Predictive analytics is a type of data analytics technique that makes predictions about events in the future based on historical data and methods such as machine learning and statistical modeling. Often, companies use historical data to build a mathematical model that captures significant trends. This model is subsequently used on current data to forecast what will happen next.
Predictive analytics can produce future insights with a substantial degree of precision. With predictive analytics tools, any business can use past and current data to consistently forecast behaviors and trends, days, or years into the future.
Benefits of Predictive Analytics
Predictive analytics does not change the motives of people who wish to know what may happen tomorrow, next month or next year, it makes looking into the future more reliable than tools available earlier.
Here is a list of benefits of predictive analytics:
- Offers the power to stop any criminal behavior: With predictive analytics, companies can study user behaviors and actions and spot abnormal activities ranging from corporate spying to cyberattacks to credit card fraud.
- Offers companies more visibility among their customers: By optimizing their marketing campaigns using predictive analytics, companies can generate new customer purchases and encourage cross-sell prospects. With predictive models, companies can attract, hold and nurture their valued customers.
- Offers an opportunity to save money: With the introduction of predictive analytics in retail, companies can estimate inventory requirements, configure store layouts and handle shipping schedules to enhance sales. Airlines use it to fix ticket prices based on past travel trends. The hospitality industry can predict the number of guests who might visit on any given day and take steps to increase revenue and occupancy.
Challenges Associated with Implementing Predictive Analytics
All company executives want faster, smarter decisions, but there is some effort needed to balance data, technology, and people when it comes to changing a business to a predictive analytics model. Executing this technology entails an ideological shift, staff training, and capital investment.
Here are some major challenges that companies face today when implementing AI-driven predictive analytics:
- Recruiting and training people for the right skills: Predictive analytics technology is quite complex, but the knowledge within the industry is not up to the standards. In fact, about 77% of the companies say that the biggest obstacle to successful digital transformation is the lack of skills among people.
- Data quality: Data is the chief inhibitor of extensive adoption of predictive analytics in companies. The most advanced technologies in the world can only use the data we provide. Therefore, it is important that companies are aware of the pitfalls and know how to evade them. More data often means better results from predictive analytics, but it needs to be the right data to solve the problem you wish to solve.
- Data Management: Each company has a source of third-party data and proprietary at their fingertips. Cloud-based solutions that store data remotely in large numbers is an answer to the question of where data should be kept. AI predictive models assess the historical data presented to them and, should a company notice that inaccurate data was fed into their predictive analytics platform, any conclusions reached will be ruled invalid.
Companies that have implemented predictive analytics techniques successfully see prescriptive analytics as the next step. Prescriptive analytics tells how companies must react in the best possible way given the prediction. Manthan offers ways to align different forms of analytics to generate prospects for businesses.
Visit website……………
Contact us
Manthan Systems, Inc.
5201 Great America Parkway,
Suite 320
Santa Clara, CA 95054
No comments:
Post a Comment