5 Benefits of Predictive Analytics Technologies

Business intelligence software is getting smarter every day — using algorithms, artificial intelligence and machine learning to better understand our business decisions and forecast what tomorrow brings. The latter is where predictive analytics software comes in, providing us with insight into growth possibilities and potential risks.

If the folks that predict these things are correct, the market for predictive analytics software is set to grow to 9.2 billion by 2020. Whether you believe this meta-prediction or not, that’s nearly four times its current market value, so we might want to take the forecast seriously.

People are using new emerging technologies to predict behavior and Predictive analytics software uses existing data to identify trends and best practices for any industry.

Marketing departments can use this software to identify emerging customer bases. Financial and insurance companies can build risk-assessment and fraud outlooks to safeguard their profitability. Manufacturing and retail firms can use it to predict fluctuations in demand or how specific process changes might affect their supply chains.

Advanced analytics enables both “optimization” and “innovation”. It can support the improvement of existing processes, for example, in the form of more precise sales planning (and therefore production and purchasing planning). Also, new insights from advanced analyses can highlight potential new business or even make new products and services possible.

The following tasks can be addressed using advanced analytics:

• Segmentation: creation of groups based on similarities between objects.
• Association: identification of the frequency of joint occurrences and, if necessary, the derivation of rules such as “From A and B (usually) follows C”
• Classification: for example, of hitherto unclassified elements;
• Correlation analysis: identification of relationships between element properties.
• Forecast: derivation of future values.

Why Advanced and Predictive Analytics Is Becoming So Important?

The current age of information and digitalization has brought with it new technologies and methods for improving business operations and maintaining competitive advantage:

New big data technologies enable cost-effective storage, processing and analysis of large amounts of data. Modern and intuitive user interfaces allow more user groups to draw insights and make informed decisions; and  Advanced analytics software enables better analysis, and analysis of relationships and future events.

Since modern techniques and technologies to accelerate or otherwise improve decisions or processes along the value chain are now widely available, it is important to carefully evaluate how advanced analytics can be used within your company in order to keep pace with the competition.

Generally, most companies see advanced and predictive analytics as one of the more important BI trends since 2017. However, there are a few differences in viewpoint across various user and company types.

Best-in-class companies and organizations in North and South America lead the way when it comes to predictive and advanced analytics.

On the other hand, the trend is much less important in telecommunications companies and the German-speaking region of Central Europe.

What 5 benefits predictive analytics important?

Organizations are turning to predictive analytics to help solve difficult problems and uncover new opportunities. Common uses include:

Detecting fraud:

Combining multiple analytics methods can improve pattern detection and prevent criminal behavior. As cybersecurity becomes a growing concern, high-performance behavioral analytics examines all actions on a network in real time to spot abnormalities that may indicate fraud, zero-day vulnerabilities and advanced persistent threats.

Optimizing marketing campaigns:

Predictive analytics are used to determine customer responses or purchases, as well as promote cross-sell opportunities. Predictive models help businesses attract, retain and grow their most profitable customers.

Improving operations:

Many companies use predictive models to forecast inventory and manage resources. Airlines use predictive analytics to set ticket prices. Hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. Predictive analytics enables organizations to function more efficiently.

Reducing risk:

Credit scores are used to assess a buyer’s likelihood of default for purchases and are a well-known example of predictive analytics. A credit score is a number generated by a predictive model that incorporates all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections.

Predictive analytics makes looking into the future more accurate and reliable than previous tools. As such it can help adopters find ways to save and earn money. Retailers often use predictive models to forecast inventory requirements, manage shipping schedules and configure store layouts to maximize sales. Airlines frequently use predictive analytics to set ticket prices reflecting past travel trends. Hotels, restaurants and other hospitality industry players can use the technology to forecast the number of guests on any given night in order to maximize occupancy and revenue.