What Is Predictive Analytics? (Definition and How to Use It)

By Indeed Editorial Team

Published 4 May 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Many organisations use predictive analytics to evaluate past data and use the information to forecast relevant opportunities for the business. This method can help organisations identify risks, examine business trends, monitor budget spending, review historical data and make important decisions in the business industry. Learning about the predictive analytics technique may encourage you to implement this strategy in the workplace. In this article, we define predictive analytics, explore why this is important for businesses, discuss how this technique works, provide a guide on how you can apply this technique and review some jobs that use predictive analytics.

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What is predictive analytics?

Predictive analytics is a method that organisations may employ to examine past statistics and trends. This information might help them predict risks in financial decisions or identify opportunities that can grow the business. For example, retailers may use predictive analytics to study the effectiveness of promotional offers and which products prove most effective for consumers.

Predictive analytics is also a modelling technique that determines if trends are likely to emerge again. This encourages organisations to adapt their resources according to the predicted data. Video game companies might, for example, monitor how players react to the game and use that information to create new marketing strategies. Some companies may only implement this technique if they want to compare previous success to current performance.

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The importance of predictive analytics

Companies that use predictive analytics can typically make appropriate judgments when spending money or generating profits. This is because the forecasting technique provides detailed information about the organisation's future success. For example, if the organisation wants to buy a range of new products, it might use the analytics technique to predict how much income it can earn from the purchase. Understanding when to make reliable decisions is important for the financial stability of the company and how it manages resource costs and budgets.

The predictive analytics method also improves customer experience by showing the products customers are most interested in and are likely to purchase again. This is important for marketing campaigns that want to target popular products in the advertisement. Along with this, companies that notice suspicious behaviour may discover fraud detection or cyber attacks on technology systems. Predictive analytics uses specific sets of trending data that contain outcomes for particular errors. Noticing these potential outcomes beforehand is very important for organisations to implement strategies that detect fraudulent behaviour and stop it from affecting the operations.

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How does predictive analytics work?

The management team might consult with analysts in the organisation to conduct data reviews and find a relevant comparison. They can also arrange meetings to discuss what they're measuring and how they can exploit those results for business growth. Once they have settled on a technique, the analyst might design a set of models that can help them compare past and present information. For example, the forecast model specialises in metric quality prediction, which envisions the number of engaged customers or product sales in the company.

Analysts can relay this information back to the management team and advise them on how to take the next steps. Some managers can plan new strategies that focus on the modelling data. If they want to increase the number of sales, they may create promotional events that display limited offers on products. The management team can also request further models that review fraudulent transactions and the nature of each purchase.

How to implement the predictive analytics strategy

Many organisations use this strategy in different ways that allow them to reach one objective. This may depend on the type of industry or sector that requires analytical data. For example, weather forecasting employs different models than law enforcement crime trend reviews. Here's a guide you can follow when enforcing this strategy:

1. Establish the objective

You can identify what you want to achieve when reviewing analytical data. This might help you create a plan that provides detailed steps on how you can implement a new strategy. Establishing your objective beforehand may give analysts a better understanding of your business requirements. Here are some examples of objectives:

  • increase product sales

  • engage with wider audiences

  • promote more product offers

  • create marketing campaigns

  • retain more customers

  • maintain or increase profits

  • improve customer satisfaction

  • invest in total quality management

  • reduce production energy

  • implement sustainable methods

  • encourage recycling

  • build high-performing teams

2. Consult with analysts

Arranging one-on-one meetings with analysts may help you understand their process of obtaining relevant data. You can learn about their data modelling techniques and how they can search for information that benefits the organisation. During this meeting, you can ask questions regarding the objective and what model is best for achieving your goals. Analysts might also question you about the objective and what you want to learn about.

The analyst may discuss potential models with you that can help them attain past and present data. Some models evaluate customer engagement, whereas other models can determine how much sales have increased since the last evaluation. This conversation might help you gain a better understanding of the model designs and how they can show differences between previous success and future predictions. Once they analyse data from their systems and gather enough information, they might continue to analyse the current data for trends and patterns.

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3. Review the data comparison

Analysts may arrange another meeting to discuss the data comparison with you and how you can take the next steps. You might discover areas of improvement when linking current data to previous achievements. For example, if you notice the sales were higher one year ago, you might find more information that determines the radical difference. This is a good way of establishing past methods and why they were effective.

Data mining is a method of turning raw data into useful information and looking for patterns that show more about customers and business performance. This can help you think of new strategies that encourage teams to reach the objective. You can get familiar with the data and identify problems, gain insights and observe subsets.

4. Create a convenient strategy

Once you've obtained enough information, you can plan strategies that help you achieve your objective. It may be good to communicate ideas with the team and listen to what they suggest for action steps. If you want to engage with more customers, for example, you might create marketing strategies that showcase products on social media platforms. This may also reach wider audiences that are more active online and prefer online shops.

Analysts can help with this process and offer advice on future strategies. They might obtain more information to help you interpret the data and make intelligent choices for the business. For example, you may request additional data from customer engagement reviews to help you determine relevant sources for your strategy.

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5. Observe progress in the business

Monitoring the progress is a good way of checking how customers react and respond to the change. This might also pertain to product sales or operational development among departments. Checking the strategy regularly may ensure that you document the results and evaluate the level of success. You can communicate concerns with analysts or request for them to note the current data and use that for other predictive analytics.

Examples of jobs that use predictive analytics

Here are some examples of jobs that might use predictive analytics:

1. Project manager

National average salary: $122,474 per year

Primary duties: Project managers can use predictive analytics to gather information about previous projects and how they performed. This helps them to guide team members on new projects and provide strategies to accomplish targets. They may also reduce costs, increase revenues and maximise the company's engagement.

2. Data analyst

National average salary: $93,396 per year

Primary duties: A data analyst might use predictive analytics to interpret data and use that to make business decisions. They can also identify problems and communicate those with the management team. Data analysts study data to find patterns and trends that represent levels of engagement in the business.

3. Network engineer

National average salary: $95,502 per year

Primary duties: Network engineers may use predictive analytics when gathering data on previous computer networks and how engineers configured them. This can help them implement troubleshooting strategies for technical issues. They might also show organisations how to maintain and develop their computer software.

Salary figures reflect data listed on Indeed Salaries at time of writing. Salaries may vary depending on the hiring organisation and a candidate's experience, academic background and location.

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