The Lowdown On Predictive Analytics

The present day market is experiencing many changes in business intelligence or BI. These changes have been brought forward by many technological innovations to meet increasing business needs. One of the latest shifts in the BI market is the movement from traditional analytics to predictive analytics. Although by default predictive analytics is under the BI umbrella, it is emerging as a distinct new software market.

In today’s modern business world, analytical tools enable greater transparency, searching and analyzing capabilities in past and present market trends. However, in the ever-changing and cut-throat world of business, the past and present insights and trend information are not enough to be competitive in business. Business enterprises will need to have their eyes on future patterns, trends and customer behavior in order to understand the market better and have an advantage. To meet this demand, many business intelligence providers and vendors developed predictive analytics to foresee future trends in buying patterns, customer behavior, new players, old players and the reasons for these patterns.

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Traditional analytical tools claim to have an all around view of any business or enterprise, but experts say that they only analyze historical data or data that has already occurred. Obviously we can’t change the past, but anyone can prepare for a better future. Decision makers want to see the predictable future, take control of it and make the necessary actions today to achieve future goals of their business.

Predictive Analytics Defined

Predictive analytics is defined in many ways. It encompasses a variety of statistical techniques from machine learning, techniques and data mining that collect and analyze current and historical facts to make calculated predictions about future events. If applied in business, predictive analytics uses patterns that are found in historical and transactional data to identify opportunities and risks.

Predictive analytics can also be defined as the branch of data mining that is focused on predicting future probabilities and trends. Predictive analytics automatically analyzes large amounts of data using different variables with the help of decision trees, clustering, neural nets, genetic algorithms, market basket analysis, regression modeling, text mining, decision analytics and hypothesis testing. Predictive analytics combines statistical analytical techniques and solid business knowledge. These insights help businesses understand how people behave as buyers, customers, distributors and sellers.

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Multiple and related predictive models can produce solid insights for making strategic company decisions like exploring new markets, retentions and acquisitions, cross selling and fraud detection. So, predictive analytics can indicate what you need to do or what path take in regards to business ventures and ideas, but it also provides ideas as to how and when you need to make changes. Predictive analytics can give you an overview of what-if scenarios for your business.

Going Deeper

Predictive analytics uses a telescopic and microscopic view of data, allowing organizations to scrutinize every detail of a business and to peek into the future. Traditional business intelligence has no way of accomplishing such functions because it works on the assumptions it creates and then will find if the statistical patterns match said assumptions. Predictive analytics goes above and beyond such assumptions and has the ability to discover new data. After gathering the data, it looks for patterns and associations between disparate information.

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6 Advantages Of Predictive Analytics

If predictive analytics is applied correctly, it can enable companies to know and respond to any new opportunities faster. The process is also useful in situations where enterprises are required to make fast decisions with large data volumes. These practices can help any enterprise in 3 major areas: risk minimizing, fraud identification and pursuing new revenue initiatives. By using predictive analytics in operational data, companies can better position themselves in identifying new revenue opportunities. Let’s say, for example, that by checking a customer’s historical data purchase patterns a business can make solid predictions about the types of promotional offers and coupons that will entice that customer further. Since almost everything today can be offered and sold online, this is where predictive analytics effectively comes into play.

Here are 6 advantages of predictive analytics:

1. Predictive Search

A customer or consumer’s interaction with a business or retailer’s website usually starts with a site search. If the said search can be made intelligently and predict what a customer is looking for, it will surely help enhance sales. Predictive search helps determine that by analyzing historical click-through behaviors and preferences in real time.

2. Promotions And Recommendations

Despite the availability of many promotional events and engines, it is still quite a challenge to know the right product recommendation or promotion that will help your business close a sale. This can be solved with predictive analytics, which learns and understands a customer’s shopping behavior. This also includes the buying history of the customer and the performance of each product on the website. This will help in making recommendations which have a higher chance of generating a sale. It also does the same with product promotions to identify those that have worked in the past and then offer the best product promotions in real-time based on the customer’s browsing history or behaviour patterns.

3. Management Of Prices

Predictive analytics studies pricing trends in correlation to sales information in determining the prices which will maximize revenue at the right time. Managing price is made possible using a predictive model that searches through historical data for sales, products and customers.

4. Fraud Management

Chargebacks and fraud management are every retailer’s nightmare. Predictive analytics can put a stop to this by analyzing a customer’s behavior and product sales and removing any products that are more prone to fraud. The fraud predictive model identifies any potential fraudulent activity before the customer completes the transaction.

5. Business Intelligence                

Understanding and knowing customer needs leads to better service and better product offers with the price they expect and effective after-sales service. Predictive analytics can make this happen by gathering customer information and developing models that identify what a specific customer may want or need. There will also be times that a particular customer may be clueless as to what they need, but predictive analytics can still make useful product recommendations that will capture their interest.

6. Management Of The Supply Chain

The process of predictive analytics helps in understanding consumer demand, which results in effective supply chain management. This includes forecasting, planning, sourcing, delivery fulfillment and returns. If a retailer has the ability to predict profits from a specific product, it will result in enhanced inventory management. It will also optimize the use of available warehouse space, enhanced cash flow and enhanced stock replenishing.

Any organization that uses predictive analytics in their daily operations will improve their business processes and enhance their decision making ability to meet business goals. Through predictive analytics any business can better manage the present and increase the chances of success in the future.


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