Predictive Analytics: Techniques and Applications

In todays’ era, constantly connected technological space is exponentially generating excess data volume to be collected and analyzed, predictive analytics plays an important character in this perspective. The modernized digital tech sector employs predictive analytics omnipresently in the business and IT domain to attain a competitive edge.

 

“Predictive analytics, an advanced form of data analytics, understands historical data behaviour and anticipates data-driven insights as future outcomes.

 

Let’s start learning “predictive analytics” in the context of techniques, workflow and applications of it.

 

 

Understanding Predictive Analytics

 

Predictive analytics is the form of advanced data analytics making predictions about future outcomes via analyzing previous data. To analyze previous data, this method combines statistical modelling, data mining and machine learning tools and techniques and makes accurate and actionable insights. The science of predictive analytics can build future insights with a significant rate of accuracy.

 

For example, with the help of predictive analytics tools and models, organizations use this technique to find patterns from past data and identify risks and opportunities.

 

Presently, companies have a flood of data residing across transactional databases, equipment log files and media files (images, videos, documents), sensors and other data resources. Data experts examine this information to gain insights and make predictions about future events using predictive analytics. 

 

For example, companies are using it to increase the bottom line and competitive advantages. The process includes various sorts of techniques, discussed below in the next section.

 

As described in the video below, predictive analytics is a practice of combining predictive algorithms with historical data to compute something likelihood will take place. 

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Predictive Analytics Techniques

 

In todays’ industries involving healthcare, life sciences, oil and gas, insurance, etc, predictive analytics is widely employed in these areas and provides most valued anticipations when business strategies and applications are clearly defined.

 

Predictive analytics incorporates a combination of scientific methods and techniques as discussed below;

 

• Data Mining: In order to manage large amounts of data sets either structured or unstructured to recognize hidden patterns and relationships among variables provided, data mining is aimed to. Once identified, these relationships can be used to understand the behaviour of the event from which data is compiled.

• Statistical Modelling: In parallel to the data mining process, statistical data models can be developed depending on the context of what needs to be anticipated using the same collected data as for data mining. Once the model builts, the new data is fed to models to predict future outcomes. For example, a business expert can build a cross-selling model using current customer data and predict what other items they will likely to purchase from the same company.

• Machine learning: ML can deploy iterative methods and techniques to identify patterns from large data sets and build models. For example, recommendation engines are widely used for online shopping recommendations as predictions are made from using customers' prior purchasing and browsing behavior.   

 

(Must read: Big data analytics)

 

 

Why is Predictive Analytics Important?

 

Turing to predictive analytics is leading to address complex business problems and discover potential opportunities, some common benefits include;

 

1. Fraud detection

 

In general, multiple analyzing methods are combined to analyze data that improve accuracy of patterns detection and catch criminal behavior, preventing frequent fraud occurrence. With the growing concern of cybersecurity,conducting high performing behavioral analytics is demanding that examines all the suspicious behavior/activities across a network in a real-time to detect fraud actions, zero-day vulnerabilities and underlying threats.

 

2. Marketing campaigns optimization

 

Predictive analytics is beneficial in optimizing marketing campaigns and promotions’ events. To determine purchasing response of customers and publicizing cross-sell opportunities, predictive models help in businesses to attract, retain and increase valuable customers. 

 

3. Minimization in risks

 

Consider a simple example of reducing risks, Credit Scores, that is highly employed to recognize the likelihood of defaulters from the user's purchasing behaviour. In practice, credit score is the amount generated by a predictive model via analyzing the data relevant to a person’s credit history. Other risk- related examples count as insurance claims and fraud claim collections.

 

4. Improvements in business operations:

Predictive analytics enables organizations to make smarter decisions by smoothing operations and functions more efficiently.

 

From managing resources to forecasting inventory in order to streamline operations efficiency, many companies are using predictive analytics. For example, airlines are using predictive analytics to confirm a range of ticket pricing, hotels are using it to predict the number of guests on a particular day/hours to maximize space occupancy and increase revenue.

 

(Also read: Why is Big Data Analytics in trends?)

 

 

5 Applications of Predictive Analytics

 

1. Marketing

 

Consumers are attracted with pool of advertising and marketing, 

 

Individuals working in the marketing domain need to look how consumers will react on a particular market campaign, or what will be their impact on the overall economy while conducting such marketing event, etc. in this case, 

 

• Predictive analytics tools could be helpful in segmenting the marketing leads by displaying ads over websites and social media platforms relating to consumer behavior and interest.

• Predictive analytics tools can explore “expect to purchase” by analyzing consumer’s behaviour on past and current available data to find people whose data matches with ideal consumers.

• Marketers could also use predictive analytics for leads scoring by analyzing data to identify which prospects are potentially most valuable for the company, or to identify how likely the prospective consumers will buy products or services and to plan how they should be contacted and with what information.

 

(Recommended blog: Marketing Analytics overview)

 

2. Retail

 

Either online or brick and mortar, each retailer looks for managing inventory and logistics, and thereby predictive analytics is extremely important. The method allows retailers to correlate huge data information such as historical sales data, purchasing products and behavior, geographical references to optimize operations and efficiencies in the following ways;

 

• Customer sales data provides personalized recommendations and promotions for individual customers, through predictive analytics, better targeting built over real-time data assists retailers for planning campaigns, making ads and promotions that buyers will respond the most.

• Sales and logistics data analysis using predictive analytics helps retailers to ensure the availability of sufficient inventory/products in warehouses, and good merchandise in stores at the right time.

• Sales and promotion timing has become an art, conducting predictive analytics over customer, inventory, and historical sales data provides suitable circumstances/timing for lowering or raising prices.

• Predictive analytics lets retailers in merchandise planning and price optimization to investigate the impact of promotional events and to figure out appropriate offers for consumers. 

 

3. Manufacturing

 

With the modernized technology and fully automated factory machines, predictive analytics tools are very significant in operating and optimizing the manufacturing process at each stage of designing, purchasing, developing, quality and inventory control, delivery, etc. Moreover, 

 

• Predictive analytics is helpful when combined with machine data in order to help in tracking and comparing machines’ performance and equipment maintenance status and predicting which particular machine will fail.

• Predictive analytics insights can lead to decrease in shipping and transportation expenses by accepting all the factors included in transferring manufacturing products at different places under the proper system.

• Considering predictions over supply chain and sales data helps in making more considerable decisions for purchasing and ensuring that no expensive raw materials get purchased unless not required. This data can also be used in aligning manufacturing processes with consumer demands.

 

4. Healthcare 

 

Healthcare industry is among dominant adapters to consider predictive analytics techniques aimed at facilitating technology to save money and improve health practices efficiencies.

 

• Predictive analytics can help medical practitioners by analyzing data concerning global diseases statistics, drug interactions, patient diagnostic history individually to provide advanced care and conduct more effective medical practices.

• Applying predictive analytics on clinics’ past appointment data helps in identifying probable no-shows or delays in cancellations more accurately and thus save time and resources.

• To detect claims frauds, the health insurance industry is using predictive analytics to discover patients at most risk of incurable or chronic disease, it helps companies in finding suitable interventions.

 

5. Finance

 

Applying to a broad spectrum of banking and financial services & activities, predictive analytics is the most valuable process helping from accessing risks to maximizing customer satisfaction. 

 

Predictive analytics are useful in;

 

• Prohibition of credit card fraud via indicating unusual transactions,

• Credit card scoring to determine whether to approve or deny loan applications, 

• Most importantly, analyzing customers’ churn data and facilitating banks to approach potential customers before they are likely to switch respective institutions.

• Measuring credit risk, maximizing cross-sell/up-sell opportunities and retaining valuable customers.

• Commonwealth bank implements predictive analytics to anticipate fraud activities for a given transaction before it is accomplished- within 40 milliseconds of the occurrence of transaction, (source)

 

Conclusion

 

To conclude the blog, predictive analytics makes use of advanced analytics techniques leveraging historical data and revealing real-time interferences to predict future events. It is a key instrument to fuel an analytical journey of organisations where classical statistical analysis methods meet advanced technologies such as machine learning and artificial intelligence. 

 

In practice, predictive analytics transform the way a business operates and performs, helping them to gain competitive advantages and opportunities to reduce risks.