What do modern businesses have in common with professional tennis superstar Novak Djokovic? Both are achieving high levels of performance and efficiency with predictive analytics.
Predictive analytics is an area of statistics that uses artificial intelligence, machine learning, data mining and predictive modeling to gather and assess historical data in order to predict future events. Businesses are increasingly using predictive analytics tools to evaluate large amounts of qualitative data in search of patterns and insights that can be used to guide corporate decisions and policies.
For several years, Djokovic has employed a “strategy coach” who uses film analysis to extract data and statistics about upcoming opponents. This analysis gives Djokovic insight about factors such when an opponent will hit the ball with a slice or topspin or where he is likely to place his first serves. The results speak for themselves — Djokovic has won 77 professional tournaments, including five Wimbledon championships, and is widely considered among the greatest players of all time.
In a similar way, businesses are upping their game with increased use of business intelligence and analytics. Applying analytics to stored data allows organizations to examine trends and histories in order to understand current operations and create roadmaps for future performance. It is influencing the way organizations serve customers, make hiring decisions, optimize supply chains, develop marketing plans and more.
According to a recent SharePost survey of more than 300 IT executives, the vast majority said analytics and BI are now strategic necessities for their organizations. More than 90 percent said they plan to increase their spending on analytics tools in the near term. That percentage held firm for both large enterprise organizations and small businesses. In fact, Forbes reports that organizations with up to 100 employees had the highest rate of BI and analytics adoption last year.
Here are just a few ways that smaller businesses can use analytics to increase efficiency:
Enhance marketing. Most small companies have limited funds for marketing. Analytics drives efficient budget allocation through better insight into customer behaviors. With better anticipation of customer needs, companies can bring innovative ideas and products to the marketplace and get an edge on the competition.
Improve customer service. By analyzing customer data from various systems and communication channels, organizations can create an end-to-end view of customer interactions across multiple communication channels. This provides insight into customer demographics, shopping habits, buying preferences, spending limits, churn rates and more — all of which can be used to create personalized experiences for customers.
Optimize the workforce. Analysis of HR data such as employee recruitment, career progression, training, productivity, performance reviews and turnover rates provides actionable information about the workforce. According to a McKinsey and Company report, companies that employ “people analytics” improve recruiting efficiency by 80 percent, staff productivity by 25 percent and staff retention by 50 percent.
Improve operations. By analyzing operational data, organizations can identify inefficient processes, wasteful practices or even fraudulent activities. Armed with this information, you can make corrections to improve efficiency and profitability.
Identify Trends. Analyzing current and historical data can help you make reasonable predictions about future events or outcomes. Businesses can use this to tailor inventory levels to meet seasonal requirements, understand patterns that impact staffing levels and optimize supply chain routing.
Data analytics provide businesses of all sizes with remarkable insights into all aspects of operations. With the ability to collect and analyze data across multiple sources and applications, companies can increase efficiency, improve the customer experience and get a jump on the competition. That all adds up to a winning play.