Predictive Analytics Consulting

Marketing has transformed impeccably through the years. Before the trend of eCommerce began, companies used outbound marketing techniques for B2C and B2B to reach leads through the offline medium of TV and Radio.

Predictive analysis for digital advertising has led to a natural conversion of leads into consumers by using the data collected from consumers to create the following marketing strategy. Many businesses are shifting to predictive analysis to upscale their profits and gain pros over contemporaries. Some of the reasons for this are as follows:

  • Increase in volume of data
  • Faster and much more cost-efficient computer systems
  • Upgraded software
  • Severe economic scenario and requirement for a better competitive market

Definition of Predictive Analysis

Predictive analysis can be elucidated as a part of advanced digital analytics. As the name goes, it is primarily used to predict futuristic events. Numerous methods were deployed for the analysis, including machine learning, reading data mining statistics, modeling, and artificial intelligence.

These processes or predictive analysis tools assist users in gaining a detailed understanding of the data at hand, thereby making predictions of future consequences. In the case of predictive analysis, technology plays an integral role, which helps in deciphering future events and influencing the results.

For example, big companies and organizations can use data relating to historical performance to reach ultimate conclusions and decide on the course of action, which can modify the outcomes significantly.

In short, predictive analysis can be extracting data from the present and deciphering future trends’ patterns. However, it only aids in predicting the directions and not emphasizing what will happen in the future.

Predictive Analytics

How Does It Work?

Now that we have understood what a digital predictive analysis consultant works, we will elucidate how it works. As mentioned, predictive analysis in digital marketing uses historical data to predict future outcomes.

In a typical scenario, historical data arises to construct a mathematical model that can pick the essential trends in the future. The model, however, can be used only on the present data to predict what will unfold in the next step.

Based on the information received, we can predict how the future event will unfold; based on this, one can bring out actions that favor the future. Additionally, the method also assists businesses and companies in identifying their leads and predicting who can turn into their potential customers.

Also, who will prove to be highly valuable for their business? Herein, it helps not only B2B companies but also e-commerce organizations in planning their strategies for generating leads, attracting clients and retaining existing ones.

Who Uses Predictive Analysis?

When it comes to carrying out predictive analysis for social media trends or digital marketing, there exist no limitations in its utilization. Predictive analysis can be used by any company or any vertical that aims to decrease the risks, streamline the operations, and thereby up-scaling the profits.

Examples of Predictive Analytics

Herein, we have listed down a few compelling examples to understand the concept in detail:

  • The banking sector is one large industry. Thus, a considerable amount of data regarding the user, as well as money, happens to be at stake. Herein is where predictive analytics comes into the picture. It assists in reducing fraudulence, calculates risks relating to credits, and expands the cross-sell and up-sell options, as well as aids in retaining valued customers. As per the predictive analytics generated by Commonwealth Bank, the chances of fraud for a given transaction, even before it gets authorized, can be done within the first 40 milliseconds before the initiation.
  • Apart from using predictive analysis in digital advertising, it has also become a pro in the retail sector. After the initial studies on buying behavior, wherein it was noted that men who bought diapers also tended to buy beer, retailers analyzed merchandising for planning and optimizing prices. It also aided in studying how effective the events were, concluding if the companies could offer customers appropriate deals. For example, – Staples gained a more in-depth insight into consumer behavior by obtaining a detailed picture of their end-users.
  • From predicting equipment failure to gaining insight into future resource requirements, studying reliability risks, and mitigating safety, the oil, gas, and utility sectors have fully utilized predictive analysis. One of the pioneer users of this technology is the Salt River Project, a public power utility that is the second largest in the US. Arizona’s largest water suppliers utilize predictive analysis for machine sensor data to predict when power-generating turbines require maintenance.
  • Government sectors have been prominent users of predictive analysis to study the service, suggest ways to improve service performance, and prevent fraud. It also renders a better understanding of consumer behavior and enhances cybersecurity.
  • Apart from the ability to detect fraud, predictive analytics in the health insurance sector aids in identifying the level of risk of chronic diseases in patients, thereby suggesting remedies. E.g., Express Scripts is known to benefit from using analytics for identifying patients who don’t adhere to treatments, thus aiding them in savings.
  • For manufacturers, it helps to identify reasons that lead to failure in production and decreased quality to optimize services and their distribution. Lenovo is one company that used the technique to gain ideas about warranty, which led to a 15% reduction in costs incurred on deposit.
  • Bing Predicts is a notable example for the Sports sector that used the prediction system by Bing of Microsoft, which is a predictive analysis for social media trends that uses social media sentiment and statistics for making assessments.
  • Predictive analysis has also improved weather forecasts by accurately predicting weather conditions since 1980.
  • Predictive analysis for digital advertising has transformed splendidly based on how information is processed about business. Predictive analysis of social media trends enables one to track user comments to respond accurately.

Difference between AI and Predictive Tools

There is a slight difference when we compare machine learning to predictive analysis. Machine learning can be elucidated as a continued process of concepts, and there lies only a little difference between the two.

Artificial Intelligence is one such system that renders the platform to generate assumptions, test, and learn newer concepts autonomously. AI can thus be defined as a combination of machine learning and technology and is used as one of the most conventional methods of utilizing personalized marketing.

Predictive Analytics Tools

The latter is riskier when comparing data analytics vs. predictive analysis as it deals primarily with aesthetically unearthing the future. Furthermore, business intelligence is considered an ancestor to analytics that follows the backward trend searching for the outcome.

This includes asking questions like – 1) who are the best customers? 2) What happens to be slow days of sales? However, in this era of cloud computing, the backward-moving trend is no longer sufficient. This thereby brings to the fore these predictive analytics tools that render appropriate outcomes.


Garnering the information, we can see that predictive analysis will be a mainstay in most industry verticals in the coming years; hence, the sky is bright, and companies need to heed it right! If you also need a predictive analysis consultant, then you are at the right spot!

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