Whether it is marketing, operations, human resources, or finance, no function can escape the massive amount of data available at their disposal. Examples include data of users based on their search browsing history and location tracking data collected through GPS. Other examples are biometrics data collected through wearable devices and records of financial transactions collected through payment apps.
Then, there are huge amounts of content generated on social media feeds, giving critical insights into customer behavior. Overall, businesses are collecting much more data than before. Irrespective of their place in the organizational hierarchy, the ability to harness the power of data serves as a demonstration of the decision-making skills of executives. Let us look at the scope of business analytics and how they can be applied to achieve different business objectives.
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Scope of Business Analytics
1. Data Analysis
A key part of business analytics is learning how to analyze data. Learning business analytics involves becoming familiar with tools and techniques to cut through the clutter and find the answers you need. Working knowledge of statistics is essential for data analysis. Concepts like probability, regression, variance, types of distribution help make sense of data and identify patterns. In addition, knowledge of tools like Python & SQL can help managers get real-time insights from data.
2. Data Visualization
Learning to analyze data is not enough. Managers should be able to visualize their analysis effectively to convince others about their findings and recommendations. Business analytics covers advanced knowledge of Microsoft Excel and data visualization tools like Tableau. Deep working knowledge and regular practice of working on these tools can give managers an edge over their non-data-literate peers.
Applications of Business Analytics
1. Improving Productivity
Business analytics can help in identifying the scope for productivity improvements based on past data. For example, Microsoft reduced meeting travel time by 46% by relocating its 1200 team to 4 buildings from an earlier number of 5 buildings. Their business analytics team analyzed the data in employee training calendars and analyzed the time spent by team members traveling for meetings. Based on their analysis, they achieved estimated annual savings worth $520,000 of employee time.
2. Improve Customer Experience
Business analytics can help businesses in understanding customer preferences and improving their service experience. For example, Blue Apron, a global meal kit delivery provider, uses predictive analytics to forecast demand for its products’ fixed menu. These insights help them reduce wastages and increase the speed of fulfilling orders.
The company uses historical data for order frequency of individual users as well as their recipe preferences. It also factors in seasonality to predict low and high seasons of demand. Based on this information, the company can provide its users with a reliable experience, while also saving costs by reducing wastages.
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3. Delivering Targeted Content
Business analytics help understand customer preferences and provide them content according to their preferences. For example, digital marketers analyze data on time spent on pages, search keywords, likes, and shares on social media. They use these insights to create content that is likely to resonate with their audience.
Netflix is another example of a brand that leverages analytics to its advantage. The company has a massive amount of data around the preferences of its 180 million-plus subscribers. It uses this data to make recommendations to its subscribers, based on their past watching history and other subscribers’ history. More than 80% of the content streamed on the Netflix platform is driven by its powerful recommendation system.
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4. Boosting the growth of Banks and Financial Services
Banks and other financial services use business analytics to manage their risks and downsides. For example, banks use past data for defaults to approve new loans. Business analytics also help financial institutions in detecting and preventing fraudulent transactions.
Banks can also improve their revenues through analytics. According to a McKinsey report, a US bank increased its revenues by 8% by using analytics to identify unnecessary discount patterns. The same McKinsey report highlights the case of an Asian bank that identified 15,000 microsegments within its customer base. Based on these insights, the bank created a product purchase model that tripled the likelihood of purchase.
5. Better Receivables Management
The use of predictive analytics can help businesses in improving their cash conversion cycle. Businesses can use past data for customers with a history of delayed payments and create different processes or reminders for such customers. They can also use data for defaulters and identify default patterns based on region, customer demographic, organization type, or other variables. Based on this data, they can reduce the likelihood of defaults in the future.
The use of analytics through dashboards can also help businesses get real-time access to their receivables situation. Through intelligent analytics, they can drill down into individual accounts and invoices as well as get a big picture view of receivables based on service/product, region, or other customer attributes. This way, finance teams can reduce bad debts by proactively following up with customers likely to delay payments.
6. Better Human Resource Management
Analytics help in all aspects of human resource management, including hiring, performance management, and retention. For example, businesses can use past data for successful hires (their institute, educational background, etc.) to improve future hires. They can also analyze data for the number of days it takes to close specific positions and improve their hiring turnaround times.
Moreover, businesses can also identify attrition trends, such as reasons for attrition, departments with high attrition, attrition percentage by managers, etc. They can use these insights to identify problem areas and proactively arrest attrition. Analytics can also help human resources in other aspects, such as the effectiveness of training efficiency.
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Business analytics applies to all walks of business. Data literacy is the way to go to succeed in the corporate world. Sound knowledge of business analytics can transform ordinary managers into savvy, data-driven decision-makers.
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What research methods do organisations use to collect data?
Data can help an organisation make critical decisions. To extract information, some of the popular methods organisations use include POS systems, websites, online surveys and questionnaires, focus group interviews, transactional history, contests, online user profile information, social media activity and mobile apps. Without quality data collection techniques, generating proper insights to correct inefficiencies and improve performance may be difficult.
What is the difference between business analytics and data analytics?
Though business analytics and data analytics are two terms that are used interchangeably, they do differ. As the name implies, data analytics involves analysing datasets to uncover trends and insights that can help an organisation make important decisions while business analytics solely focuses on business-related decisions. Usually, business analytics often uses insights drawn through data analysis. The former is mainly related to gathering and analysing data whereas the latter is more decision-oriented.
If you’re confused between the two fields, consider your interests. If working in a corporate organisation excites you, choose business analytics whereas if you’re a person who enjoys subjects such as statistics and programming, opt for data analytics.
Which are the most popular business analyst career paths?
Business analysts usually work as intermediaries by collecting, distributing and managing large amounts of data. For every company to enhance their productivity, data plays a crucial role. If you’ve just completed a data analytics program, some of the most in-demand career paths you can explore include that of a business analyst manager, IT business analyst, data analysis scientist, financial forecaster and predictive modeller, data business analyst, and quantitative and qualitative research analyst.