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Guidance and results for the data-rich, yet information-poor.

Behind The Scenes Applications of Predictive Analytics

In every industry, there are several behind-the-scenes applications of business intelligence and predictive analytics. Take a look at 3 analytics-based solutions that you might not be aware of:

  1. Retail & Merchandising Optimization

It has been the universal rule in merchandising to place a brand’s best and most popular products on shelves that are the easiest to reach. The items that do not move as fast are then placed on the lower shelves. However, with the help of data analytics and the ability to record media before combining them with other data, it is now possible to chart the paths that customers take through shops, observe which products they reach for most, and analyze how placement affects how much they prefer a product. In-store merchandising is rising to new levels thanks to this form of analytics.

  1. Identifying Law Enforcement Suspects

Right after a crime has been committed, it can be challenging for police officers to identify suspects who have left the crime scene and chase them down for arrest. During the past decade, city, county, state and federal authorities have been working together to offer continuous access to a wide range of databases at all government levels. These databases have both unstructured and structured data, such as fingerprints, arrest records, photo IDs, and text entries from various computer systems. This information is easily accessible by officers in their cars and increases the chances of quickly identifying and pursuing suspects.

  1. Collecting Patient Demographics

Due to insights gained into demographic pockets of patient groups, many more health and life saving steps can be made. One notable example includes the discovery that people from a specific geographic area have higher likelihood of getting diabetes. While preventive medicine diagnostics is not as widely used now as they will be in the future, there is significant potential for it. The ability to accurately predict conditions that are most likely to affect patients before the conditions even manifest can greatly assist patients in lessening its impact or avoiding it completely.

4 Supply Chain Opportunities with Data Analytics

With the aid of data analytics, better insights and transparency is easier to attain in the supply chain. By leveraging this data, relevant organizations can develop how they respond to risks in the supply chain or unstable demands.

Here are some examples on how data analytics can provide ways to improve the supply chain process:

  • Understand clients better and how they relate with the company.
  • Fine-tune inventory management and deliver products based on actual demand.
  • Manage supplier relationships better and to effectively understand vendors.
  • Learn how clients interact through varied channels and provide personalized product recommendations.
  • Create in-depth supplier profiles that include data from external sources

These are four major opportunities for supply chains that data analytics can address:

  • Improve Efficiency

The top priorities of many businesses in the supply chain industry are cost reduction and efficiency. Integrating data analytics in business operations results in a 10% increase in the improvement in supply chain efficiency.

  • Enhance Prediction of Customer Needs

The majority of dissatisfied clients will refuse to do business with a company that failed to rise to their expectations. Providing the right products to the right client at the right time and place is the secret to increasing customer loyalty and satisfaction. With data analytics, comprehensive buyer insight will help companies understand personal wants and needs, as well as develop an excellent brand experience.

  • Develop Advanced Assessments of Risk

Data analytics offers better predictability and visibility across supply chains – this technology aids in assessing the chances of problem happening and its likely impact to an organization. By combining the analysis of scenario planning, risk mapping and historical data, a better risk management approach is developed.

  • Prioritize Agility and Speed

The ability to readily adjust to customer expectations and objectives is considered one of the top drivers of competitive advantage across several industries. Integrating data analytics in operations can result in 400% improvement in order-to-cycle delivery times and a 41% increase in reaction time to relevant issues in the supply chain.

Predictive Analytics For Customer Retention

One of the biggest risks to companies and their growth is the silent customer. These customers do not reach out to companies and let them know if they are dissatisfied with their products or services. They then stay away from the company and cancel services altogether.

With predictive analytics, businesses can break the cycle of the silent customer churn. Here are several ways to do just that and retain customers:

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Content Marketing And Predictive Analytics

Companies should always prioritize using data analytics to measure how audiences react to content, but what’s even more important is to gather data that is actionable and helpful. With the help of predictive analytics, companies are more informed about the topics that are most relevant to their audiences. Content creators use insights to reach a specific set of people. Data mining also plays a big part due to the wealth of insights related to browsing history, purchase history, and even preferences to articles.

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Why Predictive Analytics Is A Must-Have For Human Resources

The majority of Human Resources departments are far behind when it comes to harnessing the wealth of available data. This is mostly because the hiring process traditionally relies on a human’s personal opinion of a prospective employee rather than impartial data. While it can’t be denied that a person’s ‘gut feel’ is a very valuable tool in the hiring process, it also runs the risk of hiring the wrong candidate based on a hiring manager’s bias. These hiring decisions can lead to shockingly high yet easily avoidable hiring costs, as well as a dent in employee productivity and efficiency.

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