.
How business analytics works
Business analytics involves a systematic process that transforms raw data into meaningful insights to drive better business decisions. The journey begins with collecting data from a variety of sources, such as customer interactions, sales data, and market trends. This raw data is then cleaned and organized to ensure accuracy and relevance.
Once the data is prepared, business analytics professionals use data analysis techniques, statistical models, and machine learning algorithms to analyze data and uncover patterns, correlations, and trends. Data visualization tools play a crucial role in this stage, helping to present complex information in an easily understandable format through charts, graphs, and dashboards.
By identifying trends and extracting valuable insights, business analytics aims to inform business decisions and forecast future trends. Predictive modeling is often used to anticipate customer behavior or market shifts, while statistical analysis helps organizations understand past performance and operational efficiency. Ultimately, business analytics works to provide actionable insights that enable organizations to improve business performance, optimize processes, and gain a competitive edge in their industry.
Business analytics tools
A wide range of business analytics tools are available to help organizations analyze and interpret data, identify trends, and make informed decisions. Data visualization software, such as Tableau or Power BI, enables users to create interactive charts, graphs, and dashboards that make complex data insights accessible to stakeholders. Statistical analysis software, like SAS or SPSS, allows for in-depth examination of data sets to uncover patterns and relationships.
Machine learning algorithms and data mining software are essential for predictive analytics, helping organizations forecast future trends and identify opportunities for growth. Business intelligence platforms integrate data from multiple sources—including CRM systems, ERP systems, and social media platforms—providing a comprehensive view of business performance.
These business analytics tools not only support data analysis and the creation of data visualizations, but also facilitate the integration and management of large, diverse data sets. By leveraging these tools, organizations can extract actionable data insights, identify trends, and inform business decisions with greater confidence and clarity.
Business decisions powered by analytics
Business analytics empowers organizations to make smarter, data-driven decisions across all areas of operation. Predictive analytics enables companies to forecast future trends, anticipate customer needs, and identify new opportunities for growth. Descriptive analytics helps analyze historical data to uncover patterns and understand past performance, while diagnostic analytics digs deeper to identify the root causes of business problems.
Prescriptive analytics goes a step further by providing recommendations for improvement, helping organizations optimize business processes and enhance operational efficiency. By analyzing customer behavior and market trends, business analytics can inform marketing strategies, improve supply chain management, and identify areas for cost reduction.
With the ability to identify patterns and forecast future outcomes, business analytics supports informed decisions that drive revenue growth, streamline operations, and give organizations a competitive advantage in the marketplace. Whether it’s optimizing resource allocation, refining marketing campaigns, or enhancing customer experiences, business analytics is a powerful tool for transforming data into meaningful business results.
5 ways business analytics can benefit your brand
Business analytics offers umpteen benefits to brands, including improved decision-making, operational efficiency, and enhanced customer service. Here are 5 ways you can leverage business analytics to create a highly-successful brand:
- Persona creation and audience targeting Insights derived from social media engagement, website visits and store footfalls are immensely helpful in identifying the type of people who interact with the organisation’s touch points. Business analytics will help brands create customer persona, which will enable them to design websites, create content and build products that customers will enjoy. These insights will also help organizations know where to find the ideal customers. Business analytics programs provide training in data analysis, visualization, and roles such as data scientists and analysts, equipping professionals to better understand and target audiences.
For example, if you use Google Analytics as part of your business analytics toolkit, you can use the insights that Google offers to understand who you are currently focusing on, if they’re bringing in business for you, who your target markets should be and how best to segment them for marketing. Google Analytics contains built-in tools which help you create client personas.
- Creation of content strategy Business analytics in the form of content analytics can be used to understand what type of content customers like to consume, over the various channels your business uses. This data can help plan content marketing campaigns and content strategy. Business analytics requires a combination of technical skills, critical thinking, and strong communication skills to derive actionable insights from content data and optimize your strategy.
Data analytics is a great way to understand which long-tailed keywords prospects often use to find a service. It is also very helpful in knowing the type/sources of links readers click-through when reading content online. If you use an analytics tool like WordPress or Google Analytics, you can also find out the number of page views and engagement you received over time.
- Business expansion and growth planning Organizations planning to expand to other markets can use business analytics insights to understand if the option of expansion is viable or not. Brands can also check if they will be well-received in the new market prior to the expansion. Advanced analytics and artificial intelligence are increasingly used to identify market opportunities and predict customer behavior, supporting more informed expansion decisions.
One of the best examples of this is Amazon. Using data analytics, Amazon was able to identify the varied small-scale Australian industries would be extremely profitable to them. Armed with data, they launched over 5000 sellers in a span of 42 days after their launch. Now, 14 million visitors later, Amazon Australia is the fastest growing Amazon marketplace, crossing even India. The company has already seen a rise in traffic by 90% just a month after its launch.
- Resource management and order fulfilment Big data analysis will help brands identify the best suppliers to get stock from and track resource usage and re-order levels. Business analytics will also help organizations identify a number of product returns and reasons for customer complaints, helping them improve operations. Predictive and prescriptive analytics are used to optimize supply chain operations, while key performance indicators are tracked to measure and ensure success.
Woolworths is an example of a retail brand that uses data analytics extensively. The company purchased shares worth $20 million in analytics company Quantium to analyse shopper buying behaviour. They feed the data they mine into their resource management system and ensure that their most popular products are available at the right place, at the right time and in right quantity. Not only are they satisfying customer, they are also reducing resource wastage across their stores.
- Improved customer value addition Business analytics helps organizations understand what makes customers tick. Using real-time data, brands can now work towards recognising and implementing the latest trends and creating products that customers are sure to love. Business analytics helps transform raw data into valuable insights, enabling organizations to gain insights and identify patterns in customer behavior for better value addition.
Companies like Amazon, Woolworths and American Express not only improve their operations, but also change the entire end-to-end experience that customers face. When customers are given better access to products and services, and are provided better data-driven support, value is added to their interaction with the brands.
The growing demand for business analysts and data analysts is supported by labor statistics, which highlight strong job market prospects and career stability in these roles. Business analysts play a crucial role in supporting strategic decision-making by interpreting data and communicating insights. Compared to business analytics, data science involves advanced statistical methods, algorithm development, and custom coding to explore open-ended questions. Data scientists are key in building analytics models and managing advanced analytics within organizations. According to Harvard Business School, learning data-driven decision-making techniques is essential for professionals seeking to excel in today’s data-centric business environment.
Liquid Digital can setup and manage your digital business analytics, helping ensure you get the data reports and metrics you care about for success. We can help you form insights by automating your reporting process and by formatting easy to understand dashboards and statistics that can help you monitor the pulse of your business’ success.
References:
whatusersdo.com/blog/create-personas-using-google-analytics/theaustralian.com.au/business/business-spectator/woolworths-invests-20m-in-data-analytics/thenewdaily.com.au/money/finance-news/2018/01/16/amazon-australia-marketplace-ebay/