How Predictive Analytics Can Help Solve Business Problems?

For many businesses, without intelligent software applications for analytics, data will only provide so much information.

Today, many AI solutions are increasingly becoming a critical part of many businesses in various industries across the global business landscape.

With a vast amount of unstructured and structured data that is available for business organizations to collect and analyze, businesses can apply Predictive Analytics to derive valuable insights out of that data which is unlikely possible through the traditional systems of legacy application.

Businesses organizations can use these insights to support their business making decision through data-driven predictions in order to gain a competitive advantage.

In the simplest form of explanation, predictive analytics refers to the creation of machine learning models taking into business data and statistical algorithms to forecast the probabilities of certain future outcomes.

With predictive analytics, businesses will be able to move beyond reactive operations and shift toward a more predictive and proactive activities which helps increase the chance of success.

EXAMPLES OF INDUSTRIES THAT BENEFIT FROM PREDICTIVE ANALYTICS

In recent years, the market demand for predictive analytics development has been growing strongly due to the heavy competition of businesses employing advanced, and innovative technologies to solve new business problems, at the same time gaining competitive edge from such innovations.

The sooner businesses can have their predictive analytics applications developed and integrated into their business operations, the better the competitive advantages they can gain from adopting those technologies to respond to the new business needs.

Industries that benefit from predictive analytics applications development include:

• Automotive: For many companies, reducing operation costs (in an effective way) is just as important as increasing revenue. And for logistics or transportation companies with a considerable portion of assets that are vehicles and warehouse equipment for operation, efficiently manage and maintain those assets is critical to ensure business success as well as profitability. By using predictive analytics models to aggregate and analyze various data related to the lifecycle and maintenance of that technical equipment, logistics companies can predict the timelines for both future maintenance services and replacement of equipment preventing break-down which leading to business disruption. This also helps businesses to account for maintenance costs and/or capital expenditure and avoid downtime.
• Retail: Predictive analytics can provide recommendations on the product/services items which customers do not even know that they might be interested in for purchased and/or consumed. This helps provides value to both customers and the companies as it helps provide customers with a unique and personalized shopping experience, at the same time maximize sales potential through cross-sell and upsell opportunities. Moreover, predictive analytics can be used for marketing campaigns where companies provide their existing customers with relevant discounts or promotions to keep them come back. 
• Manufacturing: Predictive analytics can be of help in forecasting production to assist with manufacturing companies in inventory order as well as warehouse management. Furthermore, with good predictive analytics, manufacturing businesses will be able to gain insights into potential product quality issues before such things arise. Poor quality control can significantly affect revenue and brand image as well as customers’ satisfaction and other myriads of issues related to warranty and repairs.
• Financial services: Businesses in the finance industry can leverage past data collected to create machine learning models to assess and avoid potential credit risks as well as approve transactions automatically.

COMMON PREDICTIVE ANALYTICS APPLICATIONS

In our previous blog post, we mentioned the use cases of predictive analytics in sales and marketing. Here are other applications of predictive analytics in businesses:

Churn prevention:

It’s said that it costs a lot more to acquire a new customer than to keep one from leaving. With predictive analytics, business can prevent churn from their customer base, by finding out the relevant signals which makes a customer or segment of customers more likely to discontinue businesses.  With that information, companies can then make necessary adjustments and put more focus to keep those customers from leaving.

Fraud detection:

Many business organizations operate in the finance or insurance industry have employed various predictive analytics methods to identify and prevent fraudulent activities from happening such as credit card frauds and/or suspicious transactions, as well as assist in other finance operations like credit scoring. In addition, advanced analytics applications can enable companies to detect cyber fraud, zero-day vulnerabilities, and advanced persistent threats.

Marketing and Sales:
As previously stated in other blog posts, business can apply predictive analytics to assist with their sales and marketing activities by predicting and identifying various cross-sell and up-sell opportunities, recommending products items which customers are interested in and likely to purchase them. 

Operations:
Predictive analytics enables businesses to use a predictive analytics model for inventory management, helping businesses to account for their inventory more accurately and effectively.

Risk management:
Companies can use predictive analytics to mitigate risk and make more informed decisions. For example, the banking sector can use information about an individual’s credit score to decide whether an applicant is adequate to receive credit or a loan. 

HOW TO USE PREDICTIVE ANALYTICS

Starting your business predictive analytics journey with employing a data-first approach which allows you and your team to have an understanding and controlling processes from the beginning. This is crucial to ensure success.

One example is big tech companies like Amazon dominate their industry is because they were the first to begin collecting customers’ data which become so large that enable them to implement various complex analytics that they are able to continuously grow their competitive advantages.

This also helps improves the types of analytics that your business can implement in the future, with a more advanced Machine Learning models.

As mentioned above, the sooner companies start to adopt Machine Learning and Predictive analytics for business, the sooner they will be able to build a large database which allows them to implement advanced predictive analytics model. Through regular  updates and improvement of each release, the advantage will be so big that it’ll become harder for a competitor to catch up

Other factors to consider for an effective Predictive Analytics Strategy

For a strong predictive analytics strategy, businesses should consider a number of critical factors as follow:

• Data: Right kind of data that is both of quality and in large quantity;
• Strong team of marketing professionals and/or Data analysts.
• The right tools and technologies for predictive analytics implementation;

Let’s look at each of these more closely.

1. A Foundation of Clean, Quality Data

Generally speaking, any predictive analytics strategy or Machine Learning development would always require a large amount of data to create and train models. Thus, when starting development, businesses need to make sure that the data gathered is clean and in the right format. A database that has a lot of variance will need to be cleansed which is a time-consuming process and takes a lot of effort.

Read more about: Data Analytics - Key factors for a good data strategy

Again, once data is clean, businesses need to make sure they have enough data that is relevant to the process they are trying to improve or the business problems they are trying to solve.

2. Gather an Experienced Data Analyst Team

Predictive analytics can help businesses identify hidden patterns and recognize trends in your data. However, predictive analytics does not completely remove human work from the process. An experienced team of business users such as marketers and/or data analysts is still needed to interpret such patterns and trends into business insights, especially when it comes to many complex business use cases involving predictive analytics.

3. The Right Tools and Technologies to Implement Predictive Analytics Strategy

From data collection across various sources to the application for predictive analytics, it always requires the right set of tools and technologies to implementing the strategy. It’s worth noting that predictive analytics involves the processing of large volumes of data which requires intense resources (e.g. Cloud computing, AI development tools, reporting tools and dashboard, etc.) and may prove to be very costly.

Thus, depends on your Predictive analytics requirements, and business needs as well as industry, you should be able to understand whether your businesses need to invest in a custom predictive analytics application development. Otherwise, you can choose to outsource the whole project to an offshore vendor who will take care of the implementation and integration tasks for you.

Based on the requirements, your industry type, the size, and needs of your business, you will be able to understand whether you are ready to invest in a custom BI tool. Otherwise, you can choose a vendor that will carry the implementation and integration burden for you.

CONCLUSION

From custom web application development to AI (Artificial Intelligence) Solutions, predictive analytics enables business organizations to forecast on the probabilities of certain outcomes. With the constant evolution of AI/Machine Learning technologies, this trend will continue to grow and business is expected to shift toward an active operation by applying predictive analytics in their operations - leveraging the present and past data for prediction of certain future events.

The application of AI solutions and Predictive Analytics could prove to be highly useful to businesses by helping them discover new revenue streams, reducing risks, and enhancing operational efficiencies to better achieve business goals.

At TP&P Technology, our skilled team of Machine Learning engineers and software developers have experience with the latest and advanced technologies for AI solutions development, and can easily build custom AI solutions to solving various business problems that your company faced.

Contact us today to talk to our dedicated specialists about your predictive analytics development needs.