Although some would argue that it’s impossible to predict the future, businesses that are harnessing the power of big data predictive models are finding they’re able to gain valuable insights about the state of their sales pipeline.
Predictive models not only help businesses anticipate what their clients’ needs will be; they can help them develop targeted, lead-generating campaigns.
Sales professionals need to know which deals in the sales cycle will close during the current quarter, which prospects to focus on, and what steps they’ll need to take to close more deals.
In the past, this was done by relying on a sales manager’s gut instincts. Today, organizations are starting to realize that predictive sales analytics can be used to answer these questions more effectively.
Advantages of Pursuing a Master’s in Business Analytics
Those who are interested in pursuing a career in big data may be interested to learn that the U.S. Bureau of Labor Statistics projects that employment in data science careers will grow by 27.9% between 2016 and 2026. Projected growth in this field is much higher than the average for other occupations, and the current projection is that roughly 50,400 jobs will soon be available.
A 2018 article from CNBC showed that Glassdoor has ranked data scientist as its No. 1 job in the U.S. for the past three years.
Glassdoor also reported that the median base salary in this field is $110,000. Professionals who have obtained a master’s in the business analytics field are likely to have more employment opportunities and higher earning potential than applicants who lack a master’s credential.
Those interested in working in the field of sales analysis will need key competencies in a number of areas. The online Master of Business Analytics (MBAn) program at Ohio University has been designed to help students develop the following skills:
- Statistical analysis. Working professionals who have a strong background in statistical analysis are likely to be given preference in the recruitment and application process. The ability to identify, interpret, and present relevant data is a key component of success for those interested in a career in sales analytics.
- Machine learning techniques. A growing number of companies have begun to rely on machine learning algorithms as a means of uncovering potential revenue opportunities. Candidates who have an understanding of how to implement systems and algorithm models in ways that augment machine learning capabilities are likely to have higher levels of success in their careers.
- Historic data sets. The effective predictive analysis involves determining which data sets can be used to help businesses identify clients and generate leads.
- Quantitative modeling. Sales analysts will need to understand how to make quantitative predictions about the future, which means they’ll need to be comfortable using statistics, machine learning, and data sets in ways that will allow them to predict future needs and outcomes.
Filling the Gaps
The depth of insight that predictive sales analytics brings to an organization’s salesforce is invaluable. Not only does it provide insights into which prospects to target, but it can also help sales teams determine which marketing campaigns will be most successful based on buyers’ personas.
Managers who incorporate statistical models into their sales strategies will have a greater understanding of which campaigns are working and which campaigns need to be tweaked. Data science can also help salespeople determine which approach will be most effective based on the persona of the prospect they’re calling.
Graduates from the Ohio University MBAn program find they have a number of career paths available to them, as talented analytics professionals are in high demand in companies across all industries around the globe.
Popular postgraduation careers include data scientist, data engineer, director of business analytics, predictive analytics specialist, business analyst, and business intelligence manager, although other opportunities may be available to qualified candidates.
Key Experiences for Career Development
Students who are interested in pursuing a Master of Business Analytics (MBAn) degree often find that the best programs go beyond conventional tools and methods. This is why the online MBAn program at Ohio University has been designed to provide students with the advanced analytical skills they’ll need to understand data sets and how to use them when making key decisions for their future employers.
Future Trends in Business Analysis
To adapt to digital trends, business analysts will need to be comfortable adopting new technologies as they become available. The use of mobile applications, cloud computing, and enhanced user interface designs are also expected to grow.
As such, working professionals who have a strong background in predictive analysis and data science are likely to find their career opportunities will be vast and continuous. Jobs in aerospace, defense, finance, and health care are expected to grow, and the need for qualified analytics professionals will continue to exceed the supply.
If you’re ready to jump-start a career in analytics, discover more about how the online MBAn program at Ohio University can provide you with the tools and educational background you’ll need to pursue a career in this field.
Ohio University Blog, “How to Empower Decision Making with Predictive Analytics”
Ohio University Blog, “Online Master of Business Analytics Program Overview”
Ohio University Blog, “Two Types of Data Analysis for Building a Business Analytics Career”
Bureau of Labor Statistics, “Big Data Adds Up to Opportunities in Math Careers”
Canadian Professional Sales Association, “What Is Predictive Sales Analytics and How Can It Help My Sales Team?”
CNBC, “The 10 Best Jobs in America According to Glassdoor”
Forbes, “5 Steps to Transition Your Career to Data Science: Step 1—Identify Your Ideal Job”
Forbes, “The Next Frontier of B2B Sales Is Predictive Analytics”
Harvard Business Review, “Predictive Analytics in Practice”
TDWI, “5 Skills You Need to Build Predictive Analytics Models”